AI-Powered Service Robotics for Independent Shopping Experiences by Elderly and Disabled People

Through human development and technological expansion, it has become apparent that the potential lies within each individual to have an essential part in the transcendence of society and the community. People less privileged than others may need more strength and determination to surpass their current resources to overcome normal and natural obstacles in order to simulate an environment where productivity and creativity exist. This paper aims to study an approach that will assist the elderly and people of determination in one of the most essential activities practiced by individuals: shopping. The study focuses on facilitating the acquirement of items from shelves and skipping the cashier line. The proposed system is a service robot supported by a robotic arm and a linear actuator as a lifting mechanism, controlled by a remote joystick to help the elderly or disabled people reach items on high shelves. The scanning system is based on barcode detection, using transfer learning. The network was designed using YOLOv2 layers connected to TinyYOLO as feature extraction layers. This network has proven to be the most practical, with 86.4% accuracy and real-time operation with 27 FPS in comparison to using the YOLOv2 layers with DarkNet or VGG19 as feature extraction layers. An anti-theft system is integrated into the robot to improve the reliability of the self-checkout feature. The system uses computer vision GMM and Kalman filter for item detection inside the cart, and the item is validated to be the one that has been scanned, using SURF for structural features, HSV for color, and load-sensors mounted to the base of the cart to measure the item’s weight.