Grounding rod hanging and removing robot with hand-eye self-calibration capability in substation

In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in...

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Bibliographic Details
Published in:Complex & intelligent systems Vol. 10; no. 5; pp. 6491 - 6507
Main Authors: Lin, Yunhan, Wang, Jiahui, Liu, Kaibo, Min, Huasong
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.10.2024
Springer Nature B.V
Springer
Subjects:
Algorithms
Calibration
Complexity
Computational Intelligence
Data Structures and Information Theory
Datasets
End effectors
Engineering
Error correction
Feature extraction
Grounding rod hanging and removing robot
Hand-eye self-calibration
Matching
Multi-stage objective function optimization
Optimization
Original Article
Robots
Self calibration
Substations
The minimization of reprojection error
Wind speed
Hand-eye self-calibration
Grounding rod hanging and removing robot
Multi-stage objective function optimization
The minimization of reprojection error
ISSN:2199-4536, 2198-6053
Online Access:Get full text
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Summary:In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-024-01492-2