Sorting Model using Robotic Arm with Image Processing

Abstract

This paper presents the design and implementation of a product sorting model using a robotic arm integrated with image processing techniques. The system consists of a conveyor belt, a vision module, and robotic manipulators that work together to identify and classify objects through a camera and computer vision algorithms that detect product characteristics. The robotic arm then performs the corresponding sorting operation according to product quality requirements. The hardware design includes the construction of the robotic arm, control circuits, and integration with actuators, while the software design focuses on developing image processing algorithms and communication between the vision system and the robot controller. Experimental results show that the system achieves an average size measurement error of approximately ±2 mm, a classification accuracy of about 95%, and an average processing time of 2–3 seconds per product. These results demonstrate reliable recognition and classification performance compared to some previous research models. The proposed model emphasizes the feasibility of combining robotic manipulation and computer vision for automated sorting tasks in industrial applications such as food processing, household tools, and medical instruments, while also serving as a practical training platform for students in technical education. Future improvements may include optimizing vision algorithms, enhancing the mechanical design of the robotic arm, integrating artificial intelligence to improve safety, and expanding the system’s capability to handle more complex classification tasks.