AI-Powered Smart Shopping Trolley

Trolleys are widely used in various places like shopping malls, hospitals, and colleges, but in supermarkets or malls, long queues during peak shopping hours often result in time loss for both shoppers and staff. This paper aims to design and develop an AI-Powered Smart Shopping Trolley to transform the conventional shopping experience by leveraging advanced technologies to enhance efficiency and customer satisfaction. The key objectives include automating checkout processes to save time, utilizing barcode scanners for accurate product tracking, and implementing real-time inventory management to prevent stock shortages and overstocking. The system also provides a personalized shopping experience by offering product recommendations based on customer preferences. Each item has a barcode linked to its price, and the trolley is equipped with indicators and alerts to assist with budgeting: a red light and buzzer activate when spending exceeds the preset budget, while a green light blinks when spending remains within limits. If an item is removed, the system recalculates the total, and the change is indicated via a buzzer and the embedded LCD screen. This allows customers to monitor their expenses and pay the total directly at the counter, eliminating traditional product scanning and reducing waiting time. By automating the billing process, the system minimizes human errors and helps shoppers prioritize essential purchases, ultimately saving time and money. Additional features, such as the ability to delete scanned products, further optimize the shopping experience. The hardware prototype is developed using an Arduino platform and comprises components such as an AI-integrated barcode scanner, LCD, push buttons, visual indicators, a Wi-Fi module, and a PIC microcontroller.

Authors

Institutions

Publication Details

Journal
International Research Journal on Advanced Engineering and Management (IRJAEM)
Published
2025-03-22
DOI
https://doi.org/10.47392/irjaem.2025.0104
Citations
1
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
1.01
Controls
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article

AI-Powered Smart Shopping Trolley

Vivek S. Vaidya, Kaustubh N. Gaigole, Sanket Chamlate, Pruthviraj P. Futan et al.
1 citations
International Research Journal on Advanced Engineering and Management (IRJAEM)
Traffic Prediction and Management Techniques
1.01
article

AI-Powered Smart Shopping Trolley

Vivek S. Vaidya, Kaustubh N. Gaigole, Sanket Chamlate, Pruthviraj P. Futan, Mohit N. Deshmukh, Nitin S. Thakare
article en
1 citations

Abstract

Trolleys are widely used in various places like shopping malls, hospitals, and colleges, but in supermarkets or malls, long queues during peak shopping hours often result in time loss for both shoppers and staff. This paper aims to design and develop an AI-Powered Smart Shopping Trolley to transform the conventional shopping experience by leveraging advanced technologies to enhance efficiency and customer satisfaction. The key objectives include automating checkout processes to save time, utilizing barcode scanners for accurate product tracking, and implementing real-time inventory management to prevent stock shortages and overstocking. The system also provides a personalized shopping experience by offering product recommendations based on customer preferences. Each item has a barcode linked to its price, and the trolley is equipped with indicators and alerts to assist with budgeting: a red light and buzzer activate when spending exceeds the preset budget, while a green light blinks when spending remains within limits. If an item is removed, the system recalculates the total, and the change is indicated via a buzzer and the embedded LCD screen. This allows customers to monitor their expenses and pay the total directly at the counter, eliminating traditional product scanning and reducing waiting time. By automating the billing process, the system minimizes human errors and helps shoppers prioritize essential purchases, ultimately saving time and money. Additional features, such as the ability to delete scanned products, further optimize the shopping experience. The hardware prototype is developed using an Arduino platform and comprises components such as an AI-integrated barcode scanner, LCD, push buttons, visual indicators, a Wi-Fi module, and a PIC microcontroller.

International Research Journal on Advanced Engineering and Management (IRJAEM)Vol. 3(03)
Cerner (United States) (US)
Openalex Percentile: Top 27%
Traffic Prediction and Management Techniques
1.01
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