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- Title
Uncalibrated visual servoing based on Kalman filter and mixed-kernel online sequential extreme learning machine for robot manipulator.
- Authors
Zhou, Zhiyu; Guo, Jiusen; Zhu, Zefei; Guo, Hanxuan
- Abstract
Visual servoing systems may suffer from interference by system noise when a Kalman filter is used to obtain a Jacobian matrix. Such interference may result in slow and poor convergence performance of the servoing system. To overcome these problems, we propose a mixed-kernel online sequential extreme learning machine (MIXEDKOSELM) with Kalman filter, which corrects the error of Kalman filtering algorithm, thus improving the accuracy of the image-based visual servoing (IBVS) system significantly. The proposed KF-MIXEDKOSELM-IBVS does not require the camera parameters in the servoing process, and it is highly robust to disturbance and noise statistical errors. The proposed KF-MIXEDKOSELM-IBVS is validated using the PUMA 560 manipulator in the MATLAB simulation environment. The simulation results clearly reveal that the KF-MIXEDKOSELM-IBVS algorithm has excellent performance by being robust and accurate.
- Subjects
KALMAN filtering; SEQUENTIAL learning; MACHINE learning; JACOBIAN matrices; STATISTICAL errors; ROBOTS; KERNEL operating systems
- Publication
Multimedia Tools & Applications, 2024, Vol 83, Issue 7, p18853
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-16381-y