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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.

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.

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Nowadays, an increasing usage of autonomous mobile robots in outdoor applications can be noticed. Identification of the terrain type is very important for efficient navigation. In this paper, a novel method is proposed for terrain classification in the case of wheeled mobile robots. The classification algorithm uses frequency domain features, which are extracted in fixed-size windows, and Multi-Layer Perceptron (MLP) neural networks as classifiers. Data from inertial sensors were collected for different outdoor terrain types using a prototype measurement system. The data of the accelerometer and the gyroscope were tested separately and together, and different processing window sizes were also applied. The achieved results show that above 99% classification efficiency can be achieved using the collected data.