PDF To download article.

DOI: 10.15507/2658-4123.26363.500-517

UDK 63:004.896:621.395.642

 

Development of Relay-Based Control System for a Robot Swarm Used for Agricultural Operations

 

Anton Yu. Taranov
Engineer of the Information Technologies and Control Processes Laboratory of the Department of Physics, Chemistry, and Computer Science, Federal Research Centre The Southern Scientific Centre of the Russian Academy of Sciences (41 Chekhov Ave., Rostov-on-Don 344006, Russian Federation), ORCID: https://orcid.org/0009-0004-7337-8140, Researcher ID: IUO-3909-2023, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract
Introduction. Agriculture requires efficient processing of large land areas (fields, orchards, pastures). The use of distributed swarm robotics systems allows automating these processes, reducing labor costs and increasing the precision of operations. The efficiency of these systems largely depends on the control methods ensuring coordination of robots, optimal task distribution, and reliable communication between them that is critical for parallel processing and minimizing downtime.
Aim of the Study. The study is aimed at improving the robot swarm control efficiency by developing an advanced method with the use of relay communication and a distributed ledger.
Materials and Methods. The object of the study was a swarm robotics system for processing agricultural areal objects. The study was conducted using a software model implemented in Python, which simulated the operation of a robot swarm with relay-based communication. At the first stage, there was implemented an improved control method involving a distributed ledger of processed targets and a mechanism for dynamic role redistribution among robots. At the second stage, the system operation was simulated using a set of synthetic test tasks with varying sizes of the objects processed and different robot numbers in the swarm. At the final stage, the improved method was compared with baseline control method terms of task execution time, followed by a statistical evaluation of the obtained results.
Results. There has been developed an improved robot swarm control method with a relay-based communication involving a distributed ledger of processed objects and a mechanism for dynamic redistribution of roles among robots. The simulation results demonstrated a 5.75% reduction in the average time of execution of full set of test tasks compared to the base approach. For performing the subset of tasks when the proposed method can realize fully its advantages, the reduction in processing time reaches 10.34%.
Conclusion. The improved robot swarm control method enables more effective task distribution and an increased degree of parallelism in processing. This has high practical significance for application in agricultural robotic systems, especially when using budget-friendly equipment. The development prospects include adaptive control, integration with other optimization methods, and the expansion of the approach to heterogeneous swarms.

Keywords: agricultural robots, UAVs, swarm control, data transmission, relay communication, distributed ledger

Conflict of interest: The author declare no conflict of interest.

For citation: Taranov A.Yu. Development of a Relay-Based Control System for a Robot Swarm Used for Agricultural Operations. Engineering Technologies and Systems. 2026;36(3):500–517. https://doi.org/10.15507/2658-4123.26363.500-517

The author has read and approved the final manuscript.

Submitted 25.12.2025;
revised 23.01.2026;
accepted 09.02.2026

 

