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Global Path Planning of Unmanned Surface Vessel Based on Multi-objective Hybrid Particle Swarm Algorithm

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Bio-inspired Computing: Theories and Applications (BIC-TA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 791))

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Abstract

A multi-objective hybrid particle swarm algorithm is proposed to solve the problem that the current unmanned surface vessel (USV) global routing algorithm is easy to fall into the local optimal solution and the optimization target is single. The snap jump feature of simulated annealing algorithm is used to improve global search capability of particle swarm algorithm, and the three objective functions of path length, path smoothness and path security are used to optimize the path. The simulation result shows that the algorithm can improve the smoothness of the inflection point and the security of the path on the shortest path.

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Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Hao Zhou, Dongming Zhao & Xuan Guo

Authors
  1. Hao Zhou
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  2. Dongming Zhao
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  3. Xuan Guo
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Corresponding author

Correspondence to Dongming Zhao .

Editor information

Editors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Cheng He

  2. Automation College, Harbin Engineering University, Harbin, China

    Hongwei Mo

  3. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Automation College, Harbin Engineering University, Harbin, China

    Yuxin Zhao

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Zhou, H., Zhao, D., Guo, X. (2017). Global Path Planning of Unmanned Surface Vessel Based on Multi-objective Hybrid Particle Swarm Algorithm. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_7

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  • DOI: https://doi.org/10.1007/978-981-10-7179-9_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7178-2

  • Online ISBN: 978-981-10-7179-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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