Design and development of full self-service smart shopping system for unmanned supermarket
Abstract Traditional supermarkets often encounter challenges such as inefficient shopping guidance, long checkout lines, and poor customer experience. To address these issues, this study proposes a fully self-service smart shopping system, comprising three key modules: a smart shopping cart, a clien...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00182-4 |
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| _version_ | 1849341708360220672 |
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| author | Xiao Liang Yu Qi Shuo Xi Guanglei Meng Zhujun Wang |
| author_facet | Xiao Liang Yu Qi Shuo Xi Guanglei Meng Zhujun Wang |
| author_sort | Xiao Liang |
| collection | DOAJ |
| description | Abstract Traditional supermarkets often encounter challenges such as inefficient shopping guidance, long checkout lines, and poor customer experience. To address these issues, this study proposes a fully self-service smart shopping system, comprising three key modules: a smart shopping cart, a client app, and a back-end management platform, with full-chain encryption using TLS (Transport Layer Security) and RSA (Rivest Shamir Adleman) to ensure secure data transmission. The smart shopping system utilizes a KNN (K Nearest Neighbor) collaborative filtering recommendation algorithm to improve the relevance and accuracy of product suggestions. By integrating Mask R-CNN and UWB (Ultra Wide Band) into SLAM (Simultaneous Localization and Mapping) framework, we construct MU-SLAM (Mask R-CNN UWB SLAM), which enables precise localization and robust obstacle avoidance in unknown supermarket environments. Additionally, integrating a distributed DIMP (Discriminative Model Prediction) visual-tracking algorithm and infrared range sensors ensures that the smart shopping cart always follows the customer at a safe distance. Comparative experiments show that the MU-SLAM algorithm’s positioning accuracy increased by 92%, while the distributed DIMP algorithm’s tracking persistence improved by 107%. Furthermore, validation of the real supermarket environment demonstrated that the smart shopping system respectively reduces average shopping time and average checkout time by 33.15% and 90.17%, significantly improving both operational efficiency and the overall customer experience. |
| format | Article |
| id | doaj-art-e2a1a8e4de35467d8660a07280631809 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-e2a1a8e4de35467d8660a072806318092025-08-20T03:43:34ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137611610.1007/s44443-025-00182-4Design and development of full self-service smart shopping system for unmanned supermarketXiao Liang0Yu Qi1Shuo Xi2Guanglei Meng3Zhujun Wang4School of Automation, Shenyang Aerospace UniversitySchool of Automation, Shenyang Aerospace UniversitySchool of Automation, Shenyang Aerospace UniversitySchool of Automation, Shenyang Aerospace UniversitySchool of Automation, Shenyang Aerospace UniversityAbstract Traditional supermarkets often encounter challenges such as inefficient shopping guidance, long checkout lines, and poor customer experience. To address these issues, this study proposes a fully self-service smart shopping system, comprising three key modules: a smart shopping cart, a client app, and a back-end management platform, with full-chain encryption using TLS (Transport Layer Security) and RSA (Rivest Shamir Adleman) to ensure secure data transmission. The smart shopping system utilizes a KNN (K Nearest Neighbor) collaborative filtering recommendation algorithm to improve the relevance and accuracy of product suggestions. By integrating Mask R-CNN and UWB (Ultra Wide Band) into SLAM (Simultaneous Localization and Mapping) framework, we construct MU-SLAM (Mask R-CNN UWB SLAM), which enables precise localization and robust obstacle avoidance in unknown supermarket environments. Additionally, integrating a distributed DIMP (Discriminative Model Prediction) visual-tracking algorithm and infrared range sensors ensures that the smart shopping cart always follows the customer at a safe distance. Comparative experiments show that the MU-SLAM algorithm’s positioning accuracy increased by 92%, while the distributed DIMP algorithm’s tracking persistence improved by 107%. Furthermore, validation of the real supermarket environment demonstrated that the smart shopping system respectively reduces average shopping time and average checkout time by 33.15% and 90.17%, significantly improving both operational efficiency and the overall customer experience.https://doi.org/10.1007/s44443-025-00182-4Smart shopping systemPersonalized recommendationMU-SLAMDistributed DIMP |
| spellingShingle | Xiao Liang Yu Qi Shuo Xi Guanglei Meng Zhujun Wang Design and development of full self-service smart shopping system for unmanned supermarket Journal of King Saud University: Computer and Information Sciences Smart shopping system Personalized recommendation MU-SLAM Distributed DIMP |
| title | Design and development of full self-service smart shopping system for unmanned supermarket |
| title_full | Design and development of full self-service smart shopping system for unmanned supermarket |
| title_fullStr | Design and development of full self-service smart shopping system for unmanned supermarket |
| title_full_unstemmed | Design and development of full self-service smart shopping system for unmanned supermarket |
| title_short | Design and development of full self-service smart shopping system for unmanned supermarket |
| title_sort | design and development of full self service smart shopping system for unmanned supermarket |
| topic | Smart shopping system Personalized recommendation MU-SLAM Distributed DIMP |
| url | https://doi.org/10.1007/s44443-025-00182-4 |
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