Abstract
For years, the manufacturing industry has been investing substantial amounts of research and development work for the implementation of hybrid teams of human workers and robotic units. The composition of hybrid teams requires an optimal coordination of individual players with fundamentally different characteristics and skills. In this paper, we present a highly configurable simulation environment supporting end-users, e.g. manufacturing planners, to optimally prepare, evaluate and improve the collaboration of hybrid teams in the scope of production lines. For generating the optimal task assignment, a GPU-based high-performance optimizer is introduced into the simulation environment. The framework is embedded in a web-based distributed infrastructure that models and provides the involved components (digital human models, robots, visualization environment) as resources. We illustrate the approach with a use case originating from the aircraft industry.
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Notes
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Unity3D: https://unity3d.com.
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- 3.
- 4.
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Not like EKs, where only the corresponding agent behaviors has access to.
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Reasoning and Validation with SPIN: https://rdf4j.org/documentation/programming/spin/.
- 8.
We are using the clingo solver from the Potsdam Answer Set Solving Collection: https://potassco.org/. To translate the RDF based AJAN knowledge base of an agent into ASP rules, we are using the approach of [20].
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Used BT lib.: https://github.com/libgdx/gdx-ai/wiki/Behavior-Trees.
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The description of resource actions respectively affordances is oriented to the action language A defined in [12].
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Node.js: https://nodejs.org/en/.
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Cytoscape: https://cytoscape.org/.
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RDFBeans is an object-RDF mapping framework for Java: https://rdfbeans.github.io.
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Plugin Framework for Java (PF4J): http://www.pf4j.org.
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Unity: https://unity3d.com.
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Open Source C# library for communicating with ROS: https://github.com/siemens/ros-sharp.
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Tecnomatix: plm.automation.siemens.com/Tecnomatix.
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FlexSim: www.FlexSim.com/FlexSim.
- 19.
visTABLEtouch: www.vistable.de/visTABLEtouch-software.
- 20.
SIMUL8: www.SIMUL8.com.
- 21.
References
Abdelkafi, O., Chebil, K., Khemakhem, M.: Parallel local search on GPU and CPU with OpenCL Language. In: Proceedings of the First International Conference on Reasoning and Optimization in Information Systems. IEEE (2013)
Antakli, A., Hermann, E., Zinnikus, I., Du, H., Fischer, K.: Intelligent distributed human motion simulation in human-robot collaboration environments. In: Proceedings of the 18th International Conference oh Intelligent Virtual Agents, pp. 319–324. ACM (2018)
Antakli, A., et al.: Agent-based web supported simulation of human-robot collaboration. In: Proceedings of the 15th International Conference on Web Information Systems and Technologies (WEBIST), pp. 88–99 (2019)
Awad, R., Fechter, M., van Heerden, J.: Integrated risk assessment and safety consideration during design of HRC workplaces. In: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–10. IEEE (2017)
Bosse, S.: Mobile multi-agent systems for the internet-of-things and clouds using the javascript agent machine platform and machine learning as a service. In: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), pp. 244–253. IEEE (2016)
Campeotto, F., Dovier, A., Fioretto, F., Pontelli, E.: A GPU implementation of large neighborhood search for solving constraint optimization problems. In: Proceedings of the Twenty-first European Conference on Artificial Intelligence (2014)
Danilewski, P., Köster, M., Leißa, R., Membarth, R., Slusallek, P.: Specialization through dynamic staging. In: Proceedings of the 13th International Conference on Generative Programming: Concepts & Experiences (GPCE), pp. 103–112. ACM (2014)
