In order to improve the intelligence of the ship and ensure the safety and reliability of the ship during navigation. Based on the deep Q-network algorithm in the field of deep reinforcement learning, this paper studies of ship intelligent collision avoidance and navigation to enable the ship to autonomously carry out collision avoidance operations and reach the goal without manual operation. According to the characteristics of actual waters, several simulated water environments are designed to study the collision avoidance and navigation training effects of ships based on algorithms. Combining collision avoidance and navigation problems with ship motion characteristics, a reasonable state space, reward function, and Q-value neural network structure are optimized. It enables the ship to choose appropriate actions according to the environmental conditions, conduct static and dynamic obstacle avoidance and navigation. There are multiple ships in the experimental environment, including different ship encounter situations, which has certain practical significance. Simulation experiments have proved the effectiveness of the algorithm. After training, the ships can smoothly complete collision avoidance and navigation operations, which can effectively reduce the risk of ship collision.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Intelligent Ships Collision Avoidance and Navigation Method Based on Deep Reinforcement Learning


    Contributors:
    Liu, Jin (author) / Xiao, Youan (author)


    Publication date :

    2021-09-01


    Size :

    1547507 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    COLREGs-Compliant Collision Avoidance Method for Autonomous Ships via Deep Reinforcement Learning

    Wang, Leihao / Zhang, Xinyu / Wang, Chengbo et al. | Springer Verlag | 2022


    Pedestrian Collision Avoidance Using Deep Reinforcement Learning

    Rafiei, Alireza / Fasakhodi, Amirhossein Oliaei / Hajati, Farshid | Springer Verlag | 2022



    Learning-Based Navigation and Collision Avoidance Through Reinforcement for UAVs

    Azzam, Rana / Chehadeh, Mohamad / Hay, Oussama Abdul et al. | IEEE | 2024

    Free access