Integrated simulation model of swarm control and adaptive routeing of UAVS in a changing air environment
DOI:
https://doi.org/10.30837/2522-9818.2025.4.032Keywords:
unmanned aerial vehicles; swarm control; adaptive routing; PID control; real-time data processing; route optimization.Abstract
Subject matter: the processes of swarm control and adaptive routing of unmanned aerial vehicles (UAVs) in complex and dynamically changing air conditions using adaptive algorithms. Goal: to develop an integrated simulation model that combines swarm control methods, adaptive PID control and adaptive routing algorithms to ensure the safety, optimality and efficiency of UAV fleet movement in conditions of a changing air environment. Tasks: to analyze existing approaches to swarm control and adaptive routing of UAVs; to develop a mathematical model of an integrated system that takes into account the specifics of interaction between UAVs, collision avoidance and dynamic changes in the air environment; to create a swarm control algorithm based on adaptive PID regulation of UAV movement parameters; to develop and implement an adaptive routing algorithm that responds to changes in traffic, weather conditions and other airspace factors; to implement the integrated model in a simulation environment and test its effectiveness; to conduct a comparative analysis of the efficiency of UAV operation with and without the developed algorithms. Methods: use of adaptive PID control methods for dynamic regulation of UAV movement trajectories and ensuring flight accuracy and stability; application of swarm control algorithms (boids-type methods) for synchronization of movement and collision avoidance in UAV groups; nonlinear optimization of routes taking into account dynamically changing conditions, which allows minimizing collision risks, energy consumption and flight time; construction of a graph-theoretic model of airspace for effective route planning and situation forecasting; creation of digital twins of the air environment for conducting simulation experiments. Results: an integrated simulation model of swarm control and adaptive routing of UAVs was developed, which takes into account air environment variables; adaptive PID control and swarm control algorithms ensured a reduction in the average positioning error and collision avoidance of UAVs; According to the results of simulation experiments, an increase in the reward of agents by ≈50%, an increase in the successful completion of episodes by ≈50%, and a reduction in agent errors on the way to the goal by ≈10%. Conclusions: created integrated model allows for effective management of UAV flotillas in conditions of a changing air environment, significantly increasing the safety and optimality of routes; the use of adaptive algorithms and graph-theoretic models provides high forecasting accuracy and risk minimization; the results of the study confirm the prospects for implementing the developed algorithms for UAV control in urban and regional conditions.
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