<p>Robust localization is a prerequisite for mobile robot autonomy. In many situations the GPS signal is not available and thus an additional localization system is required. A simple approach is to apply localization based on dead reckoning by use of wheel encoders but it results in large estimation errors. With exteroceptive sensors such as a laser range finder natural landmarks in the environment of the robot can be extracted from raw range data. Landmarks are extracted with the Hough transform and a recursive line segment algorithm. By applying data association and Kalman filtering along with process models the landmarks can be used in combination with wheel encoders for estimating the global position of the robot. If several robots can cooperate better position estimates are to be expected because robots can be seen as mobile landmarks and one robot can supervise the movement of another. The centralized Kalman filter presented in this master thesis systematically treats robots and extracted landmarks such that benefits from several robots are utilized. Experiments in different indoor environments with two different robots show that long distances can be traveled while the positional uncertainty is kept low. The benefit from cooperating robots in the sense of reduced positional uncertainty is also shown in an experiment. </p><p>Except for localization algorithms a typical autonomous robot task in the form of change detection is solved. The change detection method, which requires robust localization, is aimed to be used for surveillance. The implemented algorithm accounts for measurement- and positional uncertainty when determining whether something in the environment has changed. Consecutive true changes as well as sporadic false changes are detected in an illustrative experiment.</p>

Robust localization is a prerequisite for mobile robot autonomy. In many situations the GPS signal is not available and thus an additional localization system is required. A simple approach is to apply localization based on dead reckoning by use of wheel encoders but it results in large estimation errors. With exteroceptive sensors such as a laser range finder natural landmarks in the environment of the robot can be extracted from raw range data. Landmarks are extracted with the Hough transform and a recursive line segment algorithm. By applying data association and Kalman filtering along with process models the landmarks can be used in combination with wheel encoders for estimating the global position of the robot. If several robots can cooperate better position estimates are to be expected because robots can be seen as mobile landmarks and one robot can supervise the movement of another. The centralized Kalman filter presented in this master thesis systematically treats robots and extracted landmarks such that benefits from several robots are utilized. Experiments in different indoor environments with two different robots show that long distances can be traveled while the positional uncertainty is kept low. The benefit from cooperating robots in the sense of reduced positional uncertainty is also shown in an experiment. Except for localization algorithms a typical autonomous robot task in the form of change detection is solved. The change detection method, which requires robust localization, is aimed to be used for surveillance. The implemented algorithm accounts for measurement- and positional uncertainty when determining whether something in the environment has changed. Consecutive true changes as well as sporadic false changes are detected in an illustrative experiment.

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Sludge, produced in the waste water treatment process, can be used as fertilizer in agriculture. It is rich in nutrients but also contains other substances, such as heavy metals. Metals may enter crops which means that it also enters the food chain. Some heavy metals have been proven to cause severe damage to living organisms in high doses. It is therefore important to regulate the amount of heavy metals in the soil and in the sludge used as fertilizer. In this thesis the sources of heavy metals in the waste water system in the town of Västerås was examined. The sources of the heavy metals cadmium, chromium, mercury, copper and zinc were mainly analyzed by using the Excel tool "Source Finder". In this tool collected data of emissions or calculated emissions by the use of model values were entered. Households proved to be the single largest source of all the metals in this study, with the exeption of chromium. For cadmium and chromium water leakage from the ground water into the pipe system was a large contributing source. Business did not prove to be a large source of any metal with the exception of dental units which emitted large amounts of mercury. To be able to predict change of quality, in regards of metals, in the sludge and puried water an existing model of the water treatment plant was supplemented with processes regarding separation of copper. This was done by studying other models regarding metal partitioning and separation. The final model proved unable to describe variances in the measured data, but could describe the median concentration of copper in cleansed water and sludge.

