## 4 Conclusions  
The process of identification of the robot's base frame in a workplace using Aruco markers and OpenCV was presented and verified in this study. This approach may be used for more robots and other components of the workplace at the same time, which brings the main advantage of fast evaluation and later recalibration. The typical scenario is placing all components in their positions, placing markers on them and other points of interest, running a robot's path while measuring end-effector position by a camera and evaluating the results--obtaining the coordinates of robot's base and coordinates of points of interest (manipulated object, pallet, etc.). As the end-effector we used an 3D printed gridboard that might be replaced by a cubic with tags carried by a gripper. When observing the results provided in the Section 3.5, one can see there is a gap between the accuracy of the simulated workplace and the experiments. As already mentioned above, simulation A demonstrates the possible accuracy of this method when all conditions are close to ideal. Therefore, there are some methods and topics for another study that would help to minimize the errors. The DH parameters of the UR3 were based on its datasheet, but when the robot is manufactured it is calibrated and modified DH parameters are saved in the control unit. This parameters may be retrieved and applied in the identification calculations. In general, UR3 is not as precise as other industrial robots; depending on demands, a different manipulator should provide more accurate results. Nevertheless, the end-effector was 3D printed and assembled from three parts; more accuracy may be achieved using better manufacturing methods. In some cases, if the end-effector was manufactured precisely beforehand, only the identification of a base frame might be enough (instead of identification of the base frame and end-effector offset, as was presented). However, the biggest issue seems to be the detection of the tags, as results differed for every camera in the experiment, as shown in Table 4. Positioning of a camera (distance from a tag) seems to have a big influence on the outcome. This topic was researched in the [PERSON]'s work [25]. Additionally, alternating the OpenCV's software algorithms and filtering leads to better detection; more on this topic is discussed in [23]. A user may seriously consider the use of a self-calibrated camera with higher resolution than 1280 \(\times\) 720 px, as we used. It is important to note that the presented accuracy in simulation B and real experiment were achieved by cameras that we calibrated. There is no doubt that one with better calibrated hardware may achieve more accurate results. Another question to focus on is which trajectory and how many measured points are necessary to provide satisfying results; we tested the system with only one path of 250 points. Once this camera-based method is well optimized for a task depending on certain available equipment, and the accuracy is acceptable, it will stand as sufficient easy-to-deploy and low-cost solution for integrators and researchers. They will be able to quickly place components and robots, tag them and obtain their position coordinates based on prepared universal measurement. In addition, even the current system may serve in the manufacturing process as a continuous safety-check that all  
Figure 12: Errors for the identified values based on experiment A.  
required components, including robots, are in the place where they should be, if the detection accuracy is acceptable. In addition, this method will be used for identification of reference coordinate systems and kinematic parameters of experimental custom manipulators, the design which is a point of interest for Research Centre of Advanced Mechatronic Systems project. To make this calibration method easier to follow, the Matlab's scripts with the calculations and raw input data obtained by simulation and experiments may be found on the Github page of the Department of Robotics, VSB-Technical University of Ostrava [26].