Skip to content
DSML.KZ
Publications

Article · Eastern-European Journal of Enterprise Technologies · October 29, 2026

Development of a conceptual model for robotic manipulator-computer vision interaction in object recognition using data matrix code

Перизат Маратқызы Рахметова, Nazarbek Mussabekov, Gulmira Berdibayeva, М. К. Baktybayev, Aldabergen Bektilevov, Lazzat Kurmangaliyeva

Lazzat Kurmangaliyeva
Open via DOIPDF

Abstract

The object of the study is preprocessing, detection, motion planning, sorting, and target placement of Data Matrix images in boxes using a robotic manipulator with six degrees of freedom. The problem to be solved is that motion planning for a robotic manipulator, sorting operations by the manipulator and recognition and preprocessing of Data Matrix images are usually considered as separate tasks. Therefore, the influence of the Data Matrix image quality on the efficiency of sorting by a robotic manipulator is ambiguous. The proposed model comprises three main levels: data acquisition, software development, and planning and execution. A cascaded Gaussian filtering method was employed to process Data Matrix images distorted by Gaussian noise. The robotic arm's operation was simulated in the ROS 2 (USA) environment using RViz (USA) and MoveIt (USA) tools. As a result of the two-stage filtering, the root mean square error (RMSE) decreased across all noise levels studied: from 21.19 to 19.55 at +10 dB, from 36.36 to 33.62 at +5 dB, from 55.78 to 52.46 at 0 dB, from 72.07 to 68.57 at −5 dB, and from 82.64 to 79.11 at −10 dB. As a result of filtering, the peak signal-to-noise ratio (PSNR) increased by 0.38–0.70 dB. The time required for trajectory planning generally ranged from 0.02 to 0.04 seconds, while the length of the movement trajectories varied between 0.55 and 1.05 meters. In most cases, the root-mean-square error of angular deviation did not exceed 0.10°, and the displacement error ranged from 0.9 to 1.5 mm. The results demonstrated that image filtering enhances the stability of visual data. These metrics indicate the high efficiency of the proposed approach. The quality of image preprocessing was evaluated based on the integrity of the binary code structure, alongside metrics such as root-mean-square error (RMSE) and peak signal-to-noise ratio (PSNR). The proposed approach can be applied in warehouse and logistics systems where it is necessary to monitor noise levels in camera-captured images and reliably identify objects via codes prior to their movement and sorting

Robot Manipulation and LearningQR Code Applications and TechnologiesRobotic Mechanisms and Dynamicsrobot manipulatorscomputer visionData Matrix codes

More by Lazzat Kurmangaliyeva