Implementation of an Automata Mechanism for a Self-Organizing Swarm of Drones Platform

Authors

  • Agit F. Atashyan Institute for Informatics and Automation Problems of NAS RA
  • Artyom A. Lazyan Institute for Informatics and Automation Problems of NAS RA
  • Davit V. Hayrapetyan Institute for Informatics and Automation Problems of NAS RA
  • Vahagn S. Poghosyan Institute for Informatics and Automation Problems of NAS RA

Keywords:

Swarm of drones, Automata, Self-organizing system, Mathematical models

Abstract

Drone technology has enabled major advancements in autonomous systems, particularly in swarm robotics. This paper presents a novel automation technique aimed at enhancing the efficiency, adaptability, and robustness of self-organizing drone swarms. The system uses decentralized control algorithms and robust communication protocols to enable real-time adaptive learning and decision-making among drones. Each drone acts as an autonomous agent, adjusting its behavior based on environmental inputs and interactions with other drones. A hybrid communication model blending peer-to-peer and cluster-based protocols ensures effective information sharing and coordination. To build a scalable and resilient architecture, multi-agent systems theory is integrated with advanced self-organizing strategies. Extensive modeling and realworld testing evaluated the systems performance in complex scenarios such as disaster response, environmental monitoring, and surveillance. Results demonstrate significant improvements in swarm efficiency, resilience to failures, and adaptability to dynamic environments. The incorporation of adaptive learning algorithms further optimized task allocation and execution in real time. This work represents a substantial advancement in autonomous aerial robotics, offering a comprehensive framework for deploying intelligent, self-organizing drone swarms and highlighting the transformative potential of automata-based approaches in future autonomous systems.

References

V. Subbarayalu and M. A. Vensuslaus, “An intrusion detection system for drone swarming utilizing timed probabilistic automata”, Drones, vol. 7, no.4, 248, 2023. https://doi.org/10.3390/drones7040248

W. Jung, C. Park, S. Lee and H. Kim, Enhancing UAV Swarm Tactics with Edge AI: Adaptive Decision Making in Changing Environments, Drones, vol. 8, no. 10, 582, 2024. https://doi.org/10.3390/drones8100582

J. Kusyk, M.U.Uyar, K. Ma et al., “Artificial intelligence and game theory controlled autonomous UAV swarms”, Evol. Intel. vol. 14, pp. 17751792, 2021. https://doi.org/10.1007/s12065-020-00456-y

M. Abdelkader, S. Gler, H. Jaleel and J. S. Shamma “Aerial swarms: Recent applications and challenges”, Current robotics reports, vol.2, no. 3, pp. 309-320, 2021.

W. Y. H. Adoni, J. S. Fareedh, S. Lorenz, R. Gloaguen, Y. Madriz, A. Singh and T. D. Khne Intelligent Swarm: Concept, Design and Validation of Self-Organized UAVs Based on LeaderFollowers Paradigm for Autonomous Mission Planning, Drones, vol. 8, no. 10, 575, 2024. https://doi.org/10.3390/drones8100575

V. Poghosyan, S. Poghosyan, A. Lazyan, A. Atashyan, D. Hayrapetyan, Y. Alaverdyan and H. Astsatryan, “Self-organizing multi-user UAV swarm simulation platform”, Programming and Computer Software, vol. 49, Suppl 1, pp. S7-S15, 2023.

Y. Ding, Z. Yang, Q. V. Pham, Y. Hu, Z. Zhang and M. Shikh-Bahaei, “Distributed machine learning for uav swarms: Computing, Sensing, and Semantics”, IEEE Internet of Things Journal, vol. 11, no. 5, pp. 7447-7473, 2024.

U.S. Government Accountability Office, Science and Tech Spotlight, Drone Swarm Technologies, 2023. https://www.gao.gov/assets/gao-23-106930.pdf

P. Bak, C. Tang and K. Wiesenfeld, “Self-organized criticality: an explanation of the 1/f noise”, Phys. Rev. Lett., vol. 59, no. 4, pp. 381384, 1987. https://doi.org/10.1103/PhysRevLett.59.381

D. Dhar, “Self-organized critical state of sandpile automaton models”, Phys. Rev. Lett., vol. 64, no. 14, pp. 16131616, 1990. https://doi.org/10.1103/PhysRevLett.64.1613

P. Ruelle, “Sandpile models in the large, Front”, Phys., vol. 9, p. 641966, 2021. https://doi.org/10.3389/fphy.2021.641966

V.B. Priezzhev, D. Dhar, A. Dhar and S. Krishnamurthy, “Eulerian walkers as a model of self-organized criticality”, Phys. Rev. Lett., vol. 77, no. 25, pp. 50795082, 1996. https://doi.org/10.1103/PhysRevLett.77.5079

V.V. Papoyan, V.S. Poghosyan and V.B. Priezzhev, “A loop reversibility and subdiffusion of the rotor-router walk”, J. Phys. A: Math. Theor., vol. 48, no. 28, p. 285203, 2015. https://doi.org/10.1088/1751-8113/48/28/285203

A.E. Holroyd, L. Levine, K. Meszaros, Y. Peres, J. Propp and D.B. Wilson, “Chip-firing and rotor-routing on directed graphs”, In and Out of Equilibrium, vol. 2, pp. 331364, 2008. https://doi.org/10.1007/978-3-7643-8786-0 17

S. Poghosyan, Y. Alaverdyan, V. Poghosyan, S. Abrahamyan, A. Atashyan, H. Astsatryan and Y. Shoukourian, “Certain methods for investigating epidemics and preventing the spread of viruses in self-organizing systems”, AIP Conf. Proc., vol. 2757, no. 1., 2023. https://doi.org/10.1063/5.0135809

A. M. G. Gbagir, K. Ek, and A. Colpaert, “Open-DroneMap: multiplatform performance analysis”, Geographies, vol. 3, no. 3, pp. 446-458, 2023. https:doi.org/10.3390/geographies3030023

Downloads

Published

2025-06-01

How to Cite

Atashyan, A. F., Lazyan, A. A., Hayrapetyan, D. V., & Poghosyan, V. S. (2025). Implementation of an Automata Mechanism for a Self-Organizing Swarm of Drones Platform. Mathematical Problems of Computer Science, 63, 71–80. Retrieved from https://mpcs.sci.am/index.php/mpcs/article/view/886