Battery-aware RLExpert
Battery-Aware Reinforcement Learning for Aggressive Quadrotor Flight
This preprint introduces a battery-aware reinforcement-learning controller for aggressive quadrotor flight. On a 38 g Crazyflie Brushless, a policy with increased thrust-command authority plus a filtered voltage input cuts 3.84 m/s circle tracking error by 49.3% versus stock-authority RL and reduces 20-lap race time from 106.22 s to 95.24 s. The work shows that explicit voltage feedback can complement existing flight-controller compensation, offering a practical route to better agility on battery-powered drones.