The Autonomous Shopping-Guide Robot in Cashier-Less Convenience Stores

• Main Authors: Po-Han Lin, Cheng-Yi Lin, Chen-Ting Hung, Jen-Jee Chen, Jia-Ming Liang
• Format: Article
• Language: English
• Published: Taiwan Association of Engineering and Technology Innovation 2020-01-01
• Series: Proceedings of Engineering and Technology Innovation
• Subjects: ROS (Robot Operating System); wireless networks; navigation; autonomous driving; cashier-less convenience store; route planning
• Online Access: http://ojs.imeti.org/index.php/PETI/article/view/3961

In recent years, several cashier-less convenience stores have appeared, including Amazon’s. A store without cashiers will become a trend in the near future. To substitute the employee in traditional stores, this research proposes and designs an autonomous shopping cart robot to guide customers’ purchase according to the requested shopping list to enhance their shopping experience. The core techniques of the system are the autonomous driving robot, the traffic control center for robots and the dynamic route planning algorithm. The robot is a self-propelled vehicle developed by ROS (Robot Operating System), in which we achieve the automatic driving via image recognition, April tag identification and driving direction guidance from the path planning and traffic control services. This enables the robot to lead customers to find their commodities following the preplanned route. In conjunction with the vocal service, the robot can notify the customer when arriving at each commodity, he or she plans to buy. We also design a light APP for customers to easily set up and manage their shopping list, call for the robotic shopping cart’s help, and interact with the shopping cart robot. To enhance the shopping experience of customers, we design the dynamic route planning genetic algorithm to dynamically plan the shopping route according to the customer’s request and the traffic condition. Experiments show that our genetic algorithm can provide the most stable performance and always get efficient shopping route planning in a limited time compared to other methods.