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

• Autorzy: Mustafa Yalcin Metehan , Karaduman Burak , Kardas Geylani , Challenger Moharram

Identyfikatory

DOI

10.15439/2021F81

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

multi-agent systems; software agents; SPADE agent programming; cyber-physical production systems; SysML

PL

systemy wieloagentowe; agenci programowi; SPADE; SysML

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 521--531
• Opis fizyczny: Bibliogr. 45 poz., il.

Twórcy

autor

Mustafa Yalcin Metehan

• Department of Electric and Electronics Engineering, Ege University, Izmir, Turkey

autor

Karaduman Burak

• Department of Computer Science, University of Antwerp and Flanders Make, Belgium

autor

Kardas Geylani

• International Computer Institute, Ege University, Izmir, Turkey

autor

Challenger Moharram

• Department of Computer Science, University of Antwerp and Flanders Make, Belgium

Bibliografia

• [1] R. Baheti and H. Gill, “Cyber-physical systems,” The impact of control technology, vol. 12, no. 1, pp. 161–166, 2011, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: https://doi.org/10.1109/ICPS48405.2020.9274745.
• [37] B. Karaduman, T. A ̧sıcı, 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, doi: 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, doi: https://doi.org/10.1109/ICEEE2.2018.8391340.
• [39] J. Tavˇcar 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: https://doi.org/10.1109/SERP4IoT.2019.00012.

Uwagi

1. Track 3: Software, System and Service Engineering

2. Session: Joint 41st IEEE Software Engineering Workshop and 8th International Workshop on Cyber-Physical Systems