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Аналіз енергоефективності мобільних робототехнічних платформ і безпілотних літальних апаратів у гібридних мережах

Анотація

The paper provides a comparative analysis of the energy efficiency, functional autonomy and communication stability of mobile robotic ground platforms (UGVs) as part of hybrid telecommunications systems. The main focus is on studying the potential of UGVs as a basis for building energy-optimised and reliable next-generation networks capable of providing continuous data exchange, energy management system support and information monitoring in complex environments in both military and peacetime. Particular emphasis is placed on comparing the energy balance of ground platforms with unmanned aerial vehicles (UAVs) in order to determine the advantages of ground systems in terms of autonomous operation duration, signal stability and the possibility of using powerful antenna modules. It has been established that UGVs are characterised by low energy losses, as they do not require expenditure on maintaining altitude or stabilising their position in the air. This allows for more efficient distribution of energy resources between the motion, communication and computing systems. Due to their stable base and lack of weight restrictions, mobile ground platforms can integrate large antennas, solar panels, and energy-saving elements, this significantly increases their autonomy. The application of intelligent methods of adaptive energy consumption control using deep learning algorithms is considered. Such methods allow predicting load changes, regulating energy distribution between modules and ensuring continuity of communication even in the event of dynamic changes in network topology or the environment. It is shown that it is ground-based robotic systems that can serve as an energy-stable foundation for hybrid network infrastructure, complementing aerial elements and providing them with a stable connection in challenging conditions. This approach contributes to the creation of balanced systems in which key control, communication, and power distribution functions are implemented through autonomous ground modules capable of self-organisation and collective decision-making.

Опис

Мова

Бібліографічний опис

Михайліченко О. В., Янко А. С., Лактіонов О. І. Аналіз енергоефективності мобільних робототехнічних платформ і безпілотних літальних апаратів у гібридних мережах // Вісник Вінницького політехнічного інституту. 2025. № 6. С. 78–82. URI: https://visnyk.vntu.edu.ua/index.php/visnyk/article/view/3374.

Схвалення

Рецензія

Доповнено

Цитується в

Список використаної літератури (5)

  1. Y. Zeng, R. Zhang, and T. J. Lim, “Energy-Efficient UAV Communication With Trajectory Optimization,” IEEE Trans-actions on Wireless Communications, vol. 16, no. 6, pp. 3747-3760, Jun. 2017. https://doi.org/10.1109/TWC.2017.2688328
  2. N. Miller, N. Goulet, and B. Ayalew, “Energy-Aware Mission Planning for Unmanned Ground Vehicle Fleets,” in Pro-ceedings of the 2024 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), 2024. [Electronic resource].Available: https://ndia-mich.org. Accessed: 12.09.2025
  3. Y. Zhang, R. Zhao, D. Mishra, and D. W. K. Ng, “A Comprehensive Review of Energy-Efficient Techniques for UAV-Assisted Industrial Wireless Networks,” Energies, vol. 17, no. 18, p. 4737, 2024. https://doi.org/10.3390/en17184737 .
  4. K. Seerangan, D. Raja, M. A. Hussain, and A. Amudha, “A Novel Energy-Efficiency Framework for UAV-Assisted Рис. 4. Порівняння тривалості місії Networks using Adaptive Deep Reinforcement Learning,” Scientific Reports, vol. 14, Article рр. 22188, 2024. https://doi.org/10.1038/s41598-024-71621-x .
  5. M. Mondal, S. Ramasamy, and P. Bhounsule, “Deep Reinforcement Learning Enabled Persistent Surveillance with Energy-Aware UAV-UGV Systems for Disaster Management Applications,” arXiv preprint, 2025. https://doi.org/10.48550/arXiv.2502.02666 .