PlatROB: A Low-Cost Modular Platform for Teaching Mobile Robotics and AI to Undergraduate Mechatronic Engineering Students

This innovative practice paper describes the design, development and validation of PlatROB, an educational platform for teaching robotics and artificial intelligence (AI). The increasing demand for advanced robotic systems necessitates that engineering students enhance their system integration skills specific to robotics and AI, which requires specialized hardware and software for effective learning. PlatROB is a low-cost, modular robotic platform that fosters the development of robotic system integration abilities across key engineering disciplines including mechanics, electronics, and programming. Using Pla-tROB, students can perform different basic mobile movement types, such as Ackermann, differential and omnidirectional. The modular design allow students to experiment with each movement type by simply adding specific modules for control and processing, sensing, and actuation. Furthermore, the structure and mechanisms of PlatROB are 3D printable, familiarizing students with rapid prototyping technologies. Additionally, the platform is capable of being programmed with teleoperation algorithms and autonomous navigation using computer vision. To support the learning process, detailed manuals and test codes were provided to facilitate assembly and verify proper module integration. The learning effectiveness of PlatROB was evaluated in workshops with mechatronics engineering students from different program levels. A mixed-methods approach was utilized, combining quantitative (pre- and post-questionnaires) and qualitative (observation charts and surveys) tools. The results highlighted PlatROB's versatility as an educational tool for undergraduate students, enhancing their understanding of robotics systems integration. Early-stage students gained foundational knowledge in ground vehicle configurations, robotics technology, testing, and programming, while advanced students reinforced their understanding of complex robotics and AI concepts and implementation, including computer vision. The modular design enabled customized learning pathways, increasing students' technical skills and system-thinking abilities through hands-on, collaborative projects.