Small Unmanned Surface Vessels—A Review and Critical Analysis of Relations to Safety and Safety Assurance of Larger Autonomous Ships

Bolbot, Victor; Sandru, Andrei; Saarniniemi, Ture; Puolakka, Otto; Kujala, Pentti; Valdez Banda, Osiris A.

doi:10.3390/jmse11122387

Cited by 3 publications

(2 citation statements)

References 227 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, underwater vehicles are unsuitable for shallow remote lake shores. Recent surveys [30][31][32] have showcased a proliferation of research efforts dedicated to Autonomous Surface Vehicles (ASVs), resulting in the development of numerous commercial products of small dimensions [33]. However, conducting surveys in remote lakes requires comprehensive solutions that address challenges such as streamlined logistics and portability along high mountain trails, suitable propulsion for shallow shores, and the ability to integrate a plethora of lightweight sensors and tools for high-quality surveys.…”

Section: Background Motivation and Objectivesmentioning

confidence: 99%

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

Odetti,

Bruzzone,

Ferretti

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

show abstract

Section: Background Motivation and Objectivesmentioning

confidence: 99%

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

Odetti,

Bruzzone,

Ferretti

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Unmanned surface vehicles (USVs) have garnered considerable attention over the past few decades [1,2]. Thanks to their advantages of inherent security, autonomy, and programmability, they have been applied in different types of scenarios, such as transportation [3], environmental monitoring [4,5], marine resource exploration [6,7], disaster rescuing [8], and marine reconnaissance [9,10].…”

Section: Introductionmentioning

confidence: 99%

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Zhang,

Li,

Xiong

et al. 2024

JMSE

View full text Add to dashboard Cite

Unmanned surface vehicles (USVs) have garnered significant attention across various application fields. A sufficiently accurate kinetic model is essential for achieving high-performance navigation and control of USVs. However, time-varying unobservable internal states and external disturbances pose challenges in accurately modeling the USV’s kinetics, and existing methods face difficulties in accurately estimating unknown time-varying disturbances online while ensuring precise mechanism modeling. To address this issue, a novel grey-box modeling method based on incremental learning and mechanisms (GBM-ILM) is proposed. Its union structure combines the advantages of both incremental learning networks and physical mechanisms for estimating the USV’s full kinetics. Depending on the linear parameter-varying (LPV) mechanism, it not only adheres to physical laws but also calculates the unstructured model errors. An incremental learning network is implemented to continuously refine model errors, by accounting for the USV’s time-varying characteristics and iteratively updating the network parameters and structures to adapt to different USV states and environmental disturbances. To validate this method, we developed the ‘Salmon’ USV and conducted identification experiments in a lake. Compared to tests of other state-of-the-art methods, our method has better adaptability, with 46.34%, 14.86%, and 6.87% accuracy improvements when estimating the USV’s forward, turning, and sideslip dynamic model, respectively.

show abstract

Distributed Optimization-Based Path Planning for Multiple Unmanned Surface Vehicles to Pass through Narrow Waters

Li,

Teng,

Xiao

et al. 2024

JMSE

View full text Add to dashboard Cite

Safety and efficiency are important when Unmanned Surface Vehicles (USVs) pass through narrow waters in complex marine environments. This paper considers the issue of path planning for USVs passing through narrow waterways. We propose a distributed optimization algorithm based on a polymorphic network architecture, which maintains connectivity and avoids collisions between USVs while planning optimal paths. Firstly, the initial path through the narrow waterway is planned for each USV using the narrow water standard route method, and then the interpolating spline method is used to determine its corresponding functional form and rewrite the function as a local cost function for the USV. Secondly, a polymorphic network architecture and a distributed optimization algorithm were designed for multi-USVs to maintain connectivity and avoid collisions between USVs, and to optimize the initial paths of the multi-USV system. The effectiveness of the algorithm is demonstrated by Lyapunov stability analysis. Finally, Lingshui Harbor of Dalian Maritime University and a curved narrow waterway were selected for the simulation experiments, and the results demonstrate that the paths planned by multiple USVs were optimal and collision-free, with velocities achieving consistency within a finite time.

show abstract

Small Unmanned Surface Vessels—A Review and Critical Analysis of Relations to Safety and Safety Assurance of Larger Autonomous Ships

Cited by 3 publications

References 227 publications

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Distributed Optimization-Based Path Planning for Multiple Unmanned Surface Vehicles to Pass through Narrow Waters

Contact Info

Product

Resources

About