Abstract
This study focuses on the development and implementation of a route optimization algorithm for unmanned aerial vehicles (UAVs). The goal is to create a system that can be used to automate the process of collecting and analyzing data from UAVs in environmental and agricultural applications. The system is built using Python, QGroundControl, and the MAVLink communication protocol. The developed system aims to optimize UAV routes in order to improve the efficiency of environmental monitoring and mapping tasks. It automates the process of data collection and analysis, allowing for more accurate and timely information about the state of the environment and agricultural land. Results from testing the system demonstrate its high level of effectiveness in real-world scenarios. The conclusions from this study suggest that the proposed route optimization algorithm can be successfully applied to various environmental and agricultural use cases. Further development of the system is proposed to enhance its capabilities and expand its use in other areas.