AUTOMATED DEVELOPMENT OF CLASSIFIERS: ALGORITHMS AND SOFTWARE FOR GENERATING STRATEGIES OF DEPLOYMENT AND RELIABILITY ASSURANCE FOR UAV SWARM
DOI:
https://doi.org/10.26906/SUNZ.2025.2.195Keywords:
strategy classifier, UAV swarm deployment, reliability assurance, facet classification, decision-making automationAbstract
Motivation. Ensuring reliability and autonomy of unmanned aerial vehicles (UAVs), as well as their swarms (UAV swarms), requires effective methods for developing and selecting deployment strategies, along with ensuring functional reliability under complex operational conditions. As the number of such strategies grows, appropriate tools for their development, classification, and selection become necessary. Object of research: processes of classification and automation in constructing classifiers for deployment and reliability assurance strategies (DRAS) for mobile systems, particularly UAV swarms. Purpose of the article. Development of a method for automating the process of creating and modifying (expanding and restructuring) classifiers of DRAS for mobile systems in various subject areas, demonstrating its application through the example of UAV swarms to enhance decision-making efficiency in conditions with a large number of domain-specific factors. Research results. An analysis of existing methods has been conducted; a concept and algorithms for automating classification and modeling processes of DRAS have been proposed; software has been developed and validated; and practical examples of its application have been presented, demonstrating its capability to support decision-making regarding the formation of sets and elimination of conflicting or practically unacceptable strategies. Conclusions. The primary scientific contribution of this study is the proposed and implemented automation method based on facet-hierarchical attribute descriptions and Cartesian models for generating possible strategy configurations. This approach can enhance the reliability and effectiveness of UAV swarm deployment under complex and dynamic conditions by generating complete and internally consistent sets for selecting DRAS.Downloads
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