A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 18.3, Top 1 (SCI Q1)
    CiteScore: 28.2, Top 1% (Q1)
    Google Scholar h5-index: 95, TOP 5
Turn off MathJax
Article Contents
D. Ma, R. Huang, and L. Jin, “Cerebellum-inspired visual servoing and orientation control for redundant manipulators,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126302
Citation: D. Ma, R. Huang, and L. Jin, “Cerebellum-inspired visual servoing and orientation control for redundant manipulators,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126302

Cerebellum-Inspired Visual Servoing and Orientation Control for Redundant Manipulators

doi: 10.1109/JAS.2026.126302
Funds:  This work was supported by the National Natural Science Foundation of China (62476115), and in part by the Supercomputing Center of Lanzhou University
More Information
  • Cerebellum-inspired control schemes simulate the motor control capabilities of the cerebellum and have achieved notable progress in trajectory tracking tasks without addressing the challenges of visual servoing of the redundant manipulator. In this paper, a cerebellum model is constructed according to similarities between the cerebellum and the echo state network (ESN), and a cerebellum-inspired visual servoing and orientation control (CIVSOC) scheme is proposed based on the cerebellum model. The proposed scheme achieves the visual servoing control of the redundant manipulator while maintaining the stable orientation of the end-effector. Furthermore, to reduce the effect of noise, a Kalman filter (KF) is integrated into the control scheme and improves the robustness. Finally, simulations and experiments validate the effectiveness of the CIVSOC scheme in achieving both visual servoing and orientation control of the manipulator.

     

