Ethernet-based protocols are getting more and more important for Industry 4.0 and the Internet of Things. In this paper, we compare the features, package overhead, and performance of some of the most important protocols in this area. First, we present a general feature comparison of OPC UA, ROS, DDS, and MQTT, followed by a more detailed wire protocol evaluation, which gives an overview over the protocol overhead for establishing a connection and sending data. In the performance tests we evaluate open-source implementations of these protocols by measuring the round trip time of messages in different system states: idle, high CPU load, and high network load. The performance analysis concludes with a test measuring the round trip time for 500 nodes on the same host.

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urrent market demands require an increasingly agile production environment throughout many manufacturing branches. Traditional automation systems and industrial robots, on the other hand, are often too inflexible to provide an economically viable business case for companies with rapidly changing products. The introduction of cognitive abilities into robotic and automation systems is, therefore, a necessary step toward lean changeover and seamless human-robot collaboration. In this article, we introduce the European Union (EU)funded research project SMErobotics (http://www.smerobotics .org/), which focuses on facilitating the use of robot systems in small and medium-sized enterprises (SMEs). We analyze open challenges for this target audience and develop multiple efficient technologies to address related issues. Realworld demonstrators of several end users and from multiple application domains show the impact these smart robots can have on SMEs. This article intends to give a broad overview of the research conducted in SMErobotics. Specific details of individual topics are provided through references to our previous publications.

The current trend to lot-size-one production requires reduced integration effort and easy reuse of available devices inside the production line. These devices have to offer a uniform interface to fulfill these requirements.This paper presents a hardware-agnostic skill model using the semantic modeling capabilities of OPC UA. The model provides a standardized interface to hardware or software functionality while offering an intuitive way of grouping multiple skills to a higher hierarchical abstraction.Our skill model is based on OPC UA Programs and modeled as an open source NodeSet. We hereby focus on the reusability of the skills for many different domains. The model is evaluated by controlling three different industrial robots and their tools through the same skill interface. The evaluation shows that our generic OPC UA skill model can be used as a standardized control interface for device and software components in industrial manufacturing. With our solution new components can easily be exchanged without changing the interface. This is not only true for industrial robots, but for any device which provides a controllable functionality.

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Abstract-Today's robots are still lacking comprehensive knowledge bases about objects and their properties. Yet, a lot of knowledge is required when performing manipulation tasks to identify abstract concepts like a "handle" or the "blade of a spatula" and to ground them into concrete coordinate frames that can be used to parametrize the robot's actions. In this paper, we present a system that enables robots to use CAD models of objects as a knowledge source and to perform logical inference about object components that have automatically been identified in these models. The system includes several algorithms for mesh segmentation and geometric primitive fitting which are integrated into the robot's knowledge base as procedural attachments to the semantic representation. Bottomup segmentation methods are complemented by top-down, knowledge-based analysis of the identified components. The evaluation on a diverse set of object models, downloaded from the Internet, shows that the algorithms are able to reliably detect several kinds of object parts.

Abstract-Modern manufacturing systems require a transformation from mass production towards mass customization. This results in a trend towards more agile production lines. It also demands a reduction of configuration times when building the production line as well as faster reconfiguration when adding new hardware and product variants to the manufacturing line. This paper introduces the concept of a device adapter that allows the device to be seamlessly plugged into the agile production systems. The device adapter wraps the device functionality and offers it as a service, hiding away the low-level process capability (skill) implementation and allowing to formally represent the production steps. Preliminary tests have been performed on an industrial demonstrator that simulates a real manufacturing process.

The manufacturers are in quest for flexible and agile production facilities capable of accommodating changes to product specification. The need for flexible production facilities is stemming from the desire for customized products and fluctuating market trends. Industry 4.0 impels for adaptable manufacturing plants by employing intelligent devices and advanced communication technologies. The complexity of the configuration process determines the adaptability of production facilities to accommodate changes to the production process.We propose a systematic integration process and multi-level production system using Software-defined Networking (SDN) and OPC Unified Architecture (OPC UA) to reduce the configuration complexity to a Plug and Produce level. OPC UA, as a serviceoriented middleware, provides the tool-set for semantic modeling and automatic device discovery. However, due to the multicast nature of the OPC UA discovery mechanism, the existing approaches require intelligence at the device level to select the desired device to connect to. In contrast, our proposed solution shifts the intelligence to a centralized SDN controller to route multicast traffic to facilitate device discovery. The combination of an SDN controller and OPC UA discovery enables the integration of new devices by adding more intelligence to the device discovery.