This review paper presents an overview of depth cameras. Our goal is to describe the features and capabilities of the introduced depth sensors in order to determine their possibilities in robotic applications, focusing on objects that might appear in applications with high accuracy requirements. A series of experiments was conducted, and various depth measuring conditions were examined in order to compare the measurement results of all the depth cameras. Based on the results, all the examined depth sensors were appropriate for applications where obstacle avoidance and robot spatial orientation were required in coexistence with image vision algorithms. In robotic vision applications where high accuracy and precision were obligatory, the ZED depth sensors achieved better measurement results.

This article presents a new model of customized mass production management with Industry 4.0 standards within the food industry. The aim of this article is to develop a method for managing the production line where it is possible to produce an entire spectrum of products without reconfiguring the production line. An illustrative example is the production of fruit yoghurt of various types. The entire life cycle of the product is monitored and documentation of all relevant raw material data is carried out through the production process all the way to product packaging where each product is specifically marked with QR code. A special technique for deciding on optimum maintenance of the production line has been introduced and a multi-criteria decision model has been developed using the fuzzy analytic hierarchy process method where it is possible to achieve a high degree of minimization of maintenance costs. In this work, a fuzzy-based multi-criteria decision making methodology is developed for conceptual design evaluation in the cost reduction in maintenance of mass customization process. For the purposes of monitoring the production process itself, a LabVIEW application was created in the form of a SCADA system.

The newest and most promising type of pneumatic actuators is the pneumatic artificial muscle (PAM). Different designs have been developed, but the McKibben muscle is the most popular and is made commercially available by different companies (e. g. Fluidic Muscle manufactured by Festo Company). Pneumatic artificial muscles have a wide range of use in industrial and medical fields. There are a lot of advantages of these muscles like the high strength, good power-weight ratio, low price, little maintenance needed, great compliance, compactness, inherent safety and usage in rough environments. The main disadvantage is that their dynamic behavior is highly nonlinear. The most often mentioned characteristic of PAMs is the force as a function of pressure and contraction. In this paper our newest function approximation for the force generated by Fluidic Muscles is shown that can be generally used for different muscles made by Festo Company.

This paper focuses on the implementation of Industry 4.0-based maintenance approaches using AR technology. The primary objective is to highlight the benefits of AR in maintenance and provide a framework for implementing AR-based maintenance systems. AR can assist maintenance personnel in performing maintenance tasks more efficiently by providing real-time instructions and information. By overlaying virtual information on real equipment, AR can guide maintenance personnel through maintenance procedures, reducing human errors and improving the quality of the work. AR can also provide remote assistance to maintenance personnel, allowing experts to remotely guide less experienced personnel through complex maintenance procedures. The proposed application provides motorcycle service assistance using AR and telepresence technologies. It enables remote monitoring of the service flow and performs an automated identification of parts. The system provides instructions for disassembling more complex mechanical components, provides a 3D model of the object, and enables ordering of the necessary parts.

Terrain classification provides valuable information for both control and navigation algorithms of wheeled mobile robots. In this paper, a novel online outdoor terrain classification algorithm is proposed for wheeled mobile robots. The algorithm is based on only time-domain features with both low computational and low memory requirements, which are extracted from the inertial and magnetic sensor signals. Multilayer perceptron (MLP) neural networks are applied as classifiers. The algorithm is tested on a measurement database collected using a prototype measurement system for various outdoor terrain types. Different datasets were constructed based on various setups of processing window sizes, used sensor types, and robot speeds. To examine the possibilities of the three applied sensor types in the application, the features extracted from the measurement data of the different sensors were tested alone, in pairs and fused together. The algorithm is suitable to operate online on the embedded system of the mobile robot. The achieved results show that using the applied time-domain feature set the highest classification efficiencies on unknown data can be above 98%. It is also shown that the gyroscope provides higher classification rates than the widely used accelerometer. The magnetic sensor alone cannot be effectively used but fusing the data of this sensor with the data of the inertial sensors can improve the performance.