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 enable autonomous mission execution and integration into systematic operations. This study provides a comprehensive 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 intelligent 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, electromagnetic signals, navigation radar, and LiDAR. With advancements in multi-source information fusion technology, perception techniques have evolved from relying on single sensors to utilizing multi-sensor fusion, transitioning 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 algorithms, and AI-based approaches, have been employed to generate optimal decisions under multiple constraints, such as mission parameters, payload configurations, and environmental factors. Existing technologies facilitate global path optimization, target tracking, and emergency collision avoidance under predefined conditions. 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 regulation. Current advancements allow for precise control under design conditions; however, adaptive control remains 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 current technologies allow for target detection and identification in open seas, their accuracy significantly decreases 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 adaptability 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 research 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 optimization techniques will enhance navigation efficiency under operational constraints. These technological developments will not only expand the application scope of USVs but also significantly improve their autonomous navigation capabilities. The study emphasizes the necessity of interdisciplinary research efforts that integrate AI-driven models, control theory, and maritime engineering to accelerate the development of fully autonomous USVs capable of performing in diverse operational conditions.