Research on path planning algorithms for autonomous underwater vehicles in complex oceanic flow environments

Huang Jia; Shi Xiaodong; Liu Ziyun; Long Haowen; Chen Yuxiang; Shi Xiaotong

doi:10.11959/j.issn.2096-3750.2026.00541

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Research on path planning algorithms for autonomous underwater vehicles in complex oceanic flow environments

Smart Transportation and Intelligent Connectivity | 更新时间：2026-04-16

- Research on path planning algorithms for autonomous underwater vehicles in complex oceanic flow environments
- Chinese Journal on Internet of Things Vol. 10, Issue 1, Pages: 41-51(2026)
- 作者机构：
  
  1.海南大学海洋技术与装备学院，海南海口 570228
  2.湖北大学人工智能学院，湖北武汉 430415
  3.海南大学热带海洋工程材料及评价全国重点实验室，海南海口 570228
  4.昆明理工大学材料科学与工程学院，云南昆明 650093
- 作者简介：
- 基金信息：
  
  Technology Innovation Project of China Energy Investment Corporation Co., Ltd., “Research on Coupling Technology of Key Components for Seawater Hydrogen Production Electrolyzers”(RH2300008137)
- DOI：10.11959/j.issn.2096-3750.2026.00541
  CLC：
- Received：19 September 2025，
  
  Revised：2025-11-28，
  
  Accepted：28 November 2025，
  
  Published：30 March 2026
- 稿件说明：
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黄嘉,史晓东,刘紫芸等.面向复杂海洋流场环境的水下航行器路径规划算法研究[J].物联网学报,2026,10(01):41-51.

Huang Jia,Shi Xiaodong,Liu Ziyun,et al.Research on path planning algorithms for autonomous underwater vehicles in complex oceanic flow environments[J].Chinese Journal on Internet of Things,2026,10(01):41-51.
黄嘉,史晓东,刘紫芸等.面向复杂海洋流场环境的水下航行器路径规划算法研究[J].物联网学报,2026,10(01):41-51. DOI： 10.11959/j.issn.2096-3750.2026.00541.

Huang Jia,Shi Xiaodong,Liu Ziyun,et al.Research on path planning algorithms for autonomous underwater vehicles in complex oceanic flow environments[J].Chinese Journal on Internet of Things,2026,10(01):41-51. DOI： 10.11959/j.issn.2096-3750.2026.00541.

摘要

路径规划是自主水下航行器在动态海洋环境中实现高效与安全航行的关键。然而，在强剪切流与涡旋场等共存的复杂海洋环境中，自主水下航行器存在能耗过高、路径震荡与威胁规避不充分等问题。为此，首先，构建融合多物理场的海洋环境模型，对剪切流与涡旋场并存的复杂海洋环境进行高精度量化表征。其次，提出一种新颖的MFD-A*（multi-field-driven A*）算法，通过构建综合代价函数，将阻力能耗模型、航向协同模型与涡旋威胁场模型3类关键水动力约束协同嵌入A*搜索框架，实现能耗、航向稳定性与航行安全的全局优化。最后，仿真结果表明，在强剪切流并存的双涡旋和多涡旋海洋环境中，MFD-A*算法相较于A*算法，能耗分别降低了15.04%与22.89%，航向-流向平均夹角分别减少27.48%与34.2%，并且在两种环境中均实现了对涡旋核心区的100%规避。

Abstract

Path planning is critical for autonomous underwater vehicles to achieve efficient and safe navigation in dynamic ocean environments. However

in complex marine settings characterized by coexisting strong shear currents and vortex fields

autonomous underwater vehicles face challenges such as excessive energy consumption

path oscillations

and inadequate threat avoidance. To address these issues

firstly

a multi-physics-field-integrated ocean environment model was constructed

enabling high-fidelity characterization of complex marine environments featuring coexisting shear currents and vortex fields. Subsequently

a novel MFD-A* (multi-field-driven A*) algorithm was proposed. By formulating a comprehensive cost function

three key hydrodynamic constraints-a drag energy consumption model

a heading synergy model

and a vortex threat field model- were embedded into the A*-search framework

achieving global optimization of energy efficiency

heading stability

and navigation safety. Simulation results demonstrate that in ocean environments with strong shear currents combined with dual-vortex and multi-vortex configurations

the MFD-A* algorithm reduces energy consumption by 15.04% and 22.89%

respectively

compared to the standard A* algorithm. The average heading-current angle is reduced by 27.48% and 34.2%

while 100% avoidance of vortex core regions is achieved in both scenarios.

关键词

Keywords

references

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