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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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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