An Algorithm for Path Planning of Autonomous Ships Considering the Influence of Wind and Wave

Zhou, Yucheng; Gong, Yi; Geng, Xiongfei; Li, Dongsheng; Gao, Beili; Li, Chunxu

doi:10.1088/1742-6596/2083/3/032028

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Yucheng Zhou et al ( 2021) is an example of a representative study. In general, the authors point out that in existing studies, external factors such as wind, waves, and currents are not considered in the route planning of autonomous ships, which affects navigation safety, and propose a PSO optimization algorithm that considers external factors such as wind, waves, and currents [52].…”

Section: Topic Classification Resultsmentioning

confidence: 99%

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Kim,

Han,

Lee

et al. 2024

Preprint

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The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASS) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks like real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASS using bibliographic analysis to inform policy and future research directions in this evolving field. We analyze 3,363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. Findings reveal a rapidly growing field dominated by image recognition research. Keywords like "datum," "image," and "detection" suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with "Image analysis and classification research" as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASS and informing future policy and research agendas for the integration of electric propulsion systems in the maritime industry.

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Section: Topic Classification Resultsmentioning

confidence: 99%

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Kim,

Han,

Lee

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In general, the authors point out that, in existing studies, external factors such as wind, waves, and currents are not considered in the route planning of autonomous ships, which affects the navigation safety. They propose a PSO optimization algorithm that considers external factors such as wind, waves, and currents [51].…”

Section: Topic Classification Resultsmentioning

confidence: 99%

Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Kim,

Han,

Lee

et al. 2024

Electronics

View full text Add to dashboard Cite

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.

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“…However, the actual energy loss, size, and attitude changes of AUVs were not considered. To address the issue of path planning for autonomous surface vessels not considering external factors such as wind, waves, and currents that affect navigation safety, an adaptive inertia weight was introduced into the fitness function of an improved particle swarm optimization algorithm [20] to increase the resistance to wind and wave forces. This improved path safety but did not consider the actual energy loss of the ship.…”

Section: Introductionmentioning

confidence: 99%

An Optimal-Path-Planning Method for Unmanned Surface Vehicles Based on a Novel Group Intelligence Algorithm

Chen,

Feng,

Bao

et al. 2024

JMSE

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Path planning is crucial for unmanned surface vehicles (USVs) to navigate and avoid obstacles efficiently. This study evaluates and contrasts various USV path-planning algorithms, focusing on their effectiveness in dynamic obstacle avoidance, resistance to water currents, and path smoothness. Meanwhile, this research introduces a novel collective intelligence algorithm tailored for two-dimensional environments, integrating dynamic obstacle avoidance and smooth path optimization. The approach tackles the global-path-planning challenge, specifically accounting for moving obstacles and current influences. The algorithm adeptly combines strategies for dynamic obstacle circumvention with an eight-directional current resistance approach, ensuring locally optimal paths that minimize the impact of currents on navigation. Additionally, advanced artificial bee colony algorithms were used during the research process to enhance the method and improve the smoothness of the generated path. Simulation results have verified the superiority of the algorithm in improving the quality of USV path planning. Compared with traditional bee colony algorithms, the improved algorithm increased the length of the optimization path by 8%, shortened the optimization time by 50%, and achieved almost 100% avoidance of dynamic obstacles.

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An Algorithm for Path Planning of Autonomous Ships Considering the Influence of Wind and Wave

Cited by 4 publications

References 4 publications

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

An Optimal-Path-Planning Method for Unmanned Surface Vehicles Based on a Novel Group Intelligence Algorithm

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