STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Mechatronics and Automation Review Article

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

  1. A. P. Kinge Department of Electrical Engineering, TSSM'S Bhivarabai Sawant College of Engineering and Research, Pune
  2. R. P. Patil* Department of Electrical Engineering, TSSM'S Bhivarabai Sawant College of Engineering and Research, Pune
  3. S. P. Ingale Department of Electrical Engineering, TSSM'S Bhivarabai Sawant College of Engineering and Research, Pune
  4. P. S. Sanas Department of Electrical Engineering, TSSM'S Bhivarabai Sawant College of Engineering and Research, Pune
  5. A. M. Sawant Department of Electrical Engineering, TSSM'S Bhivarabai Sawant College of Engineering and Research, Pune

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

References (18)

  1. S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas and M. J. Marín-Jiménez, “Automatic generation and detection of highly reliable fiducial markers,” Pattern Recognition, vol. 47, no. 6,
  2. pp. 2280–2292, Jun. 2014, doi:10.1016/j.patcog.2014.01.005.
  3. Redmon J, Divvala S, Girshick R, Farhadi A. You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:779-788. doi:10.1109/cvpr.2016.91
  4. Kumar JNVRS, K B, G L, Ch C, K N, K T. IoT Based Smart Shopping Cart Integrated with Payment Gateway. 2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI). 2024:1-6. doi:10.1109/raeeucci61380.2024.10547971
  5. Patel S, Patel F, Pandya H, Modi H. Hardware implementation of smart shopping cart using IoT. AIP Conference Proceedings. 2024;3107:050022. doi:10.1063/5.0208476
  6. D. F. D. Shahila, M. N. Rahman and S. K. Ahmed, “Design and Development of IoT Enabled Smart Shopping Cart,” in Proc. Int. Conf. Communication, Computing and IoT (IC3IoT), 2024.
  7. R. Kulkarni, P. Deshmukh and S. Patil, “Smart Shopping Cart with Automated Billing Using Arduino,” Int. J. Creative Res. Thoughts (IJCRT), vol. 11, no. 10, 2023.
  8. T. K. Das, S. Roy and A. Banerjee, “A Smart Trolley for Smart Shopping,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics (SMC), 2020.
  9. G. Suciu, A. Vulpe and C. Dobre, “A Smart Shopping Platform Based on IoT Solutions,” in Proc. IEEE Int. Conf. Electronics, Computers and Artificial Intelligence (ECAI), 2020.
  10. S. Sasirekha, R. Kumar and P. Karthik,“Automated Billing Shopping Cart Using Arduino Uno,”
  11. TIJER Journal, vol. 12, no. 1, 2025.
  12. Gowda DSK, A A, Teli A, Srivastava AK, Bhandari C. DESIGN OF IOT-ENABLED SMART SHOPPING CART. IJARCCE. 2025;14(12). doi:10.17148/ijarcce.2025.141289
  13. N. Bhuvaneshwaran, R. Prakash and S. Kumar, “IoT Based Smart Shopping Cart,” Int. Journal of Engineering Research, 2021.
  14. T. K. Das, S. Roy and A. Banerjee, “Smart Shopping Trolley System Using RFID and IoT,”Int.
  15. Res. J. Modernization Eng. Sci. (IRJMS),2024.
  16. Garrido-Jurado S, Muñoz-Salinas R, Madrid-Cuevas FJ, Medina-Carnicer R. Generation of fiducial marker dictionaries using Mixed Integer Linear Programming. Pattern Recognition. 2016;51:481-491. doi:10.1016/j.patcog.2015.09.023
  17. M. A. Rahman, M. R. Islam and M. S. Hossain, “Vision-Based Autonomous Navigation System Using Raspberry Pi,” in Proc. IEEE Int. Conf. Robotics, Automation and Artificial Intelligence,2021.
  18. P. Behera, S. Nayak and B. Sethi, “Real-Time Object Tracking and Following System Using Computer Vision,” in Proc. IEEE Int. Conf. Smart Computing and Electronics Enterprise (ICSCEE), 2020.
Support