IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i1d10.1007_s13198-021-01545-6.html
   My bibliography  Save this article

A path planning algorithm for mobile robot based on edge-cloud collaborative computing

Author

Listed:
  • Taizhi Lv

    (Jiangsu Maritime Institute
    Nanjing Longyuan Microelectronic Company Limited)

  • Jun Zhang

    (Jiangsu Maritime Institute)

  • Juan Zhang

    (Jiangsu Maritime Institute)

  • Yong Chen

    (Nanjing Longyuan Microelectronic Company Limited)

Abstract

Path planning is a key problem to be solved for mobile robot to realize autonomous navigation. It is a typical computing intensive task, and high computing capacity is needed. The computing power carried by the mobile robot is difficult to support the calculation of path planning, and the traditional cloud computing model cannot meet the real-time requirement of path planning. In order to solve the problem of insufficient computing power and improve the execution efficiency and real-time performance, a real-time computing framework based on edge-cloud collaborative computing is constructed for path planning. In each control decision cycle, the mobile robot as the edge acquires the sensing data and transmits it to the cloud. By stream computing, the cloud plans the path in real-time. The edge integrates the planned path from the cloud and the partial obstacle avoidance result from the edge as a path sequence. The final path sequence is sent to the motion control layout, and drives the mobile robot to the target. By the edge-cloud collaborative computing, the computing capability of the edge is extended. By taking use of high real-time performance of stream computing, the proposed algorithm improves the efficiency of path planning. By taking use of the storage capacity of the cloud, environmental memory is realized and the problem of local traps is solved. Simulation experiment results in different environments show that the planned path by the proposed algorithm gets a higher path quality and shorter execution time comparing the other several traditional path planning algorithms. Experiments in real environment verify the feasibility and effectiveness of the algorithm.

Suggested Citation

  • Taizhi Lv & Jun Zhang & Juan Zhang & Yong Chen, 2022. "A path planning algorithm for mobile robot based on edge-cloud collaborative computing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 594-604, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01545-6
    DOI: 10.1007/s13198-021-01545-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01545-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01545-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Debadri Dutta & Akshit Pradhan & O. P. Acharya & S. K. Mohapatra, 2019. "IoT based pollution monitoring and health correlation: a case study on smart city," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 731-738, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shilpa Srivastava & Millie Pant & Ritu Agarwal, 2020. "Role of AI techniques and deep learning in analyzing the critical health conditions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 350-365, April.

    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:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01545-6. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.