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Öfjäll, Kristoffer. "Online Learning for Robot Vision." Licentiate thesis, Linköpings universitet, Datorseende, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110892.
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In tele-operated robotics applications, the primary information channel from the robot to its human operator is a video stream. For autonomous robotic systems however, a much larger selection of sensors is employed, although the most relevant information for the operation of the robot is still available in a single video stream. The issue lies in autonomously interpreting the visual data and extracting the relevant information, something humans and animals perform strikingly well. On the other hand, humans have great diculty expressing what they are actually looking for on a low level, suitable for direct implementation on a machine. For instance objects tend to be already detected when the visual information reaches the conscious mind, with almost no clues remaining regarding how the object was identied in the rst place. This became apparent already when Seymour Papert gathered a group of summer workers to solve the computer vision problem 48 years ago [35]. Articial learning systems can overcome this gap between the level of human visual reasoning and low-level machine vision processing. If a human teacher can provide examples of what to be extracted and if the learning system is able to extract the gist of these examples, the gap is bridged. There are however some special demands on a learning system for it to perform successfully in a visual context. First, low level visual input is often of high dimensionality such that the learning system needs to handle large inputs. Second, visual information is often ambiguous such that the learning system needs to be able to handle multi modal outputs, i.e. multiple hypotheses. Typically, the relations to be learned are non-linear and there is an advantage if data can be processed at video rate, even after presenting many examples to the learning system. In general, there seems to be a lack of such methods. This thesis presents systems for learning perception-action mappings for robotic systems with visual input. A range of problems are discussed, such as vision based autonomous driving, inverse kinematics of a robotic manipulator and controlling a dynamical system. Operational systems demonstrating solutions to these problems are presented. Two dierent approaches for providing training data are explored, learning from demonstration (supervised learning) and explorative learning (self-supervised learning). A novel learning method fullling the stated demands is presented. The method, qHebb, is based on associative Hebbian learning on data in channel representation. Properties of the method are demonstrated on a vision-based autonomously driving vehicle, where the system learns to directly map low-level image features to control signals. After an initial training period, the system seamlessly continues autonomously. In a quantitative evaluation, the proposed online learning method performed comparably with state of the art batch learning methods.
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Pudney, Christopher John. "Surface modelling and surface following for robots equipped with range sensors." University of Western Australia. Dept. of Computer Science, 1994. http://theses.library.uwa.edu.au/adt-WU2003.0002.
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The construction of surface models from sensor data is an important part of perceptive robotics. When the sensor data are obtained from fixed sensors the problem of occlusion arises. To overcome occlusion, sensors may be mounted on a robot that moves the sensors over the surface. In this thesis the sensors are single–point range finders. The range finders provide a set of sensor points, that is, the surface points detected by the sensors. The sets of sensor points obtained during the robot’s motion are used to construct a surface model. The surface model is used in turn in the computation of the robot’s motion, so surface modelling is performed on–line, that is, the surface model is constructed incrementally from the sensor points as they are obtained. A planar polyhedral surface model is used that is amenable to incremental surface modelling. The surface model consists of a set of model segments, where a neighbour relation allows model segments to share edges. Also sets of adjacent shared edges may form corner vertices. Techniques are presented for incrementally updating the surface model using sets of sensor points. Various model segment operations are employed to do this: model segments may be merged, fissures in model segment perimeters are filled, and shared edges and corner vertices may be formed. Details of these model segment operations are presented. The robot’s control point is moved over the surface model at a fixed distance. This keeps the sensors around the control point within sensing range of the surface, and keeps the control point from colliding with the surface. The remainder of the robot body is kept from colliding with the surface by using redundant degrees–of–freedom. The goal of surface modelling and surface following is to model as much of the surface as possible. The incomplete parts of the surface model (non–shared edges) indicate where sections of surface that have not been exposed to the robot’s sensors lie. The direction of the robot’s motion is chosen such that the robot’s control point is directed to non–shared edges, and then over the unexposed surface near the edge. These techniques have been implemented and results are presented for a variety of simulated robots combined with real range sensor data.
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Karr, Roger W. "The assembly of a microcomputer controlled low cost vision-robot system and the design of software." Ohio : Ohio University, 1985. http://www.ohiolink.edu/etd/view.cgi?ohiou1184010908.
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Sridaran, S. "Off-line robot vision system programming using a computer aided design system." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54373.
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Robots with vision capability have been taught to recognize unknown objects by comparing their shape features with those of known objects, which are stored in the vision system as a knowledge base. Traditionally, this knowledge base is created by showing the robot the set of objects that it is likely to come across. This is done with the vision system to be used and must be done in an online mode.
An approach to teach the robot in an off-line mode by integrating the robot vision system and an off-line graphic system, has been developed in this research. Instead of showing the objects that the robot is likely to come across, graphic models of the objects were created in an off-line graphic system and a FORTRAN program that processes the models to extract their shape parameters was developed. These shape parameters were passed to the vision system. A program to process an unknown object placed in front of the vision system was developed to extract its shape parameters. A program that compares the parameters of the unknown object with those of the known models was also developed.
The vision system was calibrated to measure the pixel dimensions in inches. In the vision system, shape parameters of the objects were found to vary with different orientations. The range of variation for each parameter was established and this was taken into consideration in the parameter comparison program.<br>Master of Science
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Damweber, Michael Frank. "Model independent offset tracking with virtual feature points." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/17651.
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Ma, Mo. "Navigation using one camera in structured environment /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECED%202007%20MA.
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Cipolla, Roberto. "Active visual inference of surface shape." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293392.
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Jansen, van Nieuwenhuizen Rudolph Johannes. "Development of an automated robot vision component handling system." Thesis, Bloemfontein : Central University of Technology, Free State, 2013. http://hdl.handle.net/11462/213.
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Thesis (M. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2013<br>In the industry, automation is used to optimize production, improve product quality and increase profitability. By properly implementing automation systems, the risk of injury to workers can be minimized.
Robots are used in many low-level tasks to perform repetitive, undesirable or dangerous work. Robots can perform a task with higher precision and accuracy to lower errors and waste of material.
Machine Vision makes use of cameras, lighting and software to do visual inspections that a human would normally do. Machine Vision is useful in application where repeatability, high speed and accuracy are important.
This study concentrates on the development of a dedicated robot vision system to automatically place components exiting from a conveyor system onto Automatic Guided Vehicles (AGV).
A personal computer (PC) controls the automated system. Software modules were developed to do image processing for the Machine Vision system as well as software to control a Cartesian robot. These modules were integrated to work in a real-time system.
The vision system is used to determine the parts‟ position and orientation. The orientation data are used to rotate a gripper and the position data are used by the Cartesian robot to position the gripper over the part.
Hardware for the control of the gripper, pneumatics and safety systems were developed. The automated system‟s hardware was integrated by the use of the different communication protocols, namely DeviceNet (Cartesian robot), RS-232 (gripper) and Firewire (camera).
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Ukidve, Chinmay S. "Quantifying optimum fault tolerance of manipulators and robotic vision systems." Laramie, Wyo. : University of Wyoming, 2008. http://proquest.umi.com/pqdweb?did=1605147571&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.
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Hallenberg, Johan. "Robot Tool Center Point Calibration using Computer Vision." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9520.
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<p>Today, tool center point calibration is mostly done by a manual procedure. The method is very time consuming and the result may vary due to how skilled the operators are.</p><p>This thesis proposes a new automated iterative method for tool center point calibration of industrial robots, by making use of computer vision and image processing techniques. The new method has several advantages over the manual calibration method. Experimental verifications have shown that the proposed method is much faster, still delivering a comparable or even better accuracy. The setup of the proposed method is very easy, only one USB camera connected to a laptop computer is needed and no contact with the robot tool is necessary during the calibration procedure.</p><p>The method can be split into three different parts. Initially, the transformation between the robot wrist and the tool is determined by solving a closed loop of homogeneous transformations. Second an image segmentation procedure is described for finding point correspondences on a rotation symmetric robot tool. The image segmentation part is necessary for performing a measurement with six degrees of freedom of the camera to tool transformation. The last part of the proposed method is an iterative procedure which automates an ordinary four point tool center point calibration algorithm. The iterative procedure ensures that the accuracy of the tool center point calibration only depends on the accuracy of the camera when registering a movement between two positions.</p>
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Bayraktar, Hakan. "Development Of A Stereo Vision System For An Industrial Robot." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605732/index.pdf.
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The aim of this thesis is to develop a stereo vision system to locate and
classify objects moving on a conveyor belt. The vision system determines the
locations of the objects with respect to a world coordinate system and class of the
objects. In order to estimate the locations of the objects, two cameras placed at
different locations are used. Image processing algorithms are employed to extract
some features of the objects. These features are fed to stereo matching and
classifier algorithms. The results of stereo matching algorithm are combined with
the calibration parameters of the cameras to determine the object locations.
Pattern classification techniques (Bayes and Nearest Neighbor classifiers) are
used to classify the objects. The linear velocity of the objects is determined by
using an encoder mounted to the shaft of the motor driving the conveyor belt. A
robot can plan a sequence of motion to pick the object from the conveyor belt by
using the output of the proposed system.
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Wu, Jianxin. "Visual place categorization." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29784.
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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010.<br>Committee Chair: Rehg, James M.; Committee Member: Christensen, Henrik; Committee Member: Dellaert, Frank; Committee Member: Essa, Irfan; Committee Member: Malik, Jitendra. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Brooks, Douglas Antwonne. "Control of reconfigurability and navigation of a wheel-legged robot based on active vision." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26545.
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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Howard, Ayanna; Committee Member: Egerstedt, Magnus; Committee Member: Vela, Patricio. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Brink, Wikus. "Stereo vision for simultaneous localization and mapping." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71593.
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Thesis (MScEng)--Stellenbosch University, 2012.<br>ENGLISH ABSTRACT: Simultaneous localization and mapping (SLAM) is vital for autonomous robot navigation. The robot
must build a map of its environment while tracking its own motion through that map. Although
many solutions to this intricate problem have been proposed, one of the most prominent issues that
still needs to be resolved is to accurately measure and track landmarks over time. In this thesis we
investigate the use of stereo vision for this purpose.
In order to find landmarks in images we explore the use of two feature detectors: the scale-invariant
feature transform (SIFT) and speeded-up robust features (SURF). Both these algorithms find salient
points in images and calculate a descriptor for each point that is invariant to scale, rotation and
illumination. By using the descriptors we match these image features between stereo images and
use the geometry of the system to calculate a set of 3D landmark measurements. A Taylor approximation
of this transformation is used to derive a Gaussian noise model for the measurements.
The measured landmarks are matched to landmarks in a map to find correspondences. We find that
this process often incorrectly matches ambiguous landmarks. To find these mismatches we develop
a novel outlier detection scheme based on the random sample consensus (RANSAC) framework. We
use a similarity transformation for the RANSAC model and derive a probabilistic consensus measure
that takes the uncertainties of landmark locations into account. Through simulation and practical
tests we find that this method is a significant improvement on the standard approach of using the
fundamental matrix.
With accurately identified landmarks we are able to perform SLAM. We investigate the use of three
popular SLAM algorithms: EKF SLAM, FastSLAM and FastSLAM 2. EKF SLAM uses a Gaussian
distribution to describe the systems states and linearizes the motion and measurement equations
with Taylor approximations. The two FastSLAM algorithms are based on the Rao-Blackwellized
particle filter that uses particles to describe the robot states, and EKFs to estimate the landmark
states. FastSLAM 2 uses a refinement process to decrease the size of the proposal distribution and
in doing so decreases the number of particles needed for accurate SLAM.
We test the three SLAM algorithms extensively in a simulation environment and find that all three
are capable of very accurate results under the right circumstances. EKF SLAM displays extreme
sensitivity to landmark mismatches. FastSLAM, on the other hand, is considerably more robust
against landmark mismatches but is unable to describe the six-dimensional state vector required for
3D SLAM. FastSLAM 2 offers a good compromise between efficiency and accuracy, and performs
well overall.
In order to evaluate the complete system we test it with real world data. We find that our outlier
detection algorithm is very effective and greatly increases the accuracy of the SLAM systems. We
compare results obtained by all three SLAM systems, with both feature detection algorithms, against
DGPS ground truth data and achieve accuracies comparable to other state-of-the-art systems.
From our results we conclude that stereo vision is viable as a sensor for SLAM.<br>AFRIKAANSE OPSOMMING: Gelyktydige lokalisering en kartering (simultaneous localization and mapping, SLAM) is ’n noodsaaklike
proses in outomatiese robot-navigasie. Die robot moet ’n kaart bou van sy omgewing en
tegelykertyd sy eie beweging deur die kaart bepaal. Alhoewel daar baie oplossings vir hierdie ingewikkelde
probleem bestaan, moet een belangrike saak nog opgelos word, naamlik om landmerke
met verloop van tyd akkuraat op te spoor en te meet. In hierdie tesis ondersoek ons die moontlikheid
om stereo-visie vir hierdie doel te gebruik.
Ons ondersoek die gebruik van twee beeldkenmerk-onttrekkers: scale-invariant feature transform
(SIFT) en speeded-up robust features (SURF). Altwee algoritmes vind toepaslike punte in beelde en
bereken ’n beskrywer vir elke punt wat onveranderlik is ten opsigte van skaal, rotasie en beligting.
Deur die beskrywer te gebruik, kan ons ooreenstemmende beeldkenmerke soek en die geometrie
van die stelsel gebruik om ’n stel driedimensionele landmerkmetings te bereken. Ons gebruik ’n
Taylor- benadering van hierdie transformasie om ’n Gaussiese ruis-model vir die metings te herlei.
Die gemete landmerke se beskrywers word dan vergelyk met dié van landmerke in ’n kaart om
ooreenkomste te vind. Hierdie proses maak egter dikwels foute. Om die foutiewe ooreenkomste
op te spoor het ons ’n nuwe uitskieterherkenningsalgoritme ontwikkel wat gebaseer is op die
RANSAC-raamwerk. Ons gebruik ’n gelykvormigheidstransformasie vir die RANSAC-model en lei ’n
konsensusmate af wat die onsekerhede van die ligging van landmerke in ag neem. Met simulasie en
praktiese toetse stel ons vas dat die metode ’n beduidende verbetering op die standaardprosedure,
waar die fundamentele matriks gebruik word, is.
Met ons akkuraat geïdentifiseerde landmerke kan ons dan SLAM uitvoer. Ons ondersoek die gebruik
van drie SLAM-algoritmes: EKF SLAM, FastSLAM en FastSLAM 2. EKF SLAM gebruik ’n Gaussiese
verspreiding om die stelseltoestande te beskryf en Taylor-benaderings om die bewegings- en meetvergelykings
te lineariseer. Die twee FastSLAM-algoritmes is gebaseer op die Rao-Blackwell partikelfilter
wat partikels gebruik om robottoestande te beskryf en EKF’s om die landmerktoestande af te
skat. FastSLAM 2 gebruik ’n verfyningsproses om die grootte van die voorstelverspreiding te verminder
en dus die aantal partikels wat vir akkurate SLAM benodig word, te verminder.
Ons toets die drie SLAM-algoritmes deeglik in ’n simulasie-omgewing en vind dat al drie onder die
regte omstandighede akkurate resultate kan behaal. EKF SLAM is egter baie sensitief vir foutiewe
landmerkooreenkomste. FastSLAM is meer bestand daarteen, maar kan nie die sesdimensionele
verspreiding wat vir 3D SLAM vereis word, beskryf nie. FastSLAM 2 bied ’n goeie kompromie
tussen effektiwiteit en akkuraatheid, en presteer oor die algemeen goed.
Ons toets die hele stelsel met werklike data om dit te evalueer, en vind dat ons uitskieterherkenningsalgoritme
baie effektief is en die akkuraatheid van die SLAM-stelsels beduidend verbeter. Ons
vergelyk resultate van die drie SLAM-stelsels met onafhanklike DGPS-data, wat as korrek beskou
kan word, en behaal akkuraatheid wat vergelykbaar is met ander toonaangewende stelsels.
Ons resultate lei tot die gevolgtrekking dat stereo-visie ’n lewensvatbare sensor vir SLAM is.
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Shah, Syed Irtiza Ali. "Single camera based vision systems for ground and; aerial robots." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37143.
