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Kalman Filter-Based Acoustic Guidance Docking System for Autonomous Underwater Vehicle

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Intelligent Robotics and Applications (ICIRA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15206))

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Abstract

In this paper, an ultra-short baseline positioning system-guided (USBL-guided) docking system is proposed to achieve smooth docking guidance for autonomous underwater vehicle (AUV). First, the main challenges of USBL-guided AUV docking are illustrated. Second, to address the issue of discrete USBL positioning samples due to the low positioning frequency and drifting outliers, Least Square Method is applied to select an appropriate initialize sample for USBL filter to prevent the wrong tendency of state prediction caused by the outliers of USBL. Subsequently, we introduce an adaptive exponential weight-improved Kalman Filter that dynamically adjusts the Kalman gain in update process based on the distance between prediction and measurement of state. Furthermore, the derivation process and proof of algorithm are also introduced. The feasibility and efficiency of the improved filter are validated by the comparative experiment carried out in lake trials with the ground truth of dead-reckoning of inertial navigation system (INS). The comparative result shows that the improved KF can efficiently reduce the drifting samples and get a smoother trajectory of AUV.

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Acknowledgments

This work is partially supported by National Natural Science Foundation of China (under grant 52131101 and 52071153), and Hubei Provincial Natural Science Foundation for Innovation Groups (under grant 2021CFA026).

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Authors and Affiliations

  1. School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Zhao Wang, Xianbo Xiang, Xinyang Xiong & Shaolong Yang

  2. International Science and Technology Cooperation Offshore Center for Ship and Marine Intelligent Equipment and Technology, Hubei, China

    Xianbo Xiang & Shaolong Yang

Authors
  1. Zhao Wang
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  2. Xianbo Xiang
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  3. Xinyang Xiong
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  4. Shaolong Yang
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Corresponding author

Correspondence to Xianbo Xiang .

Editor information

Editors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, China

    Xuguang Lan

  2. Xi’an Jiaotong University, Xi’an, China

    Xuesong Mei

  3. Xi’an Jiaotong University, Xi'an, China

    Caigui Jiang

  4. Xi’an Jiaotong University, Xi’an, China

    Fei Zhao

  5. Xi'an Jiaotong University, Xi'an, China

    Zhiqiang Tian

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Wang, Z., Xiang, X., Xiong, X., Yang, S. (2025). Kalman Filter-Based Acoustic Guidance Docking System for Autonomous Underwater Vehicle. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15206. Springer, Singapore. https://doi.org/10.1007/978-981-96-0792-1_6

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  • DOI: https://doi.org/10.1007/978-981-96-0792-1_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-96-0791-4

  • Online ISBN: 978-981-96-0792-1

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