Analysis of decentralized drone control model and interception trajectory calculation
DOI:
https://doi.org/10.30837/2522-9818.2024.2.033Keywords:
Cascade DataHub; automated systems; machine learning; real-time management; deep learning.Abstract
Subject matter: This article is devoted to the study of applying the innovative Cascade DataHub method for optimizing the management of automated mobile systems, especially unmanned aerial vehicles. The work analyzes both theoretical and practical aspects of implementing this method across various application sectors. Goal: The objective of the study is to conduct a comprehensive analysis of contemporary models and methods for managing a group of drones, focusing on decentralized approaches. Additionally, the study aims to develop effective algorithms for optimizing the interception trajectory, with the goal of enhancing the accuracy and reliability of managing complex automated systems through increased real-time data integration. The research is directed towards identifying the potential advantages of this method in reducing system response times and improving decision-making accuracy. Tasks: The main tasks of the research include the development of comprehensive algorithms for rapid processing and analysis of large volumes of data from various sources, creating reliable communication protocols to ensure connection stability under extreme conditions. Another important task is the integration of these developments into practical applications, which will increase their effectiveness in real operational conditions. Methods: To achieve the set goals, advanced techniques of mathematical modeling, statistical analysis, machine learning, and deep learning are used. The application of these techniques ensures high accuracy and reliability of the management systems. Results: During the research, it was found that the Cascade DataHub method significantly reduces the response time of systems to commands, increases the accuracy of task execution, and decreases data loss during their transmission. The implementation of this method also contributes to a more efficient distribution of resources among automated units, which is critically important for missions requiring high coordination and time synchronization. Conclusions: A comprehensive analysis of contemporary models and methods for managing a group of drones with a focus on decentralized approaches has been conducted. Effective algorithms for optimizing the interception trajectory have been developed, aimed at enhancing the accuracy and reliability of managing complex automated systems through real-time data integration. The study revealed the potential advantages of the proposed method in reducing system response times and improving decision-making accuracy, contributing to the more efficient functioning of automated systems.
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Fillan, J. (2023), "Autonomous inspection and maintenance missions with AI planning and the ROSPlan framework". NTNU Open. available at: https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/3094654
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Gielis, J., Shankar, A. and Prorok, A. (2022), "A Critical Review of Communications in Multi-robot Systems", Current Robot Reports, Vol. 3(3), P. 213–225. DOI: 10.1007/s43154-022-00090-9
Gielis J., Shankar A., Prorok A. A Critical Review of Communications in Multi-robot Systems // Current Robot Reports. 2022. Vol. 3(3). С. 213–225. DOI: 10.1007/s43154-022-00090-9
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