CLIMATE-ADAPTIVE AUTONOMOUS NAVIGATION FOR UAVS USING PREDICTIVE ENVIRONMENTAL MODELING

Abstract

Aerial vehicles (UAVs) frequently operate in dynamic atmospheric settings where stability and

variables like wind, temperature, and precipitation impact navigation accuracy. Traditional autopilot systems respond to disruptions after they happen, which can reduce dependability and efficiency. This paper proposes a paradigm for climate-adaptive navigation that integrates real-time control and predictive environmental modelling. The system anticipates environmental effects and makes proactive adjustments to flight parameters by integrating machine learning-based disturbance prediction, adaptive path planning, and meteorological data. When compared to conventional techniques, simulation results under various weather conditions demonstrate increased stability, decreased energy usage, and greater trajectory accuracy. The results show that UAV navigation may be made much more robust and capable of supporting dependable autonomous operations in complicated and dynamic atmospheric conditions by integrating predictive climatic intelligence. Simulation experiments conducted under diverse climatic scenarios, including turbulent wind fields, thermal updrafts, and rainfall disturbances, demonstrate significant improvements in path accuracy, energy efficiency, and flight stability compared to conventional navigation approaches. The findings indicate that integrating predictive environmental intelligence into UAV control systems enhances operational robustness, making such systems more reliable in dynamic atmospheric conditions. This capability is particularly valuable for critical applications such as disaster monitoring, precision agriculture, and long-range autonomous missions, where environmental uncertainty can strongly affect performance. Overall, the study establishes a foundation for next-generation climate-aware aerial autonomy and emphasises promising research directions at the intersection of atmospheric science, predictive modelling, and intelligent robotic navigation systems..