A robótica móvel autônoma é uma área relativamente recente que tem como objetivo a construção de mecanismos capazes de executar tarefas sem a necessidade de um controlador humano. De uma forma geral, a robótica móvel defronta com três problemas fundamentais: mapeamento de ambientes, localização e navegação do robô. Sem esses elementos, o robô dificilmente poderia se deslocar autonomamente de um lugar para outro. Um dos problemas existentes nessa área é a atuação de robôs móveis em ambientes externos como parques e regiões urbanas, onde a complexidade do cenário é muito maior em comparação aos ambientes internos como escritórios e casas. Para exemplificar, nos ambientes externos os sensores estão sujeitos às condições climáticas (iluminação do sol, chuva e neve). Além disso, os algoritmos de navegação dos robôs nestes ambientes devem tratar uma quantidade bem maior de obstáculos (pessoas, animais e vegetações). Esta dissertação apresenta o desenvolvimento de um sistema de classificação da navegabilidade de terrenos irregulares, como por exemplo, ruas e calçadas. O mapeamento do cenário é realizado através de uma plataforma robótica equipada com um sensor laser direcionado para o solo. Foram desenvolvidos dois algoritmos para o mapeamento de terrenos. Um para a visualização dos detalhes finos do ambiente, gerando um mapa de nuvem de pontos e outro para a visualização das regiões próprias e impróprias para o tráfego do robô, resultando em um mapa de navegabilidade. No mapa de navegabilidade, são utilizados métodos de aprendizado de máquina supervisionado para classificar o terreno em navegável (regiões planas), parcialmente navegável (grama, casacalho) ou não navegável (obstáculos). Os métodos empregados foram, redes neurais artificais e máquinas de suporte vetorial. Os resultados de classificação obtidos por ambos foram posteriormente comparados para determinar a técnica mais apropriada para desempenhar esta tarefa / Autonomous mobile robotics is a recent research area that focus on the construction of mechanisms capable of executing tasks without a human control. In general, mobile robotics deals with three fundamental problems: environment mapping, robot localization and navigation. Without these elements, the robot hardly could move autonomously from a place to another. One problem of this area is the operation of the mobile robots in outdoors (e.g. parks and urban areas), which are considerably more complex than indoor environments (e.g. offices and houses). To exemplify, in outdoor environments, sensors are subjected to weather conditions (sunlight, rain and snow), besides that the navigation algorithms must process a larger quantity of obstacles (people, animals and vegetation). This dissertation presents the development of a system that classifies the navigability of irregular terrains, like streets and sidewalks. The scenario mapping has been done using a robotic platform equipped with a laser range finder sensor directed to the ground. Two terrain mapping algorithms has been devolped. One for environment fine details visualization, generating a point cloud map, and other to visualize appropriated and unappropriated places to robot navigation, resulting in a navigability map. In this map, it was used supervised learning machine methods to classify terrain portions in navigable (plane regions), partially navigable (grass, gravel) or non-navigable (obstacles). The classification methods employed were artificial neural networks and support vector machines. The classification results obtained by both were later compared to determine the most appropriated technique to execute this task

This thesis presents a use of 3D laser range finder designed for purposes of autnonomous mobile systems. The 3D scanner is built as extension of 2D laser range finder with rotation module. In the first section is described laser range finder SICK LMS 291 and his pitching construction along with used software tools. Second part deals with design and implementation of algorithms for data reading and their processing with methods of surface reconstruction, octree and object segmentation with Hough transform.

This diploma thesis deals with design and realization of testing modules for detecting a defective peripheral in a mobile robot. One of the modules is intended for the diagnosis of ultra sonic range finder. The second one simulates the behaviour of these ultra sonic range finders, making it able to determine interface on which it is necessary to look for an error.

The goal of this master's thesis is design and built of platform for three-rotor helicopter development. The helicopter is also known as tricopter. Theoretical part describes principle of tricopter's flight and stabilization. There is also described basics inertial navigation system and sensors which are required for correct functionality. Practical part is dedicated to development of tricopter's frame, schematics diagram, communication between subsystems and stabilization system development. Flight stablization system is base on ATmega128A an using PID Controller. In the end is described testing of developed platform.

This master's thesis deals with determination of properties of the ultrasonic sensor SRF05. It is about finding factors that distort or have other negative effects in any way on the proper functionality of these sensors. First of all, it was necessary to program a chip mounted on signal processing board for correct functionality and operation of sensors. Then a few sets of measurements were done to determine the behaviour of sensors for different distances. In addition, we had to carry out many more measurements to find out the influence of temperature, light conditions, material of the sensed surface or cross-noise caused by the use of multiple sensors. In the next step, the approximate shape of the transmitted ultrasound was investigated to determine the zone of detection. The last task of this thesis was to compare the SRF05 sensors with other ultrasonic sensors from different manufacturers.

Yes / In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary Pentagon Support Vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy on some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results.