IDEAS home Printed from https://ideas.repec.org/a/ids/ijdsci/v8y2023i1p1-15.html
   My bibliography  Save this article

Structure design and system implementation of a supermarket shopping robot based on deep learning

Author

Listed:
  • Jinyun Jiang
  • Shiyi Ying
  • Wanxin Fu
  • Xiaoliang Jiang

Abstract

To enhance the operating efficiency of supermarkets, reduce their labour costs, and satisfy people's shopping experiences, we present the design and implementation of a supermarket shopping robot based on deep learning. Firstly, the robot adopts high-performance STM32F407 as its main control chip and is powered by 4 DC motors. It relies on 12 grey-scale sensors, gyroscopes and other devices for path planning. Through the infrared detection module, detect whether there are goods in the cargo window, and use the manipulator to accurately grasp and place the goods. Secondly, design a shopping cart to replace the manual cart to maintain the robot's control of the shopping cart during the shopping process. Finally, the AlexNet network is used as the feature extractor to realise the rapid identification of the target cargos. The experimental results show that under the simulation of the real supermarket environment, the designed robot runs flexibly, stably and reliably, and can well complete the purchase and supply of commodities, which is in line with the development trend of artificial intelligence.

Suggested Citation

  • Jinyun Jiang & Shiyi Ying & Wanxin Fu & Xiaoliang Jiang, 2023. "Structure design and system implementation of a supermarket shopping robot based on deep learning," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 8(1), pages 1-15.
    • Handle: RePEc:ids:ijdsci:v:8:y:2023:i:1:p:1-15
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=129458
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijdsci:v:8:y:2023:i:1:p:1-15. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=429 .

    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.