REFERENCES

  1. Taranov А.Yu., Ostroukhov А.Yu. Increasing Energy Efficiency in Area-Based Tasks Resolving by an Autonomous Robots Swarm through Relay Communications. Izvestiya Tula State University. 2023;(11):65–70. (In Russ., abstract in Eng.) https://elibrary.ru/bipbpg
  2. Taranov А.Yu., Rodina A.A. A Control Method for Reconfigurable Swarm of Mobile Robots with Relay Communication in Order to Monitor Areal Objects. Robotics and Technical Cybernetics. 2025;13(1):41–49. (In Russ., abstract in Eng.) https://elibrary.ru/ohganw
  3. Sait A., Al-Hadhrami T., Saeed F., Basurra S., Qasem S.N. Laser Communications System with Drones as Relay Medium for Healthcare Applications. PeerJ Computer Science. 2024;(10):e1759. https://doi.org/10.7717/peerj-cs.1759
  4. Fu S., Zhao L., Su Z., Jian X. UAV Based Relay for Wireless Sensor Networks in 5G Systems. Sensors. 2018;18(8):2413. https://doi.org/10.3390/s18082413
  5. Suetsugu A., Madokoro H., Nagayoshi T., Kikuchi T., Watanabe S., Inoue M., et al. Development and Field Testing of a Wireless Data Relay System for Amphibious Drones. Drones. 2024;8(2):38. https://doi.org/10.3390/drones8020038
  6. Tajima Y., Hiraguri T., Matsuda T., Imai T., Hirokawa J., Shimizu H., et al. Analysis of Wind Effect on Drone Relay Communications. Drones. 2023;7(3):182. https://doi.org/10.3390/drones7030182
  7. Fan J., Cui M., Zhang G., Chen Y. Throughput Improvement for Multi-Hop UAV Relaying. IEEE Access. 2019;(7):147732–147742. https://doi.org/10.1109/ACCESS.2019.2946353
  8. Yanmaz E. Positioning Aerial Relays to Maintain Connectivity During Drone Team Missions. Ad Hoc Networks. 2022;(128):102800. https://doi.org/10.1016/j.adhoc.2022.102800
  9. Burdakov O., Doherty P., Holmberg K., Kvarnström J., Olsson P.-M. Positioning Unmanned Aerial Vehicles As Communication Relays for Surveillance Tasks. In: Conference Paper: Proceedings of Robotics: Science and Systems. Seattle: The MIT Press; 2009. https://doi.org/10.15607/RSS.2009.V.033
  10. Tao C., Liu B. Distributed Coordinated Motion Control of Multiple UAVs Oriented to Optimization of Air-Ground Relay Network. Scientific Reports. 2024;(14):31501. https://doi.org/10.1038/s41598-024-83243-4
  11. Yanmaz E. Dynamic Relay Selection and Positioning for Cooperative UAV Networks. IEEE Networking Letters. 2021;3(3):114–118. https://doi.org/10.1109/LNET.2021.3080403
  12. Wang Y., Cui Y., Yang Y., Li Z., Cui X. Multi-UAV Path Planning for Air‑Ground Relay Communication Based on Mix-Greedy MAPPO Algorithm. Drones. 2024;8(12):706. https://doi.org/10.3390/drones8120706
  13. Taranov A.Yu. Algorithm of Operation of a Reconfigurable Switch Based on Multi-Agent Interaction. Proceedings of Universities. Electronics. 2025;30(2):194–207. (In Russ., abstract in Eng.) Available at: http://ivuz-e.ru/issues/.%D0%A2%D0%BE%D0%BC%2030%20%E2%84%962/ (accessed 16.08.2025).
  14. Sarabia N.S., Gashaw H., Wubben J., Hernández-Orallo E., Calafate C.T. A Safe In‑Flight Reconfiguration Solution for UAV Swarms Based on Attraction/Repulsion Principles. Electronics. 2025;14(10):3799. https://doi.org/10.3390/electronics14193799
  15. Liao J., Cheng J., Xin B., Luo D., Zheng L., Kang Y., et al. UAV Swarm Formation Reconfiguration Control Based on Variable‑Stepsize MPC–APCMPIO Algorithm. Science China Information Sciences. 2023;(66):212207. https://doi.org/10.1007/s11432-022-3735-5
  16. Bui D.N., Phung M.D., Duy H.P. Self-Reconfigurable V-Shape Formation of Multiple UAVs in Narrow Space Environments. In: 2024 IEEE/SICE International Symposium on System Integration (SII). Ha Long: IEEE; 2024. pp. 1006–1011. https://doi.org/10.1109/SII58957.2024.10417519
  17. Zhang H., Zhang G., Yang R., Feng Z., He W. Resilient Formation Reconfiguration for Leader‑Follower Multi‑UAVs. Applied Sciences. 2023;13(13):7385. https://doi.org/10.3390/app13137385
