Hand–eye calibration and grasping pose calculation with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot

Qian Zhang,Guo-Qin Gao

doi:10.1177/1729881420909012

Abstract

Due to motion constraint of 4-R(2-SS) parallel robot, it is difficult to calculate the translation component of hand–eye calibration based on the existing model solving method accurately. Additionally, the camera calibration error, robot motion error, and invalid calibration motion poses make it difficult to achieve fast and accurate online hand–eye calibration. Therefore, we propose a hand–eye calibration method with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot by improving the existing eye-to-hand model and solving method. Firstly, the eye-to-hand model of single camera is improved and the robot motion error in the improved model is compensated to reduce the influence of camera calibration error and robot motion error on model accuracy. Secondly, the vertical-component of hand–eye calibration is corrected based on vertical constraint between calibration plate and end effector in parallel robot to calculate the pose and motion error in calibration of 4-R(2-SS) parallel robot accurately. Thirdly, the nontrivial solution constraint of eye-to-hand model is constructed and adopted to remove invalid calibration motion poses and plan calibration motion. Finally, the proposed method was verified by experiments with a fruit sorting system based on 4-R(2-SS) parallel robot. Compared with random motion, the existing model, and solving method, the average time of online calibration based on planned motion decreases by 29.773 s and the average error of calibration based on the improved model and solving method decreases by 151.293. The proposed method can improve the accuracy and efficiency of hand–eye calibration of 4-R(2-SS) parallel robot effectively and further realize accurate and fast grasping.

Highlights

The automatic sorting of fruits based on robot is of great significance to the automated, large-scale, and accuracy development of agricultural production and agriculture product processing.[1,2,3] During the automatic sorting of fruits, the accurate and reliable calculation of grasping pose is the precondition to realize the accurate, fast, and nondestructive grasping control of robot.[4]
Comparison of online hand–eye calibration according to random motion and planned motion based on nontrivial solution constraint of eye-to-hand model
The planned motion based on nontrivial solution constraint of eye-to-hand model has Comparison of hand–eye calibration based on existing and improved methods

Summary

Introduction

The automatic sorting of fruits based on robot is of great significance to the automated, large-scale, and accuracy development of agricultural production and agriculture product processing.[1,2,3] During the automatic sorting of fruits, the accurate and reliable calculation of grasping pose is the precondition to realize the accurate, fast, and nondestructive grasping control of robot.[4]. The machine vision has the advantages of noncontact, good adaptability, costeffectiveness, and so on. It is more suitable for grasping pose calculation of fruits.[5] there is a challenging problem in grasping pose calculation based on machine vision, which is online hand–eye calibration with high accuracy and efficiency.[6,7,8] In addition, the parallel robot, which features strong rigidity, stable structure, and high precision, places greater demands on accuracy and efficiency of online hand–eye calibration.[9,10] Currently, the main hand–eye systems include eye-to-hand and eye-inhand according to the pose relationship between camera and end effector of robot. The eyeto-hand system with stationary camera has the advantages of high detecting accuracy and good stability, which is more suitable for the fruit sorting system of parallel robot with limited work space.[15]

Methods

Results

Conclusion