Abstract:

This paper presents an educational experience that uses a very low-cost mobile platform, the Freenove Tank Robot Kit for Raspberry Pi, to introduce undergraduate students to core concepts in robotics, mapping, and autonomous navigation. The activity is structured around a hands-on laboratory project in which small teams must program their robot to solve a maze without collisions while performing real-time object detection. The same hardware setup is shared across groups, but each team is required to design and implement its own navigation strategy (e.g., rule-based, reactive, behavior-based, or simple path-planning approaches), resulting in a diverse set of solutions to a common problem.

This design choice is pedagogically central. Allowing students to choose and justify their own algorithm fosters ownership, creativity, and critical thinking. Rather than following a fixed tutorial, students engage in authentic problem-solving: they must analyze sensor data, tune control parameters, and iteratively debug their code as the robot navigates the physical maze. Real-time object detection, implemented with lightweight vision tools, further reinforces the integration of perception and control, and exposes students to current trends in applied artificial intelligence.

The use of an inexpensive, open-hardware platform makes the proposal easily replicable in institutions with limited resources, while still providing a rich context for interdisciplinary learning that connects programming, electronics, and mathematics. We report qualitative observations and preliminary feedback indicating high levels of motivation, increased participation in class discussions, and improved understanding of abstract concepts such as uncertainty, sensor noise, and algorithmic trade-offs. The paper discusses the instructional design, the technical architecture of the lab, and recommendations for scaling and adapting the activity to different educational levels. Overall, the experience suggests that low-cost robotics projects with open algorithmic choices can be a powerful vehicle for engaging students in robotics and STEM education.

Keywords:

Robots, education, STEM, open-hardware.