In this research work, an approach for fire detection and estimation robots is presented. The approach is based on type-2 fuzzy logic system that utilizes measured temperature and light intensity to detect fires of various intensities at different distances. Type-2 fuzzy logic system (T2 FLS) is known for not needing exact mathematic model and for its capability to handle more complicated uncertain situations compared with Type-1 fuzzy logic system (T1 FLS). Due to lack of expertise for new facilities, a new approach for training experts’ expertise and setting up T2 FLS parameters from pure data is discussed in this thesis. Performance of both T1 FLS and T2 FLS regarding to same fire detection scenario are investigated and compared in this thesis. Simulation works have been done for fire detection robot of both free space scenario and new facility scenario to illustrate the operation and performance of proposed type-2 fuzzy logic system. Experiments are also performed using LEGO MINDSTROMS NXT robot to test the reliability and feasibility of the algorithm in physical environment with simple and complex situation.

Made available in DSpace on 2016-03-15T19:38:52Z (GMT). No. of bitstreams: 1 Alexandre Szabo.pdf: 2177168 bytes, checksum: 8b503cd1beb4c700f1905e07a0b08362 (MD5) Previous issue date: 2014-10-29 / Fundação de Amparo a Pesquisa do Estado de São Paulo / Clustering usually treats objects as belonging to mutually exclusive clusters, what is usually im-precise, because an object may belong to more than one cluster simultaneously with different membership degrees. The clustering algorithms, both crisp and fuzzy, have a number of parameters to be adjusted so that they present the best performance for a given database. Furthermore, it is known that no single algorithm is better than all the others for all problem classes, and the combi-nation of solutions found by various algorithms (or the same algorithm with different parameters) may lead to a global solution that is better than those found by individual algorithms, including the best one. It is within this context that the present thesis proposes a new fuzzy clustering algo-rithm inspired by the behavior of particle swarms and, then, introduces a new form of combining the clustering algorithms using concepts from Type-2 fuzzy sets. / Da maneira tradicional o agrupamento trata os objetos que compõem a base como pertencentes a grupos mutuamente exclusivos, o que nem sempre é verdade, pois um objeto pode pertencer a mais de um grupo com diferentes graus de pertinência. Os algoritmos de agrupamento, sejam eles convencionais ou nebulosos (capazes de tratar múltiplas pertinências simultaneamente), possuem diversos parâmetros a serem ajustados de tal forma que ofereçam o melhor desempenho para uma base de dados. Além disso, é sabido que nenhum algoritmo é superior a todos os outros para todas as classes de problemas e que combinar soluções fornecidas por diferentes algoritmos pode levar a uma solução global superior a todas as soluções individuais, inclusive à melhor. É nesse contexto que a presente tese propõe um novo algoritmo de agrupamento nebuloso de dados inspirado no comportamento de enxames de partículas e, em seguida, propõe uma nova forma de realizar combinações (ensembles) de algoritmos de agrupamento usando conceitos da teoria de conjuntos nebulosos do Tipo-2.