Unmanned Aerial Vehicles (UAVs) are revolutionizing logistics by providing efficient, autonomous, and cost-effective delivery solutions. This study explores how digital control systems, AI-driven navigation, and sensor integration enhance flight stability, navigation accuracy, energy efficiency, and obstacle avoidance. Experimental results indicate that PID-based control reduces oscillations by 30%, AI-powered GPS corrections improve accuracy by 20%, and optimized energy management extends flight duration by 15%. Additionally, computer vision-based obstacle detection enables a 95% success rate in urban navigation. These findings highlight the critical role of AI and digital control in optimizing UAV logistics operations, making them safer, more precise, and energy-efficient for large-scale deployment.
Unmanned Aerial Vehicles (UAVs) are revolutionizing logistics by providing efficient, autonomous, and cost-effective delivery solutions. This study explores how digital control systems, AI-driven navigation, and sensor integration enhance flight stability, navigation accuracy, energy efficiency, and obstacle avoidance. Experimental results indicate that PID-based control reduces oscillations by 30%, AI-powered GPS corrections improve accuracy by 20%, and optimized energy management extends flight duration by 15%. Additionally, computer vision-based obstacle detection enables a 95% success rate in urban navigation. These findings highlight the critical role of AI and digital control in optimizing UAV logistics operations, making them safer, more precise, and energy-efficient for large-scale deployment.
| № | Имя автора | Должность | Наименование организации |
|---|---|---|---|
| 1 | Sidikov B.M. | 2nd year master's student in the “Digital Economy” faculty | TSUE |
| № | Название ссылки |
|---|---|
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| 5 | Zhou, H., Li, X., & Wang, Y. (2022). “AI-driven UAV control for urban logistics: A reinforcement learning approach.” IEEE Robotics and Automation Letters, 7(3), 2501-2508. |
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| 9 | Cetinsaya, B., Reiners, D., & Cruz-Neira, C. (2024). “From PID to swarms: A decade of advancements in drone control and path planning – A systematic review (2013–2023).” Swarm and Evolutionary Computing. |
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| 16 | Data compiled from multiple studies, including [8], [9], [14], [15] and experimental results. |