## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Measurements** & **F Test Statistics** & \(p\)**-Value** & **Result** & \(t\) **Test Statistics** & \(p\) **Value** & **Result** \\ \hline NcM\_0\_X & 0.12035 & 0.002481 & reject & 39.706 & 2.012 \(\times\) 10\({}^{-14}\) & reject \\ NcM\_0\_Y & 0.32285 & 0.08889 & not reject & \(-\)29.105 & \(<\)2.2 \(\times\) 10\({}^{-16}\) & reject \\ NcM\_0\_Z & 0.57004 & 0.3891 & not reject & 4.9848 & 7.117 \(\times\) 10\({}^{-5}\) & reject \\ NcM\_1\_X & 0.090496 & 0.0007453 & reject & 2.7711 & 0.01717 & reject \\ NcM\_1\_Y & 2.603 & 0.1582 & not reject & 25.963 & 1.022 \(\times\) 10\({}^{-15}\) & reject \\ NcM\_1\_Z & 0.12503 & 0.002901 & reject & \(-\)9.7135 & 3.587 \(\times\) 10\({}^{-7}\) & reject \\ NcM\_2\_X & 0.00013299 & 4.261 \(\times\) 10\({}^{-16}\) & reject & \(-\)1.6714 & 0.1256 & not reject \\ NcM\_2\_Y & 2.0091 & 0.2866 & not reject & 11.625 & 2.377 \(\times\) 10\({}^{-10}\) & reject \\ NcM\_2\_Z & 3.08 & 0.0731 & not reject & 11.738 & 2.006 \(\times\) 10\({}^{-10}\) & reject \\ \hline \hline \end{tabular}
\end{table} 
Table 10: N cM F test and \(t\)-test. The following contributions also deal with the calibration of the robotic arm. In our paper, the proximity sensor MTN/EP080 Probe was used for NcM. [PERSON] et al. [29] used a laser-tracking system to calibrate the kinematic parameters of the robotic arm, which achieves high positioning accuracy using a genetic algorithm. [PERSON] et al. [30] used a contactless method to perform measurements of calibration points in space, using a stereovision system attached to the robot arm. This method is very similar to the NcM for which the MTN/EP080 Probe proximity sensor was used. Points (represented as spheres localized by a stereo system) are projected by the authors as circles in two planes of image capturing, regardless of the angle of view. The positioning error after calibration has been reduced to 1.29 mm. [PERSON] et al. [31] proposed their own method of geometric parameters' calibration of a kinematic model of a robotic arm, based on monocular vision. Similar to our paper, they used the contactless method, while in measuring accuracy of the robotic arm positioning in our paper, the CoM was also used, in which it was necessary to set the reference-calibration Cartesian coordinate system. [PERSON] et al., to determine the kinematic parameters, first used the classical Denavit-Hartenberg (D-H) modeling method. Subsequently, they implemented nonlinear optimization and parameter compensation. Their method improved the absolute accuracy of positioning the end of the robotic arm while being universal and effective, similar to the CoM method in our paper. Due to confirmed differences in positions for the same measured axis in different types of arm, we assume that Arm I--AdM, Arm I--CvM or an arm made by different technology may have dimensional deviations. From previous observations and calculations, for the research of other factors influencing the localization of the robotic arm position, these deviations in dimensions need to be eliminated. Based on the results, we can conclude that production technology does not affect the positioning accuracy of the robotic arm, but the design of Arm I--AdM needs to be changed regardless of the operating load. To further investigate, it will be appropriate to plan experimental measurements to better examine whether differences in a position of the robot arm are random or caused by loads or other factors that have not been taken into account (arm flexibility, load time, transport time, programmed trajectory of the robot arm etc.). The answer to the question will be known after analysis of accuracy of measured positions for Arm I--AdM and Arm I--CvM, depending on the measurement directions (X, Y, Z) and the load. In the future, to increase the accuracy of measurement process, it will be necessary to use the proximity sensor MTN/EP080 Probe with higher sensitivity and to increase rigidity of the proximity sensor holder.