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In view of the poor image Jacobian matrix estimation accuracy, robot system constraints, and camera field⁃of⁃view constraints in robot visual servo, this paper proposes a robot visual servo control method combining adaptive unscented Kalman filtering (AUKF) and interior point model predictive control (IP⁃MPC). Firstly, the traditional UKF algorithm is not accurate enough for noise estimation, so Sage ⁃ Husa filtering is introduced to estimate the process noise covariance matrix online to improve the online estimation accuracy of image Jacobian matrix. Secondly, for the constraints of robot system and camera field⁃ of⁃view, the model predictive controller is designed by the interior point method, and the constraint problem is transformed into the problem of minimizing quadratic programming to realize the tracking control of robot visual servo system. The experiments show that the convergence speed of the proposed method is improved by 38.1%, its average error of image feature points is reduced by 37.4%, and the end⁃effector speed fluctuation of the robot is decreased, which demonstrate that the proposed method has significant improvements in visual servo control accuracy, and system response speed and stability in comparison with the traditional UKF algorithm.
[1] Zhang Sihang, Ji Haibo, Zhang Hepeng. Adaptive IBVS and force control for uncertain robotic system with unknown dead⁃zone inputs [J]. International Journal of Control, Automation and Systems, 2021, 19(4): 1651⁃1660.
[2] Chen Zhiyuan, Li Tiemin, Jiang Yao. Image⁃based visual servoing with collision ⁃free path planning for monocular vision⁃guided assembly [J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 1⁃17.
[3] Moccia R, Ficuciello F. Autonomous endoscope control algorithm with visibility and joint limits avoidance constraints for Da Vinci research kit robot [C]// 2023 IEEE International Conference on Robotics and Automation (ICRA). London, UK: IEEE, 2023: 776⁃781.
[4] Li Tao, Yu Jinpeng, Qiu Quan, et al. Hybrid uncalibrated visual servoing control of harvesting robots with RGB⁃D cameras [J]. IEEE Transactions on Industrial Electronics, 2023, 70(3): 2729⁃ 2738.
[5] Jo K, Chwa D. Immersion⁃and⁃invariance fuzzy adaptive image ⁃ based visual servoing of omnidirectional mobile robots considering uncertain feature point dynamics [J]. IEEE Transactions on Intelligent Vehicles, 2025, 10(4): 2698⁃2711.
[6] Hernandez⁃Barragan J, Villaseñor C, Lopez⁃Franco C, et al. Image based visual servoing with kinematic singularity avoidance for mobile manipulator [J]. PeerJ Computer Science, 2024, 10: e2559.
[7] Yu Qiuda, Wei Wu, Wang Dongliang, et al. A framework for IBVS using virtual work [J]. Actuators, 2024, 13(5): 181.
[8] 李静，黄友锐，韩涛，等.矿用智能巡检机器人无标定视觉伺服控制研究[J].工矿自动化，2021，47（11）：30⁃39.
[9] 王亚威.卡尔曼滤波与模糊逻辑结合的机械手视觉伺服控制方法研究[J].自动化技术与应用，2022，41（1）：70⁃74.
[10] 焦建军，李宗刚，李龙雄，等.无标定视觉伺服多轴孔装配定位方法研究[J].中南大学学报（自然科学版），2024，55（10）： 3731⁃3741.
[11] Ren Xiaolin, Li Hongwen. Uncalibrated image⁃based visual servoing control with maximum correntropy Kalman filter [J]. IFAC⁃PapersOnLine, 2020, 53(5): 560⁃565.
[12] Liu Kenan, Zhao Wuyun, Sun Bugong, et al. Application of updated Sage⁃Husa adaptive Kalman filter in the navigation of a translational sprinkler irrigation machine [J]. Water, 2019, 11(6): 1269.
[13] 李宗刚，李彦博，焦建军，等.一种基于自适应扩展卡尔曼滤波的铆接件视觉伺服精确装配方法[J/OL].系统仿真学报： 1 ⁃ 12[2023 ⁃ 11 ⁃ 22]. https：//doi. org/10.16182/j. issn1004731x. joss.23⁃1017.
[14] 梁喜凤，彭明，路杰，等.基于自适应无迹卡尔曼滤波的采摘机械手视觉伺服控制方法[J].农业工程学报，2019，35（19）： 230⁃237.
[15] Xu Geng, Huang Yulong, Gao Zhongxing, et al. A computationally efficient variational adaptive Kalman filter for transfer alignment [J]. IEEE Sensors Journal, 2020, 20(22): 13682⁃13693. [16] 滕游，刘安东，俞立.采用内点法和图像反馈的机器人视觉伺服预测控制[J].小型微型计算机系统，2021，42（1）：196⁃200.
[17] Zhu Tianqi, Mao Jianliang, Han Linyan, et al. Fuzzy adaptive model predictive control for image ⁃ based visual servoing of robot manipulators with kinematic constraints [J]. International Journal of Control, Automation and Systems, 2024, 22(1): 311⁃322.
Basic Information:
DOI：10.16652/j.issn.1004⁃373x.2026.09.023
Citation Information:
[1]Jia Yang, Zhang Jianye, Wu Zizhao.AUKF⁃based robot visual servo model predictive control[J].Modern Electronic Technique,2026(9):156-161.DOI:10.16652/j.issn.1004⁃373x.2026.09.023.