Considering that agent is difficult to train in the real-world environment, a real-time simulation training platform for quadrotor is proposed. This article emphasizes the issue of unmanned aerial vehicle simulation training platforms based on deep reinforcement learning (DRL). Previous training platforms either could only handle low dimensional privileged information or needed strong computing power to handle high-dimensional information such as vision. This situation makes it difficult to design algorithms that utilize onboard sensors. For this reason, a real-time simulation platform for quadrotor is designed, which has been successfully used for common tasks such as quadrotor obstacle avoidance and landing. Finally, its effectiveness is verified through simulation.


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    Titel :

    Vision Real-Time Simulation Training Platform for Quadrotor


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Qu, Yi (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Fu, Wenxing (Herausgeber:in) / Li, Jiaxuan (Autor:in) / Zhang, Naizong (Autor:in) / Liu, Tianxin (Autor:in) / Fan, Quan-Yong (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2023 ; Nanjing, China September 09, 2023 - September 11, 2023



    Erscheinungsdatum :

    25.04.2024


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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