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Abstract
This study proposes an optimized hybrid visual servoing approach to overcome the imperfections of classical two-dimensional, three-dimensional and hybrid visual servoing methods. These imperfections are mostly convergence issues, non-optimized trajectories, expensive calculations and singularities. The proposed method provides more efficient optimized trajectories with shorter camera path for the robot than image-based and classical hybrid visual servoing methods. Moreover, it is less likely to lose the object from the camera field of view, and it is more robust to camera calibration than the classical position-based and hybrid visual servoing methods. The drawbacks in two-dimensional visual servoing are mostly related to the camera retreat and rotational motions. To tackle these drawbacks, rotations and translations in Z-axis have been separately controlled from three-dimensional estimation of the visual features. The pseudo-inverse of the proposed interaction matrix is approximated by a neuro-fuzzy neural network called local linear model tree. Using local linear model tree, the controller avoids the singularities and ill-conditioning of the proposed interaction matrix and makes it robust to image noises and camera parameters. The proposed method has been compared with classical image-based, position-based and hybrid visual servoing methods, both in simulation and in the real world using a 7-degree-of-freedom arm robot.
Original language | English |
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Pages (from-to) | 338-354 |
Number of pages | 17 |
Journal | Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering |
Volume | 236 |
Issue number | 2 |
Early online date | 30 Jun 2021 |
DOIs | |
Publication status | E-pub ahead of print - 30 Jun 2021 |
Bibliographical note
Funding Information:The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research was conducted as part of the project called ‘Reuse and Recycling of Lithium-Ion Batteries’ (RELIB). This work was supported by the Faraday Institution (grant no. FIRG005).
Keywords
- Hybrid visual servoing
- local linear model tree
- neuro-fuzzy neural network
- non-linear models
- optimized trajectory
ASJC Scopus subject areas
- Control and Systems Engineering
- Mechanical Engineering
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Dive into the research topics of 'Optimized hybrid decoupled visual servoing with supervised learning'. Together they form a unique fingerprint.Projects
- 1 Finished
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ReLIB - Faraday Challenge Fast Track proposal - Circular economy
Elliott, R. (Co-Investigator), Lee, R. (Co-Investigator), Allan, P. (Co-Investigator), Slater, P. (Co-Investigator), Stolkin, R. (Co-Investigator), Walton, A. (Co-Investigator), Overton, T. (Co-Investigator), Reed, D. (Co-Investigator), Anderson, P. (Principal Investigator), Windridge, D. (Co-Investigator) & Gough, R. (Co-Investigator)
Engineering & Physical Science Research Council
1/03/18 → 30/06/21
Project: Research Councils