  We tackle the problem of minimum-time flight for a quadrotor through a
sequence of waypoints in the presence of obstacles while exploiting the full
quadrotor dynamics. Early works relied on simplified dynamics or polynomial
trajectory representations that did not exploit the full actuator potential of
the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can
plan minimum-time trajectories; yet, the trajectories are executed with control
methods that do not account for obstacles. Thus, a successful execution of such
trajectories is prone to errors due to model mismatch and in-flight
disturbances. To this end, we leverage deep reinforcement learning and
classical topological path planning to train robust neural-network controllers
for minimum-time quadrotor flight in cluttered environments. The resulting
neural network controller demonstrates substantially better performance of up
to 19\% over state-of-the-art methods. More importantly, the learned policy
solves the planning and control problem simultaneously online to account for
disturbances, thus achieving much higher robustness. As such, the presented
method achieves 100% success rate of flying minimum-time policies without
collision, while traditional planning and control approaches achieve only 40%.
The proposed method is validated in both simulation and the real world, with
quadrotor speeds of up to 42km/h and accelerations of 3.6g.
