For the issue of traditional unmanned aerial vehicle (UAV) local path planning algorithms easily falling into local optima, an algorithm for local path planning that combines deep reinforcement learning and memory-guided strategy is proposed. The algorithm takes the UAV heading angle as the learning action, and detection information as the state. It integrates the Deep Q-network (DQN) algorithm to learn path planning strategies. When the UAV gets trapped in a local optimum, the memory-guided strategy utilizes historical detection information to guide the UAV out of the local optimum, completing local path planning for UAVs in unknown environments. Simulation experiments verify that the proposed algorithm achieves higher success rates and shorter average flight distances.


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    Title :

    Deep Reinforcement Learning-Based Local Path Planning with Memory-Guided


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Wang, Xu (author) / Xu, Xiaobin (author) / Lin, Shiyao (author) / Fan, Junfang (author) / Gao, Zhihao (author) / Gu, Chengyu (author)

    Conference:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Publication date :

    2025-03-11


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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