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A System for Robot Swarm Control with a Single Remote Controller

https://doi.org/10.17587/mau.26.471-479

Abstract

Robot swarm control is one of the most important tasks in the field of unmanned vehicles. The paper presents an architecture of semi-centralized system that allows one to control an entire robot swarm as a single entity with a single remote controller. The proposed system integrates intragroup communication, Local Voting Protocol-based algorithm for formation m aintenance, a method to overcome noises and communication breaks between some of the robots and the remote controller. Conducted experiments demonstrate the efficiency of the proposed system even in complex and unpredictable environments.

About the Authors

K. Amelin
Saint Petersburg State University
Russian Federation

K. Amelin, Centre for AI and Data Science

 St. Petersburg, 199034



I. Arkhipov
Saint Petersburg State University
Russian Federation

I. Arkhipov

 St. Petersburg, 199034



O. Granichin
Saint Petersburg State University
Russian Federation

Granichin Oleg N., Dr. Sc. in Physics and Mathematics, Professor

 St. Petersburg, 199034



V. Kiselev
Saint Petersburg State University
Russian Federation

V. Kiselev

 St. Petersburg, 199034



A. Chernov
Saint Petersburg State University
Russian Federation

A. Chernov

 St. Petersburg, 199034



References

1. Chen W., Liu J., Guo H., Kato N. Toward robust and intelligent drone swarm: challenges and future directions, IEEE Network, 2020, vol. 34, no, 4, pp. 278—283.

2. Skorobogatov G., Barrado C., Salami E. Multiple UAV systems: a survey, Unmanned Systems, 2020, vol. 8, no. 2, pp. 149—169.

3. Bu Y., Yan Y., Yang Y. Advancement challenges in UAV swarm formation control: a comprehensive review, Drones, 2024, vol. 8, no. 7.

4. Amala Arokia Nathan R. J., Kurmi I., Bimber O. Drone swarm strategy for the detection and tracking of occluded targets in complex environments, Communications Engineering, 2023, vol. 2.

5. Filimonov A. B., Filimonov N. B., Nguyen T. K., Pham Q. P. Planning of UAV flight routes in the problems of group patrolling of extended territories, Mekhatronika, Avtomatizatsiya, Upravlenie, 2023, vol. 24, no. 7, pp. 374—381.

6. Do H., Hua H., Nguyen M., Nguyen V.-C., Nguyen H., Nga N. Formation control algorithms for multiple-UAVs: a comprehensive survey, EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2021, vol. 8, p. 170230.

7. Amelin K., Amelina N., Granichin O., Sergeev S. Decentralized group control of autonomous robots swarm without data rou ting, Robotics and Technical Cybernetics, 2021, vol. 9, no. 1, pp. 42—48 (in Russian).

8. Kalyaev I. A., Kapustjan S., Gaiduk A. R. Self-organizing distributed control systems of intellectual robot groups constructed on the basis of network model, Large-Scale Systems Control, 2010, no. 30, pp. 605—639 (in Russian).

9. Reynolds C. W. Flocks, herds, and schools: a distributed behavioral model, Computer Graphics, 1987, vol. 21, no. 4, pp. 25—34.

10. Olfati-Saber R. Flocking for multi-agent dynamic systems: algorithms and theory, IEEE Transactions on Automatic Control, 2006, vol. 51, no. 3, pp. 401—420.

11. Lissman P., Shollenberger C. Formation flight in birds, Science, 1970, vol. 168, pp. 1003—1005.

12. Ren W. Consensus based formation control strategies for multi-vehicle systems, Proceedings of 2006 American Control Conference, 2006, p. 6.

13. Amelin K., Antal E., Vasiliev V., Granichina N. Adaptive control of autonomous group of unmanned aerial vehicles, Stochastic Optimization in Informatics, 2009, vol. 5, pp. 157—166 (in Russian).

14. Amelin K., Amelina N., Granichin O., Granichina O., Andrievsky B. Randomized algorithm for UAVs group flight optimization, IFAC Proceedings Volumes (IFAC-PapersOnline), 2013, pp. 205—208.

15. Gorodetsky V. I., Bukhvalov O., Skobelev P. O., Mayorov I. Industrial applications of multi-agent systems: current state and prospects, Large-Scale Systems Control, 2017, no. 66, pp. 94—157 (in Russian).

16. Amelina N., Fradkov A., Amelin K. Approximate consensus in multi-agent stochastic systems with switched topology and noise, 2012 IEEE International Conference on Control Applications, 2012, pp. 445—450.

17. Vergados D. J., Amelina N., Jiang Y., Kralevska K., Granichin O. Toward optimal distributed node scheduling in a multihop wireless network through local voting, IEEE Transactions on Wireless Communications, 2018, vol. 17, no. 1, p. 400—414.

18. Amelin K., Granichin O., Sergeenko A., Volkovich Z. V. Emergent intelligence via self-organization in a group of robotic devices, Mathematics, 2021, vol. 9, no. 12.

19. Amelina N., Fradkov A., Jiang Y., Vergados D. J. Approximate consensus in stochastic networks with application to load balancing, IEEE Transactions on Information Theory, 2015, vol. 61, no. 4, pp. 1739—1752.

20. Nedic A., Olshevsky A. Distributed optimization over time-varying directed graphs, IEEE Transactions on Automatic Control, 2015, vol. 60, no. 3, pp. 601—615.

21. Erofeeva V., Granichin O., Uzhva D. Meso-scale coalitional control in large-scale networks, Automatica, 2025, vol. 177, p. 112276.

22. Rawlings J. B., Mayne D. Q., Diehl M. Model predictive control: theory, computation, and design, Madison, Wisconsin, Nob Hill Publishing, 2017.

23. Kalman R. E. Contributions to the theory of optimal control, Boletin de la Sociedad Matematica Mexicana, 1960, vol. 5, pp. 102—119.

24. Cohen A., Hasidim A., Koren T., Lazic N., Mansour Y., Talwar K. Online linear quadratic control, Proceedings of the 35th International Conference on Machine Learning, 2018, vol. 80, pp. 1029—1038.

25. Goel G., Wierman A. An online algorithm for smoothed regression and LQR control, Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 2019, vol. 89, pp. 2504—2513.

26. Balharith T., Alhaidari F. Round robin scheduling algorithm in CPU and cloud computing: a review, 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), 2019, pp. 1—7.

27. Erofeeva V., Granichin O., Volodina E. Accelerated decentralized load balancing in Multi-Agent Networks, IEEE Access, 2024, vol. 12, pp. 161954—161967.


Review

For citations:


Amelin K., Arkhipov I., Granichin O., Kiselev V., Chernov A. A System for Robot Swarm Control with a Single Remote Controller. Mekhatronika, Avtomatizatsiya, Upravlenie. 2025;26(9):471-479. (In Russ.) https://doi.org/10.17587/mau.26.471-479

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ISSN 1684-6427 (Print)
ISSN 2619-1253 (Online)