Integrating Wind-Aware Optimal Turning Maneuver Strategy Into Operations for Fixed-Wing Unmanned Aircraft Vehicle (UAV)

The rapid advancement of unmanned aerial vehicle (UAV) technology has significantly enhanced aerial mapping capabilities, offering cost-effective and high-resolution data acquisition. However, fixed-wing UAVs face challenges in maintaining accuracy and efficiency due to wind disturbances and maneuvering limitations. Traditional waypoint-following methods often rely on buffer areas and overshoot points, increasing energy consumption and compromising data quality. While trochoidal paths based on Dubins' formulation have shown promise, existing solutions struggle with practical implementation challenges. To address these issues, this paper presents a methodology that integrates trochoidal path planning and reconstruction with an extended L1 controller to account for dynamic curvature and wind drift. A surrogate model is introduced for efficient bank angle prediction, mitigating inaccuracies in lateral acceleration estimation, while a frequency-domain reconstruction framework using the Discrete Cosine Transform (DCT) enables compact and smooth representation of trochoidal paths for seamless integration into autopilot systems. Simulation results demonstrate significant improvements in path compliance, robustness, and energy efficiency compared to conventional methods, bridging the gap between theoretical advancements and real-world UAV operations. This research advances unmanned systems by optimizing path-planning formulation, integration, and implementation techniques for reliable and practical aerial mapping missions.