RASTREAMENTO DE AGROBOTS EM ESTUFAS AGRÍCOLAS USANDO MODELOS OCULTOS DE MARKOV: Comparação do desempenho e da correção dos algoritmos de Viterbi e Viterbi com janela de observações deslizante

• Ano de defesa: 2015
• Autor(a) principal: Alves, Roberson Junior Fernandes
• Orientador(a): Rocha, Jose Carlos Ferreira da
• Banca de defesa: Schutz, Fabiana Costa de Araujo , Campos Junior, Arion de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNIVERSIDADE ESTADUAL DE PONTA GROSSA
• Programa de Pós-Graduação: Programa de Pós Graduação Computação Aplicada
• Departamento: Computação para Tecnologias em Agricultura
• País: BR
• Palavras-chave em Português: robôs móveis; estufas agrícolas; modelos ocultos de Markov
• Palavras-chave em Inglês: mobile robot; greenhouse; hidden Markov models
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.uepg.br/jspui/handle/prefix/132

Developing mobile and autonomous agrobots for greenhouses requires the use of procedures which allow robot autolocalization and tracking. The tracking problem can be modeled as finding the most likely sequence of states in a hidden Markov model„ whose states indicate the positions of an occupancy grid. This sequence can be estimated with Viterbi’s algorithm. However, the processing time and consumed memory, of this algorithm, grows with the dimensions of the grid and tracking duration, and, this can constraint its use for tracking agrobots. Considering it, this work presents a tracking procedure which uses two approximated implementations of Viterbi’s algorithm called Viterbi-JD(Viterbi’s algorithm with a sliding window) and Viterbi-JD-MTE(Viterbi’s algorithm with a sliding window over an hidden Markov model with sparse transition matrix). The experimental results show that the time and memory performance of tracking with this two approximated implementations are significantly higher than the Viterbi’s based tracking. The reported tracking hypothesis is suboptimal, when compared to the hypothesis generated by Viterbi, but the error does not grows substantially. Th experimentos was performed using RSSI(Received Signal Strength Indicator) simulated data.