Search Results (76)

This study proposes a safety-enhanced path planning system that integrates a Control Barrier Function (CBF)-based Safety Shield with Deep Reinforcement Learning (DRL). This framework addresses the critical limitations of conventional DRL-based Unmanned Surface Vehicle (USV) navigation models, which can output hazardous control commands in edge cases and violate the International Regulations for Preventing Collisions at Sea (COLREGs). The proposed system continuously operates during navigation via an Encounter Classifier that identifies multi-vessel situations (such as Head-on, Crossing, and Overtaking) in real time. The nominal control inputs generated by the DRL policy are verified and safely filtered through a Control Barrier Function-Quadratic Programming (CBF-QP) optimization layer immediately prior to execution, incorporating ship safety radii and asymmetric COLREGs constraints. Furthermore, we introduce a ‘Shielded Training’ mechanism that penalizes the agent based on the magnitude of the shield’s interventions during the training loop. This effectively diminishes the policy’s over-reliance on the safety filter and guides the network toward discovering robust, inherently safe trajectories. Extensive simulations conducted under diverse single- and multi-vessel encounter scenarios quantitatively demonstrate that the proposed method substantially reduces collision and COLREGs violation rates compared to baseline DRL-only or reward-shaping methods, while maintaining excellent computational scalability and real-time responsiveness. Consequently, by unifying the adaptive environmental exploration of reinforcement learning with model-based runtime safety constraints derived from control theory, this study provides a practical runtime assurance framework for future marine deployment. Full article

To address the lack of compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) in conventional timed elastic band (TEB)—based dynamic path planning for autonomous surface vessels (ASVs), this study proposes an intelligent path-planning framework that integrates an improved jump point search (JPS) algorithm with a COLREGs-aware TEB approach. At the global planning layer, an enhanced JPS algorithm incorporating redundant-node elimination and cubic Bezier curve smoothing is developed to construct the JPS–PB method, thereby reducing path redundancy and improving trajectory continuity. At the local planning layer, COLREGs encounter rules are reformulated as differentiable cost functions and embedded into the TEB optimization framework, enabling rule-compliant collision avoidance in typical maritime encounter scenarios, including head-on, crossing, and overtaking situations. Simulation results demonstrate that, compared with the conventional JPS algorithm, the proposed JPS-PB method reduces the path length by up to 8.6%, decreases the cumulative steering angle by 58.6%, and lowers the maximum steering angle by 88.6% on a 12 × 12 grid environment. Furthermore, compared with the conventional TEB algorithm, the proposed COLREGs–aware TEB (CTEB) increases the Distance at the Closest Point of Approach (DCPA) by 543%, 20.5%, 4.8%, and 13.8% in starboard-crossing encounters, head-on, overtaking, and special port-side-crossing encounter scenarios, respectively. Parameter sensitivity analyses further indicate that CTEB maintains stable collision-avoidance performance and rule-compliance capability under variations in the danger–distance threshold, danger–time threshold, and COLREGs cost weight. The results verify that the proposed JPS–PB+CTEB hybrid framework effectively improves path quality, dynamic obstacle-avoidance safety, and navigation-rule compliance for autonomous ASV operations. Full article

To address the limitations of existing perception methods for nighttime encounter situation recognition of unmanned surface vessels (USVs), this study proposes an image-based method for navigation-light recognition and encounter situation recognition. In accordance with the International Regulations for Preventing Collisions at Sea (COLREGs), a parameterized 3D geometric model of vessel navigation lights and encounter scenario models is established. Based on the camera imaging principle, a dataset of navigation-light images under various encounter situations is generated through simulation experiments. By analyzing the variation patterns of navigation-light images in different encounter situations, a feature vector composed of area-domain and azimuth-domain features is constructed, and an encounter situation recognition method is developed accordingly. To mitigate the effects of water reflections and interfering light sources in real images, a navigation-light image-processing method is designed for the stable extraction of feature parameters. Simulation results show that the classification accuracy ranges from 96.6% to 98.3% at different distance conditions. In field experiments conducted with a small USV under a three-light configuration, the proposed method achieves a navigation-light recognition accuracy of 96.2% and an encounter situation recognition accuracy of 94.94%. The proposed method provides an interpretable and lightweight complementary visual solution for nighttime encounter situation recognition, complementing existing nighttime perception technologies. Full article

In complex maritime environments, the safety of path planning for Unmanned Surface Vehicles (USVs) remains a significant challenge. Existing methods for handling dynamic obstacles often suffer from inadequate predictability and generate non-smooth trajectories. To address these issues, this paper proposes a reliable hybrid path planning approach that integrates a reinforcement learning-enhanced A* algorithm with an improved Dynamic Window Approach (DWA). Specifically, the A* algorithm is augmented by incorporating a dynamic five-neighborhood search mechanism, a reinforcement learning-based adaptive weighting strategy, and a path post-optimization procedure. These enhancements collectively shorten the path length and significantly improve trajectory smoothness. While ensuring that the global path avoids dynamic obstacles smoothly, a Kalman Filter (KF) is integrated into the Long Short-Term Memory (LSTM) network to preprocess historical data. This mechanism suppresses transient outliers and stabilizes the trajectory prediction of dynamic obstacles. Moreover, the evaluation function of the DWA is refined by incorporating the International Regulations for Preventing Collisions at Sea (COLREGs) constraints, enabling compliant navigation behaviors. Simulation results in MATLAB demonstrate that the enhanced A* algorithm better conforms to the kinematic model of the USVs. The improved DWA significantly reduces collision risks, thereby ensuring safer navigation in dynamic marine environments. Full article

With the growing demand for safe obstacle avoidance and precise trajectory tracking in the autonomous navigation of unmanned surface vessels (USVs), this paper investigates an adaptive differential evolution approach for integrated path planning and tracking control. In the path planning stage, an elite archive mechanism is first incorporated into the mutation process, and the scaling factor F and crossover rate $CR$ are adaptively adjusted to enhance population diversity and global search capability. Then, the International Regulations for Preventing Collisions at Sea (COLREGs) are embedded into the algorithmic framework to reinforce collision avoidance performance in complex encounter scenarios. A multi-objective fitness function combining six performance criteria is subsequently constructed to evaluate individual path points, thereby identifying high-quality solutions that ensure both safe navigation and route efficiency. In the tracking control stage, the optimally generated reference trajectory is then employed as the input command for the vessel’s motion control subsystem. A fuzzy logic system is introduced to approximate unknown nonlinear dynamics, and an adaptive fuzzy logic controller is designed to guarantee accurate tracking of the planned path. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness. Full article

The growing adoption of Unmanned Surface Vessels (USVs) in commercial and defense domains raises challenges for safe navigation and strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). This paper presents the implementation and field validation of three collision-avoidance approaches on a real USV: (i) behavior-based, (ii) a modified Velocity Obstacles (VO) algorithm, and (iii) a modified A* path-planning algorithm. Field trials in Guanabara Bay (Brazil) show that the behavior-based algorithm achieved the best balance between safety and efficiency, maintaining a safe mean Closest Point of Approach (30.0 m) while minimizing operational penalties: shortest total distance (179.4 m average), lowest mission completion time (174.7 s average), and smallest trajectory deviation (27.2% average increase). The VO algorithm operated with reduced safety margins (13.0 m average CPA) at the expense of larger detours (37.6% average distance increase), while the modified A* maintained equivalent safety (30.0 m average CPA) but produced the largest deviations (46.5% average increase). The trade-off analysis reveals that algorithm selection depends on operational priorities between safety margins and route efficiency. Full article

Over the past fifteen years, ship manoeuvring has evolved from a highly specialised branch of marine hydrodynamics into a key enabler within multidisciplinary research, integrating seakeeping and intact stability, and paving the way for digital twins and autonomous maritime systems. The scope of this review is to examine the existing literature in a way that paves the way forward for integration with robotics, aerial and surface drones, digital-twin (DT) ecosystems, and other interconnected autonomous platforms. This paper reviews the published articles during this period, tracing the field’s progression from classical hydrodynamic models to intelligent, data-centric, and regulation-aware maritime systems. Drawing on a structured bibliometric dataset covering 2010–2025, this study organises the literature into interconnected themes spanning physics-based manoeuvring models, adaptive and predictive control, machine learning and digital-twin (DT) technologies, collision-avoidance and regulatory reasoning, environmental performance, and cooperative autonomy. The analysis reveals the transition from static empirical modelling toward hybrid physics, artificial intelligence (AI) frameworks capable of capturing nonlinear dynamics, uncertainty, and multi-vessel interactions. At the same time, this review highlights the growing influence of Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the Second-Generation Intact Stability Criteria, and emissions-reduction targets in shaping technical developments. While learning-enabled prediction, model predictive control (MPC)-based regulatory compliance, and real-time DT synchronisation show increasing maturity, this study identifies unresolved challenges, including domain shift, model interpretability, certification barriers, multi-agent safety guarantees, and DT divergence under sparse data. By mapping both demonstrated capabilities and conceptual frontiers, this review presents manoeuvring as a central pillar of future Maritime Autonomous Surface Ships (MASS) operations and sustainable shipping. The findings outline a research agenda toward integrated, explainable, and environmentally aligned manoeuvring intelligence that can support safe, efficient, and regulation-compliant autonomous maritime systems. Full article

This comprehensive review examines the works of reinforcement learning (RL) in ship collision avoidance (SCA) from 2014 to the present, analyzing the methods designed for both single-agent and multi-agent collaborative paradigms. While prior research has demonstrated RL’s advantages in environmental adaptability, autonomous decision-making, and online optimization over traditional control methods, this study systematically addresses the algorithmic improvements, implementation challenges, and functional roles of RL techniques in SCA, such as Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Multi-Agent Reinforcement Learning (MARL). It also highlights how these technologies address critical challenges in SCA, including dynamic obstacle avoidance, compliance with Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), and coordination in dense traffic scenarios, while underscoring persistent limitations such as idealized assumptions, scalability issues, and robustness in uncertain environments. Contributions include a structured analysis of recent technological evolution, and a Large Language Model (LLM) based hierarchical architecture integrating perception, communication, decision-making, and execution layers for future SCA systems, which prioritizes the development of scalable, adaptive frameworks that ensure robust and compliant autonomous navigation in complex, real-world maritime environments. Full article

Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article

The increasing complexity of maritime traffic imposes growing demands on the safety and rationality of ship-collision-avoidance decisions. While most existing research focuses on simple encounter scenarios, autonomous collision-avoidance strategies that comply with the International Regulations for Preventing Collisions at Sea (COLREGs) in complex multi-ship environments remain insufficiently investigated. To address this gap, this study proposes a novel collision-avoidance framework that integrates a quantitative COLREGs analysis with a distributed stochastic search mechanism. The framework consists of three core components: encounter identification, safety assessment, and stage classification. A cost function is employed to balance safety, COLREGs compliance, and navigational efficiency, incorporating a distance-based weighting factor to modulate the influence of each target vessel. The use of a distributed stochastic search algorithm enables decentralized decision-making through localized information sharing and probabilistic updates. Extensive simulations conducted across a variety of scenarios demonstrate that the proposed method can rapidly generate effective collision-avoidance strategies that fully comply with COLREGs. Comprehensive evaluations in terms of safety, navigational efficiency, COLREGs adherence, and real-time computational performance further validate the method’s strong adaptability and its promising potential for practical application in complex multi-ship environments. Full article

Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) represents the first focus in this study. To provide insight into the level of understanding and knowledge regarding COLREG Rule 10, a customized, worldwide survey has been created and disseminated among marine industry professionals. The survey results reveal a notable knowledge gap in Rule 10, where we initially assumed that more than half of the respondents know COLREG regulations well. According to the probability calculation and chi-square test results, all three categories (OOW, Master, and others) have significant rule misunderstanding. In response to the COLREG misunderstanding, together with the increasing density of maritime traffic, the implementation of Decision Support Systems (DSS) in navigation has become crucial for ensuring compliance with regulatory frameworks and enhancing navigational safety in general. This study presents a structural approach to vessel prioritization and decision-making within a DSS framework, focusing on the classification and response of the own vessel (OV) to bow-crossing scenarios within the TSS. Through the real-time integration of AIS navigational status data, the proposed DSS Architecture offers a structured, rule-compliant architecture to enhance navigational safety and the decision-making process within the TSS. Furthermore, implementing a Fall-Back Strategy (FBS) represents the key innovation factor, which ensures system resilience by directing operator response if opposing vessels disobey COLREG rules. Based on the vessel’s dynamic context and COLREG hierarchy, the proposed DSS Architecture identifies and informs the navigator regarding stand-on or give-way obligations among vessels. Full article

Autonomous ship navigation systems must ensure safe and efficient route planning while complying with the International Regulations for Preventing Collisions at Sea (COLREGs). This paper presents an integrated navigation framework that combines chart-based global path planning with a Model Predictive Control (MPC) approach for local trajectory tracking and COLREG-compliant collision avoidance. The method generates feasible reference routes using maritime charts and predefined waypoints, while the MPC controller ensures precise path following and dynamic re-planning in response to nearby vessels and coastal obstacles. Coastal features and shorelines are modeled using Global Self-consistent, Hierarchical, High-resolution Geography data, enabling MPC to treat landmasses as static obstacles. Other vessels are represented as dynamic obstacles with varying speeds and headings, and COLREG rules are embedded within the MPC framework to enable rule-compliant maneuvering during encounters. To address real-time computational constraints, a simplified MPC formulation is introduced, balancing predictive accuracy with computational efficiency, making the approach suitable for embedded implementations. The navigation framework is implemented in a MATLAB-based simulation with real-time visualization supporting multi-vessel scenarios and COLREG-aware vessel interactions. Simulation results demonstrate robust performance across diverse maritime scenarios—including complex multi-ship encounters and constrained coastal navigation—while maintaining the shortest safe routes. By seamlessly integrating chart-aware path planning with COLREG-compliant, MPC-based collision avoidance, the proposed framework offers an effective, scalable, and robust solution for autonomous maritime navigation. Full article

Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (μ) and standard deviation (σ) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement. Full article

To address the challenges of manual buoy inspection, this study enhances a previously proposed Unmanned Surface Vehicle (USV) inspection system by improving its navigation and obstacle avoidance capabilities using Proximal Policy Optimization (PPO). For improved usability, the entire system adopts a fully end-to-end design, with an angular deviation weighting mechanism for stable circular navigation, a novel image-based radar encoding technique for obstacle perception and a decoupled navigation and obstacle avoidance architecture that splits the complex task into three independently trained modules. Experiments validate that both navigation modules exhibit robustness and generalization capabilities, while the obstacle avoidance module partially achieves International Regulations for Preventing Collisions at Sea (COLREGs)-compliant maneuvers. Further tests in continuous multi-buoy inspection tasks confirm the architecture’s effectiveness in integrating these modules to complete the full task. Full article

The autonomous decision-making model for ship navigation requires extensive interaction and trial-and-error in real, complex environments to ensure optimal decision-making performance and efficiency across various scenarios. However, existing approaches still encounter significant challenges in addressing the temporal features of state space and tackling complex dynamic collision avoidance tasks, primarily due to factors such as environmental uncertainty, the high dimensionality of the state space, and limited decision robustness. This paper proposes an adaptive temporal decision-making model based on reinforcement learning, which utilizes Long Short-Term Memory (LSTM) networks to capture temporal features of the state space. The model integrates an enhanced Proximal Policy Optimization (PPO) algorithm for efficient policy iteration optimization. Additionally, a simulation training environment is constructed, incorporating multi-factor coupled physical properties and ship dynamics equations. The environment maps variables such as wind speed, current velocity, and wave height, along with dynamic ship parameters, while considering the International Regulations for Preventing Collisions at Sea (COLREGs) in training the autonomous navigation decision-making model. Experimental results demonstrate that, compared to other neural network-based reinforcement learning methods, the proposed model excels in environmental adaptability, collision avoidance success rate, navigation stability, and trajectory optimization. The model’s decision resilience and state-space mapping align with real-world navigation scenarios, significantly improving the autonomous decision-making capability of ships in dynamic sea conditions and providing critical support for the advancement of intelligent shipping. Full article