In recent years, mobile robots have been widely applied in different areas, such as medicine, rescue and home service. Obstacle avoidance path planning is one of the most important issues for mobile robots. Researchers have paid attention to the methods integrated with reinforcement learning (RL). Even though the sparse reward problem of RL is proven to be solvable by Hindsight Experience Replay (HER), obstacle avoidance is still a thorny problem. Therefore, we proposed a DQN (Deep Q-learning Network)-HER RL method with proactive obstacle avoidance skill to learn the obstacle avoidance path planning task. We built simulations in Webots and designed an ablation experiment. The results showed our method improved learning stability and efficiency. In addition, we are planning to apply our method to the heterogeneous multi-robot system for multi-robot path planning in future work.


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    Titel :

    Obstacle Avoidance Path Planning Method Based on DQN-HER


    Weitere Titelangaben:

    Lect.Notes Computer


    Beteiligte:
    Yang, Huayong (Herausgeber:in) / Liu, Honghai (Herausgeber:in) / Zou, Jun (Herausgeber:in) / Yin, Zhouping (Herausgeber:in) / Liu, Lianqing (Herausgeber:in) / Yang, Geng (Herausgeber:in) / Ouyang, Xiaoping (Herausgeber:in) / Wang, Zhiyong (Herausgeber:in) / Li, Kailong (Autor:in) / Zha, Fusheng (Autor:in)

    Kongress:

    International Conference on Intelligent Robotics and Applications ; 2023 ; Hangzhou, China July 05, 2023 - July 07, 2023



    Erscheinungsdatum :

    16.10.2023


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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