Energy-based USV maritime monitoring using multi-objective evolutionary algorithms

Ouelmokhtar, Hand; Benmoussa, Yahia; Benazzouz, Djamel; Chikh, Mohamed Abdessamed Ait; Lemarchand, Laurent

doi:10.1016/j.oceaneng.2022.111182

Cited by 21 publications

(5 citation statements)

References 14 publications

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“…In study [135], the optimal collision avoidance point (OCAP) method is proposed as a new approach for USVs to proactively prevent collisions. In [136], the authors tackle the issue of monitoring missions by employing a USV that is equipped with an onboard LiDAR system. This LiDAR system enables the USV to effectively surveil regions within its coverage radius.…”

Section: From 2021 To Datementioning

confidence: 99%

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Hashali,

Yang,

Xiang

2024

JMSE

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This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques.

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Section: From 2021 To Datementioning

confidence: 99%

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Hashali,

Yang,

Xiang

2024

JMSE

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“…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]. In engineering applications, the stable navigation control of USVs is vitally important, and the cornerstone of effective navigation control in USVs is a reliable kinetic model [11,12].…”

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

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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.

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“…In recent years, the development of advanced unmanned surface vehicle (USV) has aroused widespread attention from industry professionals due to its potential applications in military, scientific, and commercial operations. 1,2 Compared with conventional manned vehicles, USV offers a multitude of superiorities, such as simple deployment and manipulation under dangerous conditions, and the ability to monitor the environment, 3 perform mine countermeasures, 4 and conduct coastal surveillance. 5 To broaden the diversity of USV mission execution, a variety of theoretical challenges and practical applications have been studied in depth, such as formation control, 6 navigation planning, 7 and tracking control.…”

Section: Introductionmentioning

confidence: 99%

Integrated fault detection filter and fault-tolerant control for the unmanned surface vehicle with deception attacks

Jin,

Liu,

Wang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article investigates the fault detection and fault-tolerant control problem for an unmanned surface vehicle exposed to wave-induced disturbances and actuator faults in the physical layer, and delays and deception attacks in the cyber layer. First, a comprehensive vehicle model that includes physical disturbances, faults, time-varying delays, and Bernoulli random variable–based deception attacks is established. Second, an integrated fault detection filter and fault-tolerant controller design are developed to simultaneously provide a high degree of sensitivity to actuator faults and robustness and stability against cyber-physical threats (disturbances, faults, delays, and attacks). Delays and deception attacks are assumed to occur on the channel from fault detection filter to fault-tolerant controller. Finally, the performance and advantages of the integrated fault detection filter and fault-tolerant controller method with the solvability of inequality matrices are evaluated via comparative simulations in the unmanned surface vehicle with both low and high forward speeds.

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Energy-based USV maritime monitoring using multi-objective evolutionary algorithms

Cited by 21 publications

References 14 publications

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

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

Integrated fault detection filter and fault-tolerant control for the unmanned surface vehicle with deception attacks

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