In recent years, mass-customization and ondemand production have spread to larger ground. To accommodate these developments, manufacturing systems are being transformed to allow more flexibility and agility. One of the technologies that allow flexibility and agility is collaborative robots. The design and implementation of intelligent manufacturing systems (IMS) is a complex activity that requires bridging between disciplines. With the introduction of collaborative robots, new disciplines are added to this activity, which need to be linked to the existing design methods and procedures. Currently, the lack of these links is a bottleneck for small-and medium-sized enterprises that have limited resources for implementation. In this paper, we introduce a human-robot coproduction design methodology, with the aim of raising the capacity of designers for reasoning on collaboration between humans and robots in manufacturing. The methodology comprises four procedural steps: analysis, modeling, simulation, and evaluation, with specific methods, tools, and instruments. The methodology has been evaluated in a laboratory environment by performing a pilot study with designers. While the current implementation of the methodology and its instrumentation is limited, it has been shown that the methodology enables quick design iterations during the conceptual design phase of human-robot coproduction, thanks to procedures that have been tailored for this novel form of organizing and structuring production processes in IMS.

In order to support the decision-making process of industry on how to implement Augmented Reality (AR) in production, this article wants to provide guidance through a set of comparative user studies. The results are obtained from the feedback of 160 participants who performed the same repair task on a switch cabinet of an industrial robot. The studies compare several AR instruction applications on different display devices (head-mounted display, handheld tablet PC and projection-based spatial AR) with baseline conditions (paper instructions and phone support), both in a single-user and a collaborative setting. Next to insights on the performance of the individual device types for the single mode operation, the study is able to show significant indications on AR techniques are being especially helpful in a collaborative setting.

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Abstract-It is expected that soon, systems consisting of a blend of humans and robots be devised in such a way that higher productivities will be achieved. The main enabler for this is expected to be the possibility of collaboration between workers and robots. HRI (Human Robot Interaction) is the field in which such phenomena are studied. A growing number of investigators treat the collaboration of robots and workers (humans) in many contexts, however attention towards the manufacturing industry is predominantly focused on full automation of human tasks. Industrial robots have long been unsafe to work in close vicinity to workers due to their duty to be fast and powerful. However, nowadays, with the drive from emerging technologies, this is changing. Safe worker-robot collaborations are beginning to take shape and the HRI community is beginning to study such scenarios. Despite being a very effective form of interaction, a key research question is whether collaboration is a suitable mode of interaction for manufacturing environments. To be able to address this question, we found a collection of ten workerrobot systems that constitute a first step in outlining coproduction characteristics. This collection allowed us to identify differences in task initiative and product handling and component handling, while we frame coproduction as an extension of man. Challenges that require additional attention are workflow planning and defining proper performance indicators. We conclude with the fact that, although the worker-robot collaboration systems are inspiring and redefine labor, no sufficient knowledge or tools exists to reproduce such qualities in different manufacturing settings. Further work will be focused on modeling and assessing the performance and bottlenecks of systems based on novel robotic systems.