Blockchain technology application prospects in group robotics

Blockchain technology application prospects in group robotics

Donat Ya. Ivanov *
PhD in Technical Sciences, Southern Federal University, Senior Research Scientist, 2, Chekhova ul., GSP-284, Taganrog, Rostov Region, 347928, Russia, tel.: +7(918)519-18-69, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID 0000-0002-2221-0231

Received 13 June 2019

Research on the use of groups of robots to solve a common group tasks has been conducted in our country and abroad since the 1980s. Progress in the field of microelectronics, computing and sensor technology has made possible and economically feasible the use of small-sized groups of mass-produced robots. At present, both academic projects for studying the possibilities of robot group use and various commercial projects in the field of group robotics are known. In the framework of group robotics, the most relevant issues are on providing the information security. Preventing the unauthorized access to the information exchange in a group of robots and to interference with the control of some members of a group is an important task. In this regard, it is necessary to use information security mechanisms in multi-robot complexes, such as identification and authentication of group members, as well as systems for detecting unauthorized access to the control of individual group members (or emulation of disabled group members). Another important issue in the field of group robotics is to ensure good scalability in solving the problem of distributed decision making. As a part of this work, an analysis of the applicability of the blockchain technology in the design of control systems for groups of robots was carried out. The advantages of using this technology are shown. Technical difficulties are indicated and possible solutions are shown.

Key words
Blockchain, group of robots, information security, distributed decision making, latency.

This work was supported by the Russian Foundation for Basic Research, project No. 19-07-00907.


Bibliographic description
Ivanov, Ya. (2019). Blockchain technology application prospects in group robotics. Robotics and Technical Cybernetics, 7(4), pp.300-305.

UDC identifier:


