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UAV Path Planning Based on Adaptive Weighted

Pigeon-Inspired Optimization Algorithm

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10639))

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Abstract

In the complex environment, using traditional pigeon-inspired optimization algorithm for the UAV route planning leads local optimum and slow convergence speed and unstable problem. In order to solve this problem, this paper introduces an adaptive weighted pigeon-inspired optimization algorithm. The adaptive weight coefficient is applied to calculate the speed and position of the individuals in the population which enhances the quality and efficiency of route planning. In addition, this paper done many simulation experiment from different aspect for providing enough evident in path planning. This paper also have these simulation experiment by designing different environment model including the simple and complex environment. The simulation results show that the adaptive weighted pigeon-inspired optimization algorithm provides a shorter route distance, a lower threat cost consumption and the algorithm running time while comparing with pigeon-inspired optimization algorithm and particle swarm optimization than basic pigeon-inspired optimization algorithm and Particle swarm intelligence algorithm. After the spline smoothing, the UAV route is flyable.

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Authors and Affiliations

  1. College of Computer Science, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China

    Na Lin, Siming Huang, Changqing Gong, Liang Zhao & Jiacheng Tang

Authors
  1. Na Lin
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  2. Siming Huang
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  3. Changqing Gong
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  4. Liang Zhao
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  5. Jiacheng Tang
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Corresponding author

Correspondence to Siming Huang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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© 2017 Springer International Publishing AG

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Lin, N., Huang, S., Gong, C., Zhao, L., Tang, J. (2017). UAV Path Planning Based on Adaptive Weighted. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_31

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  • DOI: https://doi.org/10.1007/978-3-319-70136-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70135-6

  • Online ISBN: 978-3-319-70136-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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