Energy-Efficient UAV Path Planning and Charging Integration for Precision Agriculture
Shih-Fan Chou, Chao‐Hsiang Liao · 2025
The ascent of precision agriculture has seen Unmanned Aerial Vehicles (UAVs) become increasingly pivotal for tasks such as pesticide application and crop surveillance. This study confronts the critical challenge of UAV path planning, emphasizing energy efficiency and the strategic integration of charging station deployment. We introduce the Energy-Conscious Grid Traversal Algorithm (ECGTA), a methodology that dynamically segments farmland and optimizes flight trajectories alongside charging and replenishment strategies. This approach aims to ensure uniform pesticide coverage while minimizing energy expenditure. Suboptimal energy management and inefficient routing can precipitate incomplete spraying, excessive energy consumption, and operational delays due to frequent recharging, thereby impeding large-scale UAV adoption. To validate ECGTA’s efficacy, we conduct comparative evaluations against prevalent path planning techniques, including zigzag, spiral, and reverse-spiral methods. The results underscore ECGTA’s potential to significantly enhance UAV operational efficiency within precision farming contexts.