Diaconescu, I.M., Wagner, G.: Modeling and simulation of web-of-things systems as multi-agent systems. In: Müller, J.P., Ketter, W., Kaminka, G., Wagner, G., Bulling, N. (eds.) MATES 2015. LNCS (LNAI), vol. 9433, pp. 137–153. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27343-3_8
Fielding, R.T., Taylor, R.N.: Principled design of the modern web architecture. ACM Trans. Internet Technol. (TOIT) 2(2), 115–150 (2002)
Fritzsche, L., Schönherr, R., Illmann, B.: Interactive simulation and ergonomics assessment of manual work with EMA - applications in product development and production planning. In: Advances in Applied Digital Human Modeling, pp. 49–58. AHFE (2014)
Garcia-Sanchez, F., Fernández-Breis, J.T., Valencia-Garcia, R., Gómez, J.M., Martinez-Béjar, R.: Combining semantic web technologies with multi-agent systems for integrated access to biological resources. J. Biomed. Inform. 41(5), 848–859 (2008). https://doi.org/10.1016/j.jbi.2008.05.007
Gelfond, M., Lifschitz, V.: Action languages. Electron. Trans. AI 3(16) (1998). https://ep.liu.se/ea/cis/1998/016/index.html
Gopinath, V., Johansen, K.: Risk assessment process for collaborative assembly-a job safety analysis approach. Procedia CIRP 44, 199–203 (2016)
Groß, J., Köster, M., Krüger, A.: Fast and efficient nearest neighbor search for particle simulations. In: Computer Graphics & Visual Computing (CGVC). The Eurographics Association (2019)
Guinard, D., Trifa, V.: Towards the web of things: web mashups for embedded devices. In: Workshop on Mashups, Enterprise Mashups and Lightweight Composition on the Web (MEM), vol. 15 (2009)
Heath, T., Bizer, C.: Linked data: Evolving the web into a global data space. Synth. Lect. Semant. Web Theory Technol. 1(1), 1–136 (2011)
Herrmann, E., et al.: Motion data and model management for applied statistical motion synthesis. In: Smart Tools and Apps for Graphics. The Eurographics Association (2019)
Herrmann, E., Manns, M., Du, H., Hosseini, S., Fischer, K.: Accelerating statistical human motion synthesis using space partitioning data structures. Comput. Animat. Virtual Worlds 28(3–4), e1780 (2017)
Holden, D., Komura, T., Saito, J.: Phase-functioned neural networks for character control. ACM Trans. Graph. (TOG) 36(4), 42 (2017)
Ianni, G., Martello, A., Panetta, C., Terracina, G.: Efficiently querying RDF (S) ontologies with answer set programming. J. Log. Comput. 19(4), 671–695 (2008)
Kashevnik, A., Teslya, N., Yablochnikov, E., Arckhipov, V., Kipriianov, K.: Development of a prototype cyber physical production system with help of smart-M3. In: IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 4890–4895. IEEE (2016)
Khriyenko, O., Nagy, M.: Semantic web-driven agent-based ecosystem for linked data and services. In: Proceedings of the Third International Conferences on Advanced Service Computing, pp. 25–30 (2011)
Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2149–2154. IEEE (2004)
Köster, M., Groß, J., Krüger, A.: FANG: fast and efficient successor-state generation for heuristic optimization on GPUs. In: Wen, S., Zomaya, A., Yang, L.T. (eds.) ICA3PP 2019. LNCS, vol. 11944, pp. 223–241. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38991-8_15
Köster, M., Groß, J., Krüger, A.: Parallel tracking and reconstruction of states in heuristic optimization systems on GPUs. In: Parallel and Distributed Computing, Applications and Technologies. International Conference on Parallel and Distributed Computing, Applications and Technologies. Springer (2019)
Köster, M., Krüger, A.: Adaptive position-based fluids: Improving performance of fluid simulations for real-time applications. Int. J. Comput. Graph. Animat. (IJCGA) 6(3), 1–16 (2016)
Köster, M., Leißa, R., Hack, S., Membarth, R., Slusallek, P.: Platform-specific optimization and mapping of stencil codes through refinement. In: Proceedings of the First International Workshop on High-Performance Stencil Computations, pp. 1–6 (2014)
Marzinotto, A., Colledanchise, M., Smith, C., Ögren, P.: Towards a unified behavior trees framework for robot control. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5420–5427. IEEE (2014)
Meneguzzi, F.R., Zorzo, A.F., da Costa Móra, M.: Propositional planning in BDI agents. In: Proceedings of the 2004 ACM Symposium on Applied Computing, SAC 2004, pp. 58–63. ACM (2004)
