Controle de fixação atentivo para uma cabeça robótica com visão binocular

• Ano de defesa: 2016
• Autor(a) principal: Roos, André Filipe
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial; UTFPR
• Palavras-chave em Português: Visão por computador; Visão de robô; Robôs - Sistemas de controle; Robôs; Algorítmos computacionais; Engenharia elétrica; Computer vision; Robot vision; Robots - Control systems; Robots; Computer algorithms; Electric engineering; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::ELETRONICA INDUSTRIAL, SISTEMAS E CONTROLES ELETRONICOS::CONTROLE DE PROCESSOS ELETRONICOS, RETROALIMENTACAO; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/2648

Computer vision research is still far from replicating the adaptability and performance of the Human Visual System. Most of its consolidated techniques are valid only over static scenes and restrictive conditions. Robot heads represent an advance in terms of flexibility by carrying cameras that can be freely moved to explore the surroundings. Artificial observation of dynamic environments requires the solution of at least two problems: to determine what is the relevant perceptual information to be extracted from the sensors and how to control their movement in order to shift and hold gaze on targets featuring arbitrary shapes and motions. In this work, a general binocular gaze control system is proposed, and the subsystem responsible for targeting and following lateral displacements is designed, tested and assessed in a four degrees-of-freedom robot head. The subsystem employs a popular low-level visual attention model to detect the most salient point in the scene, and a proportional-integral controller generates a conjunctive movement of the cameras to center it in the left camera image, assumed to be dominant. The development started with a detailed physical modeling of the pan and tilt mechanism that drives the cameras. Then, the linearized structure obtained was fitted via least squares estimation to experimental input-output data. Finally, the controller gains were tuned by optimization and manual adjustment. The OpenCV-based implementation in C++ allowed real-time execution at 30 Hz. Experiments demonstrate that the system is capable of fixating highly salient and static targets without any prior knowledge or strong assumptions. Targets describing harmonic motion are naturally pursued, albeit with a phase shift. In cluttered scenes, where multiple potential targets compete for attention, the system may present oscillatory behavior, requiring fine adjustment of algorithm weights for smooth operation. The addition of a controller for the neck and a vergence controller to compensate for depth displacements are the next steps towards a generic artificial observer.