Distributed deep reinforcement learning for autonomous aerial eVTOL mobility in drone taxi applications
Won Joon Yun, Soyi Jung, Joongheon Kim, Jae‐Hyun Kim
Abstract
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.
Topics & Concepts
DroneReinforcement learningTakeoffComputer scienceDestinationsTransport engineeringAeronauticsSimulationAerospace engineeringEngineeringArtificial intelligenceGeographyGeneticsTourismArchaeologyBiologyUAV Applications and OptimizationTransportation and Mobility InnovationsAir Traffic Management and Optimization