IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-97-4137-3_15.html
   My bibliography  Save this book chapter

Research on Path Planning of Mobile Robot Based on Improved Immune-Ant Colony Algorithm

In: Ieis 2023

Author

Listed:
  • Guanyi Liu

    (Beijing Jiaotong University)

  • Xuewei Li

    (Beijing Jiaotong University)

  • Yumeng Mao

    (Beijing Jiaotong University)

  • Jingxiao Sun

    (Beijing Jiaotong University)

  • Dehan Jiao

    (Beijing Jiaotong University)

  • Xuemei Li

    (Beijing Jiaotong University)

Abstract

In order to solve the problems of low search efficiency and easy to fall into local optimal solution when using traditional ant colony algorithm for path planning of mobile robots, an improved immune ant colony hybrid algorithm is proposed. Firstly, the optimal solution is obtained by using the fast global convergence of the immune algorithm, which is used as the initial pheromone distribution of the ant colony algorithm. On this basis, the improved ant colony algorithm is used for global path planning, which effectively solves the problem that the search efficiency is low due to the lack of pheromone in the early stage. By comparing the experimental results of the two algorithms, the advantages of hybrid algorithm are illustrated. The experimental results show that the improved Immune Ant Colony Algorithm can better solve the path planning problem of mobile robots in complex environments.

Suggested Citation

  • Guanyi Liu & Xuewei Li & Yumeng Mao & Jingxiao Sun & Dehan Jiao & Xuemei Li, 2024. "Research on Path Planning of Mobile Robot Based on Improved Immune-Ant Colony Algorithm," Lecture Notes in Operations Research, in: Menggang Li & Hua Guowei & Anqiang Huang & Xiaowen Fu & Dan Chang (ed.), Ieis 2023, pages 185-197, Springer.
    • Handle: RePEc:spr:lnopch:978-981-97-4137-3_15
    DOI: 10.1007/978-981-97-4137-3_15
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:lnopch:978-981-97-4137-3_15. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.