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Optimal Path Planning for Unmanned Vehicles Using Improved Ant Colony Optimization Algorithm

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Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

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Abstract

To prevent the locally optimal problem and slow convergence problem of unmanned vehicles (UVs) path planning, an improved ant colony algorithm is proposed by using a dynamic pheromone volatility coefficient. The best path is searched by selecting the appropriate pheromone volatility coefficient in ant colony algorithm, which has better searching ability, and converges to the optimal value quickly. The experimental results are illustrated to compare with other improved ant colony optimization algorithms to verify the effectiveness and efficiency of our proposed path planning method for UVs.

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Acknowledgements

This work was fund by the Science and Technology Development Fund, Macau SAR (File no. 0050/2020/A1).

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Jiangsu, 211100, China

    Hongchang Deng & Jing Zhu

  2. Institute of Systems Engineering, Macau University of Science and Technology, Macau, China

    Jing Zhu

Authors
  1. Hongchang Deng
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  2. Jing Zhu
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Corresponding author

Correspondence to Jing Zhu .

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Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Deng, H., Zhu, J. (2021). Optimal Path Planning for Unmanned Vehicles Using Improved Ant Colony Optimization Algorithm. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_50

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_50

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5187-8

  • Online ISBN: 978-981-16-5188-5

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