<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.

No description available.

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.

In some dense urban environments (e.g., a street with tall buildings around), vehicle localization result provided by Global Positioning System (GPS) receiver might not be accurate or even unavailable due to signal reflection (multi-path) or poor satellite visibility. In order to improve the accuracy and robustness of assisted navigation systems so as to guarantee driving security and service continuity on road, a vehicle localization approach is presented in this thesis by taking use of the redundancy and complementarities of multiple sensors. At first, GPS localization method is complemented by onboard dead-reckoning (DR) method (inertial measurement unit, odometer, gyroscope), stereovision based visual odometry method, horizontal laser range finder (LRF) based scan alignment method, and a 2D GIS road network map based map-matching method to provide a coarse vehicle pose estimation. A sensor selection step is applied to validate the coherence of the observations from multiple sensors, only information provided by the validated sensors are combined under a loosely coupled probabilistic framework with an information filter. Then, if GPS receivers encounter long term outages, the accumulated localization error of DR-only method is proposed to be bounded by adding a GIS building map layer. Two onboard LRF systems (a horizontal LRF and a vertical LRF) are mounted on the roof of the vehicle and used to detect building facades in urban environment. The detected building facades are projected onto the 2D ground plane and associated with the GIS building map layer to correct the vehicle pose error, especially for the lateral error. The extracted facade landmarks from the vertical LRF scan are stored in a new GIS map layer. The proposed approach is tested and evaluated with real data sequences. Experimental results with real data show that fusion of the stereoscopic system and LRF can continue to localize the vehicle during GPS outages in short period and to correct the GPS positioning error such as GPS jumps; the road map can help to obtain an approximate estimation of the vehicle position by projecting the vehicle position on the corresponding road segment; and the integration of the building information can help to refine the initial pose estimation when GPS signals are lost for long time.

Laser Diodes (LDs) are semiconductor coherent lightening devices which are widely used in many fields such as defence, industry, medical and optical communications. They have advantageous characteristics such as having higher electrical-to-optical and optical-to-optical conversion efficiencies from pump source to useful output power when compared to flash lamps, which makes them the best devices to be used in range finding applications. Optical output power of lasers depends on current through LDs. Therefore, there is a relationship between operating life and work performance of LDs and performance of drive power supply. Even, weak drive current, small fluctuations of drive current can result in much greater fluctuations of optical output power and device parameters which will reduce reliability of LDs. In this thesis, a hardware for a fast and high power pulsed LD driver is designed for laser range finder and is based on linear current source topology. The driver is capable of providing pulses up to 120A with 250&mu / s pulse width and frequencies ranging from 20Hz to 40Hz. It provides current pulses for two LD arrays controlled with a proportional-integral (PI) controller and protect LDs against overcurrents and overvoltages. The proposed current control in the thesis reduces current regulation to less than 1% and diminishes overshoots and undershoots to a value less than 1% of steady-state value, which improves safe operation of LDs. Moreover, protection functions proposed in the thesis are able to detect any failure in driver and interrupt LD firing immediately, which guarantees safe operation of LDs.