Grasping Point Detection of Randomly Placed Fruit Cluster Using Adaptive Morphology Segmentation and Principal Component Classification of Multiple Features

Precise and rapid grasping point detection based on machine vision is one of the challenging problems in automatic sorting of randomly placed fruit clusters by robot. Grasping stalk of fruit cluster can improve grasping success probability and reduce fruit damage. For the problem that the segmentation of stalk candidates for randomly placed fruit cluster based on existing morphology algorithm tends to be low precision, an improved image segmentation algorithm based on adaptive morphology is proposed. According to edge distances defined based on minimum distance between edge point and unconnected components in minimum domain, the adaptive convolution kernel is constructed. In addition, a run analysis method with different and unordered labels is designed to reduce calculation time of edge distances. For the problem that it is difficult to describe and classify unconstraint stalk by existing features, an improved region classification algorithm based on principal components of multiple features is proposed. The descriptors based on features of object region are designed and principal components of multiple features are extracted based on variance contribution to improve precision and speed of stalk extraction. The proposed grasping point detection method of randomly placed fruit cluster based on improved morphology image segmentation and region classification algorithms is verified by experiments with grape clusters based on parallel robot sorting system. The results show that, compared with existing methods, the average precisions of segmentation and extraction for stalk increase by 9.89% and 2.17% respectively, the average precision and time of grasping point detection reach 94.50% and 2.01s respectively.