  • loading
  • [1]
    F. Manara, A. Poka, D. Ludovico, A. Pistone, L. De Mari Casareto Dal Verme, C. Canali, G. Berselli, and D. G. Caldwell, “Tendon-driven hyper-redundant manipulators: A review on design solutions,” IEEE/ASME Trans. Mechatronics, vol. 30, no. 5, pp. 3852−3867, Oct. 2025. doi: 10.1109/TMECH.2025.3561597
    [2]
    D. Ma, J. Lv, C. Xu, and L. Jin, “Logic-adaptive discrete neural dynamics for distributed cooperative control of multirobot systems via minimum infinity norm optimization,” IEEE Trans. Fuzzy Syst., vol. 34, no. 2, pp. 531−539, Feb. 2026. doi: 10.1109/TFUZZ.2025.3642241
    [3]
    Y. Zhang and S. Li, “Kinematic control of serial manipulators under false data injection attack,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1009−1019, Apr. 2023. doi: 10.1109/JAS.2023.123132
    [4]
    T. Chen, W. Yang, S. Li, and X. Luo, “Data-driven calibration of industrial robots: A comprehensive survey,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 8, pp. 1544−1567, Aug. 2025. doi: 10.1109/JAS.2025.125237
    [5]
    D. Ma, S. Li, and L. Jin, “Cooperative control for multirobot systems with partially unknown structural characteristics: A game-theoretic perspective,” IEEE Trans. Ind. Informat., vol. 22, no. 8, pp. 7518−7529, Aug. 2026. doi: 10.1109/TII.2026.3686881
    [6]
    A. Poka, F. Manara, D. Ludovico, A. Pistone, L. De Mari Casareto Dal Verme, C. Canali, G. Berselli, and D. G. Caldwell, “Alternative solutions to tendon-driven hyper-redundant manipulators: A review on design and actuation strategies,” IEEE/ASME Trans. Mechatronics, vol. 30, no. 5, pp. 3938−3952, Oct. 2025. doi: 10.1109/TMECH.2025.3553289
    [7]
    Z. Xie, Y. Zheng, and L. Jin, “A data-driven image-based visual servoing scheme for redundant manipulators with unknown structure and singularity solution,” IEEE Trans. Syst., Man, Cybern. Syst., vol. 54, no. 10, pp. 6230−6241, Oct. 2024. doi: 10.1109/TSMC.2024.3420882
    [8]
    Z. Liu, K. Wang, D. Liu, Q. Wang, and J. Tan, “A motion planning method for visual servoing using deep reinforcement learning in autonomous robotic assembly,” IEEE/ASME Trans. Mechatronics, vol. 28, no. 6, pp. 3513−3524, Dec. 2023. doi: 10.1109/TMECH.2023.3275854
    [9]
    Y. Zhang and S. Li, “A neural controller for image-based visual servoing of manipulators with physical constraints,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 11, pp. 5419−5429, Nov. 2018. doi: 10.1109/TNNLS.2018.2802650
    [10]
    P. Kou, D. Liang, C. Wang, Z. Wu, and L. Gao, “Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks,” Appl. Energy, vol. 264, Art. no. 114772, Apr. 2020.
    [11]
    A. E. Urai, B. Doiron, A. M. Leifer, and A. K. Churchland, “Large-scale neural recordings call for new insights to link brain and behavior,” Nat. Neurosci., vol. 25, no. 1, pp. 11−19, Jan. 2022. doi: 10.1038/s41593-021-00980-9
    [12]
    C. I. De Zeeuw, S. G. Lisberger, and J. L. Raymond, “Diversity and dynamism in the cerebellum,” Nat. Neurosci., vol. 24, no. 2, pp. 160−167, Feb. 2021. doi: 10.1038/s41593-020-00754-9
    [13]
    H. Qiao, J. Chen, and X. Huang, “A survey of brain-inspired intelligent robots: Integration of vision, decision, motion control, and musculoskeletal systems,” IEEE Trans. Cybern., vol. 52, no. 10, pp. 11267−11280, Oct. 2022. doi: 10.1109/TCYB.2021.3071312
    [14]
    X. Wang and N. Tan, “Cerebellum-inspired model-free tracking control and visual servoing of a rigid-flexible hybrid robotic endoscope with RCM constraints,” IEEE Trans. Ind. Electron., vol. 70, no. 12, pp. 12626−12635, Dec. 2023. doi: 10.1109/TIE.2023.3239875
    [15]
    X. Chen, Z. Su, L. Jin, and S. Li, “A correntropy-based echo state network with application to time series prediction,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 2, pp. 425−435, Feb. 2025. doi: 10.1109/JAS.2024.124932
    [16]
    J. Lin, F.-L. Chung, and S. Wang, “A fast parametric and structural transfer leaky integrator echo state network for reservoir computing,” IEEE Trans. Syst., Man, Cybern. Syst., vol. 54, no. 5, pp. 3257−3269, May 2024. doi: 10.1109/TSMC.2024.3358567
    [17]
    X. Li, F. Li, S. Zheng, and Q. Liu, “NOx concentration prediction with a flexible cascaded echo-state network in a cement clinker calcination system,” IEEE Trans. Ind. Informat., vol. 20, no. 7, pp. 9644−9654, Jul. 2024. doi: 10.1109/TII.2024.3386973
    [18]
    X. Chen, L. Jin, and B. Hu, “A cerebellum-inspired control scheme for kinematic control of redundant manipulators,” IEEE Trans. Ind. Electron., vol. 71, no. 7, pp. 7539−7547, Jul. 2024. doi: 10.1109/TIE.2023.3312427
    [19]
    J. Zhang, J. Chen, S. Zhong, and H. Qiao, “A bio-inspired integration model of basal ganglia and cerebellum for motion learning of a musculoskeletal robot,” J. Syst. Sci. Complexity, vol. 37, no. 1, pp. 82−113, Feb. 2024. doi: 10.1007/s11424-024-3414-7
    [20]
    N. Tan, P. Yu, and F. Ni, “A cerebellum-inspired network model and learning approaches for solving kinematic tracking control of redundant manipulators,” IEEE Trans. Cogn. Develop. Syst., vol. 15, no. 1, pp. 150−162, Mar. 2023. doi: 10.1109/TCDS.2022.3149622
    [21]
    J. Yan, M. Liu, and L. Jin, “Cerebellum-inspired model predictive control for redundant manipulators with unknown structure information,” IEEE Trans. Cogn. Develop. Syst., vol. 16, no. 3, pp. 1198−1210, Jun. 2024. doi: 10.1109/TCDS.2023.3340179
    [22]
    M. Liu, R. Huang, Y. Yang, and X. Ma, “Cerebellum-inspired position and orientation control of redundant manipulators at joint-acceleration level,” IEEE Trans. Ind. Electron., vol. 72, no. 5, pp. 5051−5059, May 2025. doi: 10.1109/TIE.2024.3463028
    [23]
    X. Ma, Z. Xie, and M. Liu, “Discretized data-driven neural dynamics for model-adaptive kinematic control of redundant manipulators,” IEEE Trans. Autom. Sci. Eng., vol. 22, pp. 17606−17617, Jun. 2025. doi: 10.1109/TASE.2025.3582240
    [24]
    Y. Lian, X. Xiao, J. Zhang, L. Jin, J. Yu, and Z. Sun, “Neural dynamics for cooperative motion control of omnidirectional mobile manipulators in the presence of noises: A distributed approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1605−1620, Jul. 2024. doi: 10.1109/JAS.2024.124425
    [25]
    D. Ma, J. Zhang, L. Jin, and S. Li, “Discrete neural dynamics combined with Kalman filter for cooperative control of multirobot systems with constraints,” IEEE Trans. Control Syst. Technol., vol. 34, no. 4, pp. 1954−1963, Jul. 2026. doi: 10.1109/TCST.2026.3682333
    [26]
    K. Hashimoto, T. Kimoto, T. Ebine, and H. Kimura, “Manipulator control with image-based visual servo,” in Proc. IEEE Int. Conf. Robotics and Automation, Sacramento, USA, pp. 2267−2271, 1991.
    [27]
    K. Hashimoto, T. Ebine, and H. Kimura, “Visual servoing with hand-eye manipulator-optimal control approach,” IEEE Trans. Robot. Autom., vol. 12, no. 5, pp. 766−774, Oct. 1996. doi: 10.1109/70.538981
    [28]
    A. Mustafa, M. Mazouchi, and H. Modares, “Secure event-triggered distributed Kalman filters for state estimation over wireless sensor networks,” IEEE Trans. Syst., Man, Cybern. Syst., vol. 53, no. 2, pp. 1268−1283, Feb. 2023. doi: 10.1109/TSMC.2022.3195121
    [29]
    M. Boutayeb, H. Rafaralahy, and M. Darouach, “Convergence analysis of the extended Kalman filter used as an observer for nonlinear deterministic discrete-time systems,” IEEE Trans. Autom. Control, vol. 42, no. 4, pp. 581−586, Apr. 1997. doi: 10.1109/9.566674

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)

    Article Metrics

    Article views (28) PDF downloads(7) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return