A Review of Path Planning Optimization for Unmanned Surface Vehicles in Maritime Environments

CHEN Shuo

doi:10.12263/newIT.2024.04.005

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A Review of Path Planning Optimization for Unmanned Surface Vehicles in Maritime Environments

SURVEYS & TUTORIALS | 更新时间：2025-05-15

- A Review of Path Planning Optimization for Unmanned Surface Vehicles in Maritime Environments
- New Generation of Information Technology Vol. 7, Issue 4, Pages: 25-29(2024)
- 作者机构：
  
  武警士官学校，浙江杭州 311400
- 作者简介：
- 基金信息：
- DOI：10.12263/newIT.2024.04.005
  CLC： U674.9;TP301.6
- Accepted：12 March 2025，
  
  Published：15 April 2024
- 稿件说明：
移动端阅览
陈硕. 海上环境下无人艇路径规划优化方案综述[J]. 新一代信息技术, 2024, 7(4): 25-29

CHEN Shuo. A Review of Path Planning Optimization for Unmanned Surface Vehicles in Maritime Environments[J]. New Generation of Information Technology, 2024, 7(4): 25-29
陈硕. 海上环境下无人艇路径规划优化方案综述[J]. 新一代信息技术, 2024, 7(4): 25-29 DOI： 10.12263/newIT.2024.04.005.

CHEN Shuo. A Review of Path Planning Optimization for Unmanned Surface Vehicles in Maritime Environments[J]. New Generation of Information Technology, 2024, 7(4): 25-29 DOI： 10.12263/newIT.2024.04.005.

摘要

随着海洋环境复杂性和现代战争需求的提升，无人艇在军事、科研和商业领域的应用不断扩大，尤其在海上作战中展现出精准打击的重要价值。本文研究无人艇在复杂海上环境中的目标打击性能，聚焦路径规划优化、打击精度提升和作战协同方式。基于遗传算法（Genetic Algorithm，GA）、粒子群优化（Particle Swarm Optimization，PSO）和强化学习（Reinforcement Learning，RL），提出路径规划优化方案，并分析无人艇在动态环境适应、通信、自主性与智能化等方面的技术挑战。最后，展望无人艇在高效路径规划、协同作战及跨领域应用中的未来发展方向，为提升其作战能力提供理论支持。

Abstract

With the increasing complexity of marine environments and the demands of modern warfare

unmanned surface vehicles (USVs) are seeing expanded applications in military

research

and commercial fields

particularly demonstrating significant value in precision strikes during maritime operations. This paper investigates the target-strike performance of USVs in complex marine environments

focusing on path planning optimization

strike accuracy enhancement

and collaborative combat strategies. It proposes path planning solutions based on genetic algorithm (GA)

particle swarm optimization (PSO)

and reinforcement learning (RL)

while analyzing technical challenges such as dynamic environment adaptation

communication issues

autonomy

and intelligence. Furthermore

the paper explores future developments in efficient path planning

collaborative operations

and cross-domain applications

providing theoretical support to enhance USV combat capabilities.

关键词

Keywords

references

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