Abstract

The integration of mobile robots and computer vision has revolutionized industrial tasks by enabling precise and efficient automation processes. This work proposes a mobile platform for sorting Petri dishes using advanced deep-learning techniques. We utilize the YOLOv5 framework for real-time color detection and a 6-bar mechanism with a gripper for dynamic sample sorting. The implementation enhances logistics and reduces operational errors through accurate color classification. Our methodology includes creating a training dataset of over one thousand labeled RGB images and validating the system's performance. The trained network achieved over 90% accuracy during validation and testing, demonstrating precise robot positioning and effective Petri dish manipulation. This research successfully addresses automation challenges in industrial settings, offering improved efficiency and accuracy.