Designing a Supermarket Service Robot Based on Deep Convolutional Neural Networks

Abstract

In order to save people’s shopping time and reduce labor cost of supermarket operations, this paper proposes to design a supermarket service robot based on deep convolutional neural networks (DCNNs). Firstly, according to the shopping environment and needs of supermarket, the hardware and software structure of supermarket service robot is designed. The robot uses a robot operating system (ROS) middleware on Raspberry PI as a control kernel to implement wireless communication with customers and staff. So as to move flexibly, the omnidirectional wheels symmetrically installed under the robot chassis are adopted for tracking. The robot uses an infrared detection module to detect whether there are commodities in the warehouse or shelves or not, thereby grasping and placing commodities accurately. Secondly, the recently-developed single shot multibox detector (SSD), as a typical DCNN model, is employed to detect and identify objects. Finally, in order to verify robot performance, a supermarket environment is designed to simulate real-world scenario for experiments. Experimental results show that the designed supermarket service robot can automatically complete the procurement and replenishment of commodities well and present promising performance on commodity detection and recognition tasks.

Unified Software Platform for Intelligent Home Service Robots

“…Being one of the earlier service robot systems with ROS middleware, it relies upon classic machine vision techniques for perception which have been found to be unreliable, and have not been adopted in other hardware platforms. In [15], the authors presented a software framework for a service robot utilizing deep learning-based perception algorithms, but it was limited by some rather basic custom hardware used for the experiments and is unlikely to be capable of handling general home service tasks.…”