Search Results (22)

Autonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with Deep Q-Networks (DQN) to achieve robust AUV autonomy. The MEO-BIT* component delivers efficient global path planning through (1) a multithreaded batch sampling mechanism for rapid state-space exploration, (2) heuristic-driven search accelerated by KD-tree spatial indexing for optimized path discovery, and (3) an energy-aware cost function balancing path length and steering effort for enhanced endurance. Critically, the DQN component facilitates dynamic obstacle detection and adaptive local navigation, enabling the AUV to adjust its trajectory intelligently in real time. This integrated approach leverages the strengths of both algorithms. The global path intelligence of MEO-BIT* is dynamically informed and refined by the DQN’s learned perception. This allows the DQN to make effective decisions to avoid moving obstacles. Experimental validation in a simulated Achao waterway (Chile) demonstrates the MEO-BIT* + DQN system’s superiority, achieving a 46% reduction in collision rates (directly reflecting improved detection and avoidance fusion), a 15.7% improvement in path smoothness, and a 78.9% faster execution time compared to conventional RRT* and BIT* methods. This work presents a robust solution that effectively fuses two key components: the computational efficiency of MEO-BIT* and the adaptive capabilities of DQN. This fusion significantly advances the integration of navigation with dynamic obstacle detection. Ultimately, it enhances AUV operational performance and autonomy in complex maritime scenarios. Full article

The underwater conditions of the coastal ecosystem require careful monitoring to anticipate potential environmental hazards. Moreover, the unique characteristics of the marine underwater environment have presented numerous challenges for the advancement of underwater sensor networks. Current studies have not extensively integrated Digital Twins with underwater sensor networks aimed at monitoring the marine ecosystem. Consequently, this study proposes a decision-making framework based on Underwater Digital Twins (UDTs) utilizing the Exponential Hyperbolic Crisp Adaptive Network-based Fuzzy Inference System (EHC-ANFIS). The process begins with the initialization and registration of an Underwater Autonomous Vehicle (UAV). Subsequently, data are collected from the sensor network and relayed to the UDT model. The optimal path is determined using Adaptive Pheromone Ant Colony Optimization (AP-ACO) to ensure efficient data transmission. Following this, data compression is achieved through the Sliding–Huffman Coding (SHC) algorithm. The Twisted Koblitz Curve Cryptography (TKCC) method is employed to enhance data security. Additionally, an Anomaly Detection System (ADS) is trained, which involves collecting and pre-processing sensor network data. A Radial Chart is then utilized for effective visualization. Anomalies are detected using the CosLU-Variational Shake-Long Short-Term Memory (CosLU-VS-LSTM) approach. For standard data, decision-making based on the UDT model is conducted using EHC-ANFIS, with a fuzzification duration of 21,045 milliseconds. Finally, alerts are dispatched to the Maritime Alert Command Centre (MACC). This approach enhances maritime communication and monitoring along coastal areas, with specific reference to the Coromandel Coast, thereby contributing to the protection of the coastal ecosystem. Full article

Underwater Wireless Sensor Networks (UWSNs) are pivotal for advancing maritime capabilities. These networks predominantly utilize acoustic communication, characterized by an open and shared acoustic channel and energy-limited underwater nodes, which underscores the critical importance of node authentication and management. Blockchain technology, recognized for its security, confidentiality, and traceability, is particularly suitable for scenarios requiring secure data exchange. This paper proposes a blockchain-based collaborative node authentication model tailored for clustered networks in UWSNs to tackle the challenges posed by the open nature of acoustic channels and the constrained energy resources of underwater nodes. Autonomous Underwater Vehicles (AUVs) are deployed as blockchain nodes to aid cluster heads in identity verification, while all underwater acoustic nodes are integrated as lightweight blockchain nodes, thus ensuring uniform management and authentication. Furthermore, this study enhances existing clustering algorithms to prolong the operational lifespan of the network and introduces a group signature and authentication mechanism tailored to the unique conditions of underwater blockchain edge computing. This mechanism includes a robust two-round block verification scheme designed to secure the blockchain against potential consensus algorithm attacks. Comprehensive simulations are presented, validating the effectiveness of the proposed group signature solution in enhancing the security and sustainability of underwater clustered networks. Full article

