AI驱动的海上无人系统决策与控制研究进展综述

邓英杰; 徐艺菲; 闫敬; 赵丁选; 李梦霞

doi:10.11993/j.issn.2096-3920.2025-0095

AI驱动的海上无人系统决策与控制研究进展综述

doi: 10.11993/j.issn.2096-3920.2025-0095

邓英杰^{1, 2,},
徐艺菲^2,,
闫敬³,
赵丁选^{1, 2},
李梦霞⁴

1.
燕山大学起重机械关键技术全国重点实验室, 河北秦皇岛, 066004
2.
燕山大学机械工程学院, 河北秦皇岛, 066004
3.
燕山大学电气工程学院, 河北秦皇岛, 066004
4.
武汉理工大学水路交通控制全国重点实验室, 湖北武汉, 430063

基金项目: 国家自然科学基金项目(52101375); 河北省自然科学基金项目(E2024203179); 河北省高等学校科学研究项目(BJ2025114)资助.

详细信息

作者简介:
邓英杰(1993-), 男, 博士, 副教授, 主要研究方向为无人帆船及海上无人系统自主决策与控制

通讯作者:
徐艺菲(1993-), 女, 博士, 副教授, 主要研究方向为海上无人系统自主决策与控制.

中图分类号: TJ630.32; U674.941
计量
- 文章访问数: 31
- HTML全文浏览量: 11
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2025-07-25
- 修回日期: 2025-09-13
- 录用日期: 2025-09-26
- 网络出版日期: 2026-01-14

Review of Research Progress on AI Driven Decision and Control of Maritime Unmanned Systems

DENG Yingjie^{1, 2
,},
XU Yifei^2
,,
YAN Jing³,
ZHAO Dingxuan^{1, 2},
LI Mengxia⁴

1.
State Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao 066004, China
2.
School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
3.
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
4.
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China

摘要

摘要: 海上无人系统是指具有自主作业能力的智能化水面、水下以及空中无人平台, 采用AI技术提升海上无人系统的决策与控制水平是未来的必然发展趋势。尽管人工智能(AI)技术已经取得了长足的发展, 其应用于海上无人系统仍受到环境干扰和系统特性的诸多制约。文中首先阐述了海上无人系统决策与控制的基本架构, 并总结了传统技术的不足之处, 接下来, 介绍了各国AI驱动海上无人系统的发展现状, 梳理总结了AI在环境感知与定位、路径规划与制导、运动控制以及多系统协同等关键技术上的研究进展及存在的问题。最后, 讨论了AI支持海上无人系统决策与控制的挑战与发展机遇。
- 海上无人系统 /
- AI技术 /
- 环境感知与定位 /
- 路径规划与制导 /
- 运动控制 /
- 多系统协同
Abstract: Maritime unmanned systems refer to intelligent unmanned platforms on the water surface, underwater, and in the air with autonomous operation capabilities. It is an inevitable development trend in the future to adopt artificial intelligence(AI) technology to improve the decision-making and control level of maritime unmanned systems. Although AI technology has made considerable progress, its application in maritime unmanned systems is still restricted by many factors such as environmental interference and system characteristics. The basic decision-making and control framework of maritime unmanned systems are illustrated at first, and the shortcomings of traditional techniques are summarized. Then, the development status of AI-driven maritime unmanned systems in various countries is expounded, and the research progress and existing problems of AI in key technologies including environmental perception and positioning, path planning and guidance, motion control, and multi-system collaboration are summarized. Finally, the challenges and development opportunities of AI supporting the decision-making and control of maritime unmanned systems are discussed.
- maritime unmanned systems /
- AI technique /
- environmental sensing and localization /
- path planning and guidance /
- motion control /
- coordination of multiple systems

HTML全文

图 1 海上无人系统决策与控制框架

Figure 1. Decision and control framework of unmanned system at sea

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图 2 路径规划、制导、控制逻辑关系

Figure 2. Logical relationship among path planning, guidance and control

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图 3 基于BEV的多传感器数据融合

Figure 3. BEV-based data fusion of multiple sensors

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图 4 水下传感器节点信息采集任务说明

Figure 4. Task theory of underwater sensor node information collection

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图 5 云边协同海上无人系统立体决策架构

Figure 5. Three dimensional decision-making architecture of cloud edge collaborative unmanned maritime system

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表 1 海上无人系统决策与控制综述对比

Table 1. Review and comparison of decision and control of unmanned systems at sea

