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Industrial standards define safety requirements for Human-Robot Collaboration (HRC) in industrial manufacturing. The standards particularly require real-time monitoring and securing of the minimum protective distance between a robot and an operator. In this work, we propose a depth-sensor based model for workspace monitoring and an interactive Augmented Reality (AR) User Interface (UI) for safe HRC. The AR UI is implemented on two different hardware: a projector-mirror setup and a wearable AR gear (HoloLens). We experiment the workspace model and UIs for a realistic diesel motor assembly task. The AR-based interactive UIs provide 21-24% and 57-64% reduction in the task completion and robot idle time, respectively, as compared to a baseline without interaction and workspace sharing. However, subjective evaluations reveal that HoloLens based AR is not yet suitable for industrial manufacturing while the projector-mirror setup shows clear improvements in safety and work ergonomics.

Today's highly volatile production environments call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and dispatching of orders. There is a desire to support such system adaptation and reconfiguration with computer-aided decision support systems. In order to bring automation to reconfiguration decision making in a multi-vendor resource environment, a common formal resource model, representing the functionalities and constraints of the resources, is required. This paper presents the systematic development process of an OWL-based manufacturing resource capability ontology (MaRCO), which has been developed to describe the capabilities of manufacturing resources. As opposed to other existing resource description models, MaRCO supports the representation and automatic inference of combined capabilities from the representation of the simple capabilities of co-operating resources. Resource vendors may utilize MaRCO to describe the functionality of their offerings in a comparable manner, while the system integrators and end users may use these descriptions for the fast identification of candidate resources and resource combinations for a specific production need. This article presents the step-by-step development process of the ontology by following the five phases of the ontology engineering methodology: feasibility study, kickoff, refinement, evaluation, and usage and evolution. Furthermore, it provides details of the model's content and structure.

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Today’s production systems need to adapt rapidly to changing product requirements. Adaptation can be eased by formal models, representing product requirements and system capabilities, which convey the needed information for adaptation planning activities. This paper presents an adaptation approach, which is based on matching the product requirements to the resource capabilities, and applies it to a practical case study in TUT-microfactory environment.

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