Multi-Lab-Driven Learning Method Used for Robotics ROS System Development

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 experiences. Traditional lab-driven pedagogy may provide hands-on opportunities on ROS itself but may still not bring students close enough to the actual applications of ROS to their major robot projects in their electrical engineering education. In this paper, a technological content knowledge (TCK) based method is utilized 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). The multi-lab-driven method (MLDM) was employed to construct the TCK of ROS of students in the context of designing an autonomous mobile robot system. A sequence of multiple labs were assigned to students to cover various topics in the ROS. 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 lab assignments, lab reports, presentations, the final robot project, students’ input to the official course evaluation administered by the 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. Preliminary assessment of this multi-lab-driven learning method for providing robotics education supports its effectiveness.