Das eigentliche Ziel der Qualitätssicherung ist es, Ausschuss zu vermeiden und nicht Schlechtteile auszusortieren; dies ermöglichen Technologien, die unter dem Titel „Zero Defect Manufacturing“ zusammengefasst sind.
Unter dem strategischen Konzept von „Zero Defect Manufacturing“ entwickelt PROFACTOR Methoden, die ausgehend von Verfahren zur Qualitätssicherung zusätzliche Informationen liefern, und es ermöglichen die Rückkopplungsschleife zum Prozess so zu schließen, dass Ausschuß vermieden oder zumindest deutlich reduziert wird.
An erster Stelle stehen dabei Sensoren, die umfassende Information über die Produktoberfläche erzeugen. Sowohl für metallische Werkstoffe als auch für Kompositbauteile werden Verfahren wie photometric Stereo genutzt und mit physikalischen Modellen der zu untersuchenden Oberfläche ergänzt, so dass die Reflexionseigenschaften der Oberfläche komplett erfasst werden. Für Prüfungen „im Inneren“ von Bauteilen wird aktive Thermographie eingesetzt.
Für die Interpretation der Daten werden einerseits konventionelle Methoden wie grauwertbasierte Segmentierung oder Texturanalyse genutzt, und andererseits aber auch neuer lernfähiger Methoden aus dem Bereich „deep learning“, wobei vor allem semantischen Segmentierungsverfahren zur Anwendung kommen. Für industrielle Anwendung tritt bei lernfähigen Verfahren das Problem auf, dass die dazu notwendigen Trainingsdaten nicht erzeugt werden können. Daher wird auf Methoden wie „generative adverserial networks“ zurückgegriffen.
Die aus der Qualitätssicherung gewonnen Information werden dann mit Prozess- und Produktdaten kombiniert, um auf dieser Datenbasis Vorhersagemodell zu trainieren. Mit Hilfe dieser Modelle ist es mögliche, Prozesse im vorgegebenen Qualitäts-Toleranzband zu halten oder gezielte Parameteranpassungen vorzunehmen, z.B. beim Einführen neuer Produktvarianten. Dabei kommen auch Methoden zur Entscheidungsunterstützung zum Einsatz, die neben Prozess- und Qualitätsdaten auch die Logistik der Gesamtanlage berücksichtigen.
Häufig werden die beschriebenen Prüfsysteme in Form von Inspektionsrobotern realisiert, mit denen auch komplexe Bauteile inspiziert werden können. Die dazu notwendigen Methoden zur Bewegungsplanungen, Defekterkennung und Rückprojektion auf den Bauteil werden im Rahmen solche Umsetzungsprojekte entwickelt.
Projekte
Publikationen
Alexander Walch, Christian Eitzinger, Werner Palfinger, Sebastian Beyer, Pauline Meyr-Heye;Â Reactive coverage planning for robotic NDT of complex parts;Â accepted for: European Conference on NDT 2018
Edwin Lughofer, Robert Pollak, Alexandru-Ciprian Zavoianu, Mahardhika Pratama, Pauline Meyer-Heye, Helmut Zörrer, Christian Eitzinger, Julia Haim, Thomas Radauer; Self-Adaptive Evolving Forecast Models with Incremental PLS Space Update for On-line Predicting Quality of Micro-fluidic Chips: Engineering Applications of Artificial Intelligence,Volume 68, February 2018, Pages 131–151, https://doi.org/10.1016/j.engappai.2017.11.001
Edwin Lughofer, Roland Richter, Ulrich Neissl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer; Explaining classifier decisions linguistically for stimulating and improving operators labeling behavior: Information Sciences, Volume 420, December 2017, Pages 16-36, https://doi.org/10.1016/j.ins.2017.08.012
Heidl, S. Thumfart, E. Lughofer, C. Eitzinger, E. P. Klement; Machine Learning Based Analysis of Gender Differences in Visual Inspection Decision Making, Information Sciences, Vol. 224, pages 62-76, DOI: 10.1016/j.ins.2012.09.054, Mar 2013
Dittrich, T. Riklin-Raviv, G. Kasprian, R. Donner, P.C.Brugger, D. Prayer, G. Langs; A Spatio-Temporal Latent Atlas for Semi-Supervised Learning of Fetal Brain Segmentations and Morphological Age Estimation, Accepted for publication in Medical Image Analysis, 2013
Elkharraz, S. Thumfart, D. Akay, C. Eitzinger, B. Henson; Tactile texture features corresponding to human affective responses. Submitted to IEEE Transactions on Affective Computing
Heidl, S. Thumfart, E. Lughofer, C. Eitzinger, E. P. Klement; Machine Learning Based Analysis of Gender Differences in Visual Inspection Decision Making,Information Sciences, accepted, pre-press DOI: 10.1016/j.ins.2012.09.054
