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A Path Planning Method for Unmanned Surface Vessels in Dynamic Environment

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  • Robot and Applications
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Abstract

A path planning method for unmanned surface vessels (USV) in dynamic environment is proposed to address the impact of dynamic environments on path planning results and the lack of dynamic obstacle avoidance capabilities. First, the considering ocean current rapidly exploring random tree (RRT*) (COC-RRT*) algorithm was proposed for global path planning. The RRT* algorithm has been enhanced with the integration of the virtual field sampling algorithm and ocean current constraint algorithm. The COC-RRT* algorithm optimizes the global planning path by adjusting the path between the parent nodes and child nodes. Second, according to the limitations of the International Regulations for Preventing Collisions at Sea (COLREGs), the improved dynamic window approach (DWA) is applied for local path planning. To enhance the ability of avoid dynamic obstacles, the dist function in the DWA algorithm has been improved. Simulation experiments were conducted in three scenarios to validate the proposed algorithm. The experimental results demonstrate that, in comparison with other algorithms, the proposed algorithm effectively avoids dynamic obstacles and mitigates the influence of the space-varying ocean current environment on the path-planning outcome. Additionally, the proposed algorithm exhibits high efficiency and robustness. The results verified the effectiveness of the proposed algorithm in dynamic environments.

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Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Bei**g Technology and Business University, Bei**g, 100048, China

    Jiabin Yu, Zhihao Chen, Zhiyao Zhao, Ji** Xu & Yang Lu

  2. Bei**g Laboratory for Intelligent Environmental Protection, Bei**g Technology and Business University, Bei**g, 100048, China

    Jiabin Yu, Zhihao Chen, Zhiyao Zhao, Ji** Xu & Yang Lu

  3. Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Bei**g Technology and Business University, Bei**g, 100048, China

    Jiabin Yu, Zhihao Chen, Zhiyao Zhao, Ji** Xu & Yang Lu

Authors
  1. Jiabin Yu
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  2. Zhihao Chen
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  3. Zhiyao Zhao
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  4. Ji** Xu
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  5. Yang Lu
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Correspondence to Jiabin Yu.

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This work was supported in part by the National Key Research and Development Program of China(2022YFF1101103), the Project of Cultivation for young top-notch Talents of Bei**g Municipal Institutions (BPHR202203043).

Jiabin Yu received his B.S. degree from the Bei**g Technology and Business University, Bei**g, China, in 2007, an M.S. degree in automation from the Bei**g Institute of Technology, in 2009, and a Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, in 2012. He has been an Associate Professor with the Bei**g Technology and Business University, since 2017. His current research interests cover water environment evaluation and prediction, motor control, and complex system design.

Zhihao Chen received his B.S. degree in electrical engineering and automation from the Bei**g Technology and Business University, Bei**g, China, in 2021, where he is currently pursuing a master’s degree. His current research interests include path planning of mobile equipment and research on path planning algorithm.

Zhiyao Zhao received his B.S. degree in automation from the Bei**g Technology and Business University, Bei**g, China, in 2011, and a Ph.D. degree in guidance, navigation, and control from the School of Automation Science and Electrical Engineering, Beihang University, Bei**g, in 2017. He has been a Lecturer with the Bei**g Technology and Business University, since 2017. His current research interests include water environment evaluation and prediction, system health management, and stochastic hybrid systems.

Ji** Xu received his B.S. and M.S. degrees in automation from the Bei**g Technology and Business University, Bei**g, China, in 2002 and 2005, respectively, and a Ph.D. degree in control theory and control engineering from the School of Automation, Bei**g Institute of Technology, Bei**g, in 2010. He has been an Associate Professor with the Bei**g Technology and Business University, since 2010. His current research interests include water environment evaluation and prediction, and big data analysis.

Yang Lu received his B.S. degree in electrical engineering and automation from the Bei**g Technology and Business University, Bei**g. China, in2022, where he is currently pursuing a master’s degree. His current research interests include paths planning of mobile equipment and task assigning.

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Yu, J., Chen, Z., Zhao, Z. et al. A Path Planning Method for Unmanned Surface Vessels in Dynamic Environment. Int. J. Control Autom. Syst. 22, 1324–1336 (2024). https://doi.org/10.1007/s12555-022-1172-7

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  • DOI: https://doi.org/10.1007/s12555-022-1172-7

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