  Autonomous drone navigation faces a critical challenge in achieving accurate
landings on dynamic platforms, especially under unpredictable conditions such
as wind turbulence. Our research introduces TornadoDrone, a novel Deep
Reinforcement Learning (DRL) model that adopts bio-inspired mechanisms to adapt
to wind forces, mirroring the natural adaptability seen in birds. This model,
unlike traditional approaches, derives its adaptability from indirect cues such
as changes in position and velocity, rather than direct wind force
measurements. TornadoDrone was rigorously trained in the gym-pybullet-drone
simulator, which closely replicates the complexities of wind dynamics in the
real world. Through extensive testing with Crazyflie 2.1 drones in both
simulated and real windy conditions, TornadoDrone demonstrated a high
performance in maintaining high-precision landing accuracy on moving platforms,
surpassing conventional control methods such as PID controllers with Extended
Kalman Filters. The study not only highlights the potential of DRL to tackle
complex aerodynamic challenges but also paves the way for advanced autonomous
systems that can adapt to environmental changes in real-time. The success of
TornadoDrone signifies a leap forward in drone technology, particularly for
critical applications such as surveillance and emergency response, where
reliability and precision are paramount.
