Logo PTI Logo FedCSIS

Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

An Agent-based Cyber-Physical Production System using Lego Technology

, , ,

DOI: http://dx.doi.org/10.15439/2021F81

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 521531 ()

Full text

Abstract. To cope with the challenges of constructing Cyber-physical Production Systems (CPPS), many studies propose benefiting from agent systems. However, industrial processes should be mostly emulated while agent-based solutions are integrating with CPPS since it is not always possible to apply cyber-based solutions to these systems directly. The target system can be miniaturised while sustaining its functionality. Hence, in this paper, we introduce an agent-based industrial production line and discuss the system development using Lego technology while providing integration of software agents as well as focusing on low-level requirements. In this way, a CPPS is emulated while agents control the system.


  1. R. Baheti and H. Gill, “Cyber-physical systems,” The impact of control technology, vol. 12, no. 1, pp. 161–166, 2011, http://dx.doi.org/https://doi.org/10.1109/icmech.2019.8722929.
  2. L. Monostori, B. Kádár, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh, W. Sihn, and K. Ueda, “Cyber-physical systems in manufacturing,” Cirp Annals, vol. 65, no. 2, pp. 621–641, 2016, http://dx.doi.org/https://doi.org/10.1016/j.cirp.2016.06.005.
  3. K.-D. Thoben, S. Wiesner, and T. Wuest, ““industrie 4.0” and smart manufacturing-a review of research issues and application examples,” International journal of automation technology, vol. 11, no. 1, pp. 4–16, 2017, http://dx.doi.org/https://doi.org/10.20965/ijat.2017.p0004.
  4. N.-H. Tran, H.-S. Park, Q.-V. Nguyen, and T.-D. Hoang, “Development of a smart cyber-physical manufacturing system in the industry 4.0 context,” Applied Sciences, vol. 9, no. 16, p. 3325, 2019, http://dx.doi.org/https://doi.org/10.3390/app9163325.
  5. M. Challenger, B. T. Tezel, V. Amaral, M. Goulao, and G. Kardas, “Agent-based cyber-physical system development with sea_ml++,” in Multi-Paradigm Modelling Approaches for Cyber-Physical Systems, B. Tekinerdogan, V. Amaral, and H. Vangheluwe, Eds. Elsevier Pub., 2021, http://dx.doi.org/https://doi.org/10.1016/B978-0-12-819105-7.00013-1.
  6. T. Semwal, M. Bode, V. Singh, S. S. Jha, and S. B. Nair, “Tartarus: a multi-agent platform for integrating cyber-physical systems and robots,” in Proceedings of the 2015 Conference on Advances in Robotics, 2015, pp. 1–6.
  7. P. Leitao, S. Karnouskos, L. Ribeiro, J. Lee, T. Strasser, and A. W. Colombo, “Smart agents in industrial cyber–physical systems,” Proceedings of the IEEE, vol. 104, no. 5, pp. 1086–1101, 2016, http://dx.doi.org/https://doi.org/10.1109/JPROC.2016.2521931.
  8. E. Schoofs, J. Kisaakye, B. Karaduman, and M. Challenger, “Software agent-based multi-robot development: A case study,” in 2021 10th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2021, pp. 1–8, http://dx.doi.org/https://doi.org/10.1109/MECO52532.2021.9460210.
  9. B. Vogel-Heuser, J. Lee, and P. Leitão, “Agents enabling cyber-physical production systems,” at-Automatisierungstechnik, vol. 63, no. 10, pp. 777–789, 2015, http://dx.doi.org/https://doi.org/10.1515/auto-2014-1153.
  10. E. Negri, L. Fumagalli, and M. Macchi, “A review of the roles of digital twin in cps-based production systems,” Procedia Manufacturing, vol. 11, pp. 939–948, 2017, http://dx.doi.org/https://doi.org/10.1016/j.promfg.2017.07.198.
  11. J. Ding, Z. Li, and T. Pan, “Control system teaching and experiment using lego mindstorms nxt robot,” International Journal of Information and Education Technology, vol. 7, no. 4, p. 309, 2017.
  12. D. Gauntlett, “The lego system as a tool for thinking, creativity, and changing the world,” Lego studies: Examining the building blocks of a transmedial phenomenon, pp. 1–16, 2014, http://dx.doi.org/https://doi.org/10.4324/9781315858012.
  13. M. Resnick, J. Maloney, A. Monroy-Hernández, N. Rusk, E. Eastmond, K. Brennan, A. Millner, E. Rosenbaum, J. Silver, B. Silverman et al., “Scratch: programming for all,” Communications of the ACM, vol. 52, no. 11, pp. 60–67, 2009.
