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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The research project presented in this thesis is situated in the domain of design research, and focuses on the designers of production systems. In general, it aims to support the re-search towards a better understanding of design for human-robot coproduction (HRC). The specific objective of this research project was the development of support for novice HRC designers for integrating collaborative robots (Cobots) successfully in existing and new human-driven production systems. At the start of the project, it was assumed that novice HRC designers were lacking conceptual design tools for analysing, modelling, simulating and evaluating human-robot coproduction scenarios. Therefore, the design support was realized in the form of an adaptable framework methodology for conceptual design of HRC. The research for this thesis was executed as a PhD project which was supported by the EU-FP7-‘Factory in a day’ project, which enabled the generation and exploration of empirical evidence from the targeted context. In addition the research had access to a laboratory environment in which two types of Cobots were present. @en

In recent years, mass-customization and on-demand 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 is a complex activity that requires bridging between disciplines. With the introduction of Collaborative Robots, new disciplines are added to this activity, that need to be linked to existing design methods and procedures. Currently, the lack of these links is a bottleneck for Small and Medium sized Enterprises. These have limited resources for implementation. In this article, we introduce a Human-Robot Coproduction Design Methodology, with the aim of raising the capacity of Intelligent Manufacturing System 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 Intelligent Manufacturing Systems.

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Within the European Factory-in-a-day project, the aim is to improve communication between automation integrator and factory owner, in their analysis of feasibility and appropriateness of automating a manual task. A visualisation tool with preconfigured workflows and working principles, with specific focus on efficient human-robot coproduction workflows can improve this process. This paper describes the Workflow Simulation Tool, which is part of the Human-Robot Coproduction Methodology, currently in development. The tool encompasses a portable tablet PC, which runs a visual modelling environment combined with a handheld 3D scanning solution. The tool also features pre-modelled template layouts, implementation of a checklist of persistent notes and portable visual documentation. The tool's appropriateness was iteratively validated in collaboration with automation integrators. This evaluation showed that offering an interactive visual simulation enriches the dialogue during conceptual design and helps in revealing requirements that otherwise only appear during or after implementation.

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Recently, the field of Human-Robot Coproduction (HRC) has received much attention and is promising to play a crucial role in factory automation in the future. However, little is known on how to design production systems by making optimal use of such collaboration strategies. We believe it is necessary to start the design process with a suitable means of analysis, which is currently missing in this context. In this article, we propose a framework in which an existing human-machine workstation is analyzed for the purpose of improving it with HRC-based technology. The analysis is performed by observing the four actors that are present at the workstation, and expressing their interactions using qualifiers. This framework was applied to an existing situation; based on an observational method to identify matter and information flows during the production task. When compared to traditional production system design approaches, the aforementioned framework helped to identify discrepancies between actors and to highlight areas where HRC-based technology could be implemented. Future research is necessary to extend this framework to methods and tools, to be validated in HRC applications.@en

Recently, robots are making their way towards the small and medium enterprises at a quick pace. This is due to market demand and enabled by reduction in costs of robot systems. If robots can be introduced strategically in existing production workflows, productivity could be increased. However, the task division and interaction between human and robot co-workers need to be optimized in order to achieve this. This work-in-progress paper presents a pilot setup to explore such scenarios. We discuss early findings and we present our vision for the future of human robot coproduction.

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