  18. Roy D., Chowdhury A., Maitra M., Bhattacharya S. Multi-Robot Virtual Structure Switching and Formation Changing Strategy in an Unknown Occluded Environment. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid: IEEE; 2018. pp. 4854–4861. https://doi.org/10.1109/IROS.2018.8594438
  19. Cheah C.C., Hou S.P., Slotine J.J.E. Region-Based Shape Control for a Swarm of Robots. Automatica. 2009;45(10):2406–2411. https://doi.org/10.1016/j.automatica.2009.06.026
  20. Marek D., Biernacki P., Szyguła J., Domański A., Paszkuta M., Szczygieł M., et al. Collision Avoidance Mechanism for Swarms of Drones. Sensors. 2025;25(4):1141. https://doi.org/10.3390/s25041141
  21. Hu T., Zong Y., Lu N., Jiang B. Dynamic Recovery and a Resilience Metric for UAV Swarms Under Attack. Drones. 2025;9(8):589. https://doi.org/10.3390/drones9080589
  22. Gaydamaka A., Samuylov A., Moltchanov D., Ashraf M., Tan B., Koucheryavy Y. Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms. IEEE Transactions on Mobile Computing. 2024;23(5):4423–4439. https://doi.org/10.1109/TMC.2023.3293034
  23. Qiuyun T., Hongyan S., Hengwei G., Ping W. Improved Particle Swarm Optimization Algorithm for AGV Path Planning. IEEE Access. 2021;(9):33522–33531. https://doi.org/10.1109/ACCESS.2021.3061288
  24. Yang Z., Yang F., Mao T., Xiao Z., Han Z., Xia X. Reconfiguration for UAV Formation: A Novel Method Based on Modified Artificial Bee Colony Algorithm. Drones. 2023;7(10):595. https://doi.org/10.3390/drones7100595
  25. Abrahams M., Sibanda M., Dube T., Chimonyo V.G.P., Mabhaudhi T. A Systematic Review of UAV Applications for Mapping Neglected and Underutilised Crop Species’ Spatial Distribution and Health. Remote Sensing. 2023;15(19):4672. https://doi.org/10.3390/rs15194672
  26. Zhang F., Guo A., Hu Z., Liang Y. A Novel Image Fusion Method Based on UAV and Sentinel‑2 for Environmental Monitoring. Scientific Reports. 2025;(15):27256. https://doi.org/10.1038/s41598-025-13049-5
  27. Li W., Luo Y., Jiang P., Dong X., Tang K., Liang Z., et al. A Sustainable Crop Protection through Integrated Technologies: UAV‑based Detection, Real‑Time Pesticide Mixing, and Adaptive Spraying. Scientific Reports. 2025;(15):35748. https://doi.org/10.1038/s41598-025-19473-x
  28. Zhang Q., Zhang Z., Manzoor S.H., Li T., Igathinathane C., Li W., et al. A Comprehensive Review of Autonomous Flower Pollination Techniques: Progress, Challenges, and Future Directions. Computers and Electronics in Agriculture. 2025;(237):110577. https://doi.org/10.1016/j.compag.2025.110577
  29. Brown J., Latombe J.C., Montgomery K. Real-Time Knot-Tying Simulation. The Visual Computer. 2004;(20):165–179. https://doi.org/10.1007/s00371-003-0226-y
  30. Canutescu A.A., Dunbrack R.L.Jr. Cyclic Coordinate Descent: A Robotics Algorithm for Protein Loop Closure. Protein Science. 2003;12(5):963–972. https://doi.org/10.1110/ps.0242703
  31. Buss S.R., Kim J.S. Selectively Damped Least Squares For Inverse Kinematics. Journal of Graphics Tools. 2005;10(3):37–49. https://doi.org/10.1080/2151237X.2005.10129202
  32. Aristidou A., Lasenby J. FABRIK: A Fast, Iterative Solver for the Inverse Kinematics Problem. Graphical Models. 2011;73(5):243–260. https://doi.org/10.1016/j.gmod.2011.05.003
  33. Kirley M. Competition, Cooperation and Collective Behaviour: Resource Utilization in Non-Stationary Environments. In: Ieee Wic Acm International Conference on Intelligent Agent Technology. Compiegne: IEEE; 2005. pp. 572–578. https://doi.org/10.1109/IAT.2005.55
  34. He Z., Sun Y., Feng Z. Research on Resource Allocation of Autonomous Swarm Robots Based on Game Theory. Electronics. 2023;12(20):4370. https://doi.org/10.3390/electronics12204370
  35. Cowley A., Taylor C.J. Orchestrating Concurrency in Robot Swarms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego: IEEE; 2007. pp. 945–950. https://doi.org/10.1109/IROS.2007.4399426

 

Licensed under a Creative Commons
This work is licensed under a Creative Commons Attribution 4.0 License.