Autonomous underwater vehicles (AUVs) are critical components of current maritime operations. However, because of the complicated marine environment, AUVs are at significant risk of being lost, and such losses significantly impact the continuity and safety of aquatic activities. This article suggests a methodology for forecasting the trajectory of lost autonomous underwater vehicles (AUVs) based on GAT-ABiGRU. Firstly, the time-series data of the AUV are transformed into a graph structure to represent the dependencies between data points. Secondly, a graph attention network is utilized to capture the spatial features of the trajectory data, while an attention-based bidirectional gated recurrent unit network learns the temporal features of the trajectory data; finally, the predicted drift trajectory is obtained. The findings show that the GAT-ABiGRU model outperforms previous trajectory prediction models, is highly accurate and robust in drift trajectory prediction, and presents a new method for forecasting the trajectory of wrecked AUVs. Full article

Underwater autonomous path planning is a critical component of intelligent underwater vehicle system design, especially for maritime conservation and monitoring missions. Effective path planning for these robots necessitates considering various constraints related to robot kinematics, optimization objectives, and other pertinent factors. Sample-based strategies have successfully tackled this problem, particularly the rapidly exploring random tree star (RRT*) algorithm. However, conventional path-searching algorithms may face challenges in the marine environment due to unique terrain undulations, sparse and unpredictable obstacles, and inconsistent results across multiple planning iterations. To address these issues, we propose a new approach specifically tailored to the distinct features of the marine environment for navigation path planning of underwater vehicles, named bidirectional cached rapidly exploring random tree star (BCRRT*). By incorporating bidirectional path planning and caching algorithms on top of the RRT*, the search process can be expedited, and an efficient path connection can be achieved. When encountering new obstacles, ineffective portions of the cached path can be efficiently modified and severed, thus minimizing the computational workload while enhancing the algorithm’s adaptability. A certain number of simulation experiments were conducted, demonstrating that our proposed method outperformed cutting-edge techniques like the RRT* in several critical metrics such as the density of path nodes, planning time, and dynamic adaptability. Full article

The rise of the Internet of Things (IoT) has opened up exciting possibilities for new applications. One such novel application is the modernization of maritime communications. Effective maritime communication is vital for ensuring the safety of crew members, vessels, and cargo. The maritime industry is responsible for the transportation of a significant portion of global trade, and as such, the efficient and secure transfer of information is essential to maintain the flow of goods and services. With the increasing complexity of maritime operations, technological advancements such as unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and the Internet of Ships (IoS) have been introduced to enhance communication and operational efficiency. However, these technologies also bring new challenges in terms of security and network management. Compromised IT systems, with escalated privileges, can potentially enable easy and ready access to operational technology (OT) systems and networks with the same privileges, with an increased risk of zero-day attacks. In this paper, we first provide a review of the current state and modalities of maritime communications. We then review the current adoption of software-defined radios (SDRs) and software-defined networks (SDNs) in the maritime industry and evaluate their impact as maritime IoT enablers. Finally, as a key contribution of this paper, we propose a unified SDN–SDR-driven cross-layer communications framework that leverages the existing SATCOM communications infrastructure, for improved and resilient maritime communications in highly dynamic and resource-constrained environments. Full article

Task allocation is crucial for autonomous underwater vehicle (AUV) collaboration in multi-AUV maritime search and rescue missions. In real projects, there are possible target areas existing in task areas, which are not expected to be divided. Motivated by such a special situation, this paper proposes an area partitioning method to allocate the task to multiple AUVs and maintain the possible target area as a whole. First, the spatial structure of the task area is defined by the spiked Morse decomposition, which divides the task area according to a set of angles. Then, we perform a variational transformation to determine the optimal angles using the AUV order. Next, a customized backtracking method is introduced to determine the optimal AUV order which divides the task area among the multiple AUVs without disturbing the possible target areas. The proposed methodology is validated under various challenging scenarios using a different number of AUVs. The empirical results show that the divided possible target areas and workload variance were superior to the comparison methods. This indicates that the proposed method can generate stable solutions that effectively reduce the segmentation of possible target areas and keep the workload of the multiple AUVs balanced. Full article

