Robôs Humanoides na Educação: Um Mapeamento Sistemático com Base na Produção Cientifica Nacional e Internacional

O presente artigo teve como objetivo investigar como se dá o uso de robôs humanoides no âmbito educacional. Para tanto, realizamos um Mapeamento Sistemático da Literatura a partir teses, dissertações e artigos científicos publicados no período compreendido entre os anos de 2009 e 2018 nas bases de dados da Biblioteca Digital Brasileira de Teses e Dissertações – BDTD, por ser uma base de dados aberta, tendo o português como língua oficial. No contexto internacional, IEEE Xplore Digital Library e Education Resources Information Center - ERIC, por ser uma base de dados com foco em pesquisas e informações educacionais, tendo o inglês como língua oficial. Especificou-se os tipos de robôs humanoides, aspectos metodológicos, didáticos e curriculares. O resultado desta pesquisa produz fundamentação teórica para a investigação e inserção de novas tecnologias na escola, assim como uma análise mais precisa das interações e o impacto do uso de robôs humanoides na educação

Current sensing feedback for humanoid stability

For humanoid robots to function in changing environments, they must be able to maintain balance similar to human beings. At present, humanoids recover from pushes by the use of either the ankles or hips and a rigid body. This method has been proven to work, but causes excessive strain on the joints of the robot and does not maximize on the capabilities of a humanlike body. The focus of this paper is to enable advanced dynamic balancing through torque classification and balance improving positional changes. For the robot to be able to balance dynamically, external torques must be determined accurately. The proposed method of this paper uses current sensing feedback at the humanoids power source to classify external torques. Through understanding the current draw of each joint, an external torque can be modeled. After being modeled, the external torque can be nullified with balancing techniques. Current sensing has the advantage that it adds detailed feedback while requiring small adjustments to the robot. Also, current sensing minimizes additional sensors, cost, and weight to the robot. Current sensing technology lies between the power supply and drive motors, thus can be implement without altering the robot. After an external torque has been modeled, the robot will undertake balancing positions to reduce the instability. The specialized positions increase the robot\u27s balance while reducing the workload of each joint. The balancing positions incorporate the humanlike body of the robot and torque from each of the leg servos. The best balancing positions were generated with a genetic algorithm and simulated in Webots. The simulation environment provided an accurate physical model and physics engine. The genetic algorithm reduced the workload of searching the workspace of a robot with ten degrees of freedom below the waist. The current sensing theory was experimentally tested on the TigerBot, a humanoid produced by the Rochester Institute of Technology (RIT). The TigerBot has twenty three degrees of freedom that fully simulate human motion. The robot stands at thirty-one inches tall and weighs close to nine pounds. The legs of the robot have six degrees of freedom per leg, which fully mimics the human leg. The robot was awarded first place in the 2012 IEEE design competition for innovation in New York