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Virtual environments have been widely adopted for design and training tasks in the industrial domain. Low-cost automation (LCA) is a technology that automatizes some activities using mostly standard automation mechanisms available off the shelf. However, LCA systems should adapt to existing standard production lines and workstations. Thus, workers must customize standard LCA templates and perform adaptation and customization steps. This activity can be very time consuming with physical LCA systems, and in case of errors, it may be necessary to rebuild many parts from scratch. Thus, LCA systems would greatly benefit from a design and prototyping step experienced in a virtual simulation environment. An immersive virtual reality (IVR) application for rapid and easy prototyping of LCA solutions has been investigated in previous work; the assessment of the system usability proved that the users highly appreciated the proposed solutions. This research explores further improvements to exploit the existing IVR application as a training tool for LCA prototyping trainees. The proposed application now provides users with two different interaction paradigms based on the VIVE controllers and the Manus Prime II data gloves. The application’s interface has been revised to allow a proper comparison of the two interaction models. The two interfaces have been compared, involving 12 participants in an LCA building task. The System Usability Scale (SUS) and the NASA Task Load Index (TLX) questionnaires have been used to assess the usability and workload of the two solutions.

Human–robot collaboration (HRC) is a new and challenging discipline that plays a key role in Industry 4.0. Digital transformation of industrial plants aims to introduce flexible production lines able to adapt to different products quickly. In this scenario, HRC can be a booster to support flexible manufacturing, thus introducing new interaction paradigms between humans and machines. Augmented reality (AR) can convey much important information to users: for instance, information related to the status and the intention of the robot/machine the user is collaborating with. On the other hand, traditional input interfaces based on physical devices, gestures, and voice might be precluded in industrial environments. Brain–computer interfaces (BCIs) can be profitably used with AR devices to provide technicians solutions to effectively collaborate with robots. This paper introduces a novel BCI–AR user interface based on the NextMind and the Microsoft Hololens 2. Compared to traditional BCI interfaces, the NextMind provides an intuitive selection mechanism based on visual cortex signals. This interaction paradigm is exploited to guide a collaborative robotic arm for a pick and place selection task. Since the ergonomic design of the NextMind allows its use in combination with the Hololens 2, users can visualize through AR the different parts composing the artifact to be assembled, the visual elements used by the NextMind to enable the selections, and the robot status. In this way, users’ hands are always free, and the focus can be always on the objects to be assembled. Finally, user tests are performed to evaluate the proposed system, assessing both its usability and the task’s workload; preliminary results are very encouraging, and the proposed solution can be considered a starting point to design and develop affordable hybrid-augmented interfaces to foster real-time human–robot collaboration.

Until some years ago, there was a lack of affordable devices that allowed for research in order to design and implement augmented and mixed reality applications. Thanks to technology improvements, it is now possible to find several wearable devices on the market that greatly improve the synergy between the real and virtual world, thus allowing users to be part of an immersive experience in which they can easily interact with 3D assets and real objects at the same time. Thus, the number of augmented reality and mixed reality (AR/MR) applications is greatly increased, from educational or cultural heritage applications to industry ones, such as maintenance-assembly-repair processes or product inspection and building monitoring. Indeed, several improvements have been made in order to enhance this technology, but some issues still need to be tackled, namely: limited field-of-view, tracking stability, the effectiveness of user interfaces to interact with 3D contents, and many others.