The widespread implementation of Human–Robot Collaborations (HRC) in industry is becoming an obvious step in the era of Industry 4.0 or even 5.0 and the requirements of sustainable production (SP) due to the increase in productivity and quality while reducing production costs. However, due to limited studies on the establishment of collision thresholds in HRC, in-depth research is needed to determine possible disruptions for safety in HRC. So, firstly, in order to identify current issues and critical challenges in the field of HRC safety, an analysis of the literature was carried out using the Systematic Literature Review (SLR) & Mapping in Literature Reviews (MLR) methodology based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines (PRISMA). A total of 4,107 research articles were identified in Science Direct—Elsevier, Web of Science and Springer, that focussed on safety in HRC in SP. In this study, 103 items were then analysed and 53 in the second stage. The key research contributions are developed as the principles for enhancing safety in HRC in industry: (1) improving HRC workspace safety, (2) building mechanisms for monitoring HRC workspaces and (3) managing ethical issues. In line with these trends, secondly, experimental studies were provided. Next, empirical research was conducted to establish the parameters for ensuring the safety of the monitoring system for work in HRC. Therefore, analysis of the video recordings on the efficiency of detecting collision in HRC, when using the Region-Based Convolutional Neural Network (YOLOv8 Tiny), within the vision system were researched. The experimental studies indicate the necessary actions within a monitoring system in HRC in order to ensure workplace safety in the HRC environment. Finally, the research results suggest future research directions as (1) proactive HRC with attention to collaborative robots and (2) integration with the approach, to ensure safety in HRC into SP strategy in industry. This study provides new insights into HRC and gives an outlook for development of safety issues in HRC in industry. Graphical abstract

The monitoring and early avoidance of collisions in a workspace shared by collaborative robots (cobots) and human operators is crucial for assessing the quality of operations and tasks completed within manufacturing. A gap in the research has been observed regarding effective methods to automatically assess the safety of such collaboration, so that employees can work alongside robots, with trust. The main goal of the study is to build a new method for recognising collisions in workspaces shared by the cobot and human operator. For the purposes of the research, a research unit was built with two UR10e cobots and seven series of subsequent of the operator activities, specifically: (1) entering the cobot’s workspace facing forward, (2) turning around in the cobot’s workspace and (3) crouching in the cobot’s workspace, taken as video recordings from three cameras, totalling 484 images, were analysed. This innovative method involves, firstly, isolating the objects using a Convolutional Neutral Network (CNN), namely the Region-Based CNN (YOLOv8 Tiny) for recognising the objects (stage 1). Next, the Non-Maximum Suppression (NMS) algorithm was used for filtering the objects isolated in previous stage, the k-means clustering method and Simple Online Real-Time Tracking (SORT) approach were used for separating and tracking cobots and human operators (stage 2) and the Convolutional Neutral Network (CNN) was used to predict possible collisions (stage 3). The method developed yields 90% accuracy in recognising the object and 96.4% accuracy in predicting collisions accuracy, respectively. The results achieved indicate that understanding human behaviour working with cobots is the new challenge for modern production in the Industry 4.0 and 5.0 concept.

The contemporary consumer market determines the use of mass customisation by manufacturers. Therefore, from the point of view of Industry 4.0 (I4.0), concept data and communications and analytics are relevant to the implementation of the mass-customisation strategy. The problem involves at least three subjects: how to connect the currently available information system within an enterprise with new I4.0 technologies, how new information solutions can support the verification of the feasibility of tailoring an order to the needs of the customer, and, finally, how to model a knowledge base for a cyber-physical system containing a formal record of the knowledge acquired regarding customer preferences. Therefore, in this paper, we developed a new algorithm that will enable, on one hand, the collection and recording of customer preferences, and, on the other hand, the integration of those data and information that are available within the Enterprise Resource Planning (ERP) system. The main contribution of this work is the use of specialist knowledge and data from ERP regarding production capabilities implemented in a manufacturing enterprise in order to model the scenario of generating possible orders for a client, and finally applying the new solution for the operation of manufacturing according to mass-customisation strategy in a real company that manufactures mattresses.