Dr. Stefan Profanter

@inproceedings{Perzylo2020a,

title = {Toward a Knowledge-Based Data Backbone for Seamless Digital Engineering in Smart Factories},

author = {Alexander Perzylo and Ingmar Kessler and Stefan Profanter and Markus Rickert},

url = {https://profanter.me/wp-content/uploads/2020/07/Perzylo2020a.pdf},

year  = {2020},

date = {2020-07-15},

booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA)},

address = {Vienna, Austria},

abstract = {Digital transformation efforts in manufacturing companies bear the potential to reduce product costs and increase the flexibility of production systems. The semantic integration of data and information along the value chain enables the automated interpretation of interrelations between its different aspects such as product design, production process and manufacturing resources. These interrelations can be used to automatically generate semantic process descriptions and execute corresponding robot motions. An initial one-time effort to model the required knowledge of a particular application domain can make the manufacturing of high-variant products in small batches or even lot size one production more efficient. This paper introduces a knowledge-based digital engineering concept to automate engineering and production activities without human involvement. The concept was integrated and evaluated in a physical robot workcell where automotive fuse boxes are autonomously fitted with different fuse configurations.},

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@inproceedings{Profanter2019b,

title = {A Hardware-Agnostic OPC UA Skill Model for Robot Manipulators and Tools},

author = { Stefan Profanter and Ari Breitkreuz and Markus Rickert and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Profanter2019b.pdf

https://youtu.be/O9WNyua72XA},

doi = {10.1109/ETFA.2019.8869205},

year  = {2019},

date = {2019-09-01},

booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA)},

address = {Zaragoza, Spain},

abstract = {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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@inproceedings{Dorofeev2019a,

title = {Agile Operational Behavior for the Control-Level Devices in Plug & Produce Production Environments},

author = { Kirill Dorofeev and Stefan Profanter and Jose Cabral and Pedro Ferreira and Alois Zoitl},

url = {https://profanter.me/wp-content/uploads/2019/08/Dorofeev2019a.pdf},

doi = {10.1109/ETFA.2019.8869208},

year  = {2019},

date = {2019-09-01},

booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA)},

abstract = {The ongoing manufacturing systems transformation from mass production towards mass customization requires more flexible engineering solutions than the existing ones. The recently proposed control architectures target, among other plug-andproduce features, a reduction of configuration times. This is relevant for building a new production line as well as for faster reconfiguration when adding new hardware and product variants to an existing manufacturing line. This paper identifies operational requirements for such reconfiguration scenarios and proposes a way to implement them using the concept of a device adapter. The device adapter contains a device description and constantly updates it following the reconfiguration changes happening in a manufacturing system. This allows not only to detect the changes in the hardware, which appear in the production system, using the device discovery mechanisms but also automatically adapt the software. Preliminary tests have been performed on a demonstrator that shows both virtual and physical executions combined in a single system. The proposed solution supports automatic hardware and software reconfiguration on-the-fly without a need to stop and restart the whole production system.},

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@inproceedings{Madiwalar2019a,

title = {Plug and Produce for Industry 4.0 using Software-defined Networking and OPC UA},

author = {Basavaraj Madiwalar and Ben Schneider and Stefan Profanter},

url = {https://profanter.me/wp-content/uploads/2019/08/Madiwalar2019a.pdf},

doi = {10.1109/ETFA.2019.8869525},

year  = {2019},

date = {2019-09-01},

booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA)},

address = {Zaragoza, Spain},

abstract = {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 service-oriented 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.},

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@inproceedings{Perzylo2019c,

title = {OPC UA NodeSet Ontologies as a Pillar of Semantic Digital Twins of Manufacturing Resources},

author = { Alexander Perzylo and Stefan Profanter and Markus Rickert and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Perzylo2019c.pdf},

doi = {10.1109/ETFA.2019.8868954},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA)},

address = {Zaragoza, Spain},

abstract = {The effectiveness of cognitive manufacturing systems in agile production environments heavily depend on the automatic assessment of various levels of interoperability between manufacturing resources. For taking informed decisions, a semantically rich representation of all resources in a workcell or production line is required. OPC UA provides means for communication and information exchange in such distributed settings. 

This paper proposes a semantic representation of a resource's properties, in which we use OWL ontologies to encode the information models that can be found in OPC UA NodeSet specifications. We further combine these models with an OWL-based description of the resource's geometry and -- if applicable -- its kinematic model. This leads to a comprehensive semantic representation of hardware and software features of a manufacturing resource, which we call semantic digital twin. Among other things, it reduces costs through virtual prototyping and enables the automatic deployment of manufacturing tasks in production lines. As a result, small-batch assemblies become financially viable. 

