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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

Need

Solution/Innovation

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.

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Results

Our results are compared with golden reference as traditional path planning algorithm, demonstrating our method’s effectiveness.

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Impacts

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