The article deals with the research of the supplementation of industrial robot effector trajectory’s control systems by an inertial navigation system. The method of reverse validation and location of an object in a navigated reference system does not require additional calibration. The goal of the research is to verify the assumption that it is possible to control and correct the programmed mobile robot trajectory by implementing an inertial navigation system even in a case when the inertial navigation system is used as the only trajectory control device. The data obtained are processed by the proposed and detailed application.

The paper deals with ionization impact on efficient cleaning of air in a measuring chamber which has been cleaned and closed against any outer impacts (e.g. impurities, dust from another room, human odours). Smoking has an impact on the number of positive and negative ions including the concentration of particulate matter PM 10 . We investigated the ion concentration according to the presence of cigarette smoke in the room and according to the change of lit cigarette distance from the supply of ionized air. Due to the experiment there was simulated smoking at the relative air humidity φ = 37 % and φ = 39 % and temperature of 20 °C in the room. Increased PM 10 concentrations were caused only by cigarette smoke pollution or more precisely by artificially created higher humidity in the measuring room excluding ambient environment impacts. The aim of the experiments was to prove influence of ionization on the elimination of cigarette smoke. The measurements showed that the highest efficiency of PM 10 particulate removal was achieved when the distance of smoking cigarettes from ionization source was 3 m and the air humidity was 39 %. The consequent increase of the distance of smoking cigarettes from the ionization source significantly decreased the efficiency of particle removal. The difference between ionized and natural air is minimal at the bigger distance.

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QR (Quick Response) codes are one of the most famous types of two-dimensional (2D) matrix barcodes, which are the descendants of well-known 1D barcodes. The mobile robots which move in certain operational space can use information and landmarks from environment for navigation and such information may be provided by QR Codes. We have proposed algorithm, which localizes a QR Code in an image in a few sequential steps. We start with image binarization, then we continue with QR Code localization, where we utilize characteristic Finder Patterns, which are located in three corners of a QR Code, and finally we identify perspective distortion. The presented algorithm is able to deal with a damaged Finder Pattern, works well for low-resolution images and is computationally efficient.

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.