Human–robot collaboration (HRC) is one of the key aspects of Industry 4.0 (I4.0) and requires intuitive modalities for humans to communicate seamlessly with robots, such as speech, touch, or bodily gestures. However, utilizing these modalities is usually not enough to ensure a good user experience and a consideration of the human factors. Therefore, this paper presents a software component, Multi-Modal Offline and Online Programming (M2O2P), which considers such characteristics and establishes a communication channel with a robot with predefined yet configurable hand gestures. The solution was evaluated within a smart factory use case in the Smart Human Oriented Platform for Connected Factories (SHOP4CF) EU project. The evaluation focused on the effects of the gesture personalization on the perceived workload of the users using NASA-TLX and the usability of the component. The results of the study showed that the personalization of the gestures reduced the physical and mental workload and was preferred by the participants, while overall the workload of the tasks did not significantly differ. Furthermore, the high system usability scale (SUS) score of the application, with a mean of 79.25, indicates the overall usability of the component. Additionally, the gesture recognition accuracy of M2O2P was measured as 99.05%, which is similar to the results of state-of-the-art applications.

There is a trend about the adoption of Knowledge Representation and Reasoning formalisms, such as ontologies, for industrial automation. For example, semantic models are used as knowledge bases that encapsulate different type of information of manufacturing systems, e.g., statuses and capabilities of their cyber and physical resources. Moreover, these models can be updated and accessed during runtime. In this context, models are becoming a critical part of the system infrastructure for both controlling and monitoring activities. However, models tend to be designed for specific purposes and not standardized. This is an issue because the employed formalisms, such as ontologies, emerged in order to bring an engineering tool for commonly classifying, defining, and sharing information. This article proposes the development of modular ontologies based on different parts of the ISA-95 standard for describing the product, process, and resource information of manufacturing systems. In addition, this research work demonstrates a set of semantic rules that may be used for inferring implicit knowledge of the ontology that permits the automatic checking of the required machines to manufacture different product variants.

In recent years, Industry 4.0 has provided many tools to replicate, monitor, and control physical systems. The purpose is to connect production assets to build cyber-physical systems that ensure the safety, quality, and efficiency of production processes. Particularly, the concept of digital twins has been introduced to create the virtual representation of physical systems where both elements are connected to exchange information. This general definition encompasses a series of major challenges for the developers of those functionalities. Among them is how to introduce the human perspective into the virtual replica. Therefore, this paper presents an approach for incorporating human factors in digital twins. This approach introduces a methodology to offer suggestions about employee rotations based on their previous performance during a shift. Afterward, this method is integrated into a digital twin to perform human performance assessments to manage workers’ jobs. Furthermore, the presented approach is mainly comprised of a human skills modelling engine and a human scheduling engine. Finally, for demonstrating the approach, a simulated serial single-product manufacturing assembly line has been introduced.

Building on the idea of Industry 4.0, new models of the highly connected factory that leverage factory-generated data to introduce cost-effective automation and involve the human worker for creating higher added value are possible. Within this context, collaborative robots are becoming more common in industry. However, promises regarding flexibility cannot be satisfied due to the challenging process of ensuring human safety. This is because current regulations and standards require updates to the risk assessment for every change to the robotic application, including the parts involved, the robotic components, and the type of interaction within the workspace. This work presents a novel risk analysis software tool that was developed to support change management for adaptive collaborative robotic systems in the connected factory model. The main innovation of this work is the tool’s ability to automatically identify where changes have been made to components or processes within a specific application through its integration with a connected factory architecture. This allows a safety expert to easily see where updates to the risk assessment are required, helping them to maintain conformity with the CE marking process despite frequent changes. To evaluate the benefits of this tool, a user study was performed with an exemplary use-case from the SHOP4CF project. The results show that this newly developed technology for risk assessment has better usability and lower omission errors when compared to existing methods. Therefore, this study underlines the need for tools that can help safety engineers cope with changes in flexible robotics applications and reduce omission errors.