This article deals with methods of navigation and mapping of mobile robots in an indoor environment, for example, laboratories, building corridors, and so on. It explains the proposed solution of global navigation in more detail through the application of potential field method and its transformation into the topological map. Two separate software tools were designed for simulation of wheeled mobile robot behavior in the authors’ workplace. The first software uses the metric form of space representation and it can simulate tactic level of global navigation, while the second one deals with its transformation into the topological map and can be used for strategic level of global navigation. This is how we can get the so-called multilayer map system suitable for different tasks of mobile robot navigation and path planning.

The article presents the design and application of multi-software platform for solving kinematic synthesis of robot manipulator systems. It also presents a modern theoretical and application approach for modelling coupled mechanical systems, which include mobile robots. Due to high requirements for accuracy, efficiency, reliability and life cycle of technical equipment, several parameters ensuring optimal operating parameters need to be taken into account while dealing with the design. This is the reason for linking computational models to optimization algorithms that allows us to find the appropriate design parameters of analysed mechanical system, mechanisms, including mobile robots mostly by iterative way. The commercial working interface of the program ADAMS and open architecture of MATLAB programming language enable to share common data while dealing with model simulations in parallel. Both of them were used while designing and implementing the algorithm for the evaluation and optimization of parameters of technical equipment from the point of view of selected properties. While working on the task of the spatial mechanism of the six-member robot manipulator system, the algorithm solving the optimal parameters was created by applying the selected optimization techniques of the program MATLAB. Presented algorithm involves the creation of a map operating positions, which is further linked to the solution of the motion of interest points in the robotic system following a prescribed trajectory. This requires the geometry optimization of the selected members of the spatial robotic system in order to achieve such parameters so that the trajectory of the interest point of the output member would precisely match with the prescribed trajectory. It is important to note that these types of tasks create wider space for solving the assignments dealing with the development and application of technical equipment like mobile robots and their outputs that are linked to the needs of the practice.

The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding and studying its impact on crankpin machining quality and accuracy can improve machining efficiency. The paper deals with developing a comprehensive scientific and methodological approach for determining the experimental dependence parameters’ quantitative values for cutting-force calculation in cylindrical infeed grinding. The main stages of creating a method for conducting a virtual experiment to determine the cutting force depending on the array of defining parameters obtained from experimental studies are outlined. It will make it possible to get recommendations for the formation of a valid route for crankpin machining. The research’s scientific novelty lies in the developed scientific and methodological approach for determining the cutting force, based on the integrated application of an artificial neural network (ANN) and multi-parametric quasi-linear regression analysis. In particular, on production conditions, the proposed method allows the rapid and accurate assessment of the technological parameters’ influence on the power characteristics for the cutting process. A numerical experiment was conducted to study the cutting force and evaluate its value’s primary indicators based on the proposed method. The study’s practical value lies in studying how to improve the grinding performance of the main bearing and connecting rod journals by intensifying cutting modes and optimizing the structure of machining cycles.

At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. In this paper, we describe the development of a tire inspection system for the tire industry. We provide methods for processing tire sidewall data obtained from a camera and a laser sensor. The captured data comprise visual and geometric data characterizing the tire surface, providing a real representation of the captured tire sidewall. We use an unfolding process, that is, a polar transform, to further process the camera-obtained data. The principles and automation of the designed polar transform, based on polynomial regression (i.e., supervised learning), are presented. Based on the data from the laser sensor, the detection of abnormalities is performed using an unsupervised clustering method, followed by the classification of defects using the VGG-16 neural network. The inspection system aims to detect trained and untrained abnormalities, namely defects, as opposed to using only supervised learning methods.

The article deals with measurement of pose repeatability of industrial robot Fanuc LR Mate 200iC. Laser interferometer Renishaw XL-80 and digital indicator Mitutoyo Absolute Digimatic ID-F are used for the measurement. The measurement methodology is based on so-called ISO cube placed in the most used part of robot workspace. Individual steps of the solution, the analysis of measurement data and results are presented in the article.

The article discusses the possibility of object detector usage in field of automated visual inspection for objects with specific parameters, specifically various types of defects occurring on the surface of a car tire. Due to the insufficient amount of input data, as well as the need to speed up the development process, the Transfer Learning principle was applied in a designed system. In this approach, the already pre-trained convolutional neural network AlexNet was used, subsequently modified in its last three layers, and again trained on a smaller sample of our own data. The detector used in the designed camera inspection system with the above architecture allowed us to achieve the accuracy and versatility needed to detect elements (defects) whose shape, dimensions and location change with each occurrence. The design of a test facility with the application of a 12-megapixel monochrome camera over the rotational table is briefly described, whose task is to ensure optimal conditions during the scanning process. The evaluation of the proposed control system with the quantification of the recognition capabilities in the individual defects is described at the end of the study. The implementation and verification of such an approach together with the proposed methodology of the visual inspection process of car tires to obtain better classification results for six different defect classes can be considered as the main novel feature of the presented research. Subsequent testing of the designed system on a selected batch of sample images (containing all six types of possible defect) proved the functionality of the entire system while the highest values of successful defect detection certainty were achieved from 85.15% to 99.34%.