Detection and Manipulation of Heaped Fried Chicken

Abstract

It is hard for robots to place foods automatically on a launch box because detecting shapes of foods are difficult. Industrial products are usually standardized; however foods are not done. In previous research, an irregular shaped food detection method is proposed without 3D shape models. However, it needs empirical parameters and thresholds. We propose a new method which uses clustering to graph structure data converted from 3D point cloud data to detect irregular shaped foods. Our method has fewer required parameters than the previous method. We have an experiment of fried chicken serving. The experimental results show 85% success rate.