Automated QR Code Recognition and Sorting Using OpenCV and SCARA

In the rapidly evolving domain of Industry 4.0 logistics, manual material handling remains a critical bottleneck characterized by high error rates and operational inefficiencies. Addressing this challenge, this research presents the design and implementation of a cost-effective, automated sorting system that integrates machine vision with a custom-fabricated SCARA robot. The primary objective was to engineer an autonomous work-cell capable of identifying and palletizing products based on barcode data, thereby mitigating reliance on manual labor. The methodology employs a robust distributed control architecture, decoupling high-level processing from real-time actuation. A laptop utilizing a custom Python- OpenCV pipeline performs real-time image acquisition and QR code decoding via a webcam to determine sorting logic. These commands are transmitted via serial protocol to an Arduino Uno equipped with a CNC shield, which functions as the deterministic controller for the 4-DOF SCARA robot’s inverse kinematics. Experimental validation confirms that the system achieves high sorting accuracy and reliable mechanical execution, successfully bridging the gap between complex industrial requirements and accessible technology. In conclusion, this project demonstrates a scalable, economically viable solution for intelligent material handling, offering immediate applications in warehousing, e-commerce and fulfillment, and small-scale manufacturing lines.