A point cloud registration-based calibration algorithm for robot offline programming automatic loading in aero-grinding applications

Purpose>During the process of the robotic grinding and polishing operations on aero-engine blades, the key problem of calibration error lies in fixture error and uneven margin. To solve this problem, this paper aims to propose a novel method to achieve rapid online calibration of the workpiece co...

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Veröffentlicht in:Industrial robot Jg. 49; H. 6; S. 1218 - 1228
Hauptverfasser: Mu, Zixin, Cai, Zhenhua, Zeng, Chunnian, Li, Zifan, Liang, Xufeng, Yang, Fan, Chen, Tingyang, Dong, Shujuan, Deng, Chunming, Niu, Shaopeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bedford Emerald Group Publishing Limited 20.09.2022
Schlagworte:
Accuracy
Algorithms
Alignment
Calibration
Coordinates
End effectors
Error reduction
Grinding
Image segmentation
Iterative algorithms
Laser applications
Machining
Methods
Polishing
Registration
Robotics
Robots
Three dimensional models
Workpieces
ISSN:0143-991X, 0143-991X, 1758-5791
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Zusammenfassung:Purpose>During the process of the robotic grinding and polishing operations on aero-engine blades, the key problem of calibration error lies in fixture error and uneven margin. To solve this problem, this paper aims to propose a novel method to achieve rapid online calibration of the workpiece coordinate system through laser-based measurement techniques.Design/methodology/approach>The authors propose a calibration strategy based on point cloud registration algorithm. The main principle is presented as follows: aero blade mounted on clamping end-effector is hold by industry robot, the whole device is then scanned by a 3D laser scanner to obtain its surface point cloud, and a fast segmentation method is used to acquire the point cloud of the workpiece. Combining Super4PCS algorithm with trimmed iterative closest point, we can align the key points of the scanned point cloud and the sampled points of the blade model, thus obtaining the translation and rotation matrix for calculating the workpiece coordinate and machining allowance. The proposed calibration strategy is experimentally validated, and the positioning error, as well as the margin distribution, is finally analyzed.Findings>The experimental results show that the algorithm can well accomplish the task of cross-source, partial data and similar local features of blade point cloud registration with high precision. The total time spent on point cloud alignment of 100,000 order of magnitude blade is about 4.2 s, and meanwhile, the average point cloud alignment error is reduced to below 0.05 mm.Originality/value>An improved point cloud registration method is proposed and introduced into the calibration process of a robotic system. The online calibration technique improves the accuracy and efficiency of the calibration process and enhances the automation of the robotic grinding and polishing system.
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ISSN:0143-991X
0143-991X
1758-5791
DOI:10.1108/IR-12-2021-0284