Abstract
This paper presents a model-free approach to visual servoing control of a robotic manipulator operated in unknown environments. A mapping estimator with the learning network is applied to visual servoing control of model-free robotic manipulator, which can online estimate the vision-motor relationship in a stochastic environment without knowledge of noise statistics. The dynamic mapping identification problems are solved by incorporating the improved Kalman filtering (KF) and network learning techniques, moreover, an observation correlation updating method is used to estimate the variance of the noises via online learning. Various grasping positioning experiments are conducted to verify the proposed approach by using an eye-in-hand robotic manipulator without calibration.
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References
Said, H.B., Marie, R., Stéphant, J., Labbani-Igbida, O.: Skeleton-based visual servoing in unknown environments. IEEE/ASME Trans. Mechatron. 23(6), 2750–2761 (2018)
Mao, H., Xiao, J.: Real-time conflict resolution of task-constrained manipulator motion in unforeseen dynamic environments. IEEE Trans. Rob. 35(5), 1276–1283 (2019)
Yang, C., Peng, G., Li, Y., Cui, R., Cheng, L., Li, Z.: Neural networks enhanced adaptive admittance control of optimized robot–environment interaction. IEEE Trans. Cybern. 49(7), 2568–2579 (2019)
Josip, M., Mirjana, B., Mojmil, C.: Comparison of uncalibrated model-free visual servoing methods for small-amplitude movements: a simulation study. Int. Jour. Adv. Robot. Syst. 108(11), 1–16 (2014)
Farrokh, J.S., Deng, L., Wilson, W.J.: Comparison of basic visual servoing methods. IEEE/ASME Trans. Mechatron. 16(5), 967–983 (2011)
Chaumette, F., Hutchinson, S.: Visual servo control. part I: basic approaches. IEEE Robot. Autom. Mag. 13(4), 82–90 (2006)
Jiang, P., Cheng, Y., Wang, X., Feng, Z.: Unfalsified visual servoing for simultaneous object recognition and pose tracking. IEEE Trans. Cybern. 46(12), 3032–3046 (2016)
Chaumette, F., Hutchinson, S.: Visual servo control. part II: Advanced approaches. IEEE Robo. Auto. Maga. 14(1), 109–118 (2007)
Do-Hwan, P., Jeong-Hoon, K., In-Joong, H.: Novel position-based visual servoing approach to robust global stability under field-of-view constraint. IEEE Trans. Ind. Electron. 59(12), 4735–4752 (2012)
Keshmiri, M., Xie, W.: Image-based visual servoing using an optimized trajectory planning technique. IEEE/ASME Trans. Mech. 22(1), 359–370 (2017)
Shi, H., Sun, G., Wang, Y., Hwang, K.S.: Adaptive image-based visual servoing with temporary loss of the visual signal. IEEE Trans. Ind. Infor. 15(4), 1956–1965 (2019)
Liang, X., Wang, H., Liu, Y., You, B., Liu, Z., Chen, W.: Calibration-free image-based trajectory tracking control of mobile robots with an overhead camera. IEEE Trans. Autom. Sci. Eng. 17(2), 933–946 (2020)
Xie, H., Lynch, A.F., Low, K.H., Mao, S.: Adaptive output-feedback image-based visual servoing for quadrotor unmanned aerial vehicles. IEEE Trans. Control Syst. Technol. 28(3), 1034–1041 (2020)
Gibson, J.D., Koo, B., Gray, S.D.: Filtering of colored noise for speech enhancement and coding. IEEE Trans. Signal Process. 39(8), 1732–1742 (1991)
Alanis, A.Y., Sanchez, E.N., Loukianov, A.G., Perez-Cisneros, M.A.: Real-time discrete neural block control using sliding modes for electric induction motors. IEEE Trans. Control Syst. Technol. 18(1), 11–21 (2010)
Soni, T., Rao, B.D., Zeidler, J.R., Ku, W.H.: Enhancement of images using the 2-D LMS adaptive algorithm. In: Proceedings of IEEE International Conference on Acoustic Speech and Signal Processing, pp. 3029–3032. IEEE, Toronto, ON, Canada (1991)
Stephanakis, I.M., Kollias, S.D.: Wavelet-based approach to adaptive wiener filtering of images in the presence of colored noise. Opt. Eng. 39(5), 1353–1363 (2000)
Khanesar, M.A., Kayacan, E., Teshnehlab, M., Kaynak, O.: Extended Kalman filter based learning algorithm for type-2 fuzzy logic systems and its experimental evaluation. IEEE Trans. Ind. Electron. 59(11), 4443–4455 (2012)
Zhan, R., Wan, J.: Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation. IEEE Signal Process. Lett. 13(7), 445–448 (2006)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82(1), 34–45 (1960)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant (NO. 61703356), in part by the Natural Science Foundation of Fujian Province under Grant (NO. 2022J011256), in part by the Innovation Foundation of Xiamen under Grant (NO. 3502Z20206071).
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Tian, J., Zhong, X., Luo, J., Peng, X. (2023). Adapted Mapping Estimator in Visual Servoing Control for Model-Free Robotics Manipulator. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14268. Springer, Singapore. https://doi.org/10.1007/978-981-99-6486-4_9
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DOI: https://doi.org/10.1007/978-981-99-6486-4_9
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