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Efficient and effective vision systems are proposed in this work for object detection for ground&aerial robots venturing into unknown environments with minimum vision aids, i.e. a single camera. The first problem attempted is that of object search and identification in a situation similar to a disaster site. Based on image analysis, typical pixel-based characteristics of a visual marker have been established to search for, using a block based search algorithm, along with a noise and interference filter. The proposed algorithm has been successfully utilized for the International Aerial Robotics competition 2009. The second problem deals with object detection for collision avoidance in 3D environments. It has been shown that a 3D model of the scene can be generated from 2D image information from a single camera flying through a very small arc of lateral flight around the object, without the need of capturing images from all sides. The forward flight simulations show that the depth extracted from forward motion is usable for large part of the image. After analyzing various constraints associated with this and other existing approaches, Motion Estimation has been proposed. Implementation of motion estimation on videos from onboard cameras resulted in various undesirable and noisy vectors. An in depth analysis of such vectors is presented and solutions are proposed and implemented, demonstrating desirable motion estimation for collision avoidance task.
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Yung, Ho-lam, and 容浩霖. "Position and pose estimation for visual control of robot manipulators in planar tasks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43224283.
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Viljoen, Vernon. "Integration of a vision-guided robot into a reconfigurable component- handling platform." Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2014. http://hdl.handle.net/11462/120.
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Thesis (M. Tech.) -- Central University of Technology, Free State, 2010<br>The latest technological trend in manufacturing worldwide is automation. Reducing human labour by using robots to do the work is purely a business decision. The reasons for automating a plant include:
Improving productivity
Reducing labour and equipment costs
Reducing product damage
Monitoring system reliability
Improving plant safety.
The use of robots in the automation sector adds value to the production line because of their versatility. They can be programmed to follow specific paths when moving material from one point to another and their biggest advantage is that they can operate for twenty-four hours a day while delivering consistent quality and accuracy.
Vision-Guided Robots (VGRs) are developed for many different applications and therefore many different combinations of VGR systems are available. All VGRs are equipped with vision sensors which are used to locate and inspect various objects. In this study a robot and a vision system were combined for a pick-and-place application. Research was done on the design of a robot for locating, inspecting and picking selected components from a moving conveyor system.
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Irwansyah, Arif [Verfasser], Ulrich [Akademischer Betreuer] Rückert, and Franz [Akademischer Betreuer] Kummert. "Heterogeneous computing systems for vision-based multi-robot tracking / Arif Irwansyah ; Ulrich Rückert, Franz Kummert." Bielefeld : Universitätsbibliothek Bielefeld, 2017. http://d-nb.info/1140586009/34.
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Kontitsis, Michail. "Design and implementation of an integrated dynamic vision system for autonomous systems operating in uncertain domains." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0002852.
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Entschev, Peter Andreas. "Efficient construction of multi-scale image pyramids for real-time embedded robot vision." Universidade Tecnológica Federal do Paraná, 2013. http://repositorio.utfpr.edu.br/jspui/handle/1/720.
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Detectores de pontos de interesse, ou detectores de keypoints, têm sido de grande interesse para a área de visão robótica embarcada, especialmente aqueles que possuem robustez a variações geométricas, como rotação, transformações afins e mudanças em escala. A detecção de características invariáveis a escala é normalmente realizada com a construção de pirâmides de imagens em multiescala e pela busca exaustiva de extremos no espaço de escala, uma abordagem presente em métodos de reconhecimento de objetos como SIFT e SURF. Esses métodos são capazes de encontrar pontos de interesse bastante robustos, com propriedades adequadas para o reconhecimento de objetos, mas são ao mesmo tempo computacionalmente custosos. Nesse trabalho é apresentado um método eficiente para a construção de pirâmides de imagens em sistemas embarcados, como a plataforma BeagleBoard-xM, de forma similar ao método SIFT. O método aqui apresentado tem como objetivo utilizar técnicas computacionalmente menos custosas e a reutilização de informações previamente processadas de forma eficiente para reduzir a complexidade computacional. Para simplificar o processo de construção de pirâmides, o método utiliza filtros binomiais em substituição aos filtros Gaussianos convencionais utilizados no método SIFT original para calcular múltiplas escalas de uma imagem. Filtros binomiais possuem a vantagem de serem implementáveis utilizando notação ponto-fixo, o que é uma grande vantagem para muitos sistemas embarcados que não possuem suporte nativo a ponto-flutuante. A quantidade de convoluções necessária é reduzida pela reamostragem de escalas já processadas da pirâmide. Após a apresentação do método para construção eficiente de pirâmides, é apresentada uma maneira de implementação eficiente do método em uma plataforma SIMD (Single Instruction, Multiple Data, em português, Instrução Única, Dados Múltiplos) – a plataforma SIMD usada é a extensão ARM Neon disponível no processador ARM Cortex-A8 da BeagleBoard-xM. Plataformas SIMD em geral são muito úteis para aplicações multimídia, onde normalmente é necessário realizar a mesma operação em vários elementos, como pixels em uma imagem, permitindo que múltiplos dados sejam processados com uma única instrução do processador. Entretanto, a extensão Neon no processador Cortex-A8 não suporta operações em ponto-flutuante, tendo o método sido cuidadosamente implementado de forma a superar essa limitação. Por fim, alguns resultados sobre o método aqui proposto e método SIFT original são apresentados, incluindo seu desempenho em tempo de execução e repetibilidade de pontos de interesse detectados. Com uma implementação direta (sem o uso da plataforma SIMD), é mostrado que o método aqui apresentado necessita de aproximadamente 1/4 do tempo necessário para construir a pirâmide do método SIFT original, ao mesmo tempo em que repete até 86% dos pontos de interesse. Com uma abordagem completamente implementada em ponto-fixo (incluindo a vetorização com a plataforma SIMD) a repetibilidade chega a 92% dos pontos de interesse do método SIFT original, porém, reduzindo o tempo de processamento para menos de 3%.<br>Interest point detectors, or keypoint detectors, have been of great interest for embedded robot vision for a long time, especially those which provide robustness against geometrical variations, such as rotation, affine transformations and changes in scale. The detection of scale invariant features is normally done by constructing multi-scale image pyramids and performing an exhaustive search for extrema in the scale space, an approach that is present in object recognition methods such as SIFT and SURF. These methods are able to find very robust interest points with suitable properties for object recognition, but at the same time are computationally expensive. In this work we present an efficient method for the construction of SIFT-like image pyramids in embedded systems such as the BeagleBoard-xM. The method we present here aims at using computationally less expensive techniques and reusing already processed information in an efficient manner in order to reduce the overall computational complexity. To simplify the pyramid building process we use binomial filters instead of conventional Gaussian filters used in the original SIFT method to calculate multiple scales of an image. Binomial filters have the advantage of being able to be implemented by using fixed-point notation, which is a big advantage for many embedded systems that do not provide native floating-point support. We also reduce the amount of convolution operations needed by resampling already processed scales of the pyramid. After presenting our efficient pyramid construction method, we show how to implement it in an efficient manner in an SIMD (Single Instruction, Multiple Data) platform -- the SIMD platform we use is the ARM Neon extension available in the BeagleBoard-xM ARM Cortex-A8 processor. SIMD platforms in general are very useful for multimedia applications, where normally it is necessary to perform the same operation over several elements, such as pixels in images, enabling multiple data to be processed with a single instruction of the processor. However, the Neon extension in the Cortex-A8 processor does not support floating-point operations, so the whole method was carefully implemented to overcome this limitation. Finally, we provide some comparison results regarding the method we propose here and the original SIFT approach, including performance regarding execution time and repeatability of detected keypoints. With a straightforward implementation (without the use of the SIMD platform), we show that our method takes approximately 1/4 of the time taken to build the entire original SIFT pyramid, while repeating up to 86% of the interest points found with the original method. With a complete fixed-point approach (including vectorization within the SIMD platform) we show that repeatability reaches up to 92% of the original SIFT keypoints while reducing the processing time to less than 3%.
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Hussein, Mustafa Turki Verfasser], Dirk [Akademischer Betreuer] [Söffker, and Josef [Akademischer Betreuer] Pauli. "Vision-Based Control of Flexible Robot Systems / Mustafa Turki Hussein. Gutachter: Josef Pauli. Betreuer: Dirk Söffker." Duisburg, 2014. http://d-nb.info/1064264611/34.
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Hussein, Mustafa Turki [Verfasser], Dirk [Akademischer Betreuer] Söffker, and Josef [Akademischer Betreuer] Pauli. "Vision-Based Control of Flexible Robot Systems / Mustafa Turki Hussein. Gutachter: Josef Pauli. Betreuer: Dirk Söffker." Duisburg, 2014. http://d-nb.info/1064264611/34.
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Meger, David Paul. "Planning, localization, and mapping for a mobile robot in a camera network." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101623.
