255 research outputs found
Antidisturbance Vibration Suppression of the Aerial Refueling Hose during the Coupling Process
In autonomous aerial refueling (AAR), the vibration of the flexible refueling hose caused by the receiver aircraft’s excessive closure speed should be suppressed once it appears. This paper proposed an active control strategy based on the permanent magnet synchronous motor (PMSM) angular control for the timely and accurate vibration suppression of the flexible refueling hose. A nonsingular fast terminal sliding-mode (NFTSM) control scheme with adaptive extended state observer (AESO) is proposed for PMSM take-up system under multiple disturbances. The states and the “total disturbance” of the PMSM system are firstly reconstituted using the AESO under the uncertainties and measurement noise. Then, a faster sliding variable with tracking error exponential term is proposed together with a special designed reaching law to enhance the global convergence speed and precision of the controller. The proposed control scheme provides a more comprehensive solution to rapidly suppress the flexible refueling hose vibration in AAR. Compared to other methods, the scheme can suppress the flexible hose vibration more fleetly and accurately even when the system is exposed to multiple disturbances and measurement noise. Simulation results show that the proposed scheme is competitive in accuracy, global rapidity, and robustness
Docking control for probe-drogue refueling: An additive-state-decomposition-based output feedback iterative learning control method
Designing a controller for the docking maneuver in Probe-Drogue Refueling (PDR) is an important but challenging task, due to the complex system model and the high precision requirement. In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control (ILC) is
adopted in this paper. First, Additive State Decomposition (ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of NonMinimum Phase (NMP) by separating these features into two subsystems
(a primary system and a secondary system). After system decomposition, an adjoint-type ILC is applied to the Linear
Time-Invariant (LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback
is used to stabilize the secondary system with input saturation. The two controllers designed for the two subsystems
can be combined to achieve the original control goal of the PDR system. Furthermore, to compensate for the receiver-independent uncertainties, a correction action is proposed by using the terminal docking error, which can lead to a
smaller docking error at the docking moment. Simulation tests have been carried out to demonstrate the performance
of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type
ILC in the docking control of PDR
An adaptive neuro-fuzzy controller for vibration suppression of a flexible structure in aerial refueling
Air-to-air refueling (AAR) has been commonly used in military jet applications. Recently,
civilian applications of AAR have been garnering increased attention due to the high cost of air
travel, which is largely dictated by the cost of jet fuel. There are two types of AAR approaches:
probe-drogue and flying boom systems. This work explores the probe-drogue AAR system in
commercial applications. Typical AAR applications deploy a drogue connected to a long flexible
hose behind a moving aircraft tanker. The drogue is connected to a probe in a receiver aircraft
before initiating fuel transfer and is retracted back into the tanker when the fuel transfer is
completed. In order to ensure a safe and efficient refueling operation sophisticated systems need
to be developed to accommodate the turbulences encountered, particularly in respect to vibration
reduction of the flexible hose and drogue. The objective of this work is to develop a probe-drogue
system for helicopter AAR applications. The first project is to make a preliminary design of a new
AAR system for helicopter refuelling from a modified AT-802 tanker aircraft. [...
Survey of computer vision algorithms and applications for unmanned aerial vehicles
This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)
Evaluation of machine vision techniques for use within flight control systems
In this thesis, two of the main technical limitations for a massive deployment of Unmanned Aerial Vehicle (UAV) have been considered.;The Aerial Refueling problem is analyzed in the first section. A solution based on the integration of \u27conventional\u27 GPS/INS and Machine Vision sensor is proposed with the purpose of measuring the relative distance between a refueling tanker and UAV. In this effort, comparisons between Point Matching (PM) algorithms and Pose Estimation (PE) algorithms have been developed in order to improve the performance of the Machine Vision sensor. A method of integration based on Extended Kalman Filter (EKF) between GPS/INS and Machine Vision system is also developed with the goal of reducing the tracking error in the \u27pre-contact\u27 to contact and refueling phases.;In the second section of the thesis the issue of Collision Identification (CI) is addressed. A proposed solution consists on the use of Optical Flow (OF) algorithms for the detection of possible collisions in the range of vision of a single camera. The effort includes a study of the performance of different Optical Flow algorithms in different scenarios as well as a method to compute the ideal optical flow with the aim of evaluating the algorithms. An analysis on the suitability for a future real time implementation is also performed for all the analyzed algorithms.;Results of the tests show that the Machine Vision technology can be used to improve the performance in the Aerial Refueling problem. In the Collision Identification problem, the Machine Vision has to be integrated with standard sensors in order to be used inside the Flight Control System
Optimal rendezvous trajectory for unmanned aerial-ground vehicles
Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low cost aerial surveillance and mapping applications in remote regions. There is however a key limitation, which is the fact that low cost UAVs have limited fuel capacity and hence require periodic refueling to accomplish a mission.
Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in fuel
wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an
optimal trajectory in 3D taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous
behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach
Vision-Based navigation system for unmanned aerial vehicles
MenciĂłn Internacional en el tĂtulo 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 IngenierĂa ElĂ©ctrica, ElectrĂłnica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando GarcĂa Fernández.- Vocal: Pascual Campoy Cerver
Toward Automated Aerial Refueling: Relative Navigation with Structure from Motion
The USAF\u27s use of UAS has expanded from reconnaissance to hunter/killer missions. As the UAS mission further expands into aerial combat, better performance and larger payloads will have a negative correlation with range and loiter times. Additionally, the Air Force Future Operating Concept calls for \formations of uninhabited refueling aircraft...[that] enable refueling operations partway inside threat areas. However, a lack of accurate relative positioning information prevents the ability to safely maintain close formation flight and contact between a tanker and a UAS. The inclusion of cutting edge vision systems on present refueling platforms may provide the information necessary to support a AAR mission by estimating the position of a trailing aircraft to provide inputs to a UAS controller capable of maintaining a given position. This research examines the ability of SfM to generate relative navigation information. Previous AAR research efforts involved the use of differential GPS, LiDAR, and vision systems. This research aims to leverage current and future imaging technology to compliment these solutions. The algorithm used in this thesis generates a point cloud by determining 3D structure from a sequence of 2D images. The algorithm then utilizes PCA to register the point cloud to a reference model. The algorithm was tested in a real world environment using a 1:7 scale F-15 model. Additionally, this thesis studies common 3D rigid registration algorithms in an effort characterize their performance in the AAR domain. Three algorithms are tested for runtime and registration accuracy with four data sets
Design of a Mobile Underwater Charging System
Autonomous Underwater Vehicles (AUVs) are extremely capable vehicles for numerous ocean related missions. AUVs are energy limited, resulting in short mission endurance on the scale of hours to days. Underwater Gliders (UGs) are able to operate on the order of months to years by using nontraditional propulsion methods. UGs, however, are unable to perform missions requiring high speed or direct forward motion due to the nature of their buoyancy driven motion. This work reviews the current state of the art in recharging AUVs and offers an underwater recharging network concept at a significantly reduced cost to traditional methods. The solution includes the design of a UG capable of serving as charge carrying agent that couples with and charges AUVs autonomously. The vehicle design is built on the work done previously at the Nonlinear and Autonomous Systems Lab on the development of ROUGHIE (Research Oriented Underwater Glider for Hands-on Investigative Engineering). The ROUGHIE2 design is a rethinking of the original ROUGHIE capabilities to serve as a mobile charger by increasing depth rating, endurance, and payload capacity. The recharging concept presented will be easy to adapt to many different AUVs and UGs making this technology universal to small AUVs
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