###### Abstract  
In this paper, a new method for the calibration of robotic cell components is presented and demonstrated by identification of an industrial robotic manipulator's base and end-effector frames in a workplace. It is based on a mathematical approach using a Jacobian matrix. In addition, using the presented method, identification of other kinematic parameters of a robot is possible. The Universal Robot UR3 was later chosen to prove the working principle in both simulations and experiment, with a simple repeatable low-cost solution for such a task--image analysis to detect tag markers. The results showing the accuracy of the system are included and discussed. robotic manipulator; identification; calibration; Jacobian; workcell layout detection 202  
may lead to incorrect final results. Therefore, the wide deployment of laser trackers is ineffective for many manufacturers. There are other methods of robot calibration that tend to avoid the use of laser tracker. In [7], [PERSON] proposed a method using planes of a very preciously made granite cube, but acquisition of such a cube is not easy in general. [PERSON] [8] or [PERSON] [9] used additional equipment; in their case it is portable photogrammetry system. In [10] a new methodology is introduced by [PERSON], who suggested to use three flat planes in a robot's workplace with a 2D laser range finder that intersects the planes, but the simulation was not verified by an experiment. A very different approach was taken by [PERSON] [11] where the elasto-geometrical calibration method based on finite element theory and fuzzy logic modeling was presented. On the other hand, the very precise results that the methodologies above wanted to achieve are not always necessary, and some nuances in a robot's kinematic structure that appear during its manufacturing process are acceptable for the users of the robot. The problem they may face is determination of the workplace coordinate system (base frame) in which the robot is deployed and eventually the offset of the tool's center point when a tool is attached to the robot's mounting flange, when they need to position it absolutely in a world frame. For such applications the typical way to calibrate more robots is to use point markers attached to every robot, as described by [PERSON] [12]. However, one important condition is that the robots need to be close together so they can approach each other with the point markers and perform the calibration. Additionally, there are a few optical methods using a camera to improve a robot's accuracy. [PERSON] [13] used two cameras placed in specific positions to track an LED tag that was mounted on a robot; the method we propose allows us to put the camera in any place, in any orientation that will provide good visibility. In [14] [PERSON] or [PERSON] in [15] attached a camera to a robot and performed the identification process, but this cannot be used for other robots or to track positions of other components at the workplace at the same time. [PERSON] describes in [16] the process of identification for a single robot. [PERSON] presented in [17] the use of on-board measurement sensors mounted on a robot. To avoid these restrictions, we propose a solution based on the OpenCV libraries [18] for Aruco tag detection by a camera, which adds to the calibration process benefits of simplicity, repeatability and low price. The outcomes may be used in offline robot programming, in reconfigurable robotic workplaces and for tracking of components, with as many tag markers and robots as needed, if the visibility for a camera or multiple cameras is provided. There are methods for 2D camera calibration already presented, and they can be divided into two main approaches. The eye-on-hand calibration, wherein the camera is mounted on the robot and a calibration plate is static, and the eye-on-base method with the calibration marker mounted on the robot with static cameras around [19].