Advertisement

Empowering a Cyber-Physical System for a Modular Conveyor System with Self-organization

  • José Barbosa
  • Paulo Leitão
  • Joy Teixeira
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 762)

Abstract

The Industry 4.0 advent, advocating the digitalization and transformation of current production systems towards the factories of future, is introducing significant social and technological challenges. Cyber-physical systems (CPS) can be used to realize these Industry 4.0 compliant systems, integrating several emergent technologies, such as Internet of Things, big data, cloud computing and multi-agent systems. The paper analyses the advantages of using biological inspiration to empower CPS, and particularly those developed using distributed and intelligent paradigms such as multi-agent systems technology. For this purpose, the self-organization capability, as one of the main drivers in this industrial revolution is analysed, and the way to translate it to solve complex industrial engineering problems is discussed. Its applicability is illustrated by building a self-organized cyber-physical conveyor system composed by different individual modular and intelligent transfer modules.

Keywords

Cyber-physical systems Multi-agent systems Self-organization 

References

  1. 1.
    ACATECH: Cyber-physical systems: driving force for innovation in mobility, health, energy and production. Technical report, ACATECH – German National Academy of Science and Engineering, Dec 2011Google Scholar
  2. 2.
    Barbosa, J., Leitão, P., Adam, E., Trentesaux, D.: Dynamic self-organization in holonic multi-agent manufacturing systems: the ADACOR evolution. Comput. Ind. 66, 99–111 (2002)CrossRefGoogle Scholar
  3. 3.
    Bauer, H., Baur, C., Camplone, G., et. al.: Industry 4.0: how to navigate digitization of the manufacturing sector. Technical report, McKinsey Digital (2015)Google Scholar
  4. 4.
    Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE. Wiley (2007)Google Scholar
  5. 5.
    Bussmann, S., Schild, K.: Self-organizing manufacturing control: an industrial application of agent technology. In: Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS’00), pp. 87–94 (2000)Google Scholar
  6. 6.
    EFFRA: Factories of the future, multi-annual roadmap for the contractual PPP under H2020. Technical report, European Commission (2013)Google Scholar
  7. 7.
    ElMaraghy, H.: Flexible and reconfigurable manufacturing systems paradigms. Int. J. Flex. Manuf. Syst. 17, 261–271 (2006)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley (1999)Google Scholar
  9. 9.
    IEC—International Electrotechnical Commission: IEC 61131: Programmable controllers—Part 3: Programming Languages (2012)Google Scholar
  10. 10.
    IEC—International Electrotechnical Commission: IEC 61499: Function Blocks—Part 1–4 (2012)Google Scholar
  11. 11.
    Kagermann, H., Wahlster, W., Helbig, J.: Securing the future of German manufacturing industry: recommendations for implementing the strategic initiative INDUSTRIE 4.0. Technical report, ACATECH – German National Academy of Science and Engineering (2013)Google Scholar
  12. 12.
    Lee, E.A.: Cyber physical systems: design challenges. In: Proceedings of the 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC’08), pp. 363–369 (2008)Google Scholar
  13. 13.
    Leitão, P.: Holonic rationale and bio-inspiration on design of complex emergent and evolvable systems. In: Hameurlain, A., Küng, J., Wagner, R. (eds.) Transactions on Large Scale Data and Knowledge Centered Systems. Lecture Notes in Computer Science, vol. 5740, pp. 243–266. Springer (2009)Google Scholar
  14. 14.
    Leitão, P.: Agent-based distributed manufacturing control: a state-of-the-art survey. Eng. Appl. Artif. Intell. 22(7), 979–991 (2009)CrossRefGoogle Scholar
  15. 15.
    Leitão, P., Karnouskos, S., Colombo, A.W.: Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Comput. Ind. 81, 11–25 (2016)CrossRefGoogle Scholar
  16. 16.
    Leitão, P., Restivo, F.: ADACOR: a holonic architecture for agile and adaptive manufacturing control. Comput. Ind. 57(2), 121–130 (2009)CrossRefGoogle Scholar
  17. 17.
    Miller, P.: The Genius of Swarms. National Geographic (2007)Google Scholar
  18. 18.
    Murata, T.: Petri nets: properties. IEEE Anal. Appl. 77(4), 541–580 (1989)Google Scholar
  19. 19.
    Valckenaers, P., Hadeli, K., Kollingbaum, M., Brussel, H., Bochmann, O.: Stigmergy in holonic manufacturing systems. J. Integr. Comput.-Aided Eng. 9(3), 281–289 (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Polytechnic Institute of BragançaCampus Sta ApolóniaBragançaPortugal
  2. 2.LIACC–Artificial Intelligence and Computer Science LaboratoryPortoPortugal
  3. 3.INESC-TECPortoPortugal

Personalised recommendations