Industrial robots, like all machines, require energy to operate, which is why energy efficiency in industrial robotics has been a subject of consideration in recent years in many scientific and industrial centers. Interest in the topic is especially noticeable in Industry 4.0. Research on energy efficiency stems from the emergence of new possibilities in terms of making strategic decisions related to manufacturing processes. As energy-efficient production is an essential part of sustainable development, the energy efficiency of industrial robots must be considered. The need to reduce costs while maintaining quality and increasing production efficiency has necessitated the implementation of modern solutions aimed at reducing electricity consumption. The rational use of electrical energy, especially in the industrial sector, significantly reduces production costs and, consequently, contributes to a company’s profits and competitiveness. This article aims to provide an overview of energy efficiency issues based on recently published articles. This article discusses the appropriate selection of robots, their programming, energy-efficient trajectory planning, and the monitoring of the operation of the robotic system to minimize energy consumption. Typical industrial applications of robots are also mentioned and discussed.

This paper presents the design and experimental validation of an innovative robotic test stand for measuring camshaft cam geometry, intended to support preventive quality control in high-volume production. The proposed solution integrates a collaborative robot with a dedicated measurement setup to enable repeatable positioning of the inspected camshaft and automated acquisition of geometric features critical for functional performance. A complete measurement methodology was developed, including the measurement sequence, data acquisition procedure, and processing of the recorded signals to determine key cam geometry parameters. To verify the reliability of the proposed approach, measurement results obtained using the robotic stand were compared with reference data acquired using conventional metrology tools and standard inspection procedures. Experimental studies confirmed that the developed stand provides repeatable measurement results, enabling the stable identification of the examined geometric features across repeated trials. Moreover, a high level of agreement was observed between the measurement data obtained using the proposed method and the reference measurements, demonstrating the suitability of the cobot-based test stand for preventive quality control applications in industrial environments. The concept presented offers a scalable and flexible alternative to manual inspection and dedicated special-purpose gauges, with potential benefits in terms of inspection throughput and standardization of quality control workflows. The novelty of the approach lies in the indirect ultrasonic measurement model combined with a quadrant-based sensor orientation strategy and repeatable 90° camshaft indexing, enabling full-profile acquisition within the robot workspace.

The COVID-19 pandemic brought the global economy to a near standstill. The scale of the coronavirus and the associated losses have left many countries facing a humanitarian, social and economic crisis. The main objective of the research presented in this article was to determine to what extent the support provided within the activities of the Motokadra project (2018–2020) within the framework of the Operational Programme Knowledge Education Development, funded by the European Social Fund, enabled its participants to adapt to the labour market conditions during the COVID-19 pandemic. The project participants were full-time Master’s students within the faculty of Mechanical Engineering, Silesian University of Technology. They formed the experimental group. The authors examined changes in the employability of the experimental group in the pre-pandemic and post-pandemic periods. The students in the experimental group represented several fields of study, which also allowed the authors to analyse which discipline appeared to be more resilient to the COVID-19 pandemic crisis and whether the profile of the discipline affected employability. The employability of graduates in the automotive industry was particularly examined. The results of the analyses allow us to conclude that the labour market crisis linked to the COVID-19 pandemic negatively affected the experimental group’s employability level. Furthermore, it has been shown that the study of the automation and robotics discipline increases the chances of finding a job after graduation.