  1. Yurevich E.I. (Ed.). (1980). Upravlenie robotami ot EVM [Computer control of robots]. Moscow: Energy Publ. (in Russian).
  2. Andrianov, Y., Bobrikov, E. and Goncharenko, V. (1984). Robototekhnika [Robotics]. Moscow: Mechanical Engineering Publ. (in Russian).
  3. Kalyaev, I., Gaiduk, A. and Kapustian, S. (2002). Raspredelennye sistemy planirovaniya deistvii kollektivov robotov [Distributed planning systems for actions of teams of robots]. Moscow: Janus-K Publ. (in Russian).
  4. Kalyaev, I., Gaiduk, A. and Kapustyan, S. (2009). Modeli i algoritmy kollektivnogo upravleniya v gruppakh roblotov [Collective control models and algorithms in groups of robots]. Moscow: Fizmatlit Publ. (in Russian).
  5. Dorigo, M. (2009). Swarm-Bots and Swarmanoid: Two Experiments in Embodied Swarm Intelligence. In: 2009 IEEE/WIC/ACM Int. Jt. Conf. Web Intell. Intell. Agent Technol, 1. DOI: 10.1109/WI-IAT.2009.370.
  6. Valentini, G., Ferrante, E., Hamann, H. and Dorigo, M. (2016). Collective Decision with 100 Kilobots: Speed versus accuracy in binary discrimination problems. Auton. Agent. Multi. Agent. Syst., 30, pp.553–580.
  7. Cao, Y., Fukunaga, A., Kahng, A. and Meng, F. (1997). Cooperative mobile robotics: antecedents and directions. Proc. 1995 IEEE/RSJ Int. Conf. Intell. Robot. Syst. Hum. Robot Interact. Coop. Robot., 23, pp.226–234. DOI: 10.1109/IROS.1995.525801.
  8. Sahin, E. (2005). Swarm Robotics : From Sources of Inspiration, in: Swarm Robotics Workshop: State-of-the-Art Survey, pp. 10–20. DOI: 10.1007/978-3-540-30552-1_2.
  9. Karpov, V. (n.d.). Kollektivnoe povedenie robotov. Zhelaemoe i deistvitel'noe [The collective behavior of robots. Desired and valid], pp. 1–18. (in Russian).
  10. O'Grady, R. et al. (2007). Performance benefits of self-assembly in a swarm-bot, in: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference On, pp. 2381–2387.
  11. Dorigo, M. et al. (2013). Swarmanoid: A novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag., 20, pp. 60–71. DOI: 10.1109/MRA.2013.2252996.
  12. Oh, H., Shiraz, A. and Jin, Y. (2018). Morphogen diffusion algorithms for tracking and herding using a swarm of kilobots. Soft Comput., 22, pp.1833–1844.
  13. Holland, J. and O'Riordan, C. (2018). Evolving Collective Behaviours in Simulated Kilobots. In: Symposium On Applied Computing SAC'18.
  14. Dimidov, C., Oriolo, G. and Trianni, V. (2016). Random walks in swarm robotics: an experiment with kilobots. In: International Conference on Swarm Intelligence, pp.185–196.
  15. Brambilla, M., Ferrante, E., Birattari, M. and Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intell., 7, pp.1–41.
  16. Volkova, A. (2019). Ziyan prodemonstrirovala roi iz 10 dronov-bombardirovshchikov vertoletnogo tipa [Ziyan demonstrated a swarm of 10 helicopter-type drones]. [online]. Available at: [Accessed 19 May 2019].
  17. Intel (2016). Intel Lights Up the Night with 500 «Shooting Star» Drones. [online]. Available at: [Accessed 1 Apr 2017].
  18. Ferrer, E. (2018). The blockchain: a new framework for robotic swarm systems. In: Proceedings of the Future Technologies Conference, pp.1037–1058.
  19. Millard, A., Timmis, J. and Winfield, A. (2013). Towards exogenous fault detection in swarm robotic systems. In: Conference towards Autonomous Robotic Systems, pp.429–430.
  20. Zikratov, I., Kozlova, E. and Zikratova, T. (2013). Analiz uyazvimostei robototekhnicheskikh kompleksov s roevym intellektom [Analysis of vulnerabilities of robotic complexes with swarm intelligence]. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 5(87), pp.164-174. (in Russian).
  21. Zikratov, I., Lebedev, I., Gurtov, A. and Kuzmich, E. (2014). Securing swarm intellect robots with a police office model. In: 2014 IEEE 8th International Conference on Application of Information and Communication Technologies (AICT), pp.1–5.
  22. Das, G., McGinnity, T., Coleman, S. and Behera, L. (2011). A fast distributed auction and consensus process using parallel task allocation and execution. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.4716–4721.
  23. Aragues, R., Cortes, J. and Sagues, C. (2012). Distributed consensus on robot networks for dynamically merging feature-based maps. IEEE Trans. Robot. DOI: 10.1109/TRO.2012.2192012.
  24. Aragues, R., Sagues, C. and Mezouar, Y. (2014). Feature-based map merging with dynamic consensus on information increments. Auton. Robots. DOI: 10.1007/s10514-014-9406-z.
  25. Navarro, I. and Matia, F. (2011). A framework for the collective movement of mobile robots based on distributed decisions. Rob. Auton. Syst., 59, pp.685–697.
  26. Nakamoto, S. (2009). Bitcoin: A Peer-to-Peer Electronic Cash System. [online]. Available at: [Accessed 19 May 2019].
  27. Pouwelse, J., Garbacki, P., Epema, D. and Sips, H. (2005). The Bittorrent P2P file-sharing system: Measurements and analysis. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). DOI: 10.1007/11558989_19.
  28. Lopes, V. and Alexandre, L. (2019). An Overview of Blockchain Integration with Robotics and Artificial Intelligence. In: Proceedings of the First Symposium on Blockchain and Robotics. MIT Media Lab, pp. 1–6.
  29. Swan, M. (2017). Blockchain - Blueprint for a new economy. In: 2017 IEEE Technology and Engineering Management Society Conference, TEMSCON 2017. DOI: 10.1109/CANDAR.2017.50
Editorial office address: 21, Tikhoretsky pr., Saint-Petersburg, Russia, 194064, tel.: +7(812) 552-13-25 e-mail: