IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/6964875.html
   My bibliography  Save this article

Multi-UAV Collaborative Path Planning Method Based on Attention Mechanism

Author

Listed:
  • Tingzhong Wang
  • Binbin Zhang
  • Mengyan Zhang
  • Sen Zhang

Abstract

Aiming at the problem that traditional heuristic algorithm is difficult to extract the empirical model in time from large sample terrain data, a multi-UAV collaborative path planning method based on attention reinforcement learning is proposed. The method draws on a combined consideration of influencing factors, such as survival probability, path length, and load balancing and endurance constraints, and works as a support system for multimachine collaborative optimizing. The attention neural network is used to generate the cooperative reconnaissance strategy of the UAV, and a large amount of simulation data is tested to optimize the attention network using the REINFORCE algorithm. Experimental results show that the proposed method is effective in solving the multi-UAV path planning issue with high real-time requirements, and the solving time is less than the traditional algorithms.

Suggested Citation

  • Tingzhong Wang & Binbin Zhang & Mengyan Zhang & Sen Zhang, 2021. "Multi-UAV Collaborative Path Planning Method Based on Attention Mechanism," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-8, September.
    • Handle: RePEc:hin:jnlmpe:6964875
    DOI: 10.1155/2021/6964875
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/6964875.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/6964875.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/6964875?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuanying Cao & Xi Fang, 2023. "Optimized-Weighted-Speedy Q-Learning Algorithm for Multi-UGV in Static Environment Path Planning under Anti-Collision Cooperation Mechanism," Mathematics, MDPI, vol. 11(11), pages 1-28, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:6964875. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.