Maritime unmanned systems (MUS) have gained widespread usage in a diverse range of hydrographic survey activities, including harbor/port surveys, beach and coastline monitoring, environmental assessment, and military operations. The present article explains a validated, rapid, and reliable technique for processing hydrographic data that was obtained via an autonomous hydrographic survey, and which was executed by a prototype unmanned surface vessel (USV) belonging to the Unmanned Survey Solutions (USS) corporation. The experimentation was part of the annual Multinational Exercise Robotic Experimentation and Prototyping that was augmented by Maritime Unmanned Systems 22 (REPMUS22), which was held in the national waters of Portugal. The main objective of this experimentation was to assess the underwater environment over an ocean beach for an amphibious landing exercise. Moreover, the integration of the multibeam system with the autonomous prototype vessel was assessed. A short comparison between the USV survey and a traditional vessel multibeam survey is presented, whereby the advantages of performing an autonomous survey operation near the coastline is emphasized. A correlation between a known multibeam processing technique and the dissemination of a rapid but consistent product for operational use is described, highlighting the applicability of the technique for the data collected from small experimental platforms. Moreover, this study outlines the relationship between the particular errors observed in autonomous small vehicles and in conventional data processing methods. The resultant cartographic outputs from the hydrographic survey are presented, emphasizing the specific inaccuracies within the raw data and the suitability of distinct hydrographic products for various user domains. Full article

As maritime and military missions become more and more complex and multifactorial over the years, there has been a high interest in the research and development of (autonomous) unmanned underwater vehicles (UUVs). Latest efforts concern the modeling and simulation of UUVs’ collaboration in swarm formations, towards obtaining deeper insights related to the critical issues of cybersecurity and interoperability. The research topics, which are constantly emerging in this domain, are closely related to the communication, interoperability, and secure operation of UUVs, as well as to the volume, velocity, variety, and veracity of data transmitted in low bit-rate due to the medium, i.e., the water. This paper reports on specific research topics in the domain of UUVs, emphasizing interoperability and cybersecurity in swarms of UUVs in a military/search-and-rescue setting. The goal of this work is two-fold: a) to review existing methods and tools of semantic modeling and simulation for cybersecurity and interoperability on the Internet of Underwater Things (IoUT), b) to highlight open issues and challenges, towards developing a novel simulation approach to effectively support critical and life-saving decision-making of commanders of military and search-and-rescue operations. Full article

In this study, we focus on the Multi-robot Coverage Path Planning (MCPP) problem for maritime Search And Rescue (SAR) missions using a multiple Autonomous Underwater Vehicle (AUV) system, with the ultimate purpose of efficiently and accurately discovering the target from sonar images taken by Side-Scan Sonar (SSS) mounted on the AUVs. Considering the specificities of real maritime SAR projects, we propose a novel MCPP method, in which the MCPP problem is transformed into two sub-problems: Area partitioning and single-AUV coverage path planning. The structure of the task area is first defined using Morse decomposition of the spike pattern. The area partitioning problem is then formulated as an AUV ordering problem, which is solved by developing a customized backtracking method to balance the workload and to avoid segmentation of the possible target area. As for the single-AUV coverage path planning problem, the SAR-A* method is adopted, which generates a path that preferentially visits the possible target areas and reduces the number of turns to guarantee the high quality of the resulting sonar images. Simulation results demonstrate that the proposed method can maintain the workload balance and significantly improve the efficiency and accuracy of discovering the target. Moreover, our experimental results indicate that the proposed method is practical and the mentioned specificities are useful for discovering targets. Full article

Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent target search method (MATSMI) for moving and invisible objects. In the MATSMI algorithm, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we add spatial and temporal information to the reinforcement learning state and set up specialized rewards in conjunction with a maritime target search scenario. Additionally, we construct a simulation environment to simulate a multi-AUV search for the floating object. The simulation results show that the MATSMI method has about 20% higher search success rate and about 70 steps shorter search time than the traditional search method. In addition, the MATSMI method converges faster than the MADDPG method. This paper provides a novel and effective method for solving the maritime target search problem. Full article