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Networks of cameras such as building security systems can be a source of localization information for a mobile robot assuming a map of camera locations as well as calibration information for each camera is available. This thesis describes an automated system to acquire such information. A fully automated camera calibration system uses fiducial markers and a mobile robot in order to drastically improve ease-of-use compared to standard techniques. A 6DOF EKF is used for mapping and is validated experimentally over a 50 m hallway environment. Motion planning strategies are considered both in front of a single camera to maximize calibration accuracy and globally between cameras in order to facilitate accurate measurements. For global motion planning, an adaptive exploration strategy based on heuristic search allows compromise between distance traveled and final map uncertainty which provides the system a level of autonomy which could not be obtained with previous techniques.
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Adeboye, Taiyelolu. "Robot Goalkeeper : A robotic goalkeeper based on machine vision and motor control." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-27561.
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This report shows a robust and efficient implementation of a speed-optimized algorithm for object recognition, 3D real world location and tracking in real time. It details a design that was focused on detecting and following objects in flight as applied to a football in motion. An overall goal of the design was to develop a system capable of recognizing an object and its present and near future location while also actuating a robotic arm in response to the motion of the ball in flight. The implementation made use of image processing functions in C++, NVIDIA Jetson TX1, Sterolabs’ ZED stereoscopic camera setup in connection to an embedded system controller for the robot arm. The image processing was done with a textured background and the 3D location coordinates were applied to the correction of a Kalman filter model that was used for estimating and predicting the ball location. A capture and processing speed of 59.4 frames per second was obtained with good accuracy in depth detection while the ball was well tracked in the tests carried out.
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Andersson, Olov. "Methods for Scalable and Safe Robot Learning." Licentiate thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138398.
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Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to enter real-world public spaces and homes. However, robot behavior is still usually engineered for narrowly defined scenarios. To manually encode robot behavior that works within complex real world environments, such as busy work places or cluttered homes, can be a daunting task. In addition, such robots may require a high degree of autonomy to be practical, which imposes stringent requirements on safety and robustness. \setlength{\parindent}{2em}\setlength{\parskip}{0em}The aim of this thesis is to examine methods for automatically learning safe robot behavior, lowering the costs of synthesizing behavior for complex real-world situations. To avoid task-specific assumptions, we approach this from a data-driven machine learning perspective. The strength of machine learning is its generality, given sufficient data it can learn to approximate any task. However, being embodied agents in the real-world, robots pose a number of difficulties for machine learning. These include real-time requirements with limited computational resources, the cost and effort of operating and collecting data with real robots, as well as safety issues for both the robot and human bystanders.While machine learning is general by nature, overcoming the difficulties with real-world robots outlined above remains a challenge. In this thesis we look for a middle ground on robot learning, leveraging the strengths of both data-driven machine learning, as well as engineering techniques from robotics and control. This includes combing data-driven world models with fast techniques for planning motions under safety constraints, using machine learning to generalize such techniques to problems with high uncertainty, as well as using machine learning to find computationally efficient approximations for use on small embedded systems.We demonstrate such behavior synthesis techniques with real robots, solving a class of difficult dynamic collision avoidance problems under uncertainty, such as induced by the presence of humans without prior coordination. Initially using online planning offloaded to a desktop CPU, and ultimately as a deep neural network policy embedded on board a 7 quadcopter.
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Modi, Kalpesh Prakash. "Vision application of human robot interaction : development of a ping pong playing robotic arm /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/943.
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Fredriksson, Scott. "Design, Development and Control of a Quadruped Robot." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-86897.
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This thesis shows the development of a quadruped platform inspired by existing quadrupled robot designs. A robot by the name of Mjukost was designed, built, and tested. Mjukost uses 12 Dynamixel AX-12a smart servos and can extend its legs up to 19 cm with an operating height of 16 cm. All the custom parts in Mjukost are ether 3d printable or easy to manufacture, and the total estimated cost of Mjukost is around 900$. Mjukost has a simple control system that can position its body freely in 6 DOF using an inverse kinematic model and walk on flat ground using an open-loop walking algorithm. The performance experiments show that its slow control loopcauses difficulties for the robot to follow precise trajectories, but its still consistent in its motions.
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Watanabe, Yoko. "Stochastically optimized monocular vision-based navigation and guidance." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/22545.
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Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Johnson, Eric; Committee Co-Chair: Calise, Anthony; Committee Member: Prasad, J.V.R.; Committee Member: Tannenbaum, Allen; Committee Member: Tsiotras, Panagiotis.
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Damaryam, Gideon Kanji. "Vision systems for a mobile robot based on line detection using the Hough Transform and artificial neural networks." Thesis, Robert Gordon University, 2008. http://hdl.handle.net/10059/450.
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This project contributes to the problem of mobile robot self-navigation within a rectilinear framework based on visual data. It proposes a number of vision systems based on detection of straight lines in images captured by a robot using the Hough transform and artificial neural networks as core algorithms. The Hough transform is a robust method for detection of basic features (Boyce et al 1987). However, it is so computationally demanding that it is not commonly used in real time applications and applications which utilise anything but small images (Song and Lyu 2005). (Dempsey and McVey 1992) have suggested that this problem might be resolved if the Hough transform were implemented with artificial neural networks. This project investigates the feasibility of systems using these core algorithms, and systems that are hybrids of them. Prior to application of the core algorithms to a captured image, various stages of pre-processing are carried out including resizing for optimum results, edgedetection, and edge thinning using an adaptation of the thinning method of (Park, 2000) proposed by this work. An analysis of the costs and benefits of thinning as part of pre-processing has also been performed. The Hough transform based system, which has been largely successful, has involved a number of new approaches. These include a peak detection scheme; post-processing schemes which find valid sub-lines of lines found by the peak detection process, and establish which high-level features these sub-lines represent; and an appropriate navigation scheme. Two artificial neural network systems were designed based on lines detection and sub-lines detection respectively. The first was able to detect long lines, but not shorter (even though navigationally important) lines, and so was aborted. The second system has two major stages. Networks of stage 1 developed to detect sub-lines in sub-images derived by breaking down the original images, did so passibly well. A network in stage 2 designed to use the results of stage 1 to guide the robot’s motion did not do so well for most test images. The networks of stage 1, however, have been helpful with development of a hybrid vision system. Suggestions have been made on how this work can be furthered.
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Wang, Xuerui, and Li Zhao. "Navigation and Automatic Ground Mapping by Rover Robot." Thesis, Högskolan i Halmstad, Halmstad Embedded and Intelligent Systems Research (EIS), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-6185.
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This project is mainly based on mosaicing of images and similarity measurements with different methods. The map of a floor is created from a database of small-images that have been captured by a camera-mounted robot scanning the wooden floor of a living room. We call this ground mapping. After the ground mapping, the robot can achieve self-positioning on the map by using novel small images it captures as it displaces on the ground. Similarity measurements based on the Schwartz inequality have been used to achieve the ground mapping, as well as to position the robot once the ground map is available. Because the natural light affects the gray value of images, this effect must be accounted for in the envisaged similarity measurements. A new approach to mosaicing is suggested. It uses the local texture orientation, instead of the original gray values, in ground mapping as well as in positioning. Additionally, we report on ground mapping results using other features, gray-values as features. The robot can find its position with few pixel errors by using the novel approach and similarity measurements based on the Schwartz inequality.
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Kira, Zsolt. "Communication and alignment of grounded symbolic knowledge among heterogeneous robots." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33941.
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Experience forms the basis of learning. It is crucial in the development of human intelligence, and more broadly allows an agent to discover and learn about the world around it. Although experience is fundamental to learning, it is costly and time-consuming to obtain. In order to speed this process up, humans in particular have developed communication abilities so that ideas and knowledge can be shared without requiring first-hand experience.
Consider the same need for knowledge sharing among robots. Based on the recent growth of the field, it is reasonable to assume that in the near future there will be a collection of robots learning to perform tasks and gaining their own experiences in the world. In order to speed this learning up, it would be beneficial for the various robots to share their knowledge with each other. In most cases, however, the communication of knowledge among humans relies on the existence of similar sensory and motor capabilities. Robots, on the other hand, widely vary in perceptual and motor apparatus, ranging from simple light sensors to sophisticated laser and vision sensing.
