Planning UAV Flight Trajectories for Monitoring Large Areas

Modern agriculture covers vast areas, and effective monitoring of these territories plays a key role in precision farming. Wireless sensor networks are widely used to obtain real-time information on the condition of agricultural crops. However, manually collecting data from such sensors (deployed across a sensor network) is challenging. At the same time, unmanned aerial vehicles (UAVs) are increasingly used to provide automated and highly accurate data collection. This article addresses the problem of constructing an optimal UAV trajectory to efficiently collect data from distributed sensor nodes. The goal is to minimize the total route length while fully covering the sensing zones of all devices. Within the study, four route planning methods were developed and compared: the centroid-based method, the three-point method, the tangential method, and the optimal point selection method within the coverage radius boundary. Each method was implemented as a programmatic algorithm, including route construction, geometric optimization, and coverage evaluation. All methods were tested under the same conditions using a set of sensors distributed over a defined area. Evaluation criteria included total path length, number of maneuver points, and computation time, across coverage radii from 1 to 50 meters. The authors propose two approaches for trajectory optimization: a clustering-based centroid algorithm and an enhanced three-point algorithm based on the Lin-Kernighan heuristic. Experimental results showed that the proposed dual-algorithm method significantly outperforms previously studied route planning methods. Thus, this paper presents a comprehensive approach to UAV route planning for agricultural field monitoring, considering geometric, algorithmic, and computational factors. It also provides practical recommendations for selecting the most suitable method based on the spatial structure of the sensor network.