Abstract
Autonomous robots play a pivotal role in improving productivity and reducing operational costs. They excel at both precision and speed in repetitive jobs and can cooperate with humans in complex tasks within dynamic environments. Self-localization is critical to any robot that must navigate or manipulate the environment. To solve this problem, a modular localization system suitable for mobile manipulators was developed. By using LIDAR data the proposed system is capable of achieving less than a centimeter in translation error and less than a degree in rotation error while requiring only 5 to 25 milliseconds of processing time. The system was tested in two different robot platforms at different velocities and in several cluttered and dynamic environments. It demonstrated high accuracy while performing pose tracking and high reliability when estimating the initial pose using feature matching. No artificial landmarks are required and it is able to adjust its operation rate in order to use very few hardware resources when a mobile robot is not moving.

Abstract
In the growing field of Robotics, one of the many possible paths to explore is the social aspect that it can influence upon the present society. The combination of the goal-oriented development of robots with the interactivity used in games while employing mixed reality is a promising route to take in regard to designing user-friendly robots and improving problem solving featured in artificial intelligence software. In this paper, we present a competitive team-based game using Pololu's 3Pi robots moving in a projected map, capable of human interaction via game controllers. The game engine was developed utilizing the framework Qt Creator with C++ and OpenCV for the image processing tasks. The technical framework uses the ROS framework for communications that may be, in the future, used to connect different modules. Various parameters of the implementation are tested, such as position tracking errors.

Abstract
This project arose from the absence in the market of a modular smartphone controlled robot capable of encouraging high school students to program and apply the physics and math's knowledge learned into it. Therefore this project's intention was to study the best way to develop a do-it-yourself (DIY) cost effective robot using only components off the shelf (COTS) and benefit from the omnipresence of smartphones. With the objective of making this robot attractive to anyone with low programming skills, it was important to make it configurable in an easy to understand language and a simple user interface, like the ones provided by Scratch and the MIT AppInventor2. The functional, physical and non-functional requirements for this robot and the free software developed are presented and validated attesting that this project was successfully completed.

Abstract
This article describes an experience in university and high school cooperation. It is expected to foster knowledge and deep learning in secondary schools by turning extra-curricular activities into articulated subject. The robotics area is very useful and generates interest and enthusiasm, even more so when associated with competition. The experience used Lego Ev3 robot and the students learned to program with a healthy technical approach called state machine programming and the easy to use Lego Software programming tool. The participation is enthusiastic because of the participation in the national robotics festival that leads into international RoboCup Federation robotics competitions. The article proposes a set of sessions adequate for secondary school students that constitute the initial step to find a curriculum for robotics in order to simultaneously learn robotics and foster interconnections with the curricular courses in STEM areas, even extending into structured programming issues. The test involved two participations in the national robotics competition that interestingly involved a team of 3 girls and another team of 3 boys although more students were involved during the year that the experience lasted. Declarations from the involved stakeholders are mentioned, even allowing for a brief discussion for women in STEM areas and technology distance for young (wo)men. Some hints, issues and lessons learned are shown. The advocacy of such informal learning strategy is made, advantages and limitations discussed.

Abstract
Robots created for education have different purposes, from learning algorithms to learning robotics. It has been shown that robots can increase the student's interest. Our proposed robot, REDi, uses wire programming to introduce students to the basics of robotics and algorithms. With this robot, attractive, interactive demonstrations can be achieved even with students that have no background in the area.

Abstract
Assessing physical activity (PA) is a challenging task and many different approaches have been proposed. Direct observation (DO) techniques can objectively code both the behavior and the context in which it occurred, however, they have significant limitations such as the cost and burden associated with collecting and processing data. Therefore, this study evaluated the utility of an automated video analysis system (CAM) designed to record and discriminate the intensity of PA using a subject tracking methodology. The relative utility of the CAM system and DO were compared with criterion data from an objective accelerometry-based device (Actigraph GT3X+). Eight 10 year old children (three girls and five boys) wore the GT3X+ during a standard basketball session. PA was analyzed by two observers using the SOPLAY instrument and by the CAM system. The GT3X+ and the CAM were both set up to collect data at 30 Hz while the DO was performed every two minutes, with 10s of observation for each gender. The GT3X+ was processed using cut points by Evanson and the outcome measure was the percentage of time spent in different intensities of PA. The CAM data were processed similarly using the same speed thresholds as were used in establishing the Evenson cut-off points (light: <2 mph; walking: 2-4 mph; very active: >4 mph). Similar outcomes were computed from the SOPLAY default analyses. A chi-square test was used to test differences in the percentage of time at the three intensity zones (light, walking and very active). The Yates' correction was used to prevent overestimation of statistical significance for small data. When compared with GT3X+, the CAM had better results than the SOPLAY. The chi-square test yielded the following pairwise comparisons: CAM versus GT3x+ was chi(2) (5) = 24.18, p < .001; SOPLAY2 versus GT3x+ was chi(2) (5) = 144.44, p < .001; SOPLAY1 versus GT3x+ was chi(2) (5) = 119.55, p < .001. The differences were smaller between CAM and GT3x+, suggesting that the video tracking system provided better agreement than DO. The small sample size precludes a definitive evaluation but the results show that the CAM video system may have promise for automated coding of physical activity behavior.