The autonomous navigation and obstacle avoidance for USVs with ANOA deep reinforcement learning method

Xing Wu*, Haolei Chen, Changgu Chen, Mingyu Zhong, Shaorong Xie, Yi-Ke Guo, Hamido Fujita

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    79 Citations (Scopus)


    The unmanned surface vehicle (USV) has been widely used to accomplish missions in the sea or dangerous marine areas for ships with sailors, which greatly expands protective capability and detection range. When USVs perform various missions in sophisticated marine environment, autonomous navigation and obstacle avoidance will be necessary and essential. However, there are few effective navigation methods with real-time path planning and obstacle avoidance in dynamic environment. With tailored design of state and action spaces and a dueling deep Q-network, a deep reinforcement learning method ANOA (Autonomous Navigation and Obstacle Avoidance) is proposed for the autonomous navigation and obstacle avoidance of USVs. Experimental results demonstrate that ANOA outperforms deep Q-network (DQN) and Deep Sarsa in the efficiency of exploration and the speed of convergence not only in static environment but also in dynamic environment. Furthermore, the ANOA is integrated with the real control model of a USV moving in surge, sway and yaw and it achieves a higher success rate than Recast navigation method in dynamic environment.

    Original languageEnglish
    Article number105201
    JournalKnowledge-Based Systems
    Publication statusPublished - 21 May 2020

    Scopus Subject Areas

    • Management Information Systems
    • Software
    • Information Systems and Management
    • Artificial Intelligence

    User-Defined Keywords

    • Autonomous navigation
    • Obstacle avoidance
    • Reinforcement learning
    • Unmanned surface vehicle (USV)


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