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All Journal Bulletin of Electrical Engineering and Informatics
Viroch Sukontanakarn
Rajamangala University of Technology Isan, Khon Kaen Campus
Electrical & Electronics Engineering

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Vision-based color and shape classification using Dobot MG400: a low-cost educational robotics platform Paramust Juntarakod; Viroch Sukontanakarn
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11690

Abstract

This study develops an eye-to-hand vision-guided robotic system using a Dobot MG400 arm, an overhead universal serial bus (USB) camera, and an Arduino-controlled pneumatic suction gripper for automated pick-and-place operations. The system detects 12 types of workpieces based on 3 shapes (circle, triangle, and square) and 4 colors (red, green, blue, and yellow) using Python and OpenCV. To ensure precision, camera-to-robot calibration is performed using a 16-point homography matrix optimized by the random sample consensus (RANSAC) algorithm. Furthermore, an adaptive correction algorithm based on affine transformation and the least squares method is implemented to compensate for coordinate deviations. Evaluated across three illumination levels (26.9, 175.4, and 430.4 lux), the correction algorithm significantly improves positioning accuracy by 48–50%, reducing residual errors to approximately 0.056–0.064 mm with a processing time of ~5.2 seconds. The system demonstrates high precision, stability, and robustness, making it highly suitable for automated small-scale industrial sorting.
Eye-in-hand vision based PID control of a Dobot Magician with online homography calibration for precision manipulation Kriangkrai Waiyakan; Viroch Sukontanakarn; Thewin Sakunbunyong; Phisit Srinoi; Wiriya Dangton; Surasit Phokha
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.12105

Abstract

This study evaluates a vision-guided robotic pick-and-place system integrating an eye-in-hand camera with a proportional integral derivative (PID) controller and an adaptive online homography calibration algorithm. To mitigate environmental disturbances, the framework utilizes hue, saturation, and value (HSV)-based object detection and a RANSAC-refined homography method that continuously updates the mapping between image pixels and the robot workspace. The system’s precision was tested under varying lighting conditions by comparing a non-PID baseline against the PID-integrated framework using mean execution time, standard deviation (SD), and t-tests. Results indicate that while both configurations maintain stability, their performance differences are not statistically significant, suggesting the static homography model already provides high baseline accuracy. These findings underscore the importance of rigorous system identification, recommending that future research transition toward advanced control strategies and spatial trajectory tracking metrics to further enhance robotic manipulation precision.
Co-Authors Kriangkrai Waiyakan Paramust Juntarakod Phisit Srinoi Surasit Phokha Thewin Sakunbunyong Wiriya Dangton