Robot programming for complex assembly tasks is challenging and demands expert knowledge. With Augmented Reality (AR), immersive 3D visualization can be placed in the robot's intrinsic coordinate system to support robot programming. However, AR interfaces introduce usability challenges. To address these, we introduce a hybrid user interface (HUI) that combines a 2D desktop, a smartphone, and an AR head-mounted display (HMD) application, enabling operators to choose the most suitable device for each sub-task. The evaluation with an expert user study shows that an HUI can enhance efficiency and user experience by selecting the appropriate device for each sub-task. Generally, the HMD is preferred for tasks involving 3D content, the desktop for creating the program structure and parametrization, and the smartphone for mobile parametrization. However, the device selection depends on individual user characteristics and their familiarity with the devices.

Human–robot collaboration (HRC) offers promising potential for more flexible and sustainable production practices in architecture and construction. This requires HRC setups to scale up from light-payload collaborative robots to conform with the scale of building construction while considering the safety and teamwork culture for workers. This research proposes a system for large-scale multi-user HRC using head-mounted augmented reality (AR) devices. To achieve this, we contribute three methods that work in conjunction: (1) an AR system that enables multiple users to share tasks and work together with robots; (2) a dynamic human task allocation engine that reacts to the changing production teams and task types; and (3) a safety zone generation and allocation method to configure human collaboration in shared space with large-scale robots. The system is evaluated using a case study of prefabricated timber cassettes combining discrete event simulations , a user study and a fabrication process demonstrator with an industry partner.

Extensive sitting is unhealthy; thus, countermeasures are needed to react to the ongoing trend toward more prolonged sitting. A variety of studies and guidelines have long addressed the question of how we can improve our sitting habits. Nevertheless, sitting time is still increasing. Here, smart devices can provide a general overview of sitting habits for more nuanced feedback on the user’s sitting posture. Based on a literature review (N=223), including publications from engineering, computer science, medical sciences, electronics, and more, our work guides developers of posture systems. There is a large variety of approaches, with pressure-sensing hardware and visual feedback being the most prominent. We found factors like environment, cost, privacy concerns, portability, and accuracy important for deciding hardware and feedback types. Further, one should consider the user’s capabilities, preferences, and tasks. Regarding user studies for sitting posture feedback, there is a need for better comparability and for investigating long-term effects.

The use of augmented reality technology to support humans with situated visualization in complex tasks such as navigation or assembly has gained increasing importance in research and industrial applications. One important line of research regards supporting and understanding collaborative tasks. Analyzing collaboration patterns is usually done by conducting observations and interviews. To expand these methods, we argue that eye tracking can be used to extract further insights and quantify behavior. To this end, we contribute a study that uses eye tracking to investigate participant strategies for solving collaborative sorting and assembly tasks. We compare participants’ visual attention during situated instructions in AR and traditional paper-based instructions as a baseline. By investigating the performance and gaze behavior of the participants, different strategies for solving the provided tasks are revealed. Our results show that with situated visualization, participants focus more on task-relevant areas and require less discussion between collaboration partners to solve the task at hand.

Commercial videos are often used to familiarise potential buyers and users with new technologies and their possibilities. In addition, presenting visions of future applications is a way to configure users and define social worlds of technology use. We analyse 30 YouTube videos featuring augmented reality (AR) devices in industrial manufacturing and construction, to explore how these commercial videos situate AR technology and future users by showcasing techno-euphoric promises and imagined use cases. With a video analysis based on Grounded Theory and Situational Analysis, we untangle the promises of AR for manufacturing and construction work; second, we present two prevailing configurations of AR users: ‘experts in situ’ and ‘smart dummies’; and third, we discuss how YouTube videos put forward developmental expectations. In addition, we identify discrepancies between expectations and foreseeable requirements in construction work. Finally, our research could contribute to a more holistic understanding of workplaces and socially robust AR applications.

Situated analytics (SitA) uses visualization in the context of physical referents, typically by using augmented reality (AR). We want to pave the way toward studying SitA in more suitable and realistic settings. Toward this goal, we contribute a testbed to evaluate SitA based on a scenario in which participants play the role of a museum curator and need to organize an exhibition of music artifacts. We conducted two experiments: First, we evaluated an AR headset interface and the testbed itself in an exploratory manner. Second, we compared the AR headset to a tablet interface. We summarize the lessons learned as guidance for designing and evaluating SitA.

We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualizations with data streams from the Internet of Things using distributed dataflow. To this end, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.