Design and Vision-Based Control of a Low-Cost SCARA Robot

Abstract

SCARA robots are widely used in industrial automation due to their high precision and speed, particularly in pick-and-place operations. In addition to conventional programming approaches, alternative vision-based control methods have gained interest to enhance flexibility and efficiency in robotic applications. This study presents the design and implementation of a Position-Based Visual Servoing (PBVS) for the SCARA robot system capable of detecting and manipulating objects in real-time. The proposed system consists of a fixed overhead camera, a SCARA robot, and Python-based control software. The software integrates image processing algorithms, kinematic calculations, and motor control, enabling the robot to autonomously identify objects, compute their positions, and execute pick and place tasks. To enhance object detection accuracy, Kuwahara filtering, Canny edge detection, morphological transformations, and connected component analysis were applied. Experimental results demonstrated that the combination of Kuwahara filtering and Canny edge detection achieved the lowest MSE error (8.45%), ensuring precise object localization. Furthermore, inverse kinematics was employed to generate accurate joint movements, allowing smooth and reliable grasping operations. The system was tested through 100 pick-and-place trials, achieving a 100% grasping success rate when Kuwahara filtering was applied. The experimental findings confirm that vision-based control significantly improves SCARA robot performance, making it suitable for automated assembly, material handling, and quality control applications.