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Complete Coverage Path Planning of Autonomous Underwater Vehicle Based on GBNN Algorithm

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Abstract

For the shortcomings of biologically inspired neural network algorithm in the path planning of robots, such as high computational complexity, long path planning time etc Glasius Bio-inspired Neural Network (GBNN) algorithm is proposed to improve the algorithm, and applied to the complete coverage path planning of autonomous underwater vehicle (AUV). Firstly, the grid map is constructed by discretizing the two-dimensional underwater environment. Secondly, the corresponding dynamic neural network is built on the grid map. Finally, complete coverage path of AUV is planned based on the GBNN strategy and the path of AUV at the edge of obstacles is optimized by some typical path templates. The simulation results show that the AUV can completely cover the entire workspace and immediately escape from deadlocks without any waiting. Meanwhile, the efficiency of complete coverage path planning is high with short path planning time and low overlapping coverage rate by using the algorithm proposed in this paper.

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Acknowledgments

This project is supported by the National Key Research and Development Plan (2017YFC0306302), National Natural Science Foundation of China (51575336, 61503239), Creative Activity Plan for Science and Technology Commission of Shanghai (16550720200).

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Authors and Affiliations

  1. Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Pudong new district, Shanghai, 201306, China

    Daqi Zhu, Chen Tian & Bing Sun

  2. Department of Electrical and Computer Engineering, University of Detroit Mercy, Detroit, MI, 48221, USA

    Chaomin Luo

Authors
  1. Daqi Zhu
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  2. Chen Tian
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  3. Bing Sun
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  4. Chaomin Luo
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Correspondence to Daqi Zhu.

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Zhu, D., Tian, C., Sun, B. et al. Complete Coverage Path Planning of Autonomous Underwater Vehicle Based on GBNN Algorithm. J Intell Robot Syst 94, 237–249 (2019). https://doi.org/10.1007/s10846-018-0787-7

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  • DOI: https://doi.org/10.1007/s10846-018-0787-7

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