Vladimir A. Kholopov, Maxim A. Makarov, Ivan G. Blagoveshchensky

MIREA - Russian technological university,

This paper proposes a method for aligning coordinate systems in robotic technological complexes (RTCs) using machine vision and a deep learning model, without the use of special calibration objects. The method is based on "eye-to-hand" calibration and employs the YOLOv5 model for object detection and classification in the robot's working area. The proposed approach allows for automatic transformation of object coordinates from the camera coordinate system to the coordinate systems of the robot and other RTC components, ensuring precise interaction between them. Simulation results confirmed the effectiveness of the method and its suitability for industrial applications. The method reduces reconfiguration time and enhances the flexibility of RTCs, which is especially important in multi-nomenclature small-batch production environments.

robotic technological complex, RTC, machine vision, deep learning, eye-to-hand calibration, automation, multi-nomenclature small-batch production