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A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer

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Abstract

In modern war, unmanned aerial vehicle (UAV) has become an indispensable weapon equipment in the battlefield of every country. Because it has the characteristics of all-weather reconnaissance, target detection and precision fire attack. However, whether the UAV can successfully perform its mission depends on whether it can avoid the enemy's radar reconnaissance and artillery attack at the lowest cost. Therefore, the path planning of UAV has become an important research problem. In order to solve this problem more effectively, we propose an algorithm (SHOPSO) combining selfish herd optimizer (SHO) and particle swarm optimizer (PSO). In the simulation experiment, we designed five 2D complex battlefield environments and five 3D complex battlefield environments, the proposed algorithm be compared with other algorithms with better optimization performance, which are CDE, pcCS, SCPIO, RLGWO, PSO and SHO. It can see from the experimental results that in most test environments, our algorithm can find the optimal battle path of UAV than other comparison algorithms. It shows that SHOPSO can make the UAV complete the given combat mission at a very low cost, which shows that SHOPSO is a very effective algorithm for UAV find a safe way.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This paper has been awarded by the National Natural Science Foundation of China (61941113), the Fundamental Research Fund for the Central Universities (30918015103, 30918012204), Nanjing Science and Technology Development Plan Project (201805036), and “13th Five-Year” equipment field fund (61403120501), China Academy of Engineering Consulting Research Project(2019-ZD-1-02-02), National Social Science Foundation (18BTQ073), State Grid Technology Project (5211XT190033). The authors gratefully acknowledge financial support from China Scholarship Council (CSC NO. 201906840057).

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Authors and Affiliations

  1. School of Information Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China

    Ruxin Zhao & Yongli Wang

  2. School of Intelligent Manufacturing, Yangzhou Polytechnic Institute, Yangzhou, 225127, China

    Gang Xiao & Chang Liu

  3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Yongli Wang, Peng Hu & Hao Li

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  1. Ruxin Zhao
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  2. Yongli Wang
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  3. Gang Xiao
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  4. Chang Liu
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  5. Peng Hu
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  6. Hao Li
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Correspondence to Yongli Wang.

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Zhao, R., Wang, Y., Xiao, G. et al. A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer. Appl Intell 52, 16775–16798 (2022). https://doi.org/10.1007/s10489-021-02353-y

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