  Mastering autonomous drone landing on dynamic platforms presents formidable
challenges due to unpredictable velocities and external disturbances caused by
the wind, ground effect, turbines or propellers of the docking platform. This
study introduces an advanced Deep Reinforcement Learning (DRL) agent,
Lander:AI, designed to navigate and land on platforms in the presence of windy
conditions, thereby enhancing drone autonomy and safety. Lander:AI is
rigorously trained within the gym-pybullet-drone simulation, an environment
that mirrors real-world complexities, including wind turbulence, to ensure the
agent's robustness and adaptability.
  The agent's capabilities were empirically validated with Crazyflie 2.1 drones
across various test scenarios, encompassing both simulated environments and
real-world conditions. The experimental results showcased Lander:AI's
high-precision landing and its ability to adapt to moving platforms, even under
wind-induced disturbances. Furthermore, the system performance was benchmarked
against a baseline PID controller augmented with an Extended Kalman Filter,
illustrating significant improvements in landing precision and error recovery.
Lander:AI leverages bio-inspired learning to adapt to external forces like
birds, enhancing drone adaptability without knowing force magnitudes.This
research not only advances drone landing technologies, essential for inspection
and emergency applications, but also highlights the potential of DRL in
addressing intricate aerodynamic challenges.
