350 research outputs found
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
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
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)
Addressing corner detection issues for machine vision based UAV aerial refueling
The need for developing autonomous aerial refueling capabilities for an Unmanned Aerial Vehicle (UAV) has risen out of the growing importance of UAVs in military and non-military applications. The AAR capabilities would improve the range and the loiter time capabilities of UAVs. A number of AAR techniques have been proposed, based on GPS based measurements and Machine Vision based measurements. The GPS based measurements suffer from distorted data in the wake of the tanker. The MV based techniques proposed the use of optical markers which---when detected---were used to determine relative orientation and position of the tanker and the UAV. The drawback of the MV based techniques is the assumption that all the optical markers are always visible and functional. This research effort proposes an alternative approach where the pose estimation does not depend on optical markers but on Feature Extraction methods. The thesis describes the results of the analysis of specific \u27corner detection\u27 algorithms within a Machine Vision---based approach for the problem of Aerial Refueling for Unmanned Aerial Vehicles. Specifically, the performances of the SUSAN and the Harris corner detection algorithms have been compared. Special emphasis was placed on evaluating their accuracy, the required computational effort, and the robustness of both methods to different sources of noise. Closed loop simulations were performed using a detailed SimulinkRTM -based simulation environment to reproduce docking maneuvers, using the US Air Force refueling boom
An Image Based Visual Servo Method for Probe-and-Drogue Autonomous Aerial Refueling
With the high focus on autonomous aerial refueling recently, it becomes
increasingly urgent to design efficient methods or algorithms to solve AAR
problems in complicated aerial environments. Apart from the complex aerodynamic
disturbance, another problem is the pose estimation error caused by the camera
calibration error, installation error, or 3D object modeling error, which may
not satisfy the highly accurate docking. The main objective of the effort
described in this paper is the implementation of an image-based visual servo
control method, which contains the establishment of an image-based visual servo
model involving the receiver's dynamics and the design of the corresponding
controller. Simulation results indicate that the proposed method can make the
system dock successfully under complicated conditions and improve the
robustness against pose estimation error
Integrity Determination for Image Rendering Vision Navigation
This research addresses the lack of quantitative integrity approaches for vision navigation, relying on the use of image or image rendering techniques. The ability to provide quantifiable integrity is a critical aspect for utilization of vision systems as a viable means of precision navigation. This research describes the development of two unique approaches for determining uncertainty and integrity for a vision based, precision, relative navigation system, and is based on the concept of using a single camera vision system, such as an electro-optical (EO) or infrared imaging (IR) sensor, to monitor for unacceptably large and potentially unsafe relative navigation errors.
The first approach formulates the integrity solution by means of discrete detection methods, for which the systems monitors for conditions when the platform is outside of a defined operational area, thus preventing hazardously misleading information (HMI). The second approach utilizes a generalized Bayesian inference approach, in which a full pdf determination of the estimated navigation state is realized.
These integrity approaches are demonstrated, in the context of an aerial refueling application, to provide extremely high levels (10-6) of navigation integrity. Additionally, various sensitivities analyzes show the robustness of these integrity approaches to various vision sensor effects and sensor trade-offs
Optical Tracking for Relative Positioning in Automated Aerial Refueling
An algorithm is designed to extract features from video of an air refueling tanker for use in determining the precise relative position of a receiver aircraft. The algorithm is based on receiving a known estimate of the tanker aircraft\u27s position and attitude. The algorithm then uses a known feature model of the tanker to predict the location of those features on a video frame. A corner detector is used to extract features from the video. The measured corners are then associated with known features and tracked from frame to frame. For each frame, the associated features are used to calculate three dimensional pointing vectors to the features of the tanker. These vectors are passed to a navigation algorithm which uses extended Kalman filters, as well as data-linked INS data to solve for the relative position of the tanker. The algorithms were tested using data from a flight test accomplished by the USAF Test Pilot School using a C-12C as a simulated tanker and a Learjet LJ-24 as the simulated receiver. The system was able to provide at least a dozen useful measurements per frame, with and without projection error
Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems
Monocular visual navigation methods have seen significant advances in the
last decade, recently producing several real-time solutions for autonomously
navigating small unmanned aircraft systems without relying on GPS. This is
critical for military operations which may involve environments where GPS
signals are degraded or denied. However, testing and comparing visual
navigation algorithms remains a challenge since visual data is expensive to
gather. Conducting flight tests in a virtual environment is an attractive
solution prior to committing to outdoor testing.
This work presents a virtual testbed for conducting simulated flight tests
over real-world terrain and analyzing the real-time performance of visual
navigation algorithms at 31 Hz. This tool was created to ultimately find a
visual odometry algorithm appropriate for further GPS-denied navigation
research on fixed-wing aircraft, even though all of the algorithms were
designed for other modalities. This testbed was used to evaluate three current
state-of-the-art, open-source monocular visual odometry algorithms on a
fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and
ORB-SLAM2 (with loop closures disabled)
The AFIT ENgineer, Volume 2, Issue 4
In this issue: AFMC Spark Tank Semi-finalist New AFIT Patents 2020 Graduate School Award Winners Airmen and Artificial Intelligence Nuclear Treaty Monitorin
The AFIT ENgineer, Volume 2, Issue 4
In this issue: AFMC Spark Tank Semi-finalist New AFIT Patents 2020 Graduate School Award Winners Airmen and Artificial Intelligence Nuclear Treaty Monitorin
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