Time delay exists in image-based visual servo system, which will have a certain impact on the system control. To solve the impact of time delay, the time delay compensation of the object feature point image and the image Jacobian matrix is discussed in this paper. Some work is done in this paper: The estimation of the object feature point image under time delay is based on a proposed robust decorrelation Kalman filtering model, for the measurement vectors which cannot be obtained during time delay in the robust Kalman filtering model, a polynomial fitting method is proposed in which the selection of the polynomial includes the position, velocity and acceleration of the object feature point which impact the feature point trajectory, then the more accurate object feature point image can be obtained. From the estimated object feature point image under time delay, the more accurate image Jacobian matrix under time delay can be obtained. Simulation and experimental results verify the feasibility and superiority of this paper method.

1. [1] G.-W. Kim, “Uncalibrated visual servoing through the efficient estimation of the image Jacobian for large residual,” J. of Electrical Engineering and Technology, Vol.8, No.2, pp. 385-392, 2013.
2. [2] X. J. Zeng, X. H. Huang, and M. Wang, “Vision servoing based on online estimation of image Jacobian matrix of Broyden,” J. of Huazhong University of Science and Technology (Natural Science Edition), Vol.36, No.9, pp. 17-20, 2008 (in Chinese).
3. [3] X. G. Zhong, X. Y. Zhong, and X. F. Peng, “Robust Kalman filtering cooperated Elman neural network learning for vision-sensing-based robotic manipulation with global stability,” Sensors, Vol.13, No.10, pp. 13464-13486, 2013.
4. [4] J. Zhang and D. Liu, “Online estimation of image Jacobian Matrix based on robust information filter,” J. of Xi’an University of Technology, Vol.27, No.2, pp. 133-138, 2011.
5. [5] H. X. Li, Y. H. Shi, and G. R.Wang, “Visual guidance of welding robot using SVR-Jacobian estimator,” J. of South China University of Technology (Natural Science Edition), Vol.41, No.7, pp. 19-25, 2013 (in Chinese).
6. [6] F. Wang, F. Sun, J. Zhang, B. Lin, and X. Li, “Unscented particle filter for online total image Jacobian matrix estimation in robot visual servoing,” IEEE Access, Vol.7, pp. 92020-92029, 2019.
7. [7] Y. Zheng and M. Yang, “Image Jacobian matrix identification based on Kalman filtering and visual predictive control of manipulator,” Proc. of the 35th Chinese Control Conf., pp. 10273-10278, 2016.
8. [8] X. Zhong, X. Zhong, and X. Peng, “Robots visual servo control with features constraint employing Kalman-neural-network filtering scheme,” Neurocomputing, Vol.151, pp. 268-277, 2015
9. [9] Z. Gao and J. Su, “The estimation of image Jacobian matrix with time-delay compensation for uncalibrated visual servoing,” Control Theory and Applications, Vol.26, No.1, pp. 23-27, 2009 (in Chinese).
10. [10] Y. Wu, “Research on uncalibrated visual servoing for robots based on time-delay compensation,” Master’s Thesis, University of Science and Technology of China, 2015 (in Chinese).
11. [11] D. Nishio, M. Nakamura, S. Komada, and J. Hirai, “Tracking of moving object by manipulator using estimated image feature and its error correction on image planes,” The 8th IEEE Int. Workshop on Advanced Motion Control (AMC ’04), pp. 653-657, 2004.
12. [12] M. Nakadokoro, S. Komada, and T. Hori, “Stereo visual servo of robot manipulators by estimated image features without 3D reconstruction,” IEEE Conf. on Systems, Man and Cybernetics (SMC 1999), pp. 571-576, 1999.
13. [13] W. Liu, Z. Bing, S. Lu, and H. Lu, “Online estimation of image Jacobian matrix with time-delay compensation,” Computer Engineering and Applications, Vol.46, No.21, pp. 181-184, 2010.
14. [14] I. Kinbara, S. Komada, and J. Hirai, “Visual servo of active cameras and manipulators by time delay compensation of image features with simple on-line calibration,” Int. Joint Conf. on SICE-ICASE, pp. 5317-5322, 2007.
15. [15] H. Li, J. Liu, Y. Li, X. Lu, and L. Sun, “Visual servo of uncalibrated eye-in-hand system with time-delay compensation,” Proc. of the 8th World Congress on Intelligent Control and Automation, pp. 1322-1328, 2010.
16. [16] J. Xin, L. Bai, and D. Liu, “Adaptive Kalman filter-based robot 6DOF uncalibrated vision positioning,” J. of System Simulation, Vol.26, No.3, pp. 586-591, 2014 (in Chinese).
17. [17] L. Chen, G. Qi, and A. Sheng, “Distributed Kalman optimal fusion with colored measurement noises,” J. of System Simulation, Vol.21, No.14, pp. 4443-4446, 2009 (in Chinese).
18. [18] Y. G. Zhang and Y. Huang, “Gaussian approximate filter for stochastic dynamic systems with randomly delayed measurements and colored measurement noises,” Science China (Information Sciences), Vol.59, Article No.92207, 2016.