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<p>To enhance the efficiency of heterogeneous multi-unmanned surface vehicle (multi-USV) systems performing multiple tasks, this study introduces a novel multi-task allocation method. The proposed method utilizes a cost conversion relational network to enable parallel execution of multi-tasks, significantly improving overall efficiency. To address the inefficiencies associated with multi-task allocation in heterogeneous multi-USV systems, this study proposes a novel task allocation algorithm inspired by the migratory behavior of horned horse herds. Three sets of experiments were conducted to validate the proposed algorithm’s effectiveness. Experiment 1 involved a comparative analysis in a randomly generated simulated obstacle environment. Experiment 2 focused on a simulated real-world scenario, while Experiment 3 was conducted in the real-world environment of <i>Miyun</i> Reservoir. Experimental results demonstrated that the proposed algorithm exhibits higher computational efficiency and lower task costs than the benchmark algorithms.</p>

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A multi-task allocation method for heterogeneous multi-unmanned surface vehicles

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Yu Jiabin,
  • Lu Yang,
  • Chen Zhihao,
  • Xu Jiping,
  • Wang Xiaoyi,
  • Zhao Zhiyao

摘要

To enhance the efficiency of heterogeneous multi-unmanned surface vehicle (multi-USV) systems performing multiple tasks, this study introduces a novel multi-task allocation method. The proposed method utilizes a cost conversion relational network to enable parallel execution of multi-tasks, significantly improving overall efficiency. To address the inefficiencies associated with multi-task allocation in heterogeneous multi-USV systems, this study proposes a novel task allocation algorithm inspired by the migratory behavior of horned horse herds. Three sets of experiments were conducted to validate the proposed algorithm’s effectiveness. Experiment 1 involved a comparative analysis in a randomly generated simulated obstacle environment. Experiment 2 focused on a simulated real-world scenario, while Experiment 3 was conducted in the real-world environment of Miyun Reservoir. Experimental results demonstrated that the proposed algorithm exhibits higher computational efficiency and lower task costs than the benchmark algorithms.