This dissertation defines the problem of how heterogeneous robots with widely different capabilities can share experiences gained in the world in order to speed up learning. The work focus specifically on differences in sensing and perception, which can be used both for perceptual categorization tasks as well as determining actions based on environmental features. Motivating the problem, experiments first demonstrate that heterogeneity does indeed pose a problem during the transfer of object models from one robot to another. This is true even when using state of the art object recognition algorithms that use SIFT features, designed to be unique and reproducible.
It is then shown that the abstraction of raw sensory data into intermediate categories for multiple object features (such as color, texture, shape, etc.), represented as Gaussian Mixture Models, can alleviate some of these issues and facilitate effective knowledge transfer. Object representation, heterogeneity, and knowledge transfer is framed within Gärdenfors' conceptual spaces, or geometric spaces that utilize similarity measures as the basis of categorization. This representation is used to model object properties (e.g. color or texture) and concepts (object categories and specific objects).
A framework is then proposed to allow heterogeneous robots to build models of their differences with respect to the intermediate representation using joint interaction in the environment. Confusion matrices are used to map property pairs between two heterogeneous robots, and an information-theoretic metric is proposed to model information loss when going from one robot's representation to another. We demonstrate that these metrics allow for cognizant failure, where the robots can ascertain if concepts can or cannot be shared, given their respective capabilities.
After this period of joint interaction, the learned models are used to facilitate communication and knowledge transfer in a manner that is sensitive to the robots' differences. It is shown that heterogeneous robots are able to learn accurate models of their similarities and difference, and to use these models to transfer learned concepts from one robot to another in order to bootstrap the learning of the receiving robot. In addition, several types of communication tasks are used in the experiments. For example, how can a robot communicate a distinguishing property of an object to help another robot differentiate it from its surroundings? Throughout the dissertation, the claims will be validated through both simulation and real-robot experiments.
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Strineholm, Philippe. "Exploring Human-Robot Interaction Through Explainable AI Poetry Generation." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54606.
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As the field of Artificial Intelligence continues to evolve into a tool of societal impact, a need of breaking its initial boundaries as a computer science discipline arises to also include different humanistic fields. The work presented in this thesis revolves around the role that explainable artificial intelligence has in human-robot interaction through the study of poetry generators. To better understand the scope of the project, a poetry generators study presents the steps involved in the development process and the evaluation methods. In the algorithmic development of poetry generators, the shift from traditional disciplines to transdisciplinarity is identified. In collaboration with researchers from the Research Institutes of Sweden, state-of-the-art generators are tested to showcase the power of artificially enhanced artifacts. A development plateau is discovered and with the inclusion of Design Thinking methods potential future human-robot interaction development is identified. A physical prototype capable of verbal interaction on top of a poetry generator is created with the new feature of changing the corpora to any given audio input. Lastly, the strengths of transdisciplinarity are connected with the open-sourced community in regards to creativity and self-expression, producing an online tool to address future work improvements and introduce nonexperts to the steps required to self-build an intelligent robotic companion, thus also encouraging public technological literacy. Explainable AI is shown to help with user involvement in the process of creation, alteration and deployment of AI enhanced applications.
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Norén, Karl. "Obstacle Avoidance for an Autonomous Robot Car using Deep Learning." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160551.
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The focus of this study was deep learning. A small, autonomous robot car was used for obstacle avoidance experiments. The robot car used a camera for taking images of its surroundings. A convolutional neural network used the images for obstacle detection. The available dataset of 31 022 images was trained with the Xception model. We compared two different implementations for making the robot car avoid obstacles. Mapping image classes to steering commands was used as a reference implementation. The main implementation of this study was to separate obstacle detection and steering logic in different modules. The former reached an obstacle avoidance ratio of 80 %, the latter reached 88 %. Different hyperparameters were looked at during training. We found that frozen layers and number of epochs were important to optimize. Weights were loaded from ImageNet before training. Frozen layers decided how many layers that were trainable after that. Training all layers (no frozen layers) was proven to work best. Number of epochs decided how many epochs a model trained. We found that it was important to train between 10-25 epochs. The best model used no frozen layers and trained for 21 epochs. It reached a test accuracy of 85.2 %.
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Wikander, Gustav. "Three dimensional object recognition for robot conveyor picking." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18373.
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<p>Shape-based matching (SBM) is a method for matching objects in greyscale images. It extracts edges from search images and matches them to a model using a similarity measure. In this thesis we extend SBM to find the tilt and height position of the object in addition to the z-plane rotation and x-y-position. The search is conducted using a scale pyramid to improve the search speed. A 3D matching can be done for small tilt angles by using SBM on height data and extending it with additional steps to calculate the tilt of the object. The full pose is useful for picking objects with an industrial robot.</p><p>The tilt of the object is calculated using a RANSAC plane estimator. After the 2D search the differences in height between all corresponding points of the model and the live image are calculated. By estimating a plane to this difference the tilt of the object can be calculated. Using the tilt the model edges are tilted in order to improve the matching at the next scale level.</p><p>The problems that arise with occlusion and missing data have been studied. Missing data and erroneous data have been thresholded manually after conducting tests where automatic filling of missing data did not noticeably improve the matching. The automatic filling could introduce new false edges and remove true ones, thus lowering the score.</p><p>Experiments have been conducted where objects have been placed at increasing tilt angles. The results show that the matching algorithm is object dependent and correct matches are almost always found for tilt angles less than 10 degrees. This is very similar to the original 2D SBM because the model edges does not change much for such small angels. For tilt angles up to about 25 degrees most objects can be matched and for nice objects correct matches can be done at large tilt angles of up to 40 degrees.</p>
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Sattigeri, Ramachandra Jayant. "Adaptive Estimation and Control with Application to Vision-based Autonomous Formation Flight." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16272.
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The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control.
This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach.
The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties.
The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations.
Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.
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Chen, Haoyao. "Towards multi-robot formations : study on vision-based localization system /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-meem-b3008295xf.pdf.
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Thesis (Ph.D.)--City University of Hong Kong, 2009.<br>"Submitted to Department of Manufacturing Engineering and Engineering Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 87-100)
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Somani, Nikhil [Verfasser], Alois C. [Akademischer Betreuer] Knoll, Torsten [Gutachter] Kröger, and Alois C. [Gutachter] Knoll. "Constraint-based Approaches for Robotic Systems: from Computer Vision to Real-Time Robot Control / Nikhil Somani ; Gutachter: Torsten Kröger, Alois C. Knoll ; Betreuer: Alois C. Knoll." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1172414947/34.
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Buason, Gunnar. "Competitive co-evolution of sensory-motor systems." Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-733.
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<p>A recent trend in evolutionary robotics and artificial life research is to maximize self-organization in the design of robotic systems, in particular using artificial evolutionary techniques, in order to reduce the human designer bias. This dissertation presents experiments in competitive co-evolutionary robotics that integrate and extend previous work on competitive co-evolution of neural robot controllers in a predator-prey scenario with work on the ‘co-evolution’ of robot morphology and control systems. The focus here is on a systematic investigation of tradeoffs and interdependencies between morphological parameters and behavioral strategies through a series of predator-prey experiments in which increasingly many aspects are subject to self-organization through competitive co-evolution. The results show that there is a strong interdependency between morphological parameters and behavioral strategies evolved, and that the competitive co-evolutionary process was able to find a balance between and within these two aspects. It is therefore concluded that competitive co-evolution has great potential as a method for the automatic design of robotic systems.</p>
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Billing, Erik. "Cognition Rehearsed : Recognition and Reproduction of Demonstrated Behavior." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-50980.
