Monocular Vision-Based Positioning System for Object Localization in Collaborative Conveyor Applications

Abstract

This paper presents a monocular vision-based relative localization system for a collaborative robotic cell comprising a mobile conveyor and an industrial robot. The main challenge is that movement of the conveyor and/or the robot may disrupt traditional camera-robot calibration, which requires time-consuming manual readjustment. The proposed solution enables a camera, statically mounted on the conveyor frame, to detect a calibration ArUco marker attached to the robot base with known coordinates, compute the camera's position relative to the robot, and then determine the coordinates of a target object on the conveyor within the robot base coordinate system. The key advantage of this approach is that calibration is performed online in each frame, allowing the conveyor and the robot to move independently without losing positioning accuracy. A mathematical model of coordinate transformation, including rotation matrices and homogeneous transformations, is developed. The system is integrated with a real KUKA KR3 robot. The experiments demonstrated a mean absolute error in the XY plane of 2.4 mm for the X-axis and 3.1 mm for the Y-axis, which is acceptable for most object grasping tasks. These findings confirm the system's functionality and suitability for flexible manufacturing cells with frequently changing layouts.