中国舰船研究2025，Vol.20Issue(1)：3-14,12.DOI:10.19693/j.issn.1673-3185.04348
无人舰艇智能航行技术进展与前沿
Advances and frontiers of key technologies in intelligent navigation for unmanned surface vehicles
摘要
Abstract
[Objectives]The rapid integration of artificial intelligence(AI)into maritime technology has driven unprecedented advancements in unmanned surface vehicles(USVs),positioning them as a crucial force in future maritime operations and military transformations.The intelligent navigation system is the core of USVs,responsible for environmental perception,decision-making,and motion control,which collectively en-able autonomous mission execution and integration into systematic operations.This study provides a compre-hensive review of the fundamental technologies underpinning USV intelligent navigation,critically evaluates existing challenges,and proposes future research directions to advance and expand the application of intelli-gent navigation technologies for USVs.The research aims to bridge existing knowledge gaps,providing a foundation for the further development of autonomous maritime systems.[Method]This research provides a comprehensive review of the current state of intelligent navigation technologies for USVs,focusing on three critical areas:environmental perception,decision-making and planning,and motion control.(a)In the domain of environmental perception,the primary sensing modalities include visible light,infrared,sonar,electromag-netic signals,navigation radar,and LiDAR.With advancements in multi-source information fusion techno-logy,perception techniques have evolved from relying on single sensors to utilizing multi-sensor fusion,trans-itioning from object-level fusion to feature-level fusion.Despite these advancements,achieving accurate and efficient environmental perception remains a key challenge.The ability to provide real-time,comprehensive environmental awareness is essential for USVs to navigate autonomously in complex maritime conditions.(b)For decision-making and planning,a variety of methodologies,including operations research,optimization al-gorithms,and AI-based approaches,have been employed to generate optimal decisions under multiple con-straints,such as mission parameters,payload configurations,and environmental factors.Existing technologies facilitate global path optimization,target tracking,and emergency collision avoidance under predefined condi-tions.However,challenges remain in multi-objective adversarial decision-making and path planning in highly dynamic and adversarial environments,especially under strong external interferences.The ability to enhance decision-making robustness in these scenarios is crucial for advancing autonomous USV capabilities.(c)In motion control,various algorithms such as proportional-integral-derivative(PID)control,model predictive control(MPC),model-free adaptive control(MFAC),linear quadratic regulators(LQR),robust control,and sliding mode control have been applied to achieve accurate trajectory tracking,course keeping,and speed reg-ulation.Current advancements allow for precise control under design conditions;however,adaptive control re-mains a challenge in scenarios with extreme environmental variations.Moreover,effective control of roll and pitch motions remains underdeveloped,limiting USV stability in high sea states.Motion control techniques serve as the foundation of USV intelligent navigation,ensuring the successful implementation of autonomous navigation systems.[Results]The study identifies key limitations.In environmental perception,while cur-rent technologies allow for target detection and identification in open seas,their accuracy significantly de-creases under adverse weather conditions,such as fog,heavy rain,and high sea states.Real-time wave field perception remains inadequate,further compromising navigation safety in dynamic operational conditions.Decision-making and planning algorithms,though effective in structured mission scenarios,struggle with the complexity of dynamic constraints,adversarial interactions,and unexpected environmental disturbances in real operations.Motion control strategies,while efficient under nominal operating conditions,require enhanced ad-aptability to handle sudden environmental shifts and complex vessel dynamics.The inability to manage roll and pitch movements effectively limits the operational capability of USVs in high sea states.[Conclusions]To address these challenges,the study proposes four key technological advancements and re-search directions.First,the development of high-precision six-degree-of-freedom(6-DoF)motion modeling for USVs will provide a robust framework for intelligent navigation algorithms.Second,the integration of large-scale AI models for multi-modal perception and decision-making will enhance autonomous situational awareness and adaptive response capabilities.Third,the advancement of high-sea-state adaptive navigation technologies through real-time wave observation and predictive motion modeling will significantly improve USV stability and safety in complex maritime environments.Additionally,the incorporation of real-time op-timization techniques will enhance navigation efficiency under operational constraints.These technological de-velopments will not only expand the application scope of USVs but also significantly improve their autonom-ous navigation capabilities.The study emphasizes the necessity of interdisciplinary research efforts that integ-rate AI-driven models,control theory,and maritime engineering to accelerate the development of fully autonomous USVs capable of performing in diverse operational conditions.关键词
无人艇/环境感知/传感器数据融合/决策规划/运动控制/运动规划/大模型Key words
unmanned vehicles/environment perception/sensor data fusion/decision-making and plan-ning/motion control/motion planning/large models分类
交通工程引用本文复制引用
楼建坤,徐蒙源,岳林,冯伟强,王鸿东..无人舰艇智能航行技术进展与前沿[J].中国舰船研究,2025,20(1):3-14,12.基金项目
国家自然科学基金面上项目资助(52271348) （52271348）
