Rescue assignments strategy are crucial for multiple Autonomous Underwater Vehicle (multi-AUV) systems in three dimensional (3-D) complex underwater environments. Considering the requirements of rescue missions, multi-AUV systems need to be cost-effective, fast-rescuing, and less concerned about the relationship between rescue missions. The real-time rescue plays a vital role in the multi-AUV system with the characteristics mentioned above. In this paper, we propose an efficient Reward acting on Reinforcement Learning and Particle Swarm Optimization (R-RLPSO), to provide a strategy of real-time rescue assignment for the multi-AUV system in the 3-D underwater environment. This strategy consists of the following three parts. Firstly, we present a reward-based real-time rescue assignment algorithm. Secondly, we propose an Attraction Rescue Area containing a Rescue Area. For the waypoints in each Attraction Rescue Area, the reward is calculated by a linear reward function. Thirdly, to speed up the convergence of the R-RLPSO and mark the rescue states of Attraction Rescue Area and rescue area, we develop a Reward Coefficient based on the reward of all Attraction Rescue Areas and Rescue Areas. Finally, simulation results show that the system based on R-RLPSO is more cost-effective and time-saving than that of based on comparison algorithms ISOM and IACO.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reinforcement Learning and Particle Swarm Optimization Supporting Real-Time Rescue Assignments for Multiple Autonomous Underwater Vehicles


    Contributors:
    Wu, Jiehong (author) / Song, Chengxin (author) / Ma, Jian (author) / Wu, Jinsong (author) / Han, Guangjie (author)


    Publication date :

    2022-07-01


    Size :

    6831260 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    An autonomous air-sea rescue system using Particle Swarm Optimization

    Rafferty, K. J. / McGookin, E. W. | IEEE | 2013


    Adaptive Multiple Task Assignments for UAVs Using Discrete Particle Swarm Optimization

    Chen, Kun / Sun, Qibo / Zhou, Ao et al. | British Library Conference Proceedings | 2018


    Lane assignments for autonomous vehicles

    CURLANDER JAMES CHRISTOPHER / RUSSELL RYAN SCOTT / BATHURST ALLAN SCOTT et al. | European Patent Office | 2017

    Free access

    LANE ASSIGNMENTS FOR AUTONOMOUS VEHICLES

    CURLANDER JAMES CHRISTOPHER / RUSSELL RYAN SCOTT / BATHURST ALLAN SCOTT et al. | European Patent Office | 2017

    Free access