WA-LPA*: An Energy-Aware Path-Planning Algorithm for UAVs in Dynamic Wind Environments

Highlights: What are the main findings? Developed a wind-adaptive lifelong planning A* (WA-LPA*) algorithm that couples UAV energy modeling with dynamic wind-field perception to achieve energy-aware path optimization. Introduced a composite heuristic integrating wind-alignment and altitude-layer optimization, together with an adaptive replanning mechanism responsive to environ- mental changes. What are the implications of the main findings? The proposed approach enables UAVs to maintain energy-efficient and stable flight performance under complex, time-varying wind conditions. This framework offers a practical foundation for real-world UAV deployment and provides methodological guidance for intelligent navigation in energy-constrained aerial systems. Energy optimization is crucial for unmanned aerial vehicle (UAV) path planning, particularly in complex wind-field environments. Most existing path-planning algorithms rely on simplified energy consumption models, which often fail to adequately capture the effects of wind fields. To address this limitation, a wind-adaptive lifelong planning A* algorithm (WA-LPA*) is proposed for energy-aware path planning in dynamic wind environments. WA-LPA* constructs a composite heuristic function incorporating wind-field alignment factors and integrates a hierarchical height-aware optimization strategy. Meanwhile, an adaptive replanning mechanism is designed based on the change characteristics of the wind field. Simulation experiments conducted across representative scenarios demonstrate that, compared to conventional algorithms that neglect wind-field effects, WA-LPA* achieves energy efficiency improvements of 5.9–29.4%.