Grünauer, S. Zambal, K. Bühler; „Detektion von Koronararterien: Das Beste aus zwei Welten“, Bildverarbeitung für die Medizin (BVM):pp. 269-273, 2011
van Beilen, H. B ult, R. Renken, M. Stieger, S. T humfart, F. Cornelissen, V. Kooijman; Effects of Visual Priming on Taste-Odor Interaction, PLoS ONE 6(9): e23857, 2011, doi:10.1371/journal.pone.0023857
Heidl, C. Eitzinger, M. Gyimesi, F. Breitenecker; Learning over Sets with Recurrent Neural Networks: An Empirical Categorization of Aggregation
Functions, Mathematics and Computers in Simulation 82(3), pp. 442-449, doi:10.1016/j.matcom.2010.10.018, Nov 2011
Thumfart, R. H.A.H. Jacobs, E. Lughofer, C. Eitzinger, F. W. Cornelissen, W. Groissboeck, R. Richter, “Modeling human aesthetic perception of visual textures “, ACM Transactions on Applied Perception, Volume 8, Issue 5, Nov. 2011, doi:10.1145/2043603.2043609
Heidl, C. Eitzinger, M. Gyimesi, F. Breitenecker; Learning over Sets with Recurrent Neural Networks: An Empirical Categorization of Aggregation Functions, Mathematics and Computers in Simulation, ISSN 0378-4754, 2010
Groissboeck, E. Lughofer, S. Thumfart; Associating Visual Textures with Human Perceptions using Genetic Algorithms, Information Sciences, vol. 180, issue 11, pp. 2065-2084, doi:10.1016/j.ins.2010.01.035, 2010
H.A.H. Jacobs, R. Renken, S. Thumfart, F. W. Cornelissen; Different Judgments about Visual Textures Invoke Different Eye Movement Patterns, Journal of Eye Movement Research, 3(4):2, pp. 1-13, 2010
Edwin Lughofer, Robert Pollak, Alexandru-Ciprian Zavoianu, Mahardhika Pratama, Pauline Meyer-Heye, Helmut Zörrer, Christian Eitzinger, Julia Haim, Thomas Radauer; Self-Adaptive Evolving Forecast Models with Incremental PLS Space Update for On-line Predicting Quality of Micro-fluidic Chips, Engineering Applications of Artificial Intelligence,Volume 68, February 2018, Pages 131–151, https://doi.org/10.1016/j.engappai.2017.11.001
Edwin Lughofer, Roland Richter, Ulrich Neissl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer; Explaining classifier decisions linguistically for stimulating and improving operators labeling behavior, Information Sciences, Volume 420, December 2017, Pages 16-36, https://doi.org/10.1016/j.ins.2017.08.012
Alexandru-Ciprian Zavoianu, Edwin Lughofer, Robert Pollak, Pauline Meyer-Heye, Christian Eitzinger, Thomas Radauer; Multi-Objective Knowledge-Based Strategy for Process Parameter Optimization in Micro-Fluidic Chip Production, 2017 IEEE Symposium Series on Computational Intelligence, accepted
Tran, C. Eitzinger; ThermoBot – autonomous robotic system for thermographic detection of cracks. Workshop Proceedings of IAS-13, 13th Intl.Conf.on Intelligent Autonomous Systems, Padova (Italy) July 15-19,2014, ISBN 978-88-95872-06-3, pp.391-391
Eitzinger, S. Akkaladevi; Dexterous Assembler Robot Working with Embodied Intelligence, Workshop Proceedings of IAS-13, 13th Intl.Conf.on Intelligent Autonomous Systems, Padova (Italy) July 15-19,2014, ISBN 978-88-95872-06-3, pp.393-393
Eitzinger, K. Zhou; VALERI – Validation of Advanced, Collaborative Robotics for Industrial Applications. Workshop Proceedings of IAS-13, 13th Intl.Conf.on Intelligent Autonomous Systems, Padova (Italy) July 15-19,2014, ISBN 978-88-95872-06-3, pp.392-392
Eitzinger, A. Baghbanpourasl, S. Zambal; Image Processing Issues in Scanning Inspection Robots. Workshop Proceedings of IAS-13, 13th Intl.Conf.on Intelligent Autonomous Systems, Padova (Italy) July 15-19,2014, ISBN 978-88-95872-06-3, pp.394-402
Traxler, P. Thanner, G. Mahler; Temporal analysis for implicit compensation of local variations of emission coefficient applied for laser induced crack checking, 12th International Conference on Quantitative Infrared Thermography, Bordeaux, France, 7th-11th July 2014
Dittrich, T. Riklin-Raviv, G. Kasprian, R. Donner, P.C.Brugger, D. Prayer, G. Langs. A Spatio; Temporal Latent Atlas for Semi-Supervised Learning of Fetal Brain Segmentations and Morphological Age Estimation, Medical Image Analysis, Vol. 18(1), pp. 9-21, January 2014.
