The Robot Operating System (ROS), a collection of tools, libraries, and conventions, is a powerful framework for programming robot software, and ROS-based mobile robot systems are becoming increasingly significant in human life. ROS has therefore been extensively taught in robotics program in electrical engineering programs. However, although it is a low-cost solution to allowing students to perform a variety of simulations and validating new algorithms before implementing them on an actual mobile robot, teaching ROS so that students can use it efficiently and effectively is a challenging task. Regular electrical engineering courses on ROS may focus on theories but neglect hands-on opportunities. Traditional lab-driven pedagogy may provide hands-on opportunities on ROS itself but may still not bring students close enough to the actual application of ROS to their major robot projects in their electrical engineering education. We argue that the best way to learn a tool is to use the tool to solve problems that the tool is designed to solve, and we therefore argue that the knowledge we want our students to learn about the tool is the technological content knowledge (TCK), with which we suggest that we need to create learning opportunities that allow students to construct their knowledge of the technology/tool (the T) in close relation to the content/robot programming (the C).
In this paper we report the multi-lab-driven method (MLDM) that we use to help our students to construct their TCK of ROS in the context of designing an autonomous mobile robot system. A sequence of well-prepared multiple labs were assigned to students to cover various topics in the ROS such as navigation, mapping, SLAM, path planning, image processing, and localization, all of which were associated to the actual robot project and could be reviewed and further explored throughout the multiple labs over the semester. A variety of labs that reflect the ROS experiments and assist students in better understanding robotics programming were elaborately managed. Based on students’ performance on various milestone assignments, lab reports, presentations, and the final robot project, students’ input to the official course evaluation administered by the college/university, and a comparison to the instructor’s previous years of teaching experience, we propose that the MLDM is effective in helping students to learn ROS efficiently and meaningfully in the real world of engineering projects.
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