Distribution of Roles in a Dynamic Swarm of Robots in Conditions of Limited Communications

  • Donat IvanovEmail author
  • Sergey Kapustyan
  • Evgeny Petruchuk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11659)


The paper deals with the problem of the distribution of roles in coalition robots with limited communications. A formal formulation of the task of role distribution in the coalition of mobile robots is given. An analysis of existing approaches to the distribution of roles in groups of robots is given, such as solving the assignment problem by the Kuhn-Munkres algorithm, using the game theory apparatus, applying the methods of probability theory, and the method of propagating the control wave using a local conversion mechanism. An iterative approach to the distribution of roles in a group of robots, based on the strategy of decentralized control and the principles of swarm interaction, is proposed. A method for the distribution of roles in coalitions of mobile robots and an algorithm that implements this method for a separate coalition robot in the distribution of roles based on the proposed approach are described. The results of the study of the proposed approach, carried out with the help of computer simulation in coalitions of 100 robots in the distribution of three roles, are presented. The estimation of the error of the distribution of roles using the proposed algorithmically implemented method has been made and compared with the known approaches. The areas of possible practical application of the developed approach are shown.


Swarm robotics Distribution of roles Distribution of tasks Decentralized control Multi-agent technologies Limited communications 



The reported study was funded by RFBR according to the research projects №17-29-07054, №19-07-00907, №18-05-80092, and the program of RAS presidium fundamental research I.29 “Actual problems of robotic systems” (progect №AAAAA18-118020190041-1).


  1. 1.
    Casbeer, D.W., Beard, R.W., Mehra, R.K., McLain, T.W.: Forest fire monitoring with multiple small UAVs. In: Proceedings of the 2005, American Control Conference, pp. 3530–3535. IEEE (2005).
  2. 2.
    Merino, L., Caballero, F., Martínez-de Dios, J.R., Ferruz, J., Ollero, A.: A cooperative perception system for multiple UAVs: application to automatic detection of forest fires. J. Field Robot. 23, 165–184 (2006)CrossRefGoogle Scholar
  3. 3.
    Sujit, P.B., Kingston, D., Beard, R.: Cooperative forest fire monitoring using multiple UAVs. In: 2007 46th IEEE Conference on Decision and Control, pp. 4875–4880 (2007)Google Scholar
  4. 4.
    Kalyaev, I., Kapustyan, S., Ivanov, D., Korovin, I., Usachev, L., Schaefer, G.: A novel method for distribution of goals among UAVs for oil field monitoring. In: 2017 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT), pp. 1–4 (2017)Google Scholar
  5. 5.
    Ondráček, J.: Intelligent Algorithms for Monitoring of the Environment Around Oil Pipe Systems Using Unmanned Aerial Systems (2014)Google Scholar
  6. 6.
    Ivanov, D., Korovin, I., Shabanov, V.: Oil fields monitoring by groups of mobile micro-robots using distributed neural networks. In: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 588–593 (2018)Google Scholar
  7. 7.
    Ferber, J.: Multi-agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley, Reading (1999)Google Scholar
  8. 8.
    Kaliaev, I., Kapustjan, S., Ivanov, D.: Decentralized control strategy within a large group of objects based on swarm intelligence. In: 2011 IEEE 5th International Conference on Robotics, Automation Mechatronics, pp. 299–303 (2011).
  9. 9.
    Dorigo, M., Birattari, M.: Swarm intelligence. Scholarpedia 2, 1462 (2007)CrossRefGoogle Scholar
  10. 10.
    Lerman, K., Jones, C., Galstyan, A., Matarić, M.J.: Analysis of dynamic task allocation in multi-robot systems. Int. J. Rob. Res. 25, 225–241 (2006)CrossRefGoogle Scholar
  11. 11.
    Batalin, M.A., Sukhatme, G.S.: Using a sensor network for distributed multi-robot task allocation. In: IEEE International Conference on Robotics and Automation. Proceedings, ICRA 2004, pp. 158–164 (2004)Google Scholar
  12. 12.
    Mulmuley, K., Vazirani, U.V., Vazirani, V.V.: Matching is as easy as matrix inversion. Combinatorica 7, 105–113 (1987)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kuhn, H.W.: The Hungarian method for the assignment problem. In: 50 Years of Integer Programming 1958–2008: From the Early Years to the State-of-the-Art, pp. 29–47 (2010). Scholar
  15. 15.
    Hajek, B.: An Introduction to Game Theory. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign (2017)Google Scholar
  16. 16.
    Luce, R.D., Raiffa, H.: Games and Decisions: Introduction and Critical Survey. Wiley, New York (1958)CrossRefGoogle Scholar
  17. 17.
    McKinsey, J.C.C.: Introduction to the Theory of Games. RAND Corporation, Santa Monica (1952)zbMATHGoogle Scholar
  18. 18.
    Karpov, V., Karpova, I.: Leader election algorithms for static swarms. Biol. Inspired Cogn. Archit. 12, 54–64 (2015)Google Scholar
  19. 19.
    Ivanov, D.Ya.: Distribution of roles in groups of robots with limited communications based on the swarm interaction. Procedia Comput. Sci. 150, 518–523 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Southern Federal UniversityTaganrogRussia
  2. 2.Southern Scientific Center of the Russian Academy of SciencesRostov-on-DonRussia

Personalised recommendations