Fixed-vision based automated quality inspection and robotic nut sorting with Dobot Magician

Abstract

This research presents a low-cost automated nut sorting system developed through laboratory testing to provide an affordable automation solution for small and medium enterprises (SMEs). The system integrates a fixed-vision camera with a Dobot Magician robotic arm, utilizing Python, OpenCV, and homography-based coordinate transformation for precise positioning. Performance was evaluated under three controlled lighting conditions with 25 samples each. Results indicate that lighting intensity significantly affects accuracy: low lighting (27.25 lux) yielded only 16% accuracy (mu=0.16, sigma approximately 0.3666), while high lighting (481.78 lux) suffered from overexposure and reflections. In contrast, optimal laboratory conditions (129.71 lux) achieved 100% classification accuracy (mu=1, sigma=0), demonstrating perfect consistency and stability. The study concludes that while the system offers a high-efficiency, budget-friendly alternative for SMEs, maintaining controlled, optimal illumination is critical for operational success. These findings provide a technical foundation for implementing cost-effective robotic sorting in real-world SME environments where high-cost sensor arrays are not feasible.