Locomotion automation is a very challenging and complex problem to solve. Besides the obvious navigation problems, there are also problems regarding the environment in which navigation has to be performed. Terrains with obstacles such as rocks, steps or high inclinations, among others, pose serious difficulties to normal wheeled vehicles. The flexibility of legged locomotion is ideal for these types of terrains but this alternate form of locomotion brings with it its own challenges to be solved, caused by the high number of degrees of freedom inherent to it. This problem is usually computationally intensive, so an alternative, using simple and hardware amenable bio-inspired systems, was studied. The goal of this thesis was to investigate if using a biologically inspired learning algorithm, integrated in a fully biologically inspired system, can improve its performance on irregular terrain by adapting its gait to deal with obstacles in its path. At first, two different versions of a learning algorithm based on unsupervised reinforcement learning were developed and evaluated. These systems worked by correlating different events and using them to adjust the behaviour of the system so that it predicts difficult situations and adapts to them beforehand. The difference between these versions was the implementation of a mechanism that allowed for some correlations to be forgotten and suppressed by stronger ones. Secondly, a depth from motion system was tested with unsatisfactory results. The source of the problems are analysed and discussed. An alternative system based on stereo vision was implemented, together with an obstacle detection system based on neuron and synaptic models. It is shown that this system is able to detect obstacles in the path of the robot. After the individual systems were completed, they were integrated together and the system performance was evaluated in a series of 3D simulations using various scenarios. These simulations allowed to conclude that both learning systems were able to adapt to simple scenarios but only the one capable of forgetting past correlations was able to adjust correctly in the more complex experiments.

Many potential indoor applications exist for autonomous vehicles, such as automated surveillance, inspection, and document delivery. A key requirement for autonomous operation is for the vehicles to be able to detect and map obstacles in order to avoid collisions. This work develops a comprehensive 3D scene reconstruction algorithm based on known vehicle motion and vision data that is specifically tailored to the indoor environment. Visible light cameras are one of the many sensors available for capturing information from the environment, and their key advantages over other sensors are that they are light weight, power efficient, cost effective, and provide abundant information about the scene. The emphasis on 3D indoor mapping enables the assumption that a large majority of the area to be mapped is comprised of planar surfaces such as floors, walls and ceilings, which can be exploited to simplify the complex task of dense reconstruction of the environment from monocular vision data. In this thesis, the Planar Surface Reconstruction (PSR) algorithm is presented. It extracts surface information from images and combines it with 3D point estimates in order to generate a reliable and complete environment map. It was designed to be used for single cameras with the primary assumptions that the objects in the environment are flat, static and chromatically unique. The algorithm finds and tracks Scale Invariant Feature Transform (SIFT) features from a sequence of images to calculate 3D point estimates. The individual surface information is extracted using a combination of the Kuwahara filter and mean shift segmentation, which is then coupled with the 3D point estimates to fit these surfaces in the environment map. The resultant map consists of both surfaces and points that are assumed to represent obstacles in the scene. A ground vehicle platform was developed for the real-time implementation of the algorithm and experiments were done to assess the PSR algorithm. Both clean and cluttered scenarios were used to evaluate the quality of the surfaces generated from the algorithm. The clean scenario satisfies the primary assumptions underlying the PSR algorithm, and as a result produced accurate surface details of the scene, while the cluttered scenario generated lower quality, but still promising, results. The significance behind these findings is that it is shown that incorporating object surface recognition into dense 3D reconstruction can significantly improve the overall quality of the environment map.

Este trabalho apresenta um sistema de rastreamento de obstáculos, utilizando sensor laser 2D e filtro de Kalman. Este filtro não é muito eficiente em situações em que ocorrem severas perturbações na posição medida do obstáculo, como, por exemplo, um objeto rastreado passando por trás de uma barreira, interrompendo o feixe de laser por alguns instantes, tornando impossível receber do sensor as informações sobre sua posição. Este trabalho sugere um método de minimizar esse problema com o uso de um algoritmo denominado Corretor de Discrepâncias. / An obstacle detection and tracking system using a 2D laser sensor and the Kalman filter is presented. This filter is not very efficient in case of severe disturbances in the measured position of the obstacle, as for instance, when an object being tracked is behind a barrier, thus interrupting the laser beam, making it impossible to receive the sensor information about its position. This work suggests a method to minimize this problem by using an algorithm called Corrector of Discrepancies.

