An intelligent master–slave collaborative robot system for cafeteria service

Abstract

In order to improve the efficiency and reduce the labor cost of cafeterias, an intelligent master–slave collaborative robot system is developed for cafeteria service in this paper. The developed system can automatically complete the tasks of scooping dishes, taking bowls and pouring dishes into the bowl based on master–slave collaboration. Specifically, a dynamic geometry feature graph convolution network (DGG) is devised using the 3D point cloud of the dish, which can efficiently predict the scooping positions of the different dishes. Moreover, a master–slave motion planning control method is proposed to achieve fast and smooth trajectories for both arms, which can accomplish the cafeteria service tasks collaboratively. Furthermore, we establish a dataset containing point clouds and color images of various Chinese food. Experiments demonstrate that the DGG network can achieve superior performance over other state-of-the-art point cloud segmentation networks. Besides, the designed robot system can well meet the requirements of operation accuracy and speed, confirming its practicality in cafeteria services.