Coverage of Agricultural Areas by a Group of Unmanned Aerial Vehicles Considering Wind Load and Energy Consumption Constraints

P. 1–4.

Faizullin R., Mustafin M., Svinin M., Magid E.

This work addresses the problem of energy-constrained coverage of agricultural fields using a fleet of identical unmanned aerial vehicles (UAVs). Each UAV must complete a spraying mission over a designated area, subject to limited battery capacity. To ensure energy feasibility, static charging stations are deployed along the field’s perimeter. The agricultural field is first partitioned into subregions using Lloyd’s clustering algorithm, producing a balanced decomposition adapted to the number of UAVs. Within each subregion, an efficient coverage path is generated using the Boustrophedon method, with flight lines aligned with the longest region axis to minimize energy-intensive turns. The primary objective is to minimize the total mission completion time while accounting for recharging needs and stochastic wind conditions. Wind is modeled as a vector with random speed and direction, affecting UAV energy consumption depending on relative flight orientation. To assess solution robustness under uncertainty, the expected mission duration is estimated using a Monte Carlo simulation. Charging station locations are optimized using the Particle Swarm Optimization algorithm. Finally, a post-processing step iteratively reduces the number of stations to achieve more cost-effective configurations without a significant loss in performance.

Keywords: path planning, UAV, БПЛА, charging stations, precision agriculture, coverage, много-агентное планирование пути, stochastic wind model

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2025 9th International Conference on Information, Control, and Communication Technologies (ICCT-2025)

IEEE, 2025.