This article focuses on the localization and navigation of a mobile differential robot in an indoor officelike environment. These are fundamental issues to service robotics, which is a branch with a strong market growth. The work implements a vision tracking system, environment mapping, route planning and navigation for an autonomous robot application inside services buildings. One goal of the methodology is its application with low cost equipment. The test bed chosen was a Pioneer P3-DX robot [16] in a service building, with an attached USB webcam, pointed at the ceiling to take advantage of the position of the light fixtures as natural landmarks. The robot location is estimated through two distinct probabilistic methods: a particle filter, when there is no information about the starting location of the robot, and the Kalman filter, given the convergence of the particle filter. Both methods use the detection of light fixtures together with the robot kinematics as information to estimate the pose. The mapping of the environment and its obstacles is obtained from the localization estimates and the information gathered by ultrasound sensors, representing the entire navigation space discretized in the form of an occupation grid. Planning the navigation path is determined by a simple search algorithm, namely the Wavefront algorithm, based on the information contained in the occupancy grid. For a given path, navigation is performed with obstacle avoidance using the virtual forces method. Replanning is used to recover from local minima situations.
Educational robotics has had an increasing growth in the past years, mainly in teaching Science, Technology, Engineering, Arts and Mathematics (STEAM). These roboticsbased learning methods have since gone from home to be used every day in school learning activities. There still is, however, a big moat from the available resources and the effective use of these tools by teachers in K-12 schools. This study aims to gather in a single location a dataset of most available educational robotic platforms and related learning materials. The goal is to have this knowledge open, freely accessible and editable by manufactures and learning resources providers, helping to increase the adoption of educational robotics in STEAM education.
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