Development and analysis of cloud models for adaptive control of unmanned vehicle swarm systems

This article examines the problem of managing swarm systems of unmanned aerial vehicles in dynamically changing environments. To address this problem, a cloud-based mathematical model based on decentralized swarm intelligence algorithms is proposed and verified. It provides adaptive control, self-organization, and stability for a unmanned aerial vehicles group. The methodological basis of the approach is the integration of two key components: a deterministic router-rotor model for guaranteed coverage of the target zone and k-fault-tolerant gossip protocols built on Knödel graphs for reliable data exchange under conditions of unstable communication and node loss. The model was implemented on the OpenStack cloud platform, ensuring deployment flexibility and scalability of computing resources. Simulation modeling included a comparative analysis with the classical Q-Routing algorithm for various operating scenarios, including normal operation and dynamic network reconfiguration. The results demonstrated the comprehensive effectiveness of the proposed architecture. The developed solution demonstrated significantly lower and more predictable latency, high and stable throughput under increasing load, and optimal utilization of compute node memory. A critical advantage was increased system survivability, resulting in shorter recovery times after failures. The results confirm that the combination of deterministic and gossip mechanisms in a cloud environment enables the creation of highly reliable and scalable systems for monitoring and data collection tasks that require stringent real-time performance and fault tolerance.

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