BROADCAST SIGNAL TRANSMISSION MODEL WITHIN DRONE SWARM BASED ON PETRI NETS

Abstract

The presented research is devoted to one of the most relevant and rapidly developing areas of modern robotics — the coordination and control of unmanned aerial vehicles (UAVs) within swarm systems. The main goal of this work is to develop an effective approach to organizing and managing a large group of UAVs that are capable of performing joint tasks in a coordinated and autonomous manner. The proposed coordination method ensures flexible scalability of UAV swarms — from a few units to hundreds of aerial devices — while maintaining simplicity, robustness, and fault tolerance of the control system.

Particular attention is paid to the use of high-level Petri nets as a mathematical tool for modeling and controlling signal transmission processes within UAV communication networks. Petri nets enable accurate formalization of the interactions between swarm agents, taking into account time delays, synchronization, and potential communication failures. The study includes a detailed analysis of several common network topologies used in UAV swarms, such as centralized, decentralized, hierarchical, and mesh structures. For each topology, corresponding high-level Petri net models were developed to simulate coordination dynamics, information flow, and decision-making processes.

Based on simulation and experimental results, the efficiency of the proposed approach was evaluated according to two primary criteria: system simplicity (ease of implementation, minimal configuration requirements) and reliability (stability of communication and coordination under failure conditions). The findings demonstrate that the developed models allow for optimizing the trade-off between scalability and control accuracy. The research provides a foundation for future applications of Petri nets in multi-agent UAV systems, paving the way for more intelligent, adaptive, and self-organizing aerial swarms capable of performing complex missions in dynamic environments.