A 2 1/2 D visual controller for autonomous underwater vehicle

• Ano de defesa: 2017
• Autor(a) principal: Cesar, Diego Brito dos Santos
• Orientador(a): Conceição, André Gustavo Scolari
• Banca de defesa: Santos, Tito Luís Maia, Tahim, André Pires Nóbrega, Bastos, Teodiano Freire
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Escola Politécnica
• Programa de Pós-Graduação: em Engenharia Elétrica
• País: brasil
• Palavras-chave em Português: Visual Servoing; Artificial Fiducial Marker; Autonomous Underwater Vehicle
• Área do conhecimento CNPq: Engenharias
• Link de acesso: http://repositorio.ufba.br/ri/handle/ri/23362

Underwater navigation is affected by the lack of GPS due to the attenuation of the electromagnetic signals. Thereby, underwater robots rely on dead reckoning as their main navigation systems. However, localization via dead-reckoning raises uncertainties over time. Consequently, visual and acoustic sensors have been used to increase accuracy in robotic systems navigation, specially when they move in relation to a target object. This level of precision is required, for instance, for object manipulation, inspection, monitoring and docking. This work aims to develop and assess a hybrid visual controller for an autonomous underwater vehicle (AUV) using artificial fiducial markers as reference. Artificial fiducial markers are planar targets, designed to be easily detected by computer vision systems and provide means to estimate the robot’s pose in respect to the marker. They usually have high detection rate and low false positive rate, which are desirable for visual servoing tasks. On this master thesis was evaluated, from among the most popular and open-source marker systems, one that presents the best performance in underwater environments in terms of detection rate, false positives rate, maximum distance and angle for successful detection. Afterwards, the best marker was used for visual servoing purposes in an underwater robot. The firsts experiments were performed on the Gazebo robot simulation environment and, after that, on a real prototype, the FlatFish. Tests on a saltwater tank were performed in order to assess the controller using static and adaptive gains. Finally, sea trials were performed, using the controller that best behaved on the controlled environment in order to assess its performance on a real environment. The tests have shown that the visual controller was able of station-keeping in front of an artificial fiducial marker. Additionally, it was also seen that the adaptive gain brings improvements, mainly because it smooths the robot’s motion on the beginning of the task.