In order to minimize the effort of creating OWL-based UA NodeSet descriptions, we provide a software tool for the automatic transformation of XML-based NodeSet specifications that adhere to the OPC Foundation's NodeSet2 XML schema.},

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@inproceedings{Profanter2019a,

title = {OPC UA versus ROS, DDS, and MQTT: Performance Evaluation of Industry 4.0 Protocols},

author = { Stefan Profanter and Ayhun Tekat and Kirill Dorofeev and Markus Rickert and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Profanter2019a.pdf},

doi = {10.1109/ICIT.2019.8755050},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the IEEE International Conference on Industrial Technology (ICIT)},

address = {Melbourne, Australia},

abstract = {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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@inproceedings{Dorofeev2017a,

title = {Device Adapter Concept towards Enabling Plug & Produce Production Environments},

author = { Kirill Dorofeev and Chih-Hong Cheng and Magno Guedes and Pedro Ferreira and Stefan Profanter and Alois Zoitl},

url = {https://profanter.me/wp-content/uploads/2019/08/Dorofeev2017a.pdf},

doi = {10.1109/ETFA.2017.8247570},

year  = {2017},

date = {2017-09-01},

booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA)},

address = {Limassol, Cyprus},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

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@inproceedings{Profanter2017a,

title = {OPC UA for Plug & Produce: Automatic Device Discovery using LDS-ME},

author = { Stefan Profanter and Kirill Dorofeev and Alois Zoitl and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Profanter2017a.pdf},

doi = {10.1109/ETFA.2017.8247569},

year  = {2017},

date = {2017-09-01},

booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA)},

address = {Limassol, Cyprus},

abstract = {Current manufacturing and production are changing more and more into a flexible and adaptable factory layout that requires rapid changeover and short reconfiguration times of machines. Additionally the setup time for new devices should be as short as possible. 

 

In this paper we propose a hierarchical architecture for a multi-level Plug & Produce system and evaluate the proposed structure using open source OPC UA implementations for easy integration of new devices into an existing system. Aside from the requirements for such a system, basic concepts of the OPC UA Discovery Service Set are described and different open source OPC UA implementations for C/C++ and Java are compared.},

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@inproceedings{Perzylo2016a,

title = {Intuitive Instruction of Industrial Robots: Semantic Process Descriptions for Small Lot Production},

author = { Alexander Perzylo and Nikhil Somani and Stefan Profanter and Ingmar Kessler and Markus Rickert and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Perzylo2016a.pdf

https://youtu.be/bbInEMEF5zU},

doi = {10.1109/IROS.2016.7759358},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

pages = {2293--2300},

address = {Daejeon, Republic of Korea},

abstract = {In this paper, we introduce a novel robot programming paradigm. It focuses on reducing the required expertise in robotics to a level that allows shop floor workers to use robots in their application domain without the need of extensive training. Our approach is user-centric and can interpret underspecified robot tasks, enabling communication on an abstract level. Such high-level task descriptions make the system amenable for users that are experts in a particular domain, but have limited knowledge about robotics and are thus not able to specify low-level details and instructions. Semantic models for all involved entities, i.e., processes, workpieces, and workcells, enable automatic reasoning about underspecified tasks and missing pieces of information. We showcase and evaluate this methodology on two industrial use cases from the domains of assembly and woodworking, comparing it to state-of-the-art solutions provided by robot manufacturers.},

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@inproceedings{Haage2016,

title = {On Cognitive Robot Woodworking in SMErobotics},

author = {Mathias Haage and Stefan Profanter and Ingmar Kessler and Alexander Perzylo and Nikhil Somani and Olof Sornmo and Martin Karlsson and Sven Gestegård Robertz and Klas Nilsson and Ludovic Resch and Michael Marti},

url = {https://profanter.me/wp-content/uploads/2019/08/Haage2016.pdf},

doi = {10.1109/IROS.2015.7353770},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings of the International Symposium on Robotics (ISR)},

address = {Munich, Germany},

abstract = {This paper details and discusses work performed at the woodworking SME Mivelaz Techniques Bois SA within the EU FP7 project SMErobotics. The aim is to improve non-expert handling of the cell by introduction of cognitive abilities in the robot system. Three areas are considered; intuitive programming, process adaptation and system integration. Proposed cognitive components are described together with experiments performed.},

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@inproceedings{Profanter2015a,

title = {Analysis and Semantic Modeling of Modality Preferences in Industrial Human-Robot Interaction},

author = { Stefan Profanter and Alexander Perzylo and Nikhil Somani and Markus Rickert and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Profanter2015a.pdf},

doi = {10.1109/IROS.2015.7353613},

year  = {2015},

date = {2015-09-01},

booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

pages = {1812--1818},

address = {Hamburg, Germany},

abstract = {Intuitive programming of industrial robots is especially important for small and medium-sized enterprises. We evaluated four different input modalities (touch, gesture, speech, 3D tracking device) regarding their preference, usability, and intuitiveness for robot programming. A Wizard-of-Oz experiment was conducted with 30 participants and its results show that most users prefer touch and gesture input over 3D tracking device input, whereas speech input was the least preferred input modality. The results also indicate that there are gender specific differences for preferred input modalities. We show how the results of the user study can be formalized in a semantic description language in such a way that a cognitive robotic workcell can benefit from the additional knowledge of input and output modalities, task parameter types, and preferred combinations of the two.},

