In recent years, the combination of deep reinforcement learning and unmanned aerial vehicle (UAV) to achieve autonomous flight has been a hot research field. In this paper, an obstacle avoidance navigation algorithm (PA-SAC) based on priority experience buffer pool (PEBP), attention, and Soft-Actor-Critic (SAC) is proposed to solve the continuous space obstacle avoidance navigation problem of UAVs by using a deep reinforcement learning algorithm. In the two simulation experiments, the success rate of the PA-SAC algorithm was 95.6% in the known environment and 73% in the unknown environment. These results demonstrate that the PA-SAC algorithm can achieve autonomous obstacle avoidance for UAVs with deep image input.


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    Titel :

    Research on Obstacle Avoidance Algorithm of UAVs Reinforcement Learning based on Vision


    Beteiligte:
    Gao, Yuan (Autor:in) / Shi, Tianwei (Autor:in) / Xu, Teng (Autor:in) / Ding, Jianbang (Autor:in) / Jing, Rongchen (Autor:in)


    Erscheinungsdatum :

    17.11.2023


    Format / Umfang :

    745995 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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