Intercepting enemy UAV in a low-altitude flight environment is a challenging task. Imitation learning provides a feasible solution for such challenge. Generative Adversarial Imitation Learning (GAIL) can learn expert strategies without a carefully designed reward function. In this paper, we propose a GAIL-based method to improve the autonomous interception of UAVs problem. In the method, we augment the expert data and introduce semantic condition into the Generative Adversarial Network (GAN) to improve learning efficiency. In addition, in the designed interactive environment, we use the proposed method to complete the UAV interception decision-making task and compare with two baseline methods by evaluating the interception performance through three performance metrics. The simulation results show that our method is better than all baseline methods on all of the three metrics.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Autonomous UAV Interception via Augmented Adversarial Inverse Reinforcement Learning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Wang, Huan (author) / Liu, Xiaofeng (author) / Zhou, Xu (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Autonomous Drone Interception with Reinforcement Learning

    Gauffriau, Adrien / Grasset, Damien / Bertoin, David et al. | TIBKAT | 2022

    Free access

    Modeling Driver Behavior using Adversarial Inverse Reinforcement Learning

    Sackmann, Moritz / Bey, Henrik / Hofmann, Ulrich et al. | IEEE | 2022



    Multiagent modeling of pedestrian-vehicle conflicts using Adversarial Inverse Reinforcement Learning

    Nasernejad, Payam / Sayed, Tarek / Alsaleh, Rushdi | Taylor & Francis Verlag | 2023