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@inproceedings{Perzylo2015a,

title = {Multimodal Binding of Parameters for Task-Based Robot Programming Based on Semantic Descriptions of Modalities and Parameter Types},

author = { Alexander Perzylo and Nikhil Somani and Stefan Profanter and Markus Rickert and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Perzylo2015a.pdf},

year  = {2015},

date = {2015-09-01},

booktitle = {Proceedings of the Workshop on Multimodal Semantics for Robotic Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

address = {Hamburg, Germany},

abstract = {In this paper, we describe our ongoing efforts to design a cognition-enabled industrial robotic workcell, which significantly increases the efficiency of teaching and adapting robot tasks. We have designed a formalism to match task parameter and input modality types, in order to infer suitable means for binding values to those parameters. All modalities are integrated through a graphical user interface, which a human operator can use to program industrial robots in an intuitive way by arbitrarily choosing modalities according to his or her preference.},

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@inproceedings{Perzylo2015c,

title = {Ubiquitous Semantics: Representing and Exploiting Knowledge, Geometry, and Language for Cognitive Robot Systems},

author = { Alexander Perzylo and Nikhil Somani and Stefan Profanter and Andre Gaschler and Sascha Griffiths and Markus Rickert and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Perzylo2015c.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the Workshop Towards Intelligent Social Robots - Current Advances in Cognitive Robotics, IEEE/RAS International Conference on Humanoid Robots (HUMANOIDS)},

address = {Seoul, South Korea},

abstract = {In this paper, we present an integrated approach to knowledge representation for cognitive robots. We combine knowledge about robot tasks, interaction objects including their geometric shapes, the environment, and natural language in a common ontological description. This description is based on the Web Ontology Language (OWL) and allows to automatically link and interpret these different kinds of information. Semantic descriptions are shared between object detection and pose estimation, task-level manipulation skills, and human-friendly interfaces. Through lifting the level of communication between the human operator and the robot system to an abstract level, we achieve more human-suitable interaction and thus a higher level of acceptance by the user. Furthermore, it increases the efficiency of communication. The benefits of our approach are highlighted by examples from the domains of industrial assembly and service robotics.},

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@inproceedings{Perzylo2015a,

title = {Toward Efficient Robot Teach-In and Semantic Process Descriptions for Small Lot Sizes},

author = { Alexander Perzylo and Nikhil Somani and Stefan Profanter and Markus Rickert and Alois Knoll},

url = {https://profanter.me/wp-content/uploads/2019/08/Perzylo2015.pdf

http://youtu.be/B1Qu8Mt3WtQ},

doi = {10.13140/RG.2.1.3333.7440},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the Workshop on Combining AI Reasoning and Cognitive Science with Robotics, Robotics: Science and Systems (RSS)},

address = {Rome, Italy},

abstract = {We present a novel robot programming methodology that is aimed at reducing the level of robotics expert knowledge needed to operate industrial robotic systems by explicitly modeling this knowledge and abstracting it from the user. Most of the current robot programming paradigms are either user-centric and fully-specify the robot's task to the lowest detail (used mostly in large industrial robotic systems) or fully autonomous solutions that generate the tasks from a problem description (used often in service and personal robotics). We present an approach that is user-centric and can interpret underspecified robot tasks. Such task descriptions make the system amenable for users that are experts in a particular domain, but have limited knowledge about robotics and are thus not able to specify low-level details and instructions. Semantic models for all involved entities enable automatic reasoning about underspecified tasks and missing pieces of information. We demonstrate this approach on an industrial assembly use-case and present a preliminary evaluation---both qualitatively and quantitatively---vis-à-vis state-of-the-art solutions available from industrial robot manufacturers.},

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@inproceedings{Tenorth2013a,

title = {Decomposing CAD models of objects of daily use and reasoning about their functional parts},

author = {Moritz Tenorth and Stefan Profanter and Ferenec Balint-Benczedi and Michael Beetz},

url = {https://profanter.me/wp-content/uploads/2019/08/Tenorth.pdf},

doi = {10.1109/IROS.2013.6697218},

issn = {2153-0858},

year  = {2013},

date = {2013-11-01},

booktitle = {2013 IEEE/RSJ International Conference on Intelligent Robots and Systems},

pages = {5943-5949},

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. Bottom-up 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.},

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