Search and rescue, autonomous construction, and many other semi-autonomous multirobot 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 presents an approach for estimating the relative location and orientation between two or more underwater vehicles operating in tight formation. One of the vehicles is equipped with a camera of wide field of view. The other vehicle(s) are equipped with active light markers to enable the use of computer vision for pose estimation. The pose estimation addresses two scenarios, which are both important from the operational point of view. The first pertains to the availability of an acoustic communication channel which allows for exchanging attitude data and acoustic ranging, and use it in the pose estimation procedure. The second corresponds to the exclusive use of a set of 4 or more optical beacons with no acoustic information exchange, which is a capability that has not been yet proposed nor demonstrated in underwater vehicles. The contributions can be summarize as (1) a novel method of estimating the pose of an autonomous underwater vehicle using light beacons and other sensors when available, (2) an automated marker configuration analysis approach. Performance of the pose estimation approach is evaluated using synthetic and real data.

Can robots in classroom reshape K-12 STEM education, and foster new ways of learning? To sketch an answer, this article reviews, side-by-side, existing literature on robot-based learning activities featuring mathematics and physics (purposefully putting aside the well-studied field of "robots to teach robotics") and existing robot platforms and toolkits suited for classroom environment (in terms of cost, ease of use, orchestration load for the teacher, etc.). Our survey suggests that the use of robots in classroom has indeed moved from purely technology to education, to encompass new didactic fields. We however identified several shortcomings, in terms of robotic platforms and teaching environments, that contribute to the limited presence of robotics in existing curricula; the lack of specific teacher training being likely pivotal. Finally, we propose an educational framework merging the tangibility of robots with the advanced visibility of augmented reality.

Title of thesis: A SYSTEMATIC APPROXIMATION TO THE NEURAL MOTOR CONTROL OF FORWARD, BACKWARD AND LATERAL TOE-TAPPING IN CHILDREN AND ADULTS Mohammad Ehsanul Karim Master of Arts in Kinesiology, 2014 Thesis directed by: Professor Jane E. Clark, Ph.D. Department of Kinesiology The neural motor control of forward, backward and lateral toe-tapping of typically developing adults, 6and 10-year-old children was investigated using the uncontrolled manifold technique. Our results indicate that the central nervous system (CNS) controls or stabilizes three functional variables (center of mass, toe and head position) by organizing the overall joint variability structure, more than the individual joints. Results reveal: (1) children control forward and backward stepping more than adults, and (2) adults’ forward stepping is more controlled than backward or lateral stepping. The relative ranking defining the approximate neural motor control of toe-tapping is: center of mass, toe and head position; indicating the CNS focuses on balance and foot placement. The observed invariance of this structure across movement phase, tap direction, and age, reinforces the idea that the CNS controls multi-directional toe-tap motion using similar neural control strategy. A SYSTEMATIC APPROXIMATION TO THE NEURAL MOTOR CONTROL OF FORWARD, BACKWARD AND LATERAL TOE-TAPPING IN CHILDREN AND ADULTS