Abstract:

This paper presents a cyber-physical pick-and-place laboratory that integrates the Dobot Magician, computer vision, and inventory databases as an educational innovation to cultivate Industry 5.0 competencies at the University degree level during an Erasmus+ research internship. Within the CODEMO project-supported learning environment, students engage in active, project-based methodologies to design, implement, and monitor autonomous stock management solutions, fostering human–robot collaboration, data-driven decision-making, and technological readiness.

Robotic manipulators are often used in industry to improve efficiency and precision, particularly in repetitive tasks. This project presents a laboratory-scale pick-and-place system that uses the Dobot Magician robotic arm to automate inventory management. It implements real-time object detection combined with robotic automation. This system can recognise and identify items detected by a webcam using the YOLO model. After that, the Dobot robot categorises items into different spots for better inventory management. The system features a user-friendly monitoring and manual operation interface, along with a log to track facility movements. Additionally, it is connected to a database that stores all movement logs and product information, ensuring data persistence and allowing the retrieval of historical records, as well as the monitoring of inventory levels. It was found that the system can automate inventory management with minimal human intervention and reduce errors. This paper presents an application-oriented approach for engineering university students to gain knowledge on Industry 5.0 concepts.

Keywords:

Robotic Automation, Object Detection, educational, innovation, active methodologies, Erasmus+, Industry 5.0.