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

BatteryLife - Umfassende Erforschung der Verlängerung von Batterielebenszyklen durch Sekundärnutzung Die hohen Anschaffungskosten für Lithium-Ionen-Batterien(LIB)gelten als eines der größten Hindernisse für die Einführung von Elektrofahrzeugen auf dem Massenmarkt. Gealterte Traktionsbatterien ...+
FlExible assembLy manufacturIng with human-robot Collaboration and digital twin modEls (FELICE)   FELICE addresses one of the greatest challenges in robotics, i.e. that of coordinated interaction and combination of human and robot skills. The proposal targets the application priority area of ...+
SMART CIRCUIT: enabling SMARTer, CIRCUlar digITal innovation hubs to enhance Central Europe’s manufacturing eco-system towards a greener & more competitive future. Das Wachstum des produzierenden Gewerbes in Mitteleuropa (CE) ist durch einen hohen Ressourcenverbrauch, Abfälle und Emissionen g ...+
Zielsetzung Das Projekt ZERO³ adressiert die Steigerung der ökologischen, ökonomischen und sozialen Nachhaltigkeit in einzelnen Produktionsprozessen produzierender Unternehmen in Österreich durch Erhöhung der Transparenz über individuelle Engpässe und Potenziale sowie durch transparente Analy ...+

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.

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Ihr Ansprechpartner

Dr. Andreas Pichler
CTO

+43 72 52 885 306
andreas.pichler@nullprofactor.at

Gerne antworten wir…

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