  14. F. Erata, M. Challenger, B. Tekinerdogan, A. Monceaux, E. Tüzün, and G. Kardas, “Tarski: A platform for automated analysis of dynamically configurable traceability semantics,” in Proceedings of the 32nd ACM SIGAPP Symposium on Applied Computing, 2017, pp. 1607–1614, doi: https://doi.org/10.1145/3019612.3019747.
  15. J. Holt and S. Perry, SysML for systems engineering. IET, 2008, vol. 7.
  16. M. E. Gregori, J. P. Cámara, and G. A. Bada, “A jabber-based multi-agent system platform,” in Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, 2006, pp. 1282–1284.
  17. L. P. GitHub, “Lego PiStorms Lubrary,” Available:{https://github.com/mindsensors/PiStorms}, [Online; accessed 9-May-2021].
  18. S. Demirkol, S. Getir, M. Challenger, and G. Kardas, “Development of an agent based e-barter system,” in 2011 International Symposium on Innovations in Intelligent Systems and Applications. IEEE, 2011, pp. 193–198, http://dx.doi.org/https://doi.org/10.1109/INISTA.2011.5946060.
  19. M. Merdan, M. Vallee, W. Lepuschitz, and A. Zoitl, “Monitoring and diagnostics of industrial systems using automation agents,” International journal of production research, vol. 49, no. 5, pp. 1497–1509, 2011, http://dx.doi.org/https://doi.org/10.1080/00207543.2010.526368.
  20. V. Mascardi, D. Weyns, A. Ricci, C. B. Earle, A. Casals, M. Challenger, A. Chopra, A. Ciortea, L. A. Dennis, Á. F. Díaz et al., “Engineering multi-agent systems: State of affairs and the road ahead,” ACM SIGSOFT Software Engineering Notes, vol. 44, no. 1, pp. 18–28, 2019, doi: https: //doi.org/10.1145/3310013.3322175.
  21. S. Jeschke, C. Brecher, T. Meisen, D. Özdemir, and T. Eschert, “Industrial internet of things and cyber manufacturing systems,” in Industrial internet of things. Springer, 2017, pp. 3–19, http://dx.doi.org/https://doi.org/10.1007/978-3-319-42559-7_1.
  22. N. Jazdi, “Cyber physical systems in the context of industry 4.0,” in 2014 IEEE international conference on automation, quality and testing, robotics. IEEE, 2014, pp. 1–4.
  23. E. A. Lee, “Cyber physical systems: Design challenges,” in 2008 11th IEEE international symposium on object and component-oriented real-time distributed computing (ISORC). IEEE, 2008, pp. 363–369, http://dx.doi.org/https://doi.org/10.1109/ISORC.2008.25.
  24. P. Leitão, J. Barbosa, C. A. Geraldes, and J. P. Coelho, “Multi-agent system architecture for zero defect multi-stage manufacturing,” in Service Orientation in Holonic and Multi-Agent Manufacturing. Springer, 2018, pp. 13–26, http://dx.doi.org/https://doi.org/10.1007/978-3-319-73751-5_2.
  25. W. Xing, Y. Jun, L. Peihuang, and T. Dunbing, “Agent-oriented embedded control system design and development of a vision-based automated guided vehicle,” International Journal of Advanced Robotic Systems, vol. 9, no. 2, p. 37, 2012.
  26. J. Queiroz, P. Leitão, J. Barbosa, and E. Oliveira, “Distributing intelligence among cloud, fog and edge in industrial cyber-physical systems,” in 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019, 2019, pp. 447–454.
  27. I. Horváth, Z. Rusák, and Y. Li, “Order beyond chaos: Introducing the notion of generation to characterize the continuously evolving implementations of cyber-physical systems,” in ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection, 2017, http://dx.doi.org/https://doi.org/10.1115/DETC2017-67082.
  28. A. Petrovska, M. Neuss, I. Gerostathopoulos, and A. Pretschner, “Runtime reasoning from uncertain observations with subjective logic in multi-agent self-adaptive cyber-physical systems,” in 16th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS, 2021, http://dx.doi.org/https://doi.org/10.1109/SEAMS51251.2021.00026.
  29. G. Kardas, Z. Demirezen, and M. Challenger, “Towards a dsml for semantic web enabled multi-agent systems,” in Proceedings of the International Workshop on Formalization of Modeling Languages, ser. FML ’10. New York, NY, USA: Association for Computing Machinery, 2010. [Online]. Available: https://doi.org/10.1145/1943397.1943402