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The work presented in this dissertation investigates techniques for robot Learning from Demonstration (LFD). LFD is a well established approach where the robot is to learn from a set of demonstrations. The dissertation focuses on LFD where a human teacher demonstrates a behavior by controlling the robot via teleoperation. After demonstration, the robot should be able to reproduce the demonstrated behavior under varying conditions. In particular, the dissertation investigates techniques where previous behavioral knowledge is used as bias for generalization of demonstrations. The primary contribution of this work is the development and evaluation of a semi-reactive approach to LFD called Predictive Sequence Learning (PSL). PSL has many interesting properties applied as a learning algorithm for robots. Few assumptions are introduced and little task-specific configuration is needed. PSL can be seen as a variable-order Markov model that progressively builds up the ability to predict or simulate future sensory-motor events, given a history of past events. The knowledge base generated during learning can be used to control the robot, such that the demonstrated behavior is reproduced. The same knowledge base can also be used to recognize an on-going behavior by comparing predicted sensor states with actual observations. Behavior recognition is an important part of LFD, both as a way to communicate with the human user and as a technique that allows the robot to use previous knowledge as parts of new, more complex, controllers. In addition to the work on PSL, this dissertation provides a broad discussion on representation, recognition, and learning of robot behavior. LFD-related concepts such as demonstration, repetition, goal, and behavior are defined and analyzed, with focus on how bias is introduced by the use of behavior primitives. This analysis results in a formalism where LFD is described as transitions between information spaces. Assuming that the behavior recognition problem is partly solved, ways to deal with remaining ambiguities in the interpretation of a demonstration are proposed. The evaluation of PSL shows that the algorithm can efficiently learn and reproduce simple behaviors. The algorithm is able to generalize to previously unseen situations while maintaining the reactive properties of the system. As the complexity of the demonstrated behavior increases, knowledge of one part of the behavior sometimes interferes with knowledge of another parts. As a result, different situations with similar sensory-motor interactions are sometimes confused and the robot fails to reproduce the behavior. One way to handle these issues is to introduce a context layer that can support PSL by providing bias for predictions. Parts of the knowledge base that appear to fit the present context are highlighted, while other parts are inhibited. Which context should be active is continually re-evaluated using behavior recognition. This technique takes inspiration from several neurocomputational models that describe parts of the human brain as a hierarchical prediction system. With behavior recognition active, continually selecting the most suitable context for the present situation, the problem of knowledge interference is significantly reduced and the robot can successfully reproduce also more complex behaviors.
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Wernersson, Björn, and Mikael Södergren. "Automatiserad inlärning av detaljer för igenkänning och robotplockning." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170.
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<p>Just how far is it possible to make learning of new parts for recognition and robot picking autonomous? This thesis initially gives the prerequisites for the steps in learning and calibration that are to be automated. Among these tasks are to select a suitable part model from numerous candidates with the help of a new part segmenter, as well as computing the spatial extent of this part, facilitating robotic collision handling. Other tasks are to analyze the part model in order to highlight correct and suitable edge segments for increasing pattern matching certainty, and to choose appropriate acceptance levels for pattern matching. Furthermore, tasks deal with simplifying camera calibration by analyzing the calibration pattern, as well as compensating for differences in perspective at great depth variations, by calculating the centre of perspective of the image. The image processing algorithms created in order to solve the tasks are described and evaluated thoroughly. This thesis shows that simplification of steps of learning and calibration, by the help of advanced image processing, really is possible.</p>
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Rafikova, Elvira. "Controle de um robô móvel através de realimentação de estados utilizando visão estereoscópica." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264560.
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Orientador: Paulo Roberto Gardel Kurka<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-17T01:31:31Z (GMT). No. of bitstreams: 1
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Previous issue date: 2010<br>Resumo: O enfoque principal desse trabalho é o controle de trajetória e navegação no ambiente através da visão estereoscópica de um robô móvel de duas rodas de acionamento diferencial. Para o controle de posicionamento, são utilizadas: uma estratégia de controle ótima linear e uma estratégia subótima, não linear, em tempo contínuo, chamada de SDRE (State Dependent Riccati Equation), e por fim, uma estratégia de controle SDRE em tempo discreto. Todas essas estratégias são baseadas em funções de Lyapunov e aplicadas ao problema de regulação do robô a uma referência. Para a navegação do robô no ambiente é considerado um modelo navegação por odometria e um mecanismo de visão estereoscópica. A estimação do estado é realizada através do filtro de Kalman clássico. São apresentadas duas estratégias para a navegação do robô no ambiente. Uma delas, totalmente discreta com a utilização do métodos de controle SDRE discreto, observação de estado discreta através das câmeras e estimação de estado através do filtro de Kalman discreto. Outra, com a abordagem de horizonte recuável, utilizando controle SDRE contínuo e, observação e estimação de estado discretas. A eficácia dos métodos de controle e das estratégias de navegação do robô é verificada através de simulações computacionais, nas quais a estratégia de navegação com horizonte recuável se mostra eficaz para a navegação precisa no ambiente<br>Abstract: The main approach of this thesis is the trajectory control and navigation of a differential steering mobile robot in the environment. For the position control problem are used? A continuous-time, linear feedback control; a suboptimal, nonlinear, continuous-time feedback called SDRE (StateDependent Riccati Equation) control and a discrete - time SDRE control method. All of these methods are Lyapunov functions based and appplied to the reference tracking problem oh the nonholonomic robot. For the purpose of the environmental navigation a model of odometry-stereo vision state observation system is considered. Meanwhile, the state estimation is given by classic Kalman filter. Futhermore, two different navigation strategies are presented. The discret-time one, using both discret SDRE control method and state estimation. Another one, is a receding horizon strategy, using continuous-time SDRE controler and sicret-time state estimation. The control method and navigation strategies eficaccy is verified through numerical simulations. Both navigation stategies demostrate good results, although the receding horizon one provides more precise navigation<br>Doutorado<br>Mecanica dos Sólidos e Projeto Mecanico<br>Doutor em Engenharia Mecânica
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Einevik, Johan, and John Kurri. "Emulering av en produktioncell med Visionguidning : Virtuell idrifttagning." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14143.
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Genom att använda sig utav en virtuell kopia utav en produktionscell kan programmering och funktionstester av olika paneler testas i ett tidigt stadie. En virtuell kopia bidrar också till enklare felsökning och minskning av kostnader vid idrifttagning. Tanken med projektet är att undersöka i vilken utsträckning som emuleringsmodellen kan ersätta den riktiga cellen vid ett funktionstest för leverantören. Det som också undersöks är i vilken utsträckning riktiga CAD-ritningar kan användas och vilka krav som ställs på ritningarna för att underlätta emulering. Projektet hade flera utmaningar och en av dem som uppkom under projektets gång var problemet med att det inte gick att emulera säkerhetssystemen. Detta löstes genom att bygla alla säkerhetskretsar i PLC-programmet. En viktig del i emulering är kommunikation mellan de olika programvarorna i systemet. I projektets visade det sig fördelaktigt att dela upp programmen i emuleringssystemet för att fördela resurserna över tre datorer. Att använda sig utav en emuleringsmodell istället för en riktig produktionscell är fortfarande i forskningsstadiet men genom projektet har många användningsområden identifierats och skulle kunna förändra idrifttagning i framtiden.<br>Using a virtual twin of a production cell, makes it possible for programming and different functional testing of panels to be performed in early stages of development. A virtual twin contributes to a simpler debugging and to identify problems and minimize cost in commissioning of the production cell. The aim for the project is to investigate how well an emulated cell will perform compared to the real production cell in a factory acceptance test. Another objective is to investigate how you can use real CAD models in the emulation and what type of criteria the models should meet. The project had a lot of challenges and one of them was the difficulty to emulate the safety systems. This was solved by bypassing the safety in the PLC program. One important thing about emulation is communication between the different software used in the system. In this project, it proved successful to distribute the software on three computers to ease the workload of the programs used in the emulation. To use the emulated model instead of the real system is still in the research phase but in this project a lot of useful applications could be identified that could change commissioning in the future.
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Zhou, Dingfu. "Vision-based moving pedestrian recognition from imprecise and uncertain data." Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2162/document.
