The rapid progress in global port construction has resulted in a significant rise in container throughput. As the development of automated container terminals continues, it is imperative to enhance the intelligence and environmental friendliness of Automated Guided Vehicles (AGVs). This paper focuses on addressing the AGV path planning problem by employing deep reinforcement learning. The primary objectives are to optimize data collection efficiency and minimize battery consumption. The diverse layouts of terminal environments are represented using RGB images, and path learning is incorporated with a meticulously designed reward function. Here, we perform 1000 simulations using Monte Carlo to evaluate the parameters for these four scenarios. The evaluation showed that the terminal path planning battery consumption averaged around 40 to 56, the data collection rates were all above 80% on average, and more than 99% of them resulted in optimal solutions in a 32×32 size environment. The algorithm is able to reach and collect the data as fast as possible.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    AGV path planning and optimization with deep reinforcement learning model


    Beteiligte:
    Chen, Xinqiang (Autor:in) / Liu, Shuhao (Autor:in) / Li, Chaofeng (Autor:in) / Han, Bing (Autor:in) / Zhou, Yamin (Autor:in) / Zhao, Jiansen (Autor:in)


    Erscheinungsdatum :

    04.08.2023


    Format / Umfang :

    1126631 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-Obstacle Path Planning using Deep Reinforcement Learning

    Morgan, Brandon / Trigg, Lena / Stringer, Alexander et al. | IEEE | 2024


    Deep Reinforcement Learning-Based UAV Path Planning Algorithm

    Wang, Kunfu / Hui, Ma / Hou, Jiajun et al. | IEEE | 2024


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

    Wang, Xu / Xu, Xiaobin / Lin, Shiyao et al. | Springer Verlag | 2025


    UAV path planning with kinematic constraints based on deep reinforcement learning

    Gao, Mingsheng / Zhang, Xiaoxuan | British Library Conference Proceedings | 2022


    Unmanned Aerial Vehicles Path Planning Based on Deep Reinforcement Learning

    Wang, Guoqiu / Zheng, Xuanyu / Zhao, Haitong et al. | Springer Verlag | 2019