The Thymio II robot was designed to be used by teachers in their classrooms for a wide range of activities and at all levels of the curriculum, from very young children to the end of high school. Although the educationally oriented design of this innovative robot was successful and made it possible to distribute more than 800 Thymio robots in schools with a large majority in the French-speaking part of Switzerland, it was not sufficient to significantly raise the number of teachers using robot technology in their teaching after three years of commercialization. After an introduction and a first section on the design of this educational robot, this paper presents some results of a sociological analysis of the benefits and blockages identified by teachers in using robots, or not, with their pupils.
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Search and rescue, autonomous construction, and many other semi-autonomous multi-robot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication techniques such as gesture and speech. This study proposes a new implicit proximal communication technique to approach the problem of robot selection. We use electroencephalography (EEG) signals to select the robot at which the operator is looking. This is achieved using steady-state visually evoked potential (SSVEP), a repeatable neural response to a regularly blinking visual stimulus that varies predictively based on the blinking frequency. In our experiments, each robot was equipped with LEDs blinking at a different frequency, and the operator’s SSVEP neural response was extracted from the EEG signal to detect and select the robot without requiring any conscious action by the user. This study systematically investigates several parameters affecting the SSVEP neural response: blinking frequency of the LED, distance between the robot and the operator, and color of the LED. Based on these parameters, we study two signal processing approaches and critically analyze their performance on 10 subjects controlling a set of physical robots. Our results show that despite numerous artifacts, it is possible to achieve a recognition rate higher than 85% on some subjects, while the average over the ten subjects was 75%.
This paper proposed a fuzzy controller for the autonomous navigation problem of robotic systems in a dynamic and uncertain environment. In particular, we are interested in determining the robot motion to reach the target while ensuring their own safety and that of different agents that surround it. To achieve these goals, we have adopted a fuzzy controller for navigation and avoidance obstacle, taking into account the changing nature of the environment. The approach has been tested and validated on a Thymio II robots set. As application field, we have chosen a parking problem.