Research on unmanned surface vessels (USVs) has evolved significantly in recent decades. In particular, intelligent navigation technology has progressed from theoretical concepts to practical applications. As USV research in ocean engineering advances, there is an increasing demand for enhanced performance in intelligent guidance strategy and path-following control systems. This manuscript proposes future development directions for USVs by providing an overview of relevant standards for the intelligence level of these vessels and describing the current status of USV engineering practices. Based on practical ocean engineering requirements, safety considerations, and energy efficiency demands, this paper summarizes the current research status, future research challenges, and potential solutions for USV intelligent guidance and path-following control algorithms from the perspective of large ship intelligence. This manuscript provides a valuable reference for academic researchers and practitioners aiming to identify and position future development directions.

Complex multi-vessel encounter situations are a challenging problem for ships to avoid collisions, and the International Regulations for Preventing Collision at Sea, 1972 (COLREGs) do not provide a clear delineation of multi-vessel encounter situations and the responsibility of collision avoidance (CA). Furthermore, Marine Autonomous Surface Ships (MASS), which realize autonomous navigation functions, face the problem of recognizing complex multi-ship encounter situations and the corresponding CA decisions. In this study, we adopt the velocity obstacle (VO) algorithm to visualize and identify the danger of multi-ship encounters with the own ship (OS) as the first viewpoint. Additionally, we consider the motion changes in target ships (TSs) and their possible CA behaviors as the basis of the ship’s CA decision-making. According to COLREGs, a simplified method for classifying the encounter situations of multiple clustered ships is proposed, considering the coupling of collision hazards and CA responsibilities between related TSs. On this basis, the corresponding CA decisions for each classified situation are proposed, and a large number of simulation experiments are conducted based on the proposed method by considering the three-ship and four-ship encounter model in the Imazu problem as an example. The experimental results indicate that the proposed method can effectively recognize the complex multi-ship encounter situation in the Imazu problem, and it can adjust the CA measures of the OS in time according to the COLREGs and the behavior of TSs. This provides the basis and reference for MASS when facing complex multi-ship encounter situations.

Autonomous path planning and collision avoidance (CA) in complex maritime environments are critical challenges for maritime autonomous surface ships (MASSs). This article presents a novel hierarchical motion planning framework that integrates global and local path-planning algorithms. For global path planning in static-obstacle environments, a hybrid approach combining A*, Bezier curves, and particle swarm optimization (PSO) is proposed to enhance path search efficiency and ensure smooth trajectory optimization. For local path planning and CA with dynamic vessels, a multiship CA algorithm based on velocity prediction potential fields (VPPFs) is introduced. A new collision-risk identification model is developed to ensure compliance with COLREGs and enable collaborative CA in complex multiship scenarios. The framework also addresses environments with both dynamic and static obstacles through a dynamic path planner that integrates global and local planning. A key innovation is the dynamic CA decision making mechanism with alteration of course and/or speed (ACS), which provides robust real-time navigation strategies. Validation through simulations, real-ship model tests, and automatic identification system (AIS)-data analysis demonstrates the framework’s robustness and adaptability for MASS navigation. Compared to existing methods, the proposed approach significantly reduces collision-avoidance path length while maintaining safety, real-time performance, and COLREGs compliance. This work advances autonomous maritime navigation by addressing complex scenarios with a unified framework that balances efficiency, safety, and regulatory adherence.

Achieving autonomous navigation requires Maritime Autonomous Surface Ships (MASS) to overcome difficulties in recognizing intricate multi-ship encounter situations and developing appropriate collision avoidance strategies. To address the above issues, this study first integrates the Velocity Obstacle (VO) with Closest Point of Approach/Time to Closest Point of Approach (CPA/TCPA) into the collision risk identification of ships. This enables the own ship (OS) to determine the risk posed by target ships (TSs) in the scenario. Secondly, the paper further discusses complex multi-ship encounter scenarios, based on 1972 International Regulations for Preventing Collision at Sea (COLREGs), it classifies responsibilities to determine the set of give-way ship and stand-on ship obligations. Finally, the derived responsibility set is incorporated into Dynamic Window Approach (DWA), allowing the enhanced Multi-Vessels Velocity Obstacle and Improved Dynamic Window Approach (MVO-IDWA) algorithm to automatically select optimal decisions in complex multi-ship encounter scenarios and ensure the ship reaches its destination safely. Centering on the challenge of avoiding collision decision in multi-ship complex encounter scenarios, this paper proposes the integration of ship responsibility sets with the MVO-IDWA algorithm. Analysis of the results establishes that the proposed algorithm can consider the inter-ship responsibilities, risk levels, and movement trends of TSs in multi-ship encounters, thereby realizing autonomous ship collision avoidance.