  30. M. Challenger, B. T. Tezel, O. F. Alaca, B. Tekinerdogan, and G. Kardas, “Development of semantic web-enabled bdi multi-agent systems using sea_ml: An electronic bartering case study,” Applied Sciences, vol. 8, no. 5, 2018, http://dx.doi.org/https://doi.org/10.3390/app8050688. [Online]. Available: https://www.mdpi.com/2076-3417/8/5/688
  31. T. Semwal and S. B. Nair, “Agpi: Agents on raspberry pi,” Electronics, vol. 5, no. 4, p. 72, 2016.
  32. P. Leitão, S. Karnouskos, L. Ribeiro, P. Moutis, J. Barbosa, and T. I. Strasser, “Common practices for integrating industrial agents and low level automation functions,” in IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017, pp. 6665–6670, http://dx.doi.org/https://doi.org/10.1109/IECON.2017.8217164.
  33. S. Karnouskos, P. Leitao, L. Ribeiro, and A. W. Colombo, “Industrial agents as a key enabler for realizing industrial cyber-physical systems: Multiagent systems entering industry 4.0,” IEEE Industrial Electronics Magazine, vol. 14, no. 3, pp. 18–32, 2020, http://dx.doi.org/https://doi.org/10.1109/MIE.2019.2962225.
  34. I. O. for Standardization, Systems and Software Engineering: Systems and Software Quality Requirements and Evaluation (SQuaRE): Measurement of System and Software Product Quality. ISO, 2016.
  35. A. Hornsby and R. Walsh, “From instant messaging to cloud computing, an xmpp review,” in IEEE International Symposium on Consumer Electronics (ISCE 2010). IEEE, 2010, pp. 1–6.
  36. L. Sakurada and P. Leitão, “Multi-agent systems to implement industry 4.0 components,” in 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), vol. 1. IEEE, 2020, pp. 21–26, http://dx.doi.org/https://doi.org/10.1109/ICPS48405.2020.9274745.
  37. B. Karaduman, T. Aşıcı, M. Challenger, and R. Eslampanah, “A cloud and contiki based fire detection system using multi-hop wireless sensor networks,” in Proceedings of the Fourth International Conference on Engineering & MIS 2018, 2018, pp. 1–5, http://dx.doi.org/https://doi.org/10.1145/3234698.3234764.
  38. B. Karaduman, M. Challenger, and R. Eslampanah, “Contikios based library fire detection system,” in 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), 2018, pp. 247–251, http://dx.doi.org/https://doi.org/10.1109/ICEEE2.2018.8391340.
  39. J. Tavčar and I. Horváth, “A review of the principles of designing smart cyber-physical systems for run-time adaptation: Learned lessons and open issues,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 1, pp. 145–158, 2018, http://dx.doi.org/https://doi.org/10.1109/TSMC.2018.2814539.
  40. K. Thiyagarajan, S. Kodagoda, L. Van Nguyen, and R. Ranasinghe, “Sensor failure detection and faulty data accommodation approach for instrumented wastewater infrastructures,” IEEE Access, vol. 6, pp. 56 562–56 574, 2018, http://dx.doi.org/https://doi.org/10.1109/ACCESS.2018.2872506.
  41. B. T. Tezel, M. Challenger, and G. Kardas, “A metamodel for jason bdi agents,” in 5th Symposium on Languages, Applications and Technologies (SLATE’16). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2016, http://dx.doi.org/https://doi.org/10.4230/OASIcs.SLATE.2016.8.
  42. N. Karimpour, B. Karaduman, A. Ural, M. Challenger, and O. Dagdeviren, “Iot based hand hygiene compliance monitoring,” in 2019 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2019, pp. 1–6, http://dx.doi.org/https://doi.org/10.1109/ISNCC.2019.8909151.
  43. M. Challenger and H. Vangheluwe, “Towards employing abm and mas integrated with mbse for the lifecycle of scpsos,” in Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, 2020, pp. 1–7, http://dx.doi.org/https://doi.org/10.1145/3417990.3421439.
  44. B. Karaduman, M. Challenger, R. Eslampanah, J. Denil, and H. Vangheluwe, “Platform-specific modeling for riot based iot systems,” in Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops, 2020, pp. 639–646, http://dx.doi.org/https://doi.org/10.1145/3387940.3392194.
  45. T. Z. Asici, B. Karaduman, R. Eslampanah, M. Challenger, J. Denil, and H. Vangheluwe, “Applying model driven engineering techniques to the development of contiki-based iot systems,” in 2019 IEEE/ACM 1st International Workshop on Software Engineering Research & Practices for the Internet of Things (SERP4IoT). IEEE, 2019, pp. 25–32, http://dx.doi.org/https://doi.org/10.1109/SERP4IoT.2019.00012.