Autonomous Shopping Cart: A New Concept of Service Robot for Assisting Customers

Alves, Raulcezar M. F.; Linhares, Brian; Souza, Jefferson R.

doi:10.1109/lars/sbr/wre.2018.00086

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…al. [12] introduced an autonomous shopping cart that by means of a Route Scheduling System, is able to find the shortest tour that passes only through required shelves with a Genetic Algorithm using the TSP model. In 2021, Sonawani et al [13] presented a setup where using a monocular camera and Bluetooth Low-Energy (BLE) obtained a robot system which can immediately be used after placing sensors in the environment.…”

Section: Related Workmentioning

confidence: 99%

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Galdelli,

Pietrini,

Mancini

et al. 2024

IEEE Access

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In the ever-evolving landscape of retail, understanding shopper behavior is pivotal for optimizing sales and effectively managing product availability and placement. This study explores the integration of autonomous mobile robots into the shelf inspection process, leveraging advancements in automation, information, and robotics technology. Performing mapping tasks, these robots incorporate insights into customer behavior by exploiting various sources of behavioral data, including trajectories and product interactions. Motivated by the complex and dynamic nature of modern stores, our research seeks to bridge the gap in retail inventory management. Our unique contribution lies in the development of a novel path planning method for robots, specifically tailored for an automated inventory management system. By focusing on customer trajectories and product interactions, we aim to enhance the arrangement and positioning of products within retail spaces. Our research is motivated by the need to address the challenges faced by retailers in optimizing store layouts and product placements. The proposed strategy utilizes a heatmap and a vision-based system to analyze spatial and temporal patterns of shopper behavior. This information is then employed to optimize robot navigation in both highly and less-visited areas. Trajectories and product interactions data from real store installations were utilized in simulation, providing valuable insights into optimal planning for mobile robots to visit Points of Interest (PoI). The active shopping cart tracking system generated heatmaps, while a vision-based system collected shopper-products interactions data. Subsequently, our approach was deployed on a real retail robot for inventory management, and the path planning source code was released. Our findings demonstrate that the path planned by our approach not only avoids collisions with static store sections but also optimizes paths in areas with significant customer-shelf activity.

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Section: Related Workmentioning

confidence: 99%

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Galdelli,

Pietrini,

Mancini

et al. 2024

IEEE Access

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Interdisciplinary Explorations of Processes of Mutual Understanding in Interaction with Assistive Shopping Robots

Yamazaki

Krummheuer

Imai

2022

2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

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

et al. 2021

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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.

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Autonomous Shopping Cart: A New Concept of Service Robot for Assisting Customers

Cited by 3 publications

References 9 publications

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Interdisciplinary Explorations of Processes of Mutual Understanding in Interaction with Assistive Shopping Robots

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

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