Search Results (101)

Maritime Autonomous Surface Ships (MASSs) are expected to become vital participants in future maritime commerce and ocean development activities. This paper investigates a channel capacity-based scheme for estimating the persistence of ship-to-ship communication links in MASS communication scenarios. Specifically, this study presents a relative motion model for nodes within the network and estimates link persistence based on the dynamic characteristics of the links. Additionally, transmission modes tailored to maritime communication scenarios are proposed to optimize link capacity and reduce interference. Simulation results demonstrate that the proposed method can accurately estimate the duration and capacity of the links, thereby achieving higher network capacity. When used as a metric for routing protocols, the proposed link-persistence measure outperforms traditional metrics in terms of packet loss ratio, end-to-end delay, and throughput. Comparisons with other mobility models show that the proposed mobility model offers greater accuracy and reliability in describing the relative mobility of nodes. 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

Maritime Autonomous Surface Ships (MASSs) incorporate advanced digital technologies, thus rendering their systems more complex and diverse than those of conventional ships. Furthermore, the operation of MASSs, which introduces new risks not encountered in conventional ship operations, differs significantly from that of conventional manned vessels. These challenges highlight the necessity for a more systematic and structured approach to risk analysis and control. This study presents a qualitative risk assessment methodology to identify and manage hazardous scenarios associated with MASS operations systematically. The key feature of the proposed methodology is the integration of cognitive model-based functional analysis with the widely adopted hazard identification (HAZID) method, which enables a structured and comprehensive analysis process. Functional analysis is used to examine the functions required for MASS operations and to analyze interconnected systems to fulfill these functions. Subsequently, HAZID is performed to identify hazardous scenarios that may cause functional degradation or failure. To illustrate the proposed methodology, a case study is presented based on a qualitative risk assessment conducted in preparation for the field trial of an Autonomous Navigation System. Practical applications, including the presented case study, demonstrated the effectiveness of this methodology as a systematic tool for identifying and evaluating potentially hazards in MASS operations. Full article

Maritime communication networks are critical for supporting the increasing demands of oceanic and coastal activities, including shipping, fishing, and offshore operations. However, traditional systems face significant challenges in providing reliable, high-throughput connectivity due to dynamic sea environments, mobility, and non-line-of-sight (NLoS) conditions. Reconfigurable intelligent surfaces (RISs) have been proposed as a promising solution to overcome these limitations by enabling programmable control of electromagnetic wave propagation in next-generation mobile communication networks, such as beyond fifth generation and sixth generation ones (B5G/6G). This paper presents a deep learning-based (DL) scheme for beam selection in RIS-aided maritime next-generation networks. The proposed approach leverages deep learning to optimize beam selection dynamically, enhancing signal quality, coverage, and network efficiency in complex maritime environments. By integrating RIS configurations with data-driven insights, the proposed framework adapts to changing channel conditions and potential vessel mobility while minimizing latency and computational overhead. Simulation results demonstrate significant improvements in both machine learning (ML) metrics, such as beam selection accuracy, and overall communication reliability compared to traditional methods. More specifically, the proposed scheme reaches around 99% Top-$K$ Accuracy levels while jointly improving energy efficiency (ee) and spectral efficiency (SE) by approx. 2 times compared to state-of-the-art approaches. This study provides a robust foundation for employing DL in RIS-aided maritime networks, contributing to the advancement of intelligent, high-performance wireless communication systems for advanced maritime applications, such as autonomous mooring, the autonomous approach, and just-in-time arrival (JIT). Full article

The autonomy of transport systems presents a transformative opportunity to enhance logistics efficiency, improve safety, and support decarbonization. In the maritime sector, the International Maritime Organization (IMO) has been working since 2016 to develop a mandatory regulatory framework for Maritime Autonomous Surface Ships (MASSs), aiming to finalize a comprehensive code. Simultaneously, pilot projects are underway in national waters under the oversight of national administrations. Naval applications of autonomous ships demonstrate their potential, as emerging doctrines highlight their strategic and operational advantages. Although the military sector is not governed at the international level, safely managing interactions between military and commercial MASSs is crucial for ensuring safe navigation. Classification societies play a vital role in achieving high safety standards and ensuring regulatory compliance. This study aims to propose a framework for certifying maritime autonomous vessels. Through a thorough analysis of the existing literature and by identifying gaps, this study outlines a structured pathway to facilitate the certification and operation of MASSs, addressing key technical, operational, and safety considerations. This research contributes to designing a risk-informed approach for the development of autonomous surface vehicles. Full article

To address the dynamic and robust performance limitations of gas turbines in maritime autonomous surface ship applications, this paper proposes a novel nonsmooth fuzzy active disturbance rejection control (NS_FADRC) algorithm. This method combines the strengths of linear active disturbance rejection control (LADRC), nonsmooth control, and fuzzy adaptive control. First, the extended state observer (ESO) is improved by using the nonsmooth control method to enhance its convergence rate and estimation capability, while ensuring finite-time convergence characteristics. Next, fuzzy control logic is integrated to enhance the adaptability of the state error feedback (SEF), overcoming the limitations of traditional SEF in handling nonlinearities. The stability of the proposed control algorithm is further validated using Lyapunov stability analysis. Lastly, a Hardware-in-the-Loop (HIL) semi-physical simulation platform, based on automatic code generation technology, is developed to validate the algorithm’s performance. Experimental results demonstrate that, compared to the PID, FPID, and LADRC algorithms, the proposed NS_FADRC algorithm provides superior dynamic response during speed step tracking and excellent robust disturbance rejection performance in the presence of load disturbances, parameter uncertainties, and measurement noise. Full article

The cooperative control of multiple autonomous surface vehicles (ASVs) is a critical area of research due to its significant applications in maritime operations, such as search and rescue and environmental monitoring. However, challenges such as communication delays and dynamic topologies often hinder stable cooperative control in practical scenarios. This study addresses these challenges by developing a formation control method based on consensus theory, focusing on both formation control and time delay. First, a simplified ASV characteristic model is established, and a basic consensus control algorithm is designed and analyzed for stability, considering different communication topologies. Then, to handle delays, the formation control method is extended, and the stability of the revised algorithm is rigorously proven using the Lyapunov function. Simulation results demonstrate that the proposed control strategy effectively maintains formations, even in the presence of communication delays. In the end, comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed controller. Simulation results demonstrate that the proposed control strategy effectively maintains formations, even in the presence of communication delays, with a convergence time of approximately 100 s and a formation error stabilizing at around 7 m. This research lays a foundation for more reliable cooperative control systems for ships, with potential applications in a variety of maritime and autonomous systems. 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

This study deals with berth allocation for Maritime Autonomous Surface Ships (MASSs) in the context of the mixed operation of MASSs and manned vessels from the perspective of port-shipping companies’ collaboration. Two berth allocation strategies, namely the separated-type and the mixed-type, are proposed in this article. Two mixed integer nonlinear programming models aimed at minimizing the total docking cost of the vessels in the port and the waiting time for berths are developed and solved using Gurobi, respectively. A large-scale simulation of the mixed-type berth allocation model is carried out using an improved simulated annealing algorithm. Several experiments are conducted to test the effectiveness of the model and to draw insights for commercializing autonomous vessels. The presented results show that multi-objective modeling and optimization should be conducted from the collaboration of port-shipping companies, which is more efficient from the perspective of shipping companies or ports, respectively. When berth resources are limited or there is a high requirement for operational safety, the separated-type berth allocation strategy is more efficient. When the number of MASS-dedicated berths reaches a certain proportion, the total docking cost of the vessel no longer changes, indicating that more dedicated berths are not better. Full article

A hybrid probabilistic road map (PRM) path planning algorithm based on historical automatic identification system (AIS) information and Douglas–Peucker (DP) compression is proposed to address the issues of low path quality and the need for extensive sampling in the traditional PRM algorithm. This innovative approach significantly reduces the number of required samples and decreases path planning time. The process begins with the collection of historical AIS data from the autonomous vessel’s navigation area, followed by comprehensive data-cleaning procedures to eliminate invalid and incomplete records. Subsequently, an enhanced DP compression algorithm is employed to streamline the cleaned AIS data, minimizing waypoint data while retaining essential trajectory characteristics. Intersection points among various vessel trajectories are then calculated, and these points, along with the compressed AIS data, form the foundational dataset for path searching. Building upon the traditional PRM framework, the proposed hybrid PRM algorithm integrates the B-spline algorithm to smooth and optimize the generated paths. Comparative experiments conducted against the standard PRM algorithm, A*, and Dijkstra algorithms demonstrate that the hybrid PRM approach not only reduces planning time but also achieves superior path smoothness. These improvements enhance both the efficiency and accuracy of path planning for maritime autonomous surface ships (MASS), marking a significant advancement in autonomous maritime navigation. Full article

With the increasing proportion of ships in logistics and the growing prosperity of traffic in maritime, negotiation and cooperative collision avoidance between ships is becoming more and more essential for navigational safety. This paper proposes a Model Predictive Control method that utilizes intention data of the target ship and a quaternion ship domain model to achieve collision avoidance while considering COLREGs, named IQMPC. Firstly, by utilizing the intention data of other ships, trajectories of the own ship and the target ship are well predicted to detect potential collision risks and take optimal avoidance actions in advance while risks exist. Secondly, the quaternion ship domain with its adjacent area is divided into four different parts to reflect the urgency of ship encounters. Collision risk evaluation functions are designed to determine avoidance actions conforming to COLREGs. Thirdly, several different ship encounter scenarios were simulated based on IQMPC to verify its capability. Full article

The development of Maritime Autonomous Surface Ships (MASS) has seen significant advancements in recent years, yet there remains a lack of comprehensive studies that holistically address the architecture of autonomous navigation systems and explain the complexity of their individual elements. This paper aims to bridge this gap by conducting a literature review that consolidates key research in the field and presents a detailed architecture of autonomous navigation systems. The results of this study identify several major clusters essential to MASS navigation architecture, including (1) autonomous navigation architecture, (2) decision-making and action-taking system, (3) situational awareness and associated technologies, (4) sensor fusion technology, (5) collision avoidance subsystems, (6) motion control and path following, and (7) mooring and unmooring. Each cluster is further dissected into sub-clusters, highlighting the intricate and interdependent nature of the components that facilitate autonomous navigation. The implications of this study are vital for multiple stakeholders. Ship captains and seafarers must be prepared for new navigation technologies, while managers and practitioners can use this architecture to better understand and implement these systems. Researchers will find a foundation for future investigations, particularly in filling knowledge gaps related to autonomous ship operations. This study makes a substantial contribution by filling a critical gap in the maritime literature, offering a detailed explanation of the elements within autonomous navigation systems. Full article

Recently, several initiatives regarding maritime autonomous surface ships (MASSs) have been implemented worldwide. One of the fundamental technologies for attaining MASSs is the recognition and localization of surrounding ships. Traditional navigational instruments are inadequate for recognizing objects, and the authors investigated the potential of stereo vision. Conventional stereo camera systems are not suitable for localizing very distant objects. One proposed solution is to use an additional camera, thus using three-camera measurements of objects at long distances to reduce positional measurement errors, incorporating time-series averaging and keypoint-based techniques. This study evaluated experimentally the accuracy of measurements using three ship-mounted cameras. The accuracy and precision of stereo measurements depend on the distance between the camera positions, referred to as the baseline length. Conventional stereo cameras are typically used to measure objects at distances of up to 200 times the baseline length. This study indicates that, using trinocular stereo vision, a target ship at distances up to 2500 m, which is 500 times the baseline length, can be measured with an accuracy of approximately 5% of the RMSE. Full article

Currently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distributed resources such as shore-based facilities and cloud computing centers to execute a variety of ship applications. Due to the increasing number of MASS and asymmetrical distribution of traffic flows, the transportation management must design an efficient cloud–shore–ship collaboration framework and smart resource allocation strategy to improve the performance of the traffic network and provide high-quality applications to the ships. Therefore, we design a cloud–shore–ship collaboration framework, which integrates ship networking and cloud/edge computing and design the respective task collaboration process. It can effectively support the collaborative interaction of distributed resources in the cloud, onshore, and onboard. Based on the global information of the framework, we propose an intelligent resource allocation method based on Q-learning by combining the relevance, QoS characteristics, and priority of ship tasks. Simulation experiments show that our proposed approach can effectively reduce task latency and system energy consumption while supporting the concurrency of scale tasks. Compared with other analogy methods, the proposed algorithm can reduce the task processing delay by at least 15.7% and the task processing energy consumption by 15.4%. Full article

Autonomous berthing is a critical phase in the fully autonomous navigation process of MASS (Maritime Autonomous Surface Ship). However, the autonomous berthing stage of MASS is significantly influenced by environmental factors and involves a wide range of technical fields, making the technology not yet fully mature. Therefore, this paper addresses three key technological challenges related to ship path planning, guidance and motion control, as well as position and state perception. Additionally, it explores the decision-making and control system architecture for autonomous berthing of MASS. An effective autonomous berthing solution for MASS is proposed. Based on vessel berthing maneuvering, a decision-making algorithm for autonomous berthing is designed. The A-star algorithm is optimized, and an expected path for unmanned boat experiments is designed offline using this algorithm. Subsequently, an indirect ship guidance and motion control program is proposed based on a CFDL-MFAC (Compact Form Dynamic Linearization based Model-Free Adaptive Control) algorithm. Experimental results show that the proposed autonomous berthing decision-making and control system architecture can effectively assist the unmanned boat in achieving autonomous berthing and help it to berth in a stable and desirable state. Full article