Uncalibrated Visual Servoing Based on Kalman Filter Optimized by SPSA

Authors

Jinqiang Zhang, Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics and Automation, Shanghai University, Shanghai 200072, China;
Xianxia Zhang, Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics and Automation, Shanghai University, Shanghai 200072, China;

Abstract

Abstract: Considering the problem of robot uncalibrated visual servoing, this paper presents a method for online estimation of image Jacobian matrix based on Kalman filter optimized by simultaneous perturbation stochastic approximation algorithm. This method takes the robot image Jacobian matrix as the system state, and uses Kalman filter to observe the system state. In order to improve the performance of the filter, the simultaneous perturbation stochastic approximation algorithm is used to optimize the filter parameters. This method is used to estimate the image Jacobian matrix and to design the control strategy, which can avoid complicated system calibration process. The simulation results indicate that the proposed method can achieve the visual positioning of the 6-degree of freedom robot with high accuracy and stability under the uncalibrated situation.

Recommended Citation

Zhang, Jinqiang and Zhang, Xianxia (2019) "Uncalibrated Visual Servoing Based on Kalman Filter Optimized by SPSA," Journal of System Simulation: Vol. 30: Iss. 12, Article 33.
DOI: 10.16182/j.issn1004731x.joss.201812033
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss12/33

First Page

4754

Revised Date

2018-08-17

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201812033

Last Page

4759

CLC

TP24

Recommended Citation

Zhang Jinqiang, Zhang Xianxia. Uncalibrated Visual Servoing Based on Kalman Filter Optimized by SPSA[J]. Journal of System Simulation, 2018, 30(12): 4754-4759.

DOI

10.16182/j.issn1004731x.joss.201812033