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SEMS-Based Control in Locally Organized Hierarchical Structures of Robots Collectives

  • Alexander Ya. Fridman
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 95)

Abstract

Objective Development of brainware to integrate interactions of hierarchical groups of robots, built on the basis of SEMS modules, with the purpose to support making objective compromise strategic and tactical decisions. Results Coordination and planning methods are proposed for hierarchical collectives of such robots within the frame of the situational approach. Sufficient conditions are obtained for coordinability of hierarchical dynamic systems with using local quality criteria gradients and ideas of local organization of reasonable behavior. For groups of robots built in the paradigm of Dynamic Intelligent Systems (DIS), a coordination principle is proposed based on the known principles of interactions prediction. On the basis of the notion of the effective N-attainability, a procedure is developed to directly synthesize plans to control such collectives. Practical significance The proposed methods of coordination and planning will allow efficient usage of available resources in order to provide acceptable results for all (or most) modules having purposeful behavior.

Keywords

Smart electromechanical systems Coordination and planning Hierarchical collectives of intelligent robots Situational approach 

Notes

Acknowledgements

The author would like to thank the Russian Foundation for Basic Researches (grants 14-07-00257, 15-07-04760, 15-07-02757, 16-29-04424, and 16-29-12901) for partial funding of this research.

References

  1. 1.
    Lee, E.: Cyber physical systems: design challenges. University of California, Berkeley Technical Report No. UCB/EECS-2008-8. Retrieved 07 June 2008Google Scholar
  2. 2.
    NSF Cyber-Physical Systems Summit. Retrieved 01 Aug 2008Google Scholar
  3. 3.
    NSF Workshop On Cyber-Physical Systems. Retrieved 09 June 2008Google Scholar
  4. 4.
    Shkodyrev, V.P.: Technical systems control: from mechatronics to cyber-physical systems. In: Gorodetskiy, A.E. (ed.) Studies in Systems, Decision and Control, vol. 49, pp. 3–6. Smart Electromechanical Systems. Springer International Publishing, Switzerland (2016). 277 ppGoogle Scholar
  5. 5.
    Gorodetskiy, A.E.: Smart electromechanical systems modules. In: Gorodetskiy, A.E. (ed.) Studies in Systems, Decision and Control, vol. 49, pp. 7–15. Smart Electromechanical Systems. Springer International Publishing, Switzerland (2016). 277 ppGoogle Scholar
  6. 6.
    Kulik, B.A., Fridman, A.Ya.: Logical Analysis of data and knowledge with uncertainties in SEMS. In: Gorodetskiy, A.E. (ed.) Studies in Systems, Decision and Control, vol. 49, pp. 45–59. Smart Electromechanical Systems. Springer International Publishing, Switzerland (2016). 277 ppGoogle Scholar
  7. 7.
    Kulik, B., Fridman, A., Zuenko, A.: Logical inference and defeasible reasoning in N-tuple algebra. In: Naidenova, X., Ignatov, D. (eds.) Diagnostic test approaches to machine learning and commonsense reasoning systems, pp. 102–128. IGI Global, Hershey, PA (2013)CrossRefGoogle Scholar
  8. 8.
    Agapov, V.A., Gorodetskij, A.E., Kuchmin, A.J., Selivanova, E.N.: Medical microrobot. Patent RU, no. 2469752 (2011)Google Scholar
  9. 9.
    Gorod, A., Fridman, A., Saucer, B.: A quantitative approach to analysis of a system of systems operational boundaries. In: Proceedings of International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT-2010), pp. 655–661. Moscow, Russia, 18–20 Oct 2010Google Scholar
  10. 10.
    Pospelov, D.A.: Situational Control: Theory and Practice. Battelle Memorial Institute, Columbus, OH (1986)Google Scholar
  11. 11.
    Zadeh, L.A., Desoer C.A.: Linear System Theory. Krieger Pub. Co. (1979)Google Scholar
  12. 12.
    Rosario, N.M.: Hierarchical structure in financial markets. Eur. Phys. J. B 11, 193–197 (1999)Google Scholar
  13. 13.
    Baroni, M., Dinu, G., Kruszewski, G.: Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 238–247 (2014)Google Scholar
  14. 14.
    Sokolov, B., Fridman, A.: Integrated situational modeling of industry-business processes for every stage of their life cycle. In: 4th International IEEE Conference “Intelligent Systems” (IS 2008), vol. 1, pp. (8-35)–(8-40). Varna, Bulgaria, 6–8 Sep 2008Google Scholar
  15. 15.
    Mesarovic, M.D., Macko, D., Takahara, Y.: Theory of Hierarchical Multilevel Systems. Academic Press, New York, London (1970)zbMATHGoogle Scholar
  16. 16.
    Stefanuk, V.L.: Stability of local control in a system with contralinear interaction of subsystems. In: Proceedings of European Control Conference (ECC’93), vol. 1, pp. 117–119. Groningen (1993)Google Scholar
  17. 17.
    Fridman, A., Fridman, O.: Gradient coordination technique for controlling hierarchical and network systems. Syst. Res. Forum 4(2), 121–136 (2010)CrossRefGoogle Scholar
  18. 18.
    Osipov, G.: Attainable sets and knowledge base architecture in discrete dynamic knowledge-based systems. In: Proceedings of the Workshop “Applied Semiotics: Control Problems (ASC 2000)”. ECAI2000. 14th European Conference of Artificial Intelligence, pp. 39–43. Berlin, 20–25 Aug 2000Google Scholar
  19. 19.
    Sokolov, B., Ivanov, D., Fridman, A.: Situational modelling for structural dynamics control of industry-business processes and supply chains. Intelligent Systems: From Theory to Practice, pp. 279–308. Springer-Verlag Berlin Heidelberg, London (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute for Informatics and Mathematical ModellingKola Science Centre of RASApatityRussia

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