Visual Aids for the blind people is an important subject. Apparently visually impaired individuals get impeded by certain hurdles in everyday life. This work proposes an indoor navigation system for visually impaired people. In particular, the goal of this study is to develop a robust, independent and portable aid to assist a user to navigate familiar as well as unfamiliar areas. The algorithm uses the data from Microsoft Xbox Kinect 360 which makes a 3D map of the indoor areas and detects the depth and estimates the relative distance and angle to an obstacle/human. To ensure the accuracy, Kinect tool is enabled with a colour camera to capture real-time details of surroundings which are then processed accordingly. Besides, the developed aid makes the user aware of environmental changes through a Bluetooth enabled headphones used as audio output device. The trials were conducted on six blindfolded volunteers who successfully navigated across various locations in the university campus such as classrooms, hallways, and stairs. Moreover, the user could also track a particular person through output generated from processed images. Hence, the work suggests a significant improvement for existing visual aids which may be very helpful in customisation as well as the adaptability of these devices.

No description available.

In the operation of unmanned vehicles, safety is a primary concern. This thesis focuses on the use of computer vision in the development of a situational awareness system that allows for safe deployment and operation of a ground robot from an unmanned aerial vehicle (UAV). A method for detecting utility cables in 3D range images is presented. This technique finds areas of an image that represent edges in 3D space, and uses the Hough transform to find those edges that take the shape of lines, indicating potential utility cables. A mission plan for stereo image capture is laid out as well for overcoming some weaknesses of the stereo vision system; this helps ensure that all utility cables in a scene are detected. In addition, the system partitions the point cloud into best-fit planes and uses these planes to locate areas of the scene that are traversable by a ground robot. Each plane's slope is tested against an acceptable value for negotiation by the robot, and the drop-off between the plane and its neighbors is examined as well. With the results of this analysis, the system locates the largest traversable region of the terrain using concepts from graph theory. The system displays this region to the human operator with the drop-offs between planes clearly indicated. The position of the robot is also simulated in this system, and real-time feedback regarding dangerous moves is issued to the operator. After a ground robot is deployed to the chosen site, the system must be capable of tracking it in real time as well. To this end, a software routine that uses ARToolkit's marker tracking capabilities is developed. This application computes the distance to the robot, as well as the horizontal distance from camera to the robot; this allows the flight controller to issue the proper commands to keep the robot centered underneath the UAV. / Master of Science

In order to operate UAVs under human supervisory control in more complex arenas such as urban environments, an obstacle detection system is a requirement to achieve safe navigation. The development of a system capable of meeting these requirements is presented. The first stage of development was sensor selection and initial testing. After this, the sensor was combined with a servomotor to allow it to rotate and provide obstacle detection coverage in front, below, and to both sides of the UAV. Utilizing a PC-104 single board computer running LabView Real-time for on-board control of the sensor and servomotor, a stand alone obstacle detection system was developed meeting the requirements of light weight, low power, and small size. The detection performance of the system for several parameters has been fully characterized. A human subjects study was conducted to assess the any advantages resulting from the addition of the obstacle detection system compared to that of a normal nadir camera. The study demonstrated that users with access to the three-dimensional display were able to navigate an obstacle course with greater success than those with only a camera. Additional development into more advanced visualization of the environment has potential to increase effectiveness of this obstacle detection system. / Master of Science

<p>U tezi je analizirana opravdanost primene FPGA tehnologije za razvoj upravljačkog sistema za linearne aktuatore. Realizovan je upravljački sistem za servo upravljanje linearnim pneumatskim aktuatorom, čiji rad je eksperimentalno proveren. Razvijeni su i algoritmi za detekciju opterećenosti aktuatora, kao i za detekciju prepreke na nepoznatoj poziciji korišćenjem metode analize promene pritiska u komorama pneumatskog cilindra.</p> / <p>This thesis discusses the possibilities of FPGA technology application in<br />the development of a control system for linear actuators. A control system<br />for servo control of linear pneumatic actuators was realized, and<br />experimentally tested. Furthermore, algorithms were developed for<br />detection of actuator load, as well as for detection of an obstacle in<br />unknown position, by analysing pressure change in the pneumatic<br />cylinder chambers.</p>