Alexander Walch, Christian Eitzinger; A combined calibration of 2D and 3D sensors, Proceedings of the VISAPP. 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Lisbon, Portugal, 5th-8th Jan. 2014
Traxler, P. Thanner, P. Meyer Heyer; Design of and practical experience with a thermographic crack checking system using laser heating, 11th European conference on NDT, 2014 10 09 Prag, ISBN: 978-80-214-5018-9 by Brno University of Technology, http://www.ndt.net/events/ECNDT2014/app/content/Paper/166_Traxler.pdf
Traxler; Unterdrückung des Emissionsgradeinflusses in der Laser angeregten Rissprüfung, Tagungsband der ÖGfTh (Österreichische Gesellschaft für Thermografie), 26.9.2014 Eugendorf/Austria
Walch, C. Eitzinger. A combined calibration of 2D and 3D sensors, Proceedings of the VISAPP. 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Lisbon, Portugal, 5th-8th Jan. 2014
Heidl, S. Thumfart, and C. Eitzinger, Humans Differ; So Should Models. Systematic Differences Call for Per-Subject Modeling, ICAART 2012: Proceedings of the 4th Int. Conf. on Agents and Artificial Intelligence, pages 413-418, Vilamoura, Portugal, February 6th-8th, 2012
Heidl, S. Thumfart, E. Lughofer, C. Eitzinger, E. P. Klement; Classifier-based analysis of visual inspection: Gender differences in decision-making, Proc. of SMC 2010, IEEE Conference on Systems, Man and Cybernetics, pp. 113-120, Istanbul, Turkey, October 2010
Thumfart, J. Scharinger, C. Eitzinger; Pixel based Texture Mixing, Proc. of the 34th Workshop of the Austrian Association for Pattern Recognition, pp. 147-154, Zwettl, Austria, May 27-28th 2010
Henson, G. Elkharraz, S. Thumfart, D. Akay, C. Eitzinger; Machine vision approach to predicting affective properties of tactile textures, In Proceedings of the International Conference on Kansei Engineering and Emotion Research, KEER 2010, Paris, France, March 2- 4, ISBN 978-4-9905104-0-4, pp. 2261 – 2270, 2010.
Thumfart, W. Palfinger, M Stöger, C. Eitzinger; Accurate Fibre Orientation Measurement for Carbon Fibre Surfaces, accepted for presentation at CAIP 2013, York, UK, Aug 27-29th, 2013
Eitzinger, S.Ghidoni, E. Menegatti; ThermoBot: towards semi-autonomous, thermographic detection of crack, Proc. of the International Conference on Heating by Electromagnetic Source HES-13, pp. 461-468, Padua, May 21-24, 2013
Eitzinger, PROFACTOR, Steyr-Gleink, Österreich, G. Mahler, InfraTec, Dresden; Konzeption und Aufbau einer robotergestützten Plattform für optisch angeregte Wärmefluss-Thermografie. Presented at DGZFP, Thermographie-Kolloquium 2013, 26. – 27. September 2013, Leinfelden-Echterdingen
Traxler, PROFACTOR, Steyr-Gleink, Österreich, S. Koch, Institut Dr. Foerster, Reutlingen; Inline-Prüfung von warmgewalzten Stahlknüppeln mittels Wärmeflussthermographie, Presented at DGZFP, Thermographie-Kolloquium 2013, 26. – 27. September 2013, Leinfelden-Echterdingen
Thanner, G. Traxler, Design for Thermographic Crack Checking System using Laser Induced Heat Flux Technology, Presented at Factory Automation Conference 2012, Veszprem, Hungary, 21-22 May 2012 Proceedings of Factory Automation 2012, pages 122-125, Veszprem, Hungary
Thumfart, W. Palfinger, C. Eitzinger; Vision based sensors enabling automated production of composite material. In the Proc. of SAMPE / SEMAT 2012, Munich, May 24th – 25th, pp. 301 – 306, ISBN: 978-3-952 3565-6-2, 2012
Eitzinger, S. Thumfart: Optimizing Feature Calculation in Adaptive Machine Vision Systems, M. Sayed-Mouchaweh and E. Lughofer (eds.), Learning in Non-Stationary Environments: Methods and Applications, DOI 10.1007/978-1-4419-8020-5_13, Springer Science+Business Media New York 2012
S.Thumfart, PhD Thesis: Genetic Texture Synthesis. Johannes Kepler University Linz, Department of Computational Perception, Feb 2012
Dittrich; Ein Atlas der frühen Gehirnentwicklung. Published online at ORF Science, July 2013
Thanner: “Defect Avoidance, Machine-vision system catches defects in seamless steel tube production using linescan cameras and nearinfrared imaging„, Vision Systems Design (VSD) Magazin, 1.6. 2010
Wögerer, P. Thanner, G. Traxler: “Measurement of Material properties with Thermography„, FACTORY AUTOMATION 2011 Conference, Györ, Hungary, 24-26 May 2011
Wögerer, P. Thanner, G. Traxler: „Thermografic methods for online control for steel pipes„, FACTORY AUTOMATION 2011 Conference, Györ, Hungary, 24-26 May 2011
Petra Thanner „Mülltrennung mit Infrarottechnologie“, Newsletter E!AT aktuell, März 2010
Thumfart; “Pixel based Texture Mixing„, ÖAGM 2010 – 34th annual workshop of the Austrian Association for Pattern Recognition (AAPR) – Computer Vision in a Global Society, Zwettl, 28. Mai 2010
Thanner, W. Palfinger, “Qualitätssicherung von Carbonfaserteilen mit Bildverarbeitung“
Handhabungstechnik – Der Schlüssel für eine automatisierte Herstellung von Composite-Bauteilen, Augsburg, 8. Juli 2010
Thanner, W. Palfinger, G. Traxler, “Wärmeflussauswertung für die induktiv angeregte Rissprüfung„, Thermografieforum Eugendorf, Eugendorf, 10. September 2010
Eitzinger; “Adaptive Produktion„, 25 Jahre Eureka, Linz, 7. Oktober 2010
Thanner; “EM80 – OIDIPUS, Optimized InGaAS Detectors for Imaging Applications and Industrial Spectroscopy„, 25 Jahre Eureka, Linz, 7. Oktober 2010
Heidl; „Classifier – based analysis of visual inspection: Gender differences in decision-making„, SMC2010, IEEE International Conference on Systems, Man and Cybernetics, Istanbul, 11. Oktober 2010
Thanner, G. Traxler; “Advanced Evaluation for Thermographic Crack Detection with Inductive Excitation for Steele Billets„, 20th Manufuturing Confernece, Budapest, 20. Oktober 2010
Traxler; „Automatisierte Inline-Prüfmöglichkeit mit aktiver Thermographie“, Seminar Wärmefluss-Thermographie, Erlangen, 4. November 2010
Petra Thanner; Defect Avoidance, Machine-vision system catches defects in seamless steel tube production using linescan cameras and near-infrared imaging,Vision Systems Design (VSD) Magazi, 1.6. 2010
Thanner, G. Traxler; Qualitätssicherung von Carbonfaserteilen mittels Bildverarbeitung, 8. Juli 2010, Handhabungstechnik – Der Schlüssel für eine automatisierte Herstellung von Composite-Bauteilen, Augsburg, Germany
Die Robotergeneration der Gegenwart basiert auf Systemen, die mit Standardprodukten rund um intelligente Sensorik ausgestattet sind.
Diese Maschinen sind in der Regel in einen starren Regelapparat eingebettet und verfügen kaum über kognitive Fähigkeiten.
Im Fokus der Forschung von PROFACTOR stehen innovative Technologielösungen und Systeme, deren autonome Entscheidungsfähigkeit sich durch eine zunehmende Komplexität auszeichnen. Ziel sind Maschinen und Systeme, die ihre Assistenz möglichst autonom, der Situation entsprechend, adaptiv und in Echtzeit anpassen. Die Akzeptanz solcher Lösungen durch den Menschen wird dabei – ungeachtet der ex obligo stehenden Sicherheitsfragen – berücksichtigt.
PROFACTOR forscht in seinen Projekten derzeit an folgenden Themen:
- Kooperative Robotik: Im Mittelpunkt steht die Interaktion von Mensch und Maschine im gleichen Arbeitsraum und am gleichen Objekt.
- Situative Assistenz: Ziel ist ein gesteigertes Erkennen des Nutzerverhaltens durch das System in Echtzeit. Der Mensch wird nicht nur als Hindernis angesehen, das Verhalten des Menschen wird klassifiziert, um die in Echtzeit nötige Assistenz anbieten zu können.
- Dezentrale und verteile Systemarchitekturen: Sie vereinfachen die Programmierung von Systemen mit heterogenen Elementen wie Sensor, Roboter, und Steuerung.
- Automatische Prozessplanung: Sie ermöglicht eine (semi-) automatische Planung von Handlings- und Inspektionsprozessen, die automatische Planung robotischer Bearbeitung und eine Optimierung von adaptiv generierten Prozessplänen.
- Neue Aktionsmuster und Modalitäten: Im Mittelpunkt stehen die Muster und Modalitäten einer Mensch-Roboter Kooperation – auch unter Berücksichtigung der Akzeptanz durch den Menschen.
Projekte
Publikationen
Zörrer, H., Weichhart, G., Schmoigl Tonis, M., Bieg, T., Propst, M., Schuster, D., Sturm, N., Nativel, C., Salomon, G., Strohmeier, F., Sackl, A., Eberle, M., & Pichler, A. (2023). Enabling End-Users in Designing and Executing of Complex, Collaborative Robotic Processes. Applied System Innovation, 6(3), 56. https://doi.org/10.3390/asi6030056
Deshpande, K., Möhl, P., Hämmerle, A., Weichhart, G., Zörrer, H., & Pichler, A. (2022). Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience. Energies, 15(19). https://doi.org/10.3390/en15197381
Haemmerle, A., Deshpande, K., Moehl, P., & Weichhart, G. (2022). Training an Energy Management Simulation with Multi-Agent Reinforcement Learning. Researchgate.Net, May. https://www.researchgate.net/profile/Kapil-Deshpande-5/publication/360912718_Training_an_Energy_Management_Simulation_with_Multi-Agent_Reinforcement_Learning/links/62920b1455273755ebbda8af/Training-an-Energy-Management-Simulation-with-Multi-Agent-Reinforc
Pratheepkumar, A., Hofmann, M., Ikeda, M., & Pichler, A. (2022). Domain Adaptation With Evolved Target Objects for AI Driven Grasping. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2022-Septe. https://doi.org/10.1109/ETFA52439.2022.9921470
Zörrer, H., Propst, M., Weichhart, G., Pichler, A., Strohmeier, F., & Schmoigl-Tonis, M. (2022). ROBxTASK RTE – a lightweight runtime environment to implement collaborative processes across different robotic systems. IFAC-PapersOnLine, 55(10), 2647–2652. https://doi.org/10.1016/j.ifacol.2022.10.109
Akkaladevi, S. C., Plasch, M., Hofmann, M., & Pichler, A. (2021). Semantic knowledge based reasoning framework for human robot collaboration. Procedia CIRP, 97, 373–378. https://doi.org/10.1016/j.procir.2020.05.253
Ikeda, M., Chitturi, N., Ganglbauer, M., & Pichler, A. (2021). ScienceDirect Knowledge Based Accuracy Improvement in Programming by Demonstration of Point Based Processes. 00(2019).
Ikeda, M., Ganglbauer, M., Chitturi, N., & Pichler, A. (2021). ScienceDirect Geometric Reasoning enabled One Shot Learning for Robotic Tasks. 00(2019).
Plasch, M., Akkaladevi, S. C., Hofmann, M., Wögerer, C., & Pichler, A. (2021). Event-driven knowledge engineering as enabling technology towards configuration of assistance systems in industrial assembly. Smart Innovation, Systems and Technologies, 200, 261–272. https://doi.org/10.1007/978-981-15-8131-1_24
Viktor, A., Ikeda, M., & Pichler, A. (2021). ScienceDirect Panorama Image Based Code Free Programming of Line Based Robotic Operations. 00(2019).
Weichhart, G., Mangler, J., Raschendorfer, A., Mayr-Dorn, C., Huemer, C., Hämmerle, A., & Pichler, A. (2021). An adaptive system-of-systems approach for resilient manufacturing. Elektrotechnik Und Informationstechnik, 138(6), 341–348. https://doi.org/10.1007/s00502-021-00912-2
Weichhart, G., Pichler, A., Strohmeier, F., Schmoigl, M., & Zorrer, H. (2021). The ROBxTASK architecture for interoperability of robotic systems. 2021 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2021 – Proceedings, 449–453. https://doi.org/10.1109/MetroInd4.0IoT51437.2021.9488560
AKKALADEVI, Sharath Chandra, et al. Semantic knowledge based reasoning framework for human robot collaboration. Procedia CIRP, 2021, 97. Jg., S. 373-378.
Chandra, S., Plasch, M., Hofmann, M., & Pichler, A. (2020). ScienceDirect Semantic Knowledge Based Reasoning Framework for Human Robot Collaboration. 00(2019), 1–6.
Spitzer, F., Lindorfer, R., Froschauer, R., Hofmann, M., & Ikeda, M. (2020). A generic Approach for the Industrial Application of Skill-based Engineering using OPC UA. Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 8.
Weichhart, G., Ikeda, M., & Propst, M. (2020). PlugBot Architecture for Modular Manufacturing Enterprise Interoperability may be seen as one end of a continuum ranging from tight integration of.
Akkaladevi, S. C., Plasch, M., Pichler, A., & Ikeda, M. (2019). Towards reinforcement based learning of an assembly process for human robot collaboration. Procedia Manufacturing, 38(Faim 2019), 1491–1498. https://doi.org/10.1016/j.promfg.2020.01.138
Heindl, C., Ikeda, M., Stübl, G., Pichler, A., & Scharinger, J. (2019). Enhanced Human-Machine Interaction by Combining Proximity Sensing with Global Perception. 2020. http://arxiv.org/abs/1910.02445
Heindl, C., Ikeda, M., Stübl, G., Pichler, A., & Scharinger, J. (2019). Metric Pose Estimation for Human-Machine Interaction Using Monocular Vision. 1. http://arxiv.org/abs/1910.03239
Akkaladevi, S. C., Plasch, M., Eitzinger, C., Pichler, A., & Rinner, B. (2018). Towards a Context Enhanced Framework for Multi Object Tracking in Human Robot Collaboration. IEEE International Conference on Intelligent Robots and Systems, 8435–8442. https://doi.org/10.1109/IROS.2018.8593842
Akkaladevi, S. C., Plasch, M., Maddukuri, S., Eitzinger, C., Pichler, A., & Rinner, B. (2018). Toward an interactive reinforcement based learning framework for human robot collaborative assembly processes. Frontiers Robotics AI, 5(NOV), 1–15. https://doi.org/10.3389/frobt.2018.00126
Fast-Berglund, A., Thorvald, P., Billing, E., Palmquist, A., Romero, D., & Weichhart, G. (2018). Conceptualizing Embodied Automation to Increase Transfer of Tacit knowledge in the Learning Factory. 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 – Proceedings, 358–364. https://doi.org/10.1109/IS.2018.8710482
Weichhart, G., Fast-Berglund, A., Romero, D., & Pichler, A. (2018). An Agent- and Role-based Planning Approach for Flexible Automation of Advanced Production Systems. 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 – Proceedings, 391–399. https://doi.org/10.1109/IS.2018.8710546
Wögerer, C., Mühlberger, M., Ikeda, M., Kastner, J., & Chitturi, N. C. (2018). Inkjet Printings on FFF printed curved surfaces. Fraunhofer Direct Digital Manufacturing Conference, March, 2–5.
Hämmerle, A., & Weichhart, G. (2017). Variable neighbourhood search solving sub-problems of a lagrangian flexible scheduling problem. ICORES 2017 – Proceedings of the 6th International Conference on Operations Research and Enterprise Systems, 2017-Janua, 234–241. https://doi.org/10.5220/0006114102340241
Weichhart, G., & Hämmerle, A. (2017). Lagrangian relaxation realised in the NgMPPS multi actor architecture. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10413 LNAI, 138–155. https://doi.org/10.1007/978-3-319-64798-2_9
Akkaladevi, S. C., Ankerl, M., Fritz, G., & Pichler, A. (2016). Real-time tracking of rigid objects using depth data. Computer Vision and Robotics.
Akkaladevi, S., Ankerl, M., Heindl, C., & Pichler, A. (2016). Tracking multiple rigid symmetric and non-symmetric objects in real-time using depth data. Proceedings – IEEE International Conference on Robotics and Automation, 2016-June, 5644–5649. https://doi.org/10.1109/ICRA.2016.7487784
Maddukuri, S. C., Heidl, W., Eitzinger, C., & Pichler, A. (2016). Structural Synthesis based on PCA: Methodology and Evaluation. 348–355. https://doi.org/10.5220/0005721403480355
Plasch, M., Ebenhofer, G., Hofmann, M., Rooker, M., Akkaladevi, S., & Pichler, A. (2016). Workspace Sharing Assembly Robots: Applying IEC 61499 to System Integration and Application Development. 397–422. https://doi.org/10.1201/b19391-23
Weichhart, G., & Hämmerle, A. (2016). Multi-actor architecture for schedule optimisation based on lagrangian relaxation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9872 LNAI, 190–197. https://doi.org/10.1007/978-3-319-45889-2_14
Wögerer, C., Mühlberger, M., & Ikeda, M. (2016). ADDMANU – An Austrian Lighthouse Project for Additive Manfacturing. c, 35–40.
Akkaladevi, S., & Eitzinger, C. (2015). Performance Evaluation of a Cognitive Robotic System. Austrian Robotic Workshop, Klagenfurt, Austria, 43(0), 7–9.
Akkaladevi, S., Heindl, C., Angerer, A., & Minichberger, J. (2015). Action Recognition for Industrial Applications using Depth Sensors Action Recognition for Industrial Applications using Depth Sensors. 43(November), 1–4.
Zhou, K., Rooker, M., Akkaladevi, S. C., Fritz, G., & Pichler, A. (2015). How industrial robots benefit from affordances. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8926, 455–458. https://doi.org/10.1007/978-3-319-16181-5_35
Plasch, M., Hofmann, M., Ebenhofer, G., & Rooker, M. (2014). Reduction of development time by using scriptable IEC 61499 function blocks in a dynamically loadable type library. 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014. https://doi.org/10.1109/ETFA.2014.7005164
Rooker, M., Hofmann, M., Minichberger, J., Ikeda, M., Ebenhofer, G., & Pichler, A. (2014). Quality Inspection performed by a Flexible Robot System. 47–51.
Rooker, M., Hofmann, M., Minichberger, J., Ikeda, M., Ebenhofer, G., Fritz, G., & Pichler, A. (2014). Flexible and assistive quality checks with industrial robots. Proceedings for the Joint Conference of ISR 2014 – 45th International Symposium on Robotics and Robotik 2014 – 8th German Conference on Robotics, ISR/ROBOTIK 2014, 184–189.
Akkaladevi, S. C., & Eitzinger, C. (2013). DARWIN – D extrous A ssembler R obot W orking with embodied INtelligence ). 43(0), 7252.
Capco, J., Rooker, M., & Pichler, A. (2013). RRT planner for the binpicking problem. 9th International Workshop on Robot Motion and Control, RoMoCo 2013 – Workshop Proceedings, 1, 154–160. https://doi.org/10.1109/RoMoCo.2013.6614601
Hämmerle, A., & Ankerl, M. (2013). Solving a vehicle routing problem with ant colony optimisation and stochastic ranking. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8111 LNCS(PART 1), 259–266. https://doi.org/10.1007/978-3-642-53856-8-33
Plasch, M., Ebenhofer, G., Hofmann, M., Bauer, H., Wögerer, C., Rooker, M., & Pichler, A. (2013). Ein modulares Roboterassistenzsystem zur Effizienzsteigerung in der Produktion. Internationales Forum Meachtronik.
Rooker, M., Angerer, A., Wallhoff, F., Blume, J., Bannatt, A., Ferrara, P., Olarra, A., Kiirikki, J., & Pichler, A. (2013). Flexible Assistance System for Packaging Electronic Consumer Goods using Industrial Robots *.
Plasch, M., Pichler, A., & Bauer, H. (2012). A Plug & Produce Approach to Design Robot Assistants in a Sustainable Manufacturing Environment. 22nd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2012), 43(0), 8. http://www.locobot.eu/wp-content/uploads/2011/02/A-Plug-Produce-Approach-to-Design-Robot-Assistants-in-a-Sustainable-Manufacturing-Environment_FAIM2012.pdf
Wögerer, C., Bauer, H., Rooker, M., Ebenhofer, G., Rovetta, A., Robertson, N., & Pichler, A. (2012). LOCOBOT – Low cost toolkit for building robot co-workers in assembly lines. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7507 LNAI(PART 2), 449–459. https://doi.org/10.1007/978-3-642-33515-0_45
Ankerl, M., & Hämmerle, A. (2009). Applying Ant colony optimisation to dynamic pickup and delivery. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5717 LNCS, 721–728. https://doi.org/10.1007/978-3-642-04772-5_93
Pichler, A., Ikeda, M., & Stübl, G. (2009). Ein lernfähiger adaptiver Roboter für Handhabung komplexer hochvarianter Teile. Internationales Forum Meachtronik.
Rooker, M. N., Ebenhofer, G., & Strasser, T. (2009). Reconfigurable control in distributed automation systems. Proceedings of the 2009 ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR 2009, 705–714.
Rooker, M. N., Strasser, T., Ebenhofer, G., Hofmarm, M., & Osuna, R. V. (2008). Modeling flexible mechatronical based assembly systems through simulation support. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 452–455. https://doi.org/10.1109/ETFA.2008.4638434
Strasser, T., Rooker, M. N., & Ebenhofer, G. (2008). Distributed Control Concept for a 6-DOF Reconfigurable Robot Arm. Control.
Hämmerle, A., Karageorgos, A., Pirker, M., Reitbauer, A., & Weichhart, G. (2004). A role-based infrastructure for customised agent system development in supply networks. Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics, 5, 4692–4699. https://doi.org/10.1109/ICSMC.2004.1401272
Stalker, I. D., Mehandjiev, N. D., Weichhart, G., & Fessl, K. (2004). Agents for Decentralised Process Design. Proceedings of the 12th International Conference on Cooperative Information Systems, 23–25.
Hämmerle, A., Karageorgos, A., Mehandjiev, N., & Weichhart, G. (2003). A GENTCITIES TECHNICAL NOTE Integrated Logisitcs and Prodction Planning. Agentcities Technote Series, 1–15.
Hämmerle, A., Weichhart, G., & Fessl, K. (2002). The MaBE project: An agent-based environment for business networks. Proceedings – International Workshop on Database and Expert Systems Applications, DEXA, 2002-Janua, 646–650. https://doi.org/10.1109/DEXA.2002.1045971
Hämmerle, A., Schleicher, M., Kollingbaum, M., & Lane, M. (1999). Skilled Resources in Holonic Manufacturing Systems.
Hämmerle, A., Dupré, C., & Hingerl, K. (1997). Distributed Knowledge and an Approach To Path-Planning. 8th DAAAM INTERNATIONAL SYMPOSIUM, October.
Akkaladevi, S., Ebenhofer, G., & Matthias, P. (n.d.). Benchmarking a Cognitive System. 4321–4323.
Hofmann, M., Ikeda, M., Minichberger, J., Fritz, G., & Pichler, A. (n.d.). Automatic Coverage Planning System for Quality Inspection of Complex Objects *. 3–4.
Hofmann, M., Propst, M., Ikeda, M., Pichler, A., Spitzer, F., & Froschauer, R. (2023). Framework for Reducing the Complexity of Programming Robot Skills. Lecture Notes in Mechanical Engineering, 592–604. https://doi.org/10.1007/978-3-031-17629-6_62
Sackl, A., Pretolesi, D., Burger, S., Ganglbauer, M., & Tscheligi, M. (2022). Social Robots as Coaches: How Human-Robot Interaction Positively Impacts Motivation in Sports Training Sessions. RO-MAN 2022 – 31st IEEE International Conference on Robot and Human Interactive Communication: Social, Asocial, and Antisocial Robots, 141–148. https://doi.org/10.1109/RO-MAN53752.2022.9900600
Akkaladevi, S. C., Plasch, M., Chitturi, N. C., Hofmann, M., & Pichler, A. (2020). Programming by interactive demonstration for a human robot collaborative assembly. Procedia Manufacturing, 51(2019), 148–155. https://doi.org/10.1016/j.promfg.2020.10.022
Chandra, S., Plasch, M., Chowdhary, N., & Pichler, A. (2020). ScienceDirect Programming by Interactive Demonstration for a Human Robot Collaborative Assembly. 00(2019), 1–8.
Chitturi, N. C., Kulha, P., Plasch, M., Ganglbauer, M., Weinbacher, A., & Pichler, A. (2020). Intuitive Human-Robot Interaction with Inkjet Printed Capacitive Sensors. March, 1–6.
Ganglbauer, M., Plasch, M., Ikeda, M., & Pichler, A. (2020). ScienceDirect Human in the loop online estimation of robotic speed limits for safe human robot collaboration. 00(2019), 0–6.
AKKALADEVI, Sharath Chandra, et al. Programming-free approaches for human–robot collaboration in assembly tasks. In: Advanced Human-Robot Collaboration in Manufacturing. Cham: Springer International Publishing, 2021. S. 283-317.
HOFMANN, Michael, et al. Towards Human and Robot Collaborative Ergonomic Handling of Long Parts with a Loose Grip. In: Smart Technologies for Precision Assembly: 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, Virtual Event, December 14–15, 2020, Revised Selected Papers 9. Springer International Publishing, 2021. S. 249-259.
PROPST, Matthias, et al. Human and Workcell Event Recognition and Its Application Areas in Industrial Assembly. In: Smart Technologies for Precision Assembly: 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, Virtual Event, December 14–15, 2020, Revised Selected Papers 9. Springer International Publishing, 2021. S. 260-275.
Akkaladevi, S. C., Pichler, A., Plasch, M., Ikeda, M., & Hofmann, M. (2019). Skill-based programming of complex robotic assembly tasks for industrial application. Elektrotechnik Und Informationstechnik, 136(7), 326–333. https://doi.org/10.1007/s00502-019-00741-4
Chitturi, N. C., Ganglbauer, M., Plasch, M., Kulha, P., & Pichler, A. (2019). HolisafeHRC: Holistic Safety Concepts ni Human-Robot Collaboration. Systems Biology, 71–75. https://doi.org/10.3217/978-3-85125-663-5-11
Ikeda, M., Ganglbauer, M., Ashok, P., Maddukuri, S., Hofmann, M., & Pichler, A. (2019). Instrumented Tool based Robot Programming – Parameterization of Screwing Process Macros. Procedia Manufacturing, 38(2019), 415–422. https://doi.org/10.1016/j.promfg.2020.01.053
Weichhart, G., Ferscha, A., Mutlu, B., Brillinger, M., Diwold, K., Lindstaedt, S., Schreck, T., & Mayr-Dorn, C. (2019). Human/machine/roboter: technologies for cognitive processes. Elektrotechnik Und Informationstechnik, 136(7), 313–317. https://doi.org/10.1007/s00502-019-00740-5
Wögerer, C., Plasch, M., Tscheligi, M., & Lampl, S. E. (2019). Industrial assistance as an I4.0 topic—MMassist: assistance in production in the context of human–machine cooperation. Smart Innovation, Systems and Technologies, 155, 399–410. https://doi.org/10.1007/978-981-13-9271-9_33
Ikeda, M., Ebenhofer, G., Minichberger, J., Stübl, G., Pichler, A., Huber, A., & Weiss, A. (2018). User Experience in kollaborativen Roboteranwendungen. 1–12.
Ikeda, M., Maddukuri, S., Hofmann, M., Pichler, A., Zhang, X., Polydoros, A., Piater, J., Winkler, K., Brenner, K., Harton, I., & Neugebauer, U. (2018). FlexRoP – flexible, assistive robots for customized production (pp. 53–58). https://doi.org/10.15203/3187-22-1-11
Weichhart, G. (2018). Representing processes of human robot collaboration. CEUR Workshop Proceedings, 2074.
Weichhart, G., Åkerman, M., Akkaladevi, S. C., Plasch, M., Fast-Berglund, Å., & Pichler, A. (2018). Models for Interoperable Human Robot Collaboration. IFAC-PapersOnLine, 51(11), 36–41. https://doi.org/10.1016/j.ifacol.2018.08.231
Wögerer, C., Plasch, M., Tscheligi, M., Egger-Lampl, S., & Pichler, A. (2018). MMAssist_II: Assistance in production in the context of human – machine cooperation. 49–52. https://doi.org/10.15203/3187-22-1-10
Akkaladevi, S. C., Plasch, M., & Pichler, A. (2017). Skill-basiertes Lernen für Montageprozesse. Elektrotechnik Und Informationstechnik, 134(6), 312–315. https://doi.org/10.1007/s00502-017-0514-2
Ikeda, M., Ebenhofer, G., Minichberger, J., Fritz, G., Pichler, A., Huber, A., & Weiss, A. (2017). User-Centered Assistive Robotics for Production Human-Robot Interaction Concepts in the AssistMe project. 45–50. https://doi.org/10.3217/978-3-85125-524-9-09
Pichler, A., Akkaladevi, S. C., Ikeda, M., Hofmann, M., Plasch, M., Wögerer, C., & Fritz, G. (2017). Towards Shared Autonomy for Robotic Tasks in Manufacturing. Procedia Manufacturing, 11(June), 72–82. https://doi.org/10.1016/j.promfg.2017.07.139
Sharath Chandra, A., Plasch, M., Eitzinger, C., & Rinner, B. (2017). Context enhanced multi object tracker for human robot collaboration. ACM/IEEE International Conference on Human-Robot Interaction, 61–62. https://doi.org/10.1145/3029798.3038406
Akkaladevi, S. C., & Heindl, C. (2016). Action recognition for human robot interaction in industrial applications. 2015 IEEE International Conference on Computer Graphics, Vision and Information Security, CGVIS 2015, 94–99. https://doi.org/10.1109/CGVIS.2015.7449900
Chowdhary, M. S., & Rooker Martijn, et al. (2015). Towards Safe Human Robot Collaboration: Sonar Based Collision Avoidance for Robot’s End-Effector. Austrian Robotics Workshop 2015, 36–37.
Rooker, M., Maddukuri, S., Minichberger, J., Pichler, A., Feyrer, C., & Nöhmayer, H. (2015). Interactive Workspace Modelling for Assistive Robot Systems with the Aid of Ultrasonic Sensors. 43(July). https://doi.org/10.13140/RG.2.1.3056.8807
Barattini, P., Morand, C., Almajai, I., Robertson, N., Hopgood, J., Ferrara, P., Bonasso, M., Strassmair, C., Rottenbacher, M., Staehr, M., Neumann, R., Tornari, M., Rovetta, A., Plasch, M., Bauer, H., Capco, J., Woegerer, C., & Pichler, A. (2013). Towards tailor made robot co workers based on a plug&produce framework. Proceedings – 2013 IEEE International Symposium on Assembly and Manufacturing, ISAM 2013, 1–7. https://doi.org/10.1109/isam.2013.6643496
Wögerer, C., Rooker, M., Angerer, A., Blume, J., Bannatt, A., Ferrara, P., Kiirikki, J., & Pichler, A. (2013). A Robotic Assistance System for Flexible Packaging of Consumer Goods in Electronic Industry. Raad 2013, 1–8.
Plasch, M., Pichler, A., & Rooker, M. (2012). Simplified Programming of Modular Robotic Systems Based on Workflow Modeling. Austrian Robotics Workshop.
Plasch, M., Pichler, A., Rooker, M., & Ebenhofer, G. (2012). Ein modellbasierter Plug&Produce-Ansatz für Roboterassistenzsysteme in der Produktion. Internationales Forum Meachtronik.
Oppl, S., & Weichhart, G. (2005). Requirements for Collaborative Process Design. Workshop-Proceedings Der 5. Fachuebergreifenden Konferenz Mensch Und Computer 2005, 197, 15–22.