Traditional nonintrusive object inspection methods are complex or extremely expensive to apply in certain cases, such as inspection of enormous objects, underwater or maritime inspection, an unobtrusive inspection of a crowded place, etc. With the latest advances in robotics, autonomous self-driving vehicles could be applied for this task. The present study is devoted to a review of the existing and novel technologies and methods of using autonomous self-driving vehicles for nonintrusive object inspection. Both terrestrial and maritime self-driving vehicles, their typical construction, sets of sensors, and software algorithms used for implementing self-driving motion were analyzed. The standard types of sensors used for nonintrusive object inspection in security checks at the control points, which could be successfully implemented at self-driving vehicles, along with typical areas of implementation of such vehicles, were reviewed, analyzed, and classified. Full article

Indonesia is a maritime country that has vast coastal resources and biodiversity. To support the Indonesian maritime program, a topography mapping tool is needed. The ideal topography mapping tool is the Unmanned Surface Vehicle (USV). This paper proposes the design, manufacture, and development of an affordable autonomous USV. The USV which is composed of thruster and rudder is quite complicated to build. This study employs rudderless and double thrusters as the main actuators. PID compensator is utilized as the feedback control for the autonomous USV. Energy consumption is measured when the USV is in autonomous mode. The Dynamics model of USV was implemented to study the roll stability of the proposed USV. Open-source Mission Planner software was selected as the Ground Control Station (GCS) software. Performance tests were carried out by providing the USV with an autonomous mission to follow a specific trajectory. The results showed that the developed USV was able to complete autonomous mission with relatively small errors, making it suitable for underwater topography mapping. Full article

This paper presents the technical and economic feasibility analysis of the subsea shuttle tanker (SST). The SST is proposed as an alternative to subsea pipelines and surface tankers with the primary purpose of transporting CO₂ autonomously underwater from onshore facilities to subsea wells for direct injection at marginal subsea fields. In contrast to highly weather-dependent surface tanker operations, the SST can operate in any condition underwater. The technical–economic analysis is performed in two steps. First, the SST’s technical feasibility is evaluated by investigating designs with lower and higher capacities. The purpose is to observe the appearance of technical limits (if present) when the SST is scaled down or up in size. Second, an economic analysis is performed using the well-reviewed cost models from the publicly available Zero Emissions Platform (ZEP) and Maritime Un-manned Navigation through Intelligence in Networks (MUNIN) D9.3 reports. The scenarios considered are CO₂ transport volumes of 1 to 20 million tons per annum (mtpa) with transport distances of 180 km to 1500 km in which the cost per ton of CO₂ is compared between offshore pipelines, crewed/autonomous tanker ships, and SST. The results show that SSTs with cargo capacities 10,569 m³, 23,239 m³, and 40,730 m³ are technically feasible. Furthermore, the SSTs are competitive for short and intermediate distances of 180–750 km and smaller CO₂ volumes of 1–2.5 mtpa. Lastly, it is mentioned that the SST design used the DNVGL Rules for Classification for Naval Vessels, Part 4 Sub-surface ships, Chapter 1 Submarine, DNVGL-RU-NAVAL-Pt4Ch1, which is primarily catered towards military submarine design. It is expected that a dedicated structural design code that is optimized for the SST would reduce the structural weight and corresponding capital expenditure (CAPEX). Full article

Coastal zones are subjected to a wide range of phenomena acting on very different temporal and spatial scales: from decades to days and from hundreds of kilometers to tens of meters. Planning the management of such areas, thus, requires an accurate and updated knowledge of the ongoing processes. While standard monitoring activities are functional for the medium-long time scale and medium-large spatial scale, they struggle to provide adequate information concerning the short period (i.e., days) and small range (i.e., few meters). In addition, such operations are affected by high costs and logistic complexity since they generally involve the deployment of specific aircraft or maritime vehicles. On the contrary, the employment of robotic devices can represent a solution to these issues. Their proper use can allow for frequent surveys and enhance the coverage of the acquired data due to optimized mission strategies. Marine robotics has the potential to arise as an efficient complementary tool to standard monitoring techniques. Nevertheless, the use of marine robots is still limited and should be improved. The purpose of this paper is to discuss the current state of robotic technology, identifying both the benefits and shortcomings of its use for micro-tidal marine-coastal monitoring. The discussion will be supported by actual results, taken as an example, achieved using FeelHippo AUV, the compact Autonomous Underwater Vehicle (AUV) developed by the Department of Industrial Engineering at the University of Florence, Italy. Full article