Optimal Path Planning of Unmanned Aerial Vehicle with Wind Field

This paper proposes a novel A∗∗This work was not supported by any organization algorithm for unmanned aerial vehicles (UAVs) path planning that integrates wind field dynamics into the energy optimization process. First, based on the dynamics model, an energy consumption model is established to characterize the effects of wind during trajectory tracking tasks. Building upon this, we introduce a wind-augmented A∗ algorithm that dynamically adjusts its heuristic estimates based on real-time energy cost predictions influenced by local wind conditions. This enables UAVs to strategically exploit favorable winds and mitigate adverse flows, achieving energy-efficient navigation. Numerical experiments in composite wind field demonstrate that the proposed method reduces energy consumption by an average of approximately 6 % compared to conventional A∗ algorithms, validating its effectiveness for intelligent UAVs operations in complex atmospheric environments.