Design of an ArUco Marker-Guided Smart Trolley with Integrated Billing Estimation

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | Issue : 01 | Page :
    By

    A. P. Kinge,

  • R. P. Patil*,

  • S. P. Ingale,

  • P. S. Sanas,

  • A. M. Sawant,

  1. Professor, Department of Electrical Engineering, TSSM’S Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India
  2. Professor, Department of Electrical Engineering, TSSM’S Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India
  3. Student, Department of Electrical Engineering, TSSM’S Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India
  4. Student, Department of Electrical Engineering, TSSM’S Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India
  5. Student, Department of Electrical Engineering, TSSM’S Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India

Abstract

The growing adoption of automation in retail environments has increased the need for intelligent systems that improve user convenience and reduce manual effort. This paper presents the design and development of a human-following smart shopping trolley with an integrated automatic billing system based on computer vision. The proposed system employs ArUco marker–based human tracking to achieve reliable and real-time following behaviour. A Raspberry Pi serves as the central processing unit, acquiring visual data from a USB camera and controlling DC motors to enable autonomous movement of the trolley.

Human-following functionality is achieved by detecting ArUco markers and estimating the relative position of the user with respect to the trolley. Based on the detected marker position within the camera frame, the system generates appropriate control signals to drive the motors in forward, left, or right directions. To ensure safe navigation, an ultrasonic sensor is incorporated for real-time obstacle detection, allowing the trolley to stop automatically when an obstacle is detected within a predefined threshold distance.

In addition to navigation, the system integrates an automatic billing mechanism using barcode recognition. The onboard camera captures product barcodes, which are decoded using image processing techniques. The extracted barcode data are mapped to a predefined product database containing item names and prices. The system updates the total bill dynamically and displays the information on an LCD interface. A delay-based filtering mechanism is implemented to prevent duplicate scanning of the same product within a short time interval, thereby improving billing accuracy.

The proposed system operates in dual modes, namely follow mode and billing mode, controlled through a user interface switch. Experimental results demonstrate that the system performs efficiently in real- time conditions, providing a cost-effective and practical solution for smart retail automation.

Keywords: Human-Following Robot, Smart Shopping Trolley, ArUco Marker, Computer Vision, Raspberry Pi, Barcode Detection, Automatic Billing System, Autonomous Navigation

[This article belongs to Journal of Mechatronics and Automation ]

How to cite this article:
A. P. Kinge, R. P. Patil*, S. P. Ingale, P. S. Sanas, A. M. Sawant. Design of an ArUco Marker-Guided Smart Trolley with Integrated Billing Estimation. Journal of Mechatronics and Automation. 2026; 13(01):-.
How to cite this URL:
A. P. Kinge, R. P. Patil*, S. P. Ingale, P. S. Sanas, A. M. Sawant. Design of an ArUco Marker-Guided Smart Trolley with Integrated Billing Estimation. Journal of Mechatronics and Automation. 2026; 13(01):-. Available from: https://journals.stmjournals.com/joma/article=2026/view=241812


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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 06/04/2026
Accepted 29/04/2026
Published 29/04/2026
Publication Time 23 Days
8

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