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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    UAV Autonomous Reconnaissance Route Planning Based on Deep Reinforcement Learning


    Contributors:
    Xu, Tonghua (author) / Wang, Nan (author) / Lin, Hong (author) / Sun, Zhaomei (author)


    Publication date :

    2019-10-01


    Size :

    2053713 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    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