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<p>In this study, a hybrid path planning algorithm for ships in ice areas is proposed. First, the operation space of ships in ice areas is established in accordance with the raster method. Second, improvement strategies are introduced to address the slow iteration speed and poor solution quality of the traditional ant colony algorithm, and the Ant Colony Optimization (ACO) algorithm is used to plan the global path in a static environment. Then, the node deletion algorithm is proposed to optimize the global path. Finally, the dynamic window approach (DWA) algorithm is optimized, and the subnodes of the preprocessed path are considered the subobjectives to be optimized. The final path in the dynamic environment is solved using the improved DWA algorithm. The simulation results show that: the ACO algorithm can plan a high-quality global path with a high iteration speed, the node deletion algorithm can effectively eliminate redundant nodes and reduce the computational complexity of the subsequent algorithms, and the improved DWA algorithm can efficiently avoid dynamic obstacles in the environment on an original global path and solve the path that meets the motion performance of the ship in ice areas.</p>

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Ship Path Planning in Ice Based on an Ant Colony-Dynamic Window Hybrid Algorithm

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Yanzhuo Xue,
  • Xinyue Zhang,
  • Wenbo Liu,
  • Yang Lu,
  • Menghang Liu,
  • Yuejun Liu

摘要

In this study, a hybrid path planning algorithm for ships in ice areas is proposed. First, the operation space of ships in ice areas is established in accordance with the raster method. Second, improvement strategies are introduced to address the slow iteration speed and poor solution quality of the traditional ant colony algorithm, and the Ant Colony Optimization (ACO) algorithm is used to plan the global path in a static environment. Then, the node deletion algorithm is proposed to optimize the global path. Finally, the dynamic window approach (DWA) algorithm is optimized, and the subnodes of the preprocessed path are considered the subobjectives to be optimized. The final path in the dynamic environment is solved using the improved DWA algorithm. The simulation results show that: the ACO algorithm can plan a high-quality global path with a high iteration speed, the node deletion algorithm can effectively eliminate redundant nodes and reduce the computational complexity of the subsequent algorithms, and the improved DWA algorithm can efficiently avoid dynamic obstacles in the environment on an original global path and solve the path that meets the motion performance of the ship in ice areas.