Machine Learning for Unmanned Aerial Vehicle Routing on Rough Terrain

Sidenko, Ievgen; Trukhov, Artem; Kondratenko, Galyna; Zhukov, Yuriy; Kondratenko, Yuriy

doi:10.1007/978-3-031-36118-0_56

Cited by 1 publication

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The design and implementation of advanced IoT-based control systems would enable UAVs to have enhanced decision-making capabilities, allowing them to navigate complex environments, adapt to changing conditions, and perform meteorological measurement tasks with minimal human intervention [34,35]. This includes the integration of various artificial intelligence [36,37] and machine learning [38] algorithms to allow the UAV to learn from data and improve its performance over time. As a result, such integration will make it possible to create universal mobile meteorological stations based on UAVs, capable of performing tasks in dynamic and unpredictable conditions.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Intelligent IoT-based Control System of the UAV for Meteorological Measurements

Kozlov,

Kondratenko,

Skakodub

2024

JMM

View full text Add to dashboard Cite

This study focuses on the creation and examination of an intelligent automated control system for UAVs utilized in meteorological measurements based on the Internet of Things (IoT) and mobile technologies. The proposed system enables the achievement of commendable flight control standards for UAVs during meteorological data gathering, thereby markedly enhancing the overall effectiveness of meteorological stations. Notably, this system is constructed on the foundation of three integrated principles: (a) a hierarchical two-level approach for control and data collection based on IoT and mobile technologies, (b) a straightforward and dependable fuzzy logic control characterized by high performance, and (c) the effective optimization of fuzzy control components through the application of bio-inspired multi-agent computing techniques. To assess the performance of the suggested intelligent system, this study involves the creation and bioinspired optimization of the climb speed fuzzy controller. Subsequent simulation experiments are conducted to evaluate the automatic control of UAV’s flight processes under different modes. The analysis of the simulation results indicates that the developed system, utilizing fuzzy control, exhibits superior efficiency and higher quality metrics when compared to existing counterparts, especially in diverse flight scenarios such as uniform climbing, gradual approach to designated altitude levels, and smooth landing during meteorological measurements.

show abstract