文献	题目	研究对象	主要内容	不足
[1]	水下无人系统跨域协同控制: 研究进展与挑战	跨域集群	从信息交互、任务部署、路径规划及协同控制等多角度分析水下无人系统跨域协同控制的现状及难点。	偏重于跨域集群的智能化控制。
[2]	海上无人系统跨域集群发展现状及其关键技术	跨域集群	分析海上无人系统跨域集群的关键问题, 从任务规划、组网传输及协同控制的角度综述海上跨域集群现状。	偏重于跨域集群的总体决策与控制问题。
[8]	新一代航运系统的未来船舶技术展望	水面船舶	针对船—岸—云的技术构架展开分析, 综合分析船端、岸端以及通信的关键技术。	偏重于水面船, 未能论述具体技术。
[9]	水域无人系统平台自主航行及协同控制研究进展	无人船	从智能感知、自主航行及协同控制等角度分析了水域无人系统决策与控制的现状及机遇。	偏重于无人船的智能化决策与控制。
[10]	无人帆船研究现状与展望	无人帆船	从船体结构、操纵特性与航行控制的角度分析无人帆船装备的发展现状及未来趋势。	偏重于无人帆船装备和控制算法。
[11]	海上无人系统发展及关键技术研究	无人系统	从战略规划、装备研发以及关键技术等角度分析海上无人系统的发展现状, 并提出对策建议。	偏重于作战规划, 未能论述具体技术。
[12]	海上无人机及蜂群作战指挥控制系统发展	无人机	从作战应用和指挥系统的角度分析海上无人机发展现状及未来启示。	偏重于指挥系统, 未能论述具体技术。
[13]	Intelligent decision and planning for unmanned surface vehicle: A review of machine learning techniques	无人船	提出一种机器学习——任务部署, 动态决策, 路径规划的无人船决策与控制架构, 分析该架构下基于机器学习的研究进展及相关挑战。	仅针对无人船决策与控制问题, AI技术上仅考虑机器学习, 未涉及通信层面的决策。
[14]	A comprehensive review of datasets and deep learning techniques for vision in unmanned surface vehicles	无人船	针对水面无人船视觉技术, 从数据集、深度学习算法两个角度展开论述, 分析不同传感器的视觉数据特点, 以及单传感器及多传感器融合技术。	近针对无人船的智能感知问题, AI技术上关注深度学习。
[15]	Intelligent motion control of unmanned surface vehicles: A critical review	无人船	针对无人船智能控制方法, 分析控制模型、任务分类及关键难点, 分别论述了基于神经网络、模糊逻辑系统、强化学习、自适应动态规划技术的研究现状。	仅针对无人帆船的智能控制问题, 偏重于非线性控制理论。
[16]	Artificial intelligence algorithms in unmanned surface vessel task assignment and path planning: A survey	无人船	针对无人船的路径规划和任务分配问题, 论述AI技术的应用, 分析限制性因素及未来发展趋势。	偏重于无人船的路径规划和任务分配, 未涉及通信层面的决策。

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表 2 AI与AGI应用于海上无人系统不同能力维度的对比

Table 2. Comparison of different capability dimensions of AI and AgI applied to unmanned systems at sea

对比维度	传统AI驱动海上无人系统	AGI驱动海上无人系统
跨模态感知能力	依赖单一模态或简单数据拼接, 恶劣海况下易失效, 无法互补修正	基于Transformer等架构实现视觉、激光点云、声呐等多模态深度关联, 强光/遮挡场景下可自主调整融合权重, 鲁棒性较高
群体行为预测能力	基于预设规则, 无法预判群体内冲突(如无人机交汇), 对突发干扰时响应滞后	通过图神经网络构建全局交互模型, 实时整合设备状态与环境数据, 提前预测冲突风险, 生成避让策略
任务推理能力	按固定流程执行(如预设巡逻路线), 突发状况下需人工干预, 无法自主切换任务优先级	结合知识图谱与强化学习, 动态规划备选方案, 可实现分层决策及任务自组织学习
环境适应能力	仅适配训练场景, 新场景需重新标注数据、训练模型, 适应周期长	通过元学习快速迁移知识, 新场景下仅需少量数据即可适配, 适应周期短
故障容错能力	单设备故障时(如传感器失效)整体系统性能骤降, 需人工排查故障点, 恢复时间较长	多模态数据交叉验证(如视觉失效时用雷达补位), 自主定位故障源并切换备用方案, 恢复时间短

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