Inferring probabilistic automata from sensor data for robot navigation

We address the problem of guiding a robot in such a way, that it can decide, based on perceived sensor data, which future actions to choose, in order to reach a goal. In order to realize this guidance, the robot has access to a (probabilistic) automaton (PA), whose final states represent concepts, which have to be recognized in order to verify, that a goal has been achieved. The contribution of this work is to learn these PA's from classified sensor data of robot traces through known environments. Within this framework, we account for the uncertainties arising from ambiguous perceptions. We introduce a knowledge structure, called prefix tree , in which the sample data, represented as cases, is organized. The prefix tree is used to derive and estimate the parameters of deterministic, as well as probabilistic automata models, which reflect the inherent knowledge, implicit in the data, and which are used for recognition in a restricted first-order logic framework

An algorithm for segmenting range imagery

This report describes the technical accomplishments of the FY96 Cross Cutting and Advanced Technology (CC&AT) project at Los Alamos National Laboratory. The project focused on developing algorithms for segmenting range images. The image segmentation algorithm developed during the project is described here. In addition to segmenting range images, the algorithm can fuse multiple range images thereby providing true 3D scene models. The algorithm has been incorporated into the Rapid World Modelling System at Sandia National Laboratory

Radius of curvature and location estimation of cylindrical objects with sonar using a multi-sensor configuration

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references leaves 130-133.Despite their limitations, sonar sensors are very popular in time-of-flight measuring systems since they are inexpensive and convenient. One of the most important limitations of sonar is its low angular resolution. An adjustable multi-sonar configuration consisting of three transmitter/receiver ultrasonic transducers is used to improve the resolution. The radius of curvature estimation of cylindrical objects is accomplished with this configuration. Two different ways of rotating the transducers are considered. First, the sensors are rotated around their joints. Second, the sensors are rotated around their centers. Also, two methods of tirne-of-flight estimation are implemented which are thresholding and curve-fitting. Sensitivity analysis of the radius of curvature with respect to some important parameters is made. The bias-variance combinations of both estimators are compared to the Cramer-Rao lower bound. Theory and simulations are verified by experimental data from real sonar systems. Data is smoothed by extended Kalman filtering. Rotating around the center works better than rotating around the joint. Curve-fitting method is shown to be better than thresholding method both in the absence and presence of noise. The best results are obtained w'hen the sensors are rotated around their centers and the curve-fitting method is used to estimate the time- of-flight. There is about 30% improvement in the absence of noise and 50% improvement in the presence of noise.Sekmen, Ali ŞafakM.S

Construção de mapas locais em robótica móvel

Dissertação de Mestrado em Sistemas e Automação, na especialidade de Automação Industrial apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraA construção de mapas é muito importante para dotar sistemas que envolvem robôs móveis com níveis crescentes de autonomia. No Instituto de Sistemas e Robótica (ISR-Coimbra) existem projectos a decorrer relacionados com a navegação de robôs móveis orientados-ahumanos, mais concretamente envolvendo uma cadeira de rodas motorizada. Os mapas locais são uma componente importante a incorporar na navegação deste tipo de robôs móveis. O objectivo do trabalho apresentado nesta dissertação consistiu no desenvolvimento de uma arquitectura para a construção de um mapa de grelha de células que modelize dinamicamente as características locais do meio envolvente do robô móvel, em termos de regiões ocupadas e regiões desocupadas. Esse mapa pode ser construído utilizando várias fontes de informação sensorial e é independente da disposição dos sensores. A arquitectura é constituída por módulos. Um dos módulos implementa uma rede neuronal artificial com propagação para a frente, através da qual se interpreta a informação sensorial, tendo por objectivo determinar, para cada célula, a probabilidade de estar ocupada. Depois de conhecida a referida probabilidade, as células são actualizadas através da aplicação de uma fórmula baseada no teorema de Bayes. Foram ainda implementados outros módulos para a pesquisa de todas as células e para a escolha dos sensores mais adequados à actualização de cada célula. O método apresentado foi testado em ambientes reais e simulados. Os mapas obtidos em cada ambiente foram analisados segundo determinados parâmetros