In order to improve the autonomous reconnaissance efficiency of unmanned aerial vehicle (UAV) in an uncertain environment, situation and observation information acquired by UAV are input into the replay buffer. Model-free training is performed on the data of the replay buffer by deep reinforcement learning (DRL) method, so as to generate the corresponding network model. The reward function is designed for UAV regional reconnaissance missions to further improve the generalization ability of the model. The simulation results show that the UAV autonomous reconnaissance route planning algorithm based on DRL has a high degree of sustainable coverage and its patrol path is unpredictable.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    UAV Autonomous Reconnaissance Route Planning Based on Deep Reinforcement Learning


    Beteiligte:
    Xu, Tonghua (Autor:in) / Wang, Nan (Autor:in) / Lin, Hong (Autor:in) / Sun, Zhaomei (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    2053713 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Multivehicle reconnaissance route and sensor planning

    Moitra, A. / Mattheyses, R.M. / DiDomizio, V.A. et al. | IEEE | 2003




    Deep Reinforcement Learning for Autonomous Aerobraking Maneuver Planning

    Falcone, Giusy / Putnam, Zachary R. | AIAA | 2022