This thesis aims at detecting obstacles using a single camera in an unknown 3D world for 3D motion of the robot/UAV. Obstacle detection is a pre-requisite for collision-free motion of robots/UAVs. Most of the research in this area has been for 2D motion of the ground robots and with active sensors e.g Laser range finders, Ultrasonic sensors, SONAR, RADAR etc. The passive camera based research has mostly been done either using triangulation/stereo vision (using more than one camera), or, developing an expectation map pre-hand, of the world and comparing it with the new image data. In contrast, this thesis, aims at finding solution of the problem using just a single camera in a perfectly unknown world. This requirement is based on the fact that at least a single camera would be carried by almost all robots/UAVs anyway in foreseeable future. Hence the attempt is to use the same camera for obstacle detection and avoidance task as well, so as to come up with a low cost and light weight solution, in order to facilitate building miniature robots/UAVs.

Cette thèse porte sur la fonction de détection d'obstacles dans le domaine ferroviaire à partir de la vision par ordinateur. Il s'agit d'assurer une perception de l'environnement situé à l'avant du train afin de détecter et d'évaluer les distances des obstacles situés sur la voie.Nous avons donc proposé un module détection de rails à partir des images à niveaux de gris, pour déterminer une zone libre d'obstacles à l'avant du train. Cette détection est basée dans l'algorithme de RANSAC et une estimation de la voie par un polynôme de degré 2. Elle s'est montrée robuste à notre base de données et a permis de détecter les rails à des distances supérieures à la distance d'arrêt. Aussi, un algorithme d'étalonnage des caméras installées dans le train a été proposé à partir de la morphologie de la voie.Comme support de la reconnaissance de rails, nous présentons un module de détection et classification des appareils de voie basé dans le descripteur HOG extrait des images IPM (Inverse Perspective Mapping). Un classifieur SVM (Support Vector Machines) binaire a été utilisé pour la détection et un SVM multi-classe pour différencier les appareils de voie existants sur la ligne.Après avoir élaboré le module de détection des rails, nous avons implémenté un détecteur de trains. À partir d'un échantillon des images de trains de la ligne en question et des images négatives comme des voitures ou des bus, nous avons créé une base de données d'obstacles pour trouver un descripteur robuste qui arrive à décrire la forme des trains et permet à un classifieur SVM de discriminer les images et détecter les trains. Par la suite, ce classifieur est utilisé par le système global pour déterminer la présence d'un train au-delà de la détection de la voie. À la distance maximale de détection, un rectangle de la taille d'un train est extrait de l'image pour vérifier la présence d'un train. Ces rectangles font l'objet d'une classification au moyen de descripteurs globaux de type HOG et une structure SVM binaire.Cette étude permettra non seulement de déboucher sur des applications concrètes, mais surtout d'évaluer la maturité des technologies de traitements d'images pour réaliser des fonctions sûres appliquées aux systèmes ferroviaires. / This thesis deals with obstacle detection in a railway setting using computer vision. The main task is to provide perception of the environment in front ofthe train using an optical sensor to detect and evaluate distances to obstacles along the track path.We present a module for detecting rails from grayscale images to determine an obstacle-free zone in front of the train. This detection is based on the RANSACalgorithm and fitting the track to a second degree polynomial. The method has shown itself robust to our dataset and allows detecting the rails at distancesgreater than the emergency stopping distance. In addition, a method for calibrating the cameras installed on the train is proposed based on the morphology ofthe track.To supplement rail detection, we present a new module for detecting and classifying junctions based on the HOG descriptor extracted from InversePerspective Mapping (IPM) images. A Support Vector Machines (SVM) binary classifier was used for detection and a multi-class SVM for distinguishing ofjunctions along the rails.In the sequel, a train detector was implemented. Using a set of images of trains found on the studied line and negative images such as cars or buses, we havecreated a database of obstacles to find a robust descriptor which is able to model the form of trains and allows a SVM classifier to distinguish images anddetect trains. Next this classifier is used by the overall system to determine the presence of a train in addition to detecting rails. At the maximum detectiondistance, a train-sized rectangle is extracted from the image in order to confirm the presence of a train. These rectangles are classified by means of HOG-typeglobal descriptors and a binary SVM structure.In addition to its applications to concrete problems, this study permits to evaluate the maturity of image processing technologies forfail-safe railway systems.