Queues Theory for the Task Flows Management of UAV Swarms

As modern combat experience shows, the use of individual or swarms of unmanned aerial vehicles is an effective tool for striking enemy targets. However, managing such parallelized strike resources is a challenging task that requires real-time information transfer, planning, and dynamic replanning of strike processes organization. Synchronization and control of strike or reconnaissance resources necessitate the development of mathematical support, algorithms, and automated control programs using adequate mathematical models and artificial intelligence. The relevance of these research results is due to the application of the mathematical apparatus of queueing theory, which allows with a high degree of reliability to describe and analyze service processes in systems of unmanned aerial vehicles and artificial intelligence as platforms for data transformation and control over enemy resources. The aim of the research is to analyze and substantiate the possibility of applying queueing theory, based on which the development of algorithmic and software tools for managing artificial intelligence is proposed. The goal was achieved through the implementation of methods for analyzing incoming task streams, classifying and prioritizing them, and planning and executing service tasks. The novelty of this work lies in the application of probabilistic mathematical models that enable the efficient servicing of incoming tasks in unmanned aerial vehicle systems. Another aspect is the use of artificial intelligence tools. The practical aspect involves the development of network software and control systems for unmanned aerial vehicles. This article presents the first part of the research; subsequent results will be presented in future papers in this series.