Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

Shi, Binghua; Guo, Jia; Wang, Chen; Su, Yixin; Di, Yi; AbouOmar, Mahmoud S.

doi:10.1038/s41598-022-14355-y

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…Since that, The BBPSO has arisen great attention from researchers. The BBPSO and its variants have been widely applied to surface vehicles [24], knapsack problems [25], and other fields.…”

Section: Introductionmentioning

confidence: 99%

A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

et al. 2023

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Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy, evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of selfrectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.

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“…Since that, The BBPSO has arisen great attention from researchers. The BBPSO and its variants have been widely applied to surface vehicles [24], knapsack problems [25], and other fields.…”

Section: Introductionmentioning

confidence: 99%

A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

et al. 2023

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“…In this paper, we aim to address the above horizon line detection issues in a complex maritime scenario based on our previous work [15][16][17]. For this, the criteria for defining a complex scenario need to be clearly defined as a first step, which will also document the various challenging scenarios.…”

Section: Introductionmentioning

confidence: 99%

Research on a Horizon Line Detection Method for Unmanned Surface Vehicles in Complex Environments

Shi

Guo

et al. 2023

JMSE

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A critical step in the visual navigation of unmanned surface vehicles (USVs) is horizon line detection, which can be used to adjust the altitude as well as for obstacle avoidance in complex environments. In this paper, a real-time and accurate detection method for the horizon line is proposed. Our approach first differentiates the complexity of navigational scenes using the angular second moment (ASM) parameters in the grey level co-occurrence matrix (GLCM). Then, the region of interest (ROI) is initially extracted using minimal human interaction for the complex navigation scenes, while subsequent frames are dynamically acquired using automatic feature point matching. The matched ROI can be maximally removed from the complex background, and the Zernike-moment-based edges are extracted from the obtained ROI. Finally, complete sea horizon information is obtained through a linear fitting of the lower edge points to the edge information. Through various experiments carried out on a classical dataset, our own datasets, and that of another previously published paper, we illustrate the significance and accuracy of this technique for various complex environments. The results show that the performance has potential applications for the autonomous navigation and control of USVs.

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“…However, current solutions in the market still face several challenges. Most autonomous navigation systems have high development and testing costs, limited functionality that fails to meet user requirements, and low integration [9,10,11]. They lack a comprehensive, real-time, and accurate assessment of navigation risks, only calculating collision risk when a potential danger is visually detected [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Multi-functional and Practical Adaptive Collision Avoidance Decision-making System for Autonomous Ships

Zheng,

Zhang,

Liu

et al. 2023

J. Phys.: Conf. Ser.

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In the current landscape, autonomous navigation systems face challenges due to incomplete functionality and low integration. They also lack a comprehensive, real-time, and accurate navigation risk assessment, while operating independently from one another. To address these issues, a multi-functional and practical adaptive collision avoidance decision-making system for autonomous ships is developed in this paper. The system primarily relies on the electronic navigational chart (ENC) to effectively display the ship’s navigation situation. It establishes a collision avoidance decision-making model centered around the own ship, offering collision risk analysis, avoidance methods, and optimal timing to ensure safe navigation. The system efficiently integrates and processes multimodal maritime data from various devices, utilizing ontology-based approaches for comprehensive navigation situation understanding, which integration provides invaluable support for intelligent decision-making processes. With a user-friendly interface, excellent portability, and cross-platform interoperability, this system has undergone collaborative efforts and joint debugging with China Shipbuilding Navigation Technology Co., Ltd. during the project’s first and second phases. Notably, the system has been successfully implemented on the “High-performance Integrated Bridge System” platform of China Shipbuilding Navigation, showcasing its potential for advancement from principle prototype development to actual equipment application. By offering accurate collision avoidance decision support, this system significantly contributes to enhancing the safety of ship navigation.

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Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

Cited by 7 publications

References 38 publications

A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

Research on a Horizon Line Detection Method for Unmanned Surface Vehicles in Complex Environments

Multi-functional and Practical Adaptive Collision Avoidance Decision-making System for Autonomous Ships

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