Drone in the wind
Urban air mobility (UAM) is perceived as a revolutionary aspect of future urban transportation, with drones playing a key role.
Its potential lies in
offer an alternative to existing ground transportation systems
unlock traffic capacity in urban low-altitude space
provide a much faster way to traverse across cities
We modified advanced deep reinforcement learning algorithm with high-volume computational fluid dynamics (CFD) simulation data to achieve energy saving for UAV delivery tasks in urban environments.
Our results are compared with golden reference as traditional path planning algorithm, demonstrating our method’s effectiveness.