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Article · International Journal of Advanced Computer Science and Applications · January 1, 2026

A Coordinate Transformation Framework for Vision-Guided Robotic Positioning in Automated Vehicle Body Marking

Olzhas Olzhayev, Azhar Tursynova, Sayat Ibrayev

Azhar Tursynova
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Abstract

Vision-guided robotic positioning plays a critical role in modern manufacturing by enabling robots to accurately adapt to variations in workpiece position. This study proposes a coordinate transformation framework for automated vehicle body marking that converts visually detected target coordinates into executable robot motions through a unified processing pipeline. The proposed framework integrates visual target localization, homogeneous coordinate transformation, Tool Center Point (TCP) pose generation, and inverse kinematics to achieve accurate robotic positioning. Experimental validation was performed using a vision-guided robotic marking platform consisting of an AR4-MK3 six-degree-of-freedom manipulator, an overhead monocular camera, and a 50 W electromagnetic marking device. Five target positions with two repeated trials per position were used to evaluate positioning accuracy, repeatability, and computational performance. The proposed framework achieved an average positioning error of 2.19 mm, a maximum error of 2.69 mm, and a total processing time of approximately 27 ms, demonstrating stable and reliable robotic positioning during the marking process. The proposed framework provides a practical and computationally efficient solution for transforming visual information into robot-executable poses and offers a promising foundation for future vision-guided robotic marking systems operating in industrial environments.

Robot Manipulation and LearningRobotic Mechanisms and DynamicsIndustrial Automation and Control Systemscoordinate transformationinverse kinematicshomogeneous transformation

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