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A Novel Hybrid Discrete Grey Wolf Optimizer Algorithm for Multi-UAV Path Planning

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Abstract

With the development of the fifth-generation wireless network, autonomous moving platforms such as unmanned aerial vehicles (UAV) have been widely used in modern smart cities. In some applications, the UAVs need to perform certain monitoring tasks within a specified time. However, due to the energy constraints of UAVs, such tasks require using multiple UAVs to monitor multiple points. To solve this practical problem, this paper proposes a multi-UAV path planning model with the energy constraint (MUPPEC). The MUPPEC considers the energy consumption of a UAV in different states, such as acceleration, cruising speed, deceleration, and hovering, and the main objective of the MUPPEC is to minimize the total monitoring time. Also, a hybrid discrete intelligence algorithm based on the grey wolf optimizer (HDGWO) is proposed to solve the MUPPEC. In the HDGWO, the discrete grey wolf update operators are implemented, and the integer coding and greedy algorithms are used to transform between the grey wolf space and discrete problem space. Furthermore, the central position operation and stagnation compensation grey wolf update operation are introduced to improve the global convergence ability, and a two-opt with azimuth is designed to enhance the local search ability of the algorithm. Experimental results show that the HDGWO can solve the MUPPEC effectively, and compared to the traditional grey wolf optimizer(GWO), the discrete operators and the two-opt local search strategy with azimuth can effectively improve the optimization ability of the GWO.

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Availability of data and material

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Code Availability

The custom code used to obtain the experiment results during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is partially supported by the National Nature Science Foundation of China under grant No. 6217070288, the Science and Technology Program of Tianhe District, Guangzhou under grant No. 2018CX005, the Key Project in Higher Education of Guangdong Province, China under grant No. 2020ZDZX3030, and the Young Innovation Talents Project in Higher Education of Guangdong Province, China under grant No. 2018KQNCX333.

Funding

This work is partially supported by the National Nature Science Foundation of China under grant No. 6217070288, the Science and Technology Program of Tianhe District, Guangzhou under grant No. 2018CX005, the Key Project in Higher Education of Guangdong Province, China under grant No. 2020ZDZX3030, and the Young Innovation Talents Project in Higher Education of Guangdong Province, China under grant No. 2018KQNCX333.

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Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China

    Gewen Huang, Yanguang Cai & Jianqi Liu

  2. Information and Network Center, Jiaying University, Meizhou, Guangdong, 514015, China

    Gewen Huang

  3. School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, Guangdong, 528402, China

    Yuanhang Qi

  4. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China

    Yuanhang Qi

  5. School of Material and Energy, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China

    Xiaozhou Liu

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  1. Gewen Huang
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  2. Yanguang Cai
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  4. Yuanhang Qi
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  5. Xiaozhou Liu
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Contributions

Conceptualization, Y. C. and G. H.; methodology, J. L., Y. Q. and X. L.; formal analysis, G. H. and J. L. and Y. Q.; writing—original draft preparation, G. H. and X. L.; writing—review and editing, J. L. and G. H. and Y. Q.; funding acquisition, Y. C. and Y. Q.. All authors have read and agreed to the published version of the manuscrip

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Correspondence to Jianqi Liu or Yuanhang Qi.

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Huang, G., Cai, Y., Liu, J. et al. A Novel Hybrid Discrete Grey Wolf Optimizer Algorithm for Multi-UAV Path Planning. J Intell Robot Syst 103, 49 (2021). https://doi.org/10.1007/s10846-021-01490-3

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