ICT Express · 2021 · 59 citations · 9 references
EngineeringAutonomous SystemsComputer ScienceAerial RoboticsAutonomous VehiclesTransportationAutonomous LandingAutonomous TakeoffAviation TransportationFlight Path OptimizationPolicy LearningReinforcement LearningDeep Reinforcement LearningFlight RoutesMulti-agent LearningMulti-agent Reinforcement LearningReinforcement Learning For LandingTaxi OperationsUrban Air MobilityVertical Takeoff Control
The urban aerial mobility (UAM) system, such as drone taxi or air taxi, is one of future on-demand transportation networks. Among them, electric vertical takeoff and landing (eVTOL) is one of UAM systems that is for identifying the locations of passengers, flying to the positions where the passengers are located, loading the passengers, and delivering the passengers to their destinations. In this paper, we propose a distributed deep reinforcement learning where the agents are formulated as eVTOL vehicles that can compute the optimal passenger transportation routes under the consideration of passenger behaviors, collisions among eVTOL, and eVTOL battery status.
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