Grasping model and pose calculation of parallel robot for fruit cluster

Grasping pose calculation with high accuracy and efficiency is precondition to realize the accurate, fast and nondestructive automatic sorting based on machine vision for a robot. Due to the unconstraint shape and location of stalk, the variability of cluster morphology, and unobvious difference between the stalk region and non-stalk region in the image of randomly placed fruit cluster, it is difficult to acquire the grasping pose parameters of robot for fruit cluster sorting based on machine vision accurately. Therefore, in this paper, a grasping pose calculation method of parallel robot for fruit cluster based on 3D grasping model is proposed by fitting stalk skeleton of fruit cluster and constructing 3D grasping model for the machine vision automatic sorting system for fruit cluster based on 4-R(2-SS) parallel robot. Firstly, according to the stalk node, the relationship between the finger length of robot clamping mechanism and centerline of stalk, the stalk of fruit cluster is divided into four categories: the long stalk without node, the short stalk without node, the long stalk with node and the short stalk with node. Four 3D grasping models of randomly placed fruit cluster based on the stalk skeleton are constructed according to the 3D pose of stalk and the features of robot clamping mechanism. The models does not need to calculate the contour parameters of stalk without constraint shape and location, and solve the problem that it is difficult to grasp randomly placed fruit cluster stably and effectively adopting the 2D grasping pose based on plane contour parameters. Secondly, a morphological image segmentation method for extracting stem region of fruit cluster is designed based on the distance between the contours of stem region and berry region. Then a multi-dimensional feature vector is constructed based on the descriptors of region. The Gaussian mixture model that can learn the object features independently is adopted to extract the stalk region from the stem region. It can solve the problem that it is difficult to extract and fit the stalk skeleton accurately because of the variability of cluster morphology, unobvious difference between the stalk region and non-stalk region in the image of randomly placed fruit cluster. Thirdly, the grasping pose parameters including spatial position, rotation angle about Z-axis and finger opening width of clamping mechanism under different grasping conditions are calculated based on the stalk skeleton of fruit cluster and 3D grasping model. Then the transformation matrix of clamping mechanism from the current pose to the grasping pose in world coordinate system is calculated based on the closed loop of coordinate transformation chain, which can be directly used to realize the automatic and stable grasping of fruit cluster. Finally, the proposed grasping pose calculation method is verified by experiments with the self-developed machine vision automatic sorting system for fruit cluster based on 4-R(2-SS) parallel robot. The average errors of pose parameters x, y, z, θ and w calculated by the proposed grasping pose calculation method are 1.400 mm, 1.217 mm, 1.837 mm, 3.331° and 0.833 mm respectively. Compared with the existing 2D grasping pose calculation method, the success rates of grasping for the fruit cluster with stalk node and the fruit cluster without stalk node, and the average success rate of grasping based on the proposed method increased by 14, 12 and 13 percentage points respectively. Experimental results demonstrate that the proposed grasping pose calculation method of parallel robot for fruit cluster based on 3D grasping model can effectively improve the grasping accuracy of 4-R(2-SS) parallel robot for randomly placed fruit cluster based on machine vision, and realize accurate and fast automatic sorting of fruit clusters.