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UAV Path Planning Based on Enhanced PSO-GA

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Artificial Intelligence (CICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14474))

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Abstract

Path planning for unmanned aerial vehicles (UAV) is a key technology for UAV intelligent system in the aspect of model construction. In order to improve the rapidity and optimality of UAV path planning, we propose a hybrid approach for UAV path planning in 2D environment. First, an enhanced particle swarm optimization algorithm (EPSO) combine with genetic algorithm (GA) which named as EPSO-GA is utilized to obtain the initial paths of UAV. In EPSO-GA, a hybrid initialization of Q-learning and random initial solutions is adopted to find the better initial paths for the UAV, which improves the quality of initial paths and accelerates the convergence of the EPSO-GA. The acceleration coefficients of EPSO-GA are designed as adaptive ones by the fitness value to make full use of all particles and strengthen the global search ability of the algorithm. Finally, the effectiveness of the proposed algorithm is proved by the experiments of UAV path planning.

This work was supported by the National Natural Science Foundation of China (61973285, 62076226), Hubei Provincial Natural Science Foundation of China (2022CFB438), the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (Grant No. GLAB 2023ZR08).

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Authors and Affiliations

  1. Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan, 430074, China

    Hongbo Xiang, Xiaobo Liu, Xinsheng Song & Wen Zhou

  2. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Hongbo Xiang, Xiaobo Liu, Xinsheng Song & Wen Zhou

Authors
  1. Hongbo Xiang
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  2. Xiaobo Liu
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  3. Xinsheng Song
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  4. Wen Zhou
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Corresponding author

Correspondence to Xiaobo Liu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Jian Pei

  3. Shanghai Jiao Tong Univeristy, Shanghai, China

    Guangtao Zhai

  4. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Xiang, H., Liu, X., Song, X., Zhou, W. (2024). UAV Path Planning Based on Enhanced PSO-GA. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14474. Springer, Singapore. https://doi.org/10.1007/978-981-99-9119-8_25

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  • DOI: https://doi.org/10.1007/978-981-99-9119-8_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9118-1

  • Online ISBN: 978-981-99-9119-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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