Design and development of full self-service smart shopping system for unmanned supermarket - Xiao Liang; Yu Qi; Shuo Xi; Guanglei Meng; Zhujun Wang (Journal of King Saud University: Computer and Information Sciences)
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.