Authors

Xueqi Mou, Haisheng Yu, Qingkun Guo, Qing Yang, Xiangxiang Meng

Abstract

Traditional uncalibrated visual servo control faces challenges in achieving accurate target positioning at the robot end-effector, since the filtering parameters are not sensitive to environmental changes. In this article, the designed adaptive Kalman filtering algorithm is utilized to estimate the image Jacobian matrix, reinforcing the robustness of the robot system. Dependent on this estimation, the visual servo controller is formulated, generating the joint control signal of the robot driven by direct current motors. Besides, considering energy transformation based control, error port-controlled Hamiltonian method is introduced to enhance the stability and accurate performance of joint servo control. Simulation and experimental results demonstrate that the designed control strategy achieves faster and more accurate location of the target.

Citation

  • Journal: 2025 37th Chinese Control and Decision Conference (CCDC)
  • Year: 2025
  • Volume:
  • Issue:
  • Pages: 6354–6359
  • Publisher: IEEE
  • DOI: 10.1109/ccdc65474.2025.11090592

BibTeX

@inproceedings{Mou_2025,
  title={{Uncalibrated Visual Servo Control Using Adaptive Kalman Filtering for Robots Driven by DC Motors}},
  DOI={10.1109/ccdc65474.2025.11090592},
  booktitle={{2025 37th Chinese Control and Decision Conference (CCDC)}},
  publisher={IEEE},
  author={Mou, Xueqi and Yu, Haisheng and Guo, Qingkun and Yang, Qing and Meng, Xiangxiang},
  year={2025},
  pages={6354--6359}