针对无人船在动态环境下的路径规划问题，提出一种改进哈里斯鹰优化算法（IHHO）混合动态窗口算法（DWA）的融合算法（IHHO-DWA）。首先，针对哈里斯鹰算法收敛精度低和易陷入局部最优等问题，对哈里斯鹰算法进行混沌初始化改进，增加种群生成的离散度，避免算法过早陷入局部最优；引入高斯变异机制对哈里斯鹰最优个体进行变异扰动，提高算法开发能力。其次，引入动态窗口算法提高无人船在动态环境下规划路径的平滑度，将全局规划路径信息融入局部避障算法中，兼顾实时避障与规划安全最优路径。最后，在不同复杂度环境下进行对比仿真实验，实验结果表明，所提方法兼顾安全避障与路径最优，更有利于无人船的航行。
To address the path planning challenge for unmanned vessels in dynamic environments, a novel fusion algorithm (IHHO-DWA), which combines the improved Harris Hawk optimization algorithm (IHHO) with the dynamic window algorithm (DWA), is introduced. Initially, the Harris Hawk algorithm is enhanced with chaotic initialization to increase population diversity and prevent early convergence to suboptimal solutions. Gaussian mutation is also applied to the algorithm’s best individuals, boosting its solution exploration capabilities. Furthermore, the integration of the dynamic window algorithm smoothens path planning in dynamic environments by merging global path planning data with local obstacle avoidance strategies, ensuring both real-time safety and optimal route planning. Comparative simulations in environments of varying complexity demonstrate that this method effectively balances safety and route optimization, significantly benefiting the navigation of unmanned vessels.
2025,47(19): 99-106 收稿日期：2024-10-21
DOI：10.3404/j.issn.1672-7649.2025.19.016
分类号：U664.82; TP18
基金项目：山东省交通运输厅科技项目（2022B104）
作者简介：王孝帅(1999-),男,硕士研究生,研究方向为船舶路径规划与视景仿真
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