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La mise en oeuvre de systèmes avancés d’aide à la conduite (ADAS) basée vision, est une tâche complexe et difficile surtout d’un point de vue robustesse en conditions d’utilisation réelles. Une des fonctionnalités des ADAS vise à percevoir et à comprendre l’environnement de l’ego-véhicule et à fournir l’assistance nécessaire au conducteur pour réagir à des situations d’urgence. Dans cette thèse, nous nous concentrons sur la détection et la reconnaissance des objets mobiles car leur dynamique les rend plus imprévisibles et donc plus dangereux. La détection de ces objets, l’estimation de leurs positions et la reconnaissance de leurs catégories sont importants pour les ADAS et la navigation autonome. Par conséquent, nous proposons de construire un système complet pour la détection des objets en mouvement et la reconnaissance basées uniquement sur les capteurs de vision. L’approche proposée permet de détecter tout type d’objets en mouvement en fonction de deux méthodes complémentaires. L’idée de base est de détecter les objets mobiles par stéréovision en utilisant l’image résiduelle du mouvement apparent (RIMF). La RIMF est définie comme l’image du mouvement apparent causé par le déplacement des objets mobiles lorsque le mouvement de la caméra a été compensé. Afin de détecter tous les mouvements de manière robuste et de supprimer les faux positifs, les incertitudes liées à l’estimation de l’ego-mouvement et au calcul de la disparité doivent être considérées. Les étapes principales de l’algorithme sont les suivantes : premièrement, la pose relative de la caméra est estimée en minimisant la somme des erreurs de reprojection des points d’intérêt appariées et la matrice de covariance est alors calculée en utilisant une stratégie de propagation d’erreurs de premier ordre. Ensuite, une vraisemblance de mouvement est calculée pour chaque pixel en propageant les incertitudes sur l’ego-mouvement et la disparité par rapport à la RIMF. Enfin, la probabilité de mouvement et le gradient de profondeur sont utilisés pour minimiser une fonctionnelle d’énergie de manière à obtenir la segmentation des objets en mouvement. Dans le même temps, les boîtes englobantes des objets mobiles sont générées en utilisant la carte des U-disparités. Après avoir obtenu la boîte englobante de l’objet en mouvement, nous cherchons à reconnaître si l’objet en mouvement est un piéton ou pas. Par rapport aux algorithmes de classification supervisée (comme le boosting et les SVM) qui nécessitent un grand nombre d’exemples d’apprentissage étiquetés, notre algorithme de boosting semi-supervisé est entraîné avec seulement quelques exemples étiquetés et de nombreuses instances non étiquetées. Les exemples étiquetés sont d’abord utilisés pour estimer les probabilités d’appartenance aux classes des exemples non étiquetés, et ce à l’aide de modèles de mélange de gaussiennes après une étape de réduction de dimension réalisée par une analyse en composantes principales. Ensuite, nous appliquons une stratégie de boosting sur des arbres de décision entraînés à l’aide des instances étiquetées de manière probabiliste. Les performances de la méthode proposée sont évaluées sur plusieurs jeux de données de classification de référence, ainsi que sur la détection et la reconnaissance des piétons. Enfin, l’algorithme de détection et de reconnaissances des objets en mouvement est testé sur les images du jeu de données KITTI et les résultats expérimentaux montrent que les méthodes proposées obtiennent de bonnes performances dans différents scénarios de conduite en milieu urbain<br>Vision-based Advanced Driver Assistance Systems (ADAS) is a complex and challenging task in real world traffic scenarios. The ADAS aims at perceiving andunderstanding the surrounding environment of the ego-vehicle and providing necessary assistance for the drivers if facing some emergencies. In this thesis, we will only focus on detecting and recognizing moving objects because they are more dangerous than static ones. Detecting these objects, estimating their positions and recognizing their categories are significantly important for ADAS and autonomous navigation. Consequently, we propose to build a complete system for moving objects detection and recognition based on vision sensors. The proposed approach can detect any kinds of moving objects based on two adjacent frames only. The core idea is to detect the moving pixels by using the Residual Image Motion Flow (RIMF). The RIMF is defined as the residual image changes caused by moving objects with compensated camera motion. In order to robustly detect all kinds of motion and remove false positive detections, uncertainties in the ego-motion estimation and disparity computation should also be considered. The main steps of our general algorithm are the following : first, the relative camera pose is estimated by minimizing the sum of the reprojection errors of matched features and its covariance matrix is also calculated by using a first-order errors propagation strategy. Next, a motion likelihood for each pixel is obtained by propagating the uncertainties of the ego-motion and disparity to the RIMF. Finally, the motion likelihood and the depth gradient are used in a graph-cut-based approach to obtain the moving objects segmentation. At the same time, the bounding boxes of moving object are generated based on the U-disparity map. After obtaining the bounding boxes of the moving object, we want to classify the moving objects as a pedestrian or not. Compared to supervised classification algorithms (such as boosting and SVM) which require a large amount of labeled training instances, our proposed semi-supervised boosting algorithm is trained with only a few labeled instances and many unlabeled instances. Firstly labeled instances are used to estimate the probabilistic class labels of the unlabeled instances using Gaussian Mixture Models after a dimension reduction step performed via Principal Component Analysis. Then, we apply a boosting strategy on decision stumps trained using the calculated soft labeled instances. The performances of the proposed method are evaluated on several state-of-the-art classification datasets, as well as on a pedestrian detection and recognition problem.Finally, both our moving objects detection and recognition algorithms are tested on the public images dataset KITTI and the experimental results show that the proposed methods can achieve good performances in different urban scenarios
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Roos, André Filipe. "Controle de fixação atentivo para uma cabeça robótica com visão binocular." Universidade Tecnológica Federal do Paraná, 2016. http://repositorio.utfpr.edu.br/jspui/handle/1/2648.
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A pesquisa em visão computacional ainda está distante de replicar a adaptabilidade e o desempenho do Sistema Visual Humano. Grande parte das técnicas consolidadas são válidas apenas em cenas estáticas e condições restritivas. Cabeças robóticas representam um avanço em flexibilidade, pois carregam câmeras que podem ser movimentadas livremente para a exploração dos arredores. A observação artificial de um ambiente dinâmico exige a solução de pelo menos dois problemas: determinar quais informações perceptuais relevantes extrair dos sensores e como controlar seu movimento para mudar e manter a fixação de alvos com forma e movimento arbitrários. Neste trabalho, um sistema de controle de fixação binocular geral é proposto, e o subsistema responsável pela seleção de alvos e fixação de deslocamentos laterais é projetado, experimentado e avaliado em uma cabeça robótica com quatro graus de liberdade. O subsistema emprega um popular modelo de atenção visual de baixo nível para detectar o ponto mais saliente da cena e um controlador proporcional-integral gera um movimento conjuntivo das duas câmeras para centralizá-lo na imagem da câmera esquerda, assumida como dominante. O desenvolvimento do sistema envolveu primeiramente a modelagem física detalhada do mecanismo de pan e tilt das câmeras. Então, a estrutura linearizada obtida foi ajustada por mínimos quadrados aos dados experimentais de entrada-saída. Por fim, os ganhos do controlador foram sintonizados por otimização e ajuste manual. A implementação em C++ com a biblioteca OpenCV permitiu operação em tempo real a 30 Hz. Experimentos demonstram que o sistema é capaz de fixar alvos estáticos e altamente salientes sem conhecimento prévio ou fortes suposições. Alvos em movimento harmônico são perseguidos naturalmente, embora com defasamento. Em cenas visualmente densas, onde múltiplos alvos em potencial competem pela atenção, o sistema pode apresentar comportamento oscilatório, exigindo o ajuste fino dos pesos do algoritmo para operação suave. A adição de um controlador para o pescoço e de um controlador de vergência para a compensação de deslocamentos em profundidade são os próximos passos rumo a um observador artificial genérico.<br>Computer vision research is still far from replicating the adaptability and performance of the Human Visual System. Most of its consolidated techniques are valid only over static scenes and restrictive conditions. Robot heads represent an advance in terms of flexibility by carrying cameras that can be freely moved to explore the surroundings. Artificial observation of dynamic environments requires the solution of at least two problems: to determine what is the relevant perceptual information to be extracted from the sensors and how to control their movement in order to shift and hold gaze on targets featuring arbitrary shapes and motions. In this work, a general binocular gaze control system is proposed, and the subsystem responsible for targeting and following lateral displacements is designed, tested and assessed in a four degrees-of-freedom robot head. The subsystem employs a popular low-level visual attention model to detect the most salient point in the scene, and a proportional-integral controller generates a conjunctive movement of the cameras to center it in the left camera image, assumed to be dominant. The development started with a detailed physical modeling of the pan and tilt mechanism that drives the cameras. Then, the linearized structure obtained was fitted via least squares estimation to experimental input-output data. Finally, the controller gains were tuned by optimization and manual adjustment. The OpenCV-based implementation in C++ allowed real-time execution at 30 Hz. Experiments demonstrate that the system is capable of fixating highly salient and static targets without any prior knowledge or strong assumptions. Targets describing harmonic motion are naturally pursued, albeit with a phase shift. In cluttered scenes, where multiple potential targets compete for attention, the system may present oscillatory behavior, requiring fine adjustment of algorithm weights for smooth operation. The addition of a controller for the neck and a vergence controller to compensate for depth displacements are the next steps towards a generic artificial observer.
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Pan, Wendy. "A simulated shape recognition system using feature extraction /." Online version of thesis, 1989. http://hdl.handle.net/1850/10496.
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Mikhalsky, Maxim. "Efficient biomorphic vision for autonomous mobile robots." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16206/.
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Autonomy is the most enabling and the least developed robot capability. A mobile robot is autonomous if capable of independently attaining its objectives in unpredictable environment. This requires interaction with the environment by sensing, assessing, and responding to events. Such interaction has not been achieved. The core problem consists in limited understanding of robot autonomy and its aspects, and is exacerbated by the limited resources available in a small autonomous mobile robot such as energy, information, and space. This thesis describes an efficient biomorphic visual capability that can provide purposeful interaction with environment for a small autonomous mobile robot. The method used for achieving this capability comprises synthesis of an integral paradigm of a purposeful autonomous mobile robot, formulation of requirements for the visual capability, and development of efficient algorithmic and technological solutions. The paradigm is a product of analysis of fundamental aspects of the problem, and the insights found in inherently autonomous biological organisms. Based on this paradigm, analysis of the biological vision and the available technological basis, and the state-of-the-art in vision algorithms, the requirements were formulated for a biomorphic visual capability that provides the situation awareness capability for a small autonomous mobile robot. The developed visual capability is comprised of a sensory and processing architecture, an integral set of motion vision algorithms, and a method for visual ranging of still objects that is based on them. These vision algorithms provide motion detection, fixation, and tracking functionality with low latency and computational complexity. High temporal resolution of CMOS imagers is exploited for reducing the logical complexity of image analysis, and consequently the computational complexity of the algorithms. The structure of the developed algorithms conforms to the arithmetic and memory resources available in a system on a programmable chip (SoPC), which allows complete confinement of the high-bandwidth datapath within a SoPC device and therefore high-speed operation by design. The algorithms proved to be functional, which validates the developed visual capability. The experiments confirm that high temporal resolution imaging simplifies image motion structure, and ultimately the design of the robot vision system.
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Benlamri, Rachid. "A multiple-sensor based system for image inspection." Thesis, University of Manchester, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307427.
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Ng, Romney K. H. "Geon recognition using a mobile robot vision system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0025/MQ50648.pdf.
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Lagarde, Matthieu, Philippe Gaussier, and Pierre Andry. "Apprentissage de nouveaux comportements: vers le développement épigénétique d'un robot autonome." Phd thesis, Université de Cergy Pontoise, 2010. http://tel.archives-ouvertes.fr/tel-00749761.
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La problématique de l'apprentissage de comportements sur un robot autonome soulève de nombreuses questions liées au contrôle moteur, à l'encodage du comportement, aux stratégies comportementales et à la sélection de l'action. Utiliser une approche développementale présente un intérêt tout particulier dans le cadre de la robotique autonome. Le comportement du robot repose sur des mécanismes de bas niveau dont les interactions permettent de faire émerger des comportements plus complexes. Le robot ne possède pas d'informations a priori sur ses caractéristiques physiques ou sur l'environnement, il doit apprendre sa propre dynamique sensori-motrice. J'ai débuté ma thèse par l'étude d'un modèle d'imitation bas niveau. Du point de vue du développement, l'imitation est présente dès la naissance et accompagne, sous de multiples formes, le développement du jeune enfant. Elle présente une fonction d'apprentissage et se révèle alors être un atout en terme de temps d'acquisition de comportements, ainsi qu'une fonction de communication participant à l'amorce et au maintien d'interactions non verbales et naturelles. De plus, même s'il n'y a pas de réelle intention d'imiter, l'observation d'un autre agent permet d'extraire suffisamment d'informations pour être capable de reproduire la tâche. Mon travail a donc dans un premier temps consisté à appliquer et tester un modèle développemental qui permet l'émergence de comportements d'imitation de bas niveau sur un robot autonome. Ce modèle est construit comme un homéostat qui tend à équilibrer par l'action ses informations perceptives frustres (détection du mouvement, détection de couleur, informations sur les angles des articulations d'un bras de robot). Ainsi, lorsqu'un humain bouge sa main dans le champ visuel du robot, l'ambigüité de la perception de ce dernier lui fait confondre la main de l'humain avec l'extrémité de son bras. De l'erreur qui en résulte émerge un comportement d'imitation immédiate des gestes de l'humain par action de l'homéostat. Bien sûr, un tel modèle implique que le robot soit capable d'associer au préalable les positions visuelles de son effecteur avec les informations proprioceptives de ses moteurs. Grace au comportement d'imitation, le robot réalise des mouvements qu'il peut ensuite apprendre pour construire des comportements plus complexes. Comment alors passer d'un simple mouvement à un geste plus complexe pouvant impliquer un objet ou un lieu ? Je propose une architecture qui permet à un robot d'apprendre un comportement sous forme de séquences temporelles complexes (avec répétition d'éléments) de mouvements. Deux modèles différents permettant l'apprentissage de séquences ont été développés et testés. Le premier apprend en ligne le timing de séquences temporelles simples. Ce modèle ne permettant pas d'apprendre des séquences complexes, le second modèle testé repose sur les propriétés d'un réservoir de dynamiques, il apprend en ligne des séquences complexes. A l'issue de ces travaux, une architecture apprenant le timing d'une séquence complexe a été proposée. Les tests en simulation et sur robot ont montré la nécessité d'ajouter un mécanisme de resynchronisation permettant de retrouver les bons états cachés pour permettre d'amorcer une séquence complexe par un état intermédiaire. Dans un troisième temps, mes travaux ont consisté à étudier comment deux stratégies sensorimotrices peuvent cohabiter dans le cadre d'une tâche de navigation. La première stratégie encode le comportement à partir d'informations spatiales alors que la seconde utilise des informations temporelles. Les deux architectures ont été testées indépendamment sur une même tâche. Ces deux stratégies ont ensuite été fusionnées et exécutées en parallèle. La fusion des réponses délivrées par les deux stratégies a été réalisée avec l'utilisation de champs de neurones dynamiques. Un mécanisme de "chunking" représentant l'état instantané du robot (le lieu courant avec l'action courante) permet de resynchroniser les dynamiques des séquences temporelles. En parallèle, un certain nombre de problème de programmation et de conception des réseaux de neurones sont apparus. En effet, nos réseaux peuvent compter plusieurs centaines de milliers de neurones. Il devient alors difficile de les exécuter sur une seule unité de calcul. Comment concevoir des architectures neuronales avec des contraintes de répartition de calcul, de communications réseau et de temps réel ? Une autre partie de mon travail a consisté à apporter des outils permettant la modélisation, la communication et l'exécution en temps réel d'architecture distribuées. Pour finir, dans le cadre du projet européen Feelix Growing, j'ai également participé à l'intégration de mes travaux avec ceux du laboratoire LASA de l'EPFL pour l'apprentissage de comportements complexes mêlant la navigation, le geste et l'objet. En conclusion, cette thèse m'a permis de développer à la fois de nouveaux modèles pour l'apprentissage de comportements - dans le temps et dans l'espace, de nouveaux outils pour maîtriser des réseaux de neurones de très grande taille et de discuter à travers les limitations du système actuel, les éléments importants pour un système de sélection de l'action.
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Berg, Paula M. "Intergrating vision into a computer integrated manufacturing system." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/43754.
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An industrial vision system is a useful and often integral part of a computer integrated manufacturing system. Successful integration of vision capabilities into a manufacturing system involves extracting from image data the information which has meaning to the task at hand, and communicating that information to the larger system.
The goal of this research was to integrate the activities of a stand-alone vision system into the operation of a manufacturing system; more specifically, the host controller and vision system were expected to work together to determine the status of pallets moving through the system.
Pallet status was based on whether the objects on the pallet were correct in shape, location, and orientation, as compared to a pallet model generated using the microcomputer-based CADKEY CAD program. Cadd.c, a C language program developed for this research, extracts object area, perimeter, centroid, and principal angle from the CAD KE Y model for comparison to counterparts generated by the vision system. This off-line approach to supplying known parameters to the vision system was chosen over the traditional "teach by showing" method to take advantage of existing CAD data and to avoid disruption of the production system.
The actual comparison of model and image data was performed by a program written in VPL, the resident language of the GE Optomation II Vision System. The comparison program relies on another short VPL program to obtain a pixel/inch ratio which equates the disparate units of the two systems.
Model parameters are passed to the vision system via hardware and software links developed as part of this research. Three C language programs enable the host computer to communicate commands and parameters, and receive program results from the vision system.
Preliminary testing of the system revealed that the object location and surface texture, lighting conditions, and pallet background all affected the image parameter calculations and hence the comparison process.<br>Master of Science