Min, J., Chai, J.: Motion graphs++: a compact generative model for semantic motion analysis and synthesis. ACM Trans. Graph. (TOG) 31(6), 153 (2012)
Nguyen, H., Ciocarlie, M.T., Hsiao, K., Kemp, C.C.: ROS commander (ROSCo): behavior creation for home robots. In: 2013 IEEE International Conference on Robotics and Automation, pp. 467–474 (2013)
Ore, F., Hanson, L., Delfs, N., Wiktorsson, M.: Virtual evaluation of industrial human-robot cooperation: an automotive case study. In: 3rd International Digital Human Modeling Symposium. Elsevier (2014)
Parastatidis, S., Webber, J., Silveira, G., Robinson, I.S.: The role of hypermedia in distributed system development. In: Proceedings of the First International Workshop on RESTful Design, pp. 16–22. ACM (2010)
Paxton, C., Hundt, A., Jonathan, F., Guerin, K., Hager, G.D.: CoSTAR: instructing collaborative robots with behavior trees and vision. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 564–571. IEEE (2017)
Pedrocchi, N., Vicentini, F., Matteo, M., Tosatti, L.M.: Safe human-robot cooperation in an industrial environment. Int. J. Adv. Robot. Syst. (2013)
Pfisterer, D., et al.: SPITFIRE: toward a semantic web of things. IEEE Commun. Mag. 49(11), 40–48 (2011). https://doi.org/10.1109/MCOM.2011.6069708
Quigley, M., e al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, Kobe, Japan, vol. 3, p. 5 (2009)
Schreiber, W., Zürl, K., Zimmermann, P. (eds.): Web-basierte Anwendungen Virtueller Techniken: Das ARVIDA-Projekt - Dienste-basierte Software-Architektur und Anwendungsszenarien für die Industrie. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-52956-0
Schubotz, R., Vogelgesang, C., Antakli, A., Rubinstein, D., Spieldenner, T.: Requirements and specifications for robots, linked data and all the REST. In: Proceedings of Workshop on Linked Data in Robotics and Industry 4.0 (LIDARI). CEUR (2017)
Sheth, A.P.: Changing focus on interoperability in information systems: from system, syntax, structure to semantics. In: Goodchild, M., Egenhofer, M., Fegeas, R., Kottman, C. (eds.) Interoperating Geographic Information Systems. The Springer International Series in Engineering and Computer Science, vol. 495, pp. 5–29. Springer, Boston (1999). https://doi.org/10.1007/978-1-4615-5189-8_2
Spieldenner, T., Schubotz, R., Guldner, M.: ECA2LD: from entity-component-attribute runtimes to linked data applications. In: Proceedings of the International Workshop on Semantic Web of Things for Industry 4.0. Springer (2018)
Tsarouchi, P., Makris, S., Chryssolouris, G.: Human-robot interaction review and challenges on task planning and programming. Int. J. Comput. Integr. Manuf. 29(8), 916–931 (2016)
Verborgh, R., Steiner, T., Van Deursen, D., Van de Walle, R., Valles, J.G.: Efficient runtime service discovery and consumption with hyperlinked RESTdesc. In: 2011 7th International Conference on Next Generation Web Services Practices, pp. 373–379. IEEE (2011)
Villani, V., Pini, F., Leali, F., Secchi, C.: Survey on human-robot collaboration in industrial settings: safety, intuitive interfaces and applications. Mechatronics 55, 248–266 (2018)
W3C: SPARQL 1.1 Protocol (2008). https://www.w3.org/TR/sparql11-protocol/. Accessed 16 Jan 2020
W3C: SPARQL 1.1 Query Language. https://www.w3.org/TR/2013/REC-sparql11-query-20130321/ (2013). Accessed 16 Jan 2020
W3C: SPARQL 1.1 Update. https://www.w3.org/TR/sparql11-update/ (2013). Accessed 16 Jan 2020
Xu, X., Bessis, N., Cao, J.: An autonomic agent trust model for IoT systems. Procedia Comput. Sci. 21, 107–113 (2013)
Zinnikus, I., et al.: Integrated semantic fault analysis and worker support for cyber-physical production systems. In: 2017 IEEE 19th Conference on Business Informatics (CBI), vol. 1, pp. 207–216. IEEE (2017)
Acknowledgements
The work described in this paper has been funded by the ITEA 3 project MOSIM (grant no. 01IS18060C) as well as by the German Federal Ministry of Education and Research (BMBF) through the projects Hybr-iT (grant no. 01IS16026A) and REACT (grant no. 01/W17003).
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Antakli, A. et al. (2020). Optimized Coordination and Simulation for Industrial Human Robot Collaborations. In: Bozzon, A., Domínguez Mayo, F.J., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST 2019. Lecture Notes in Business Information Processing, vol 399. Springer, Cham. https://doi.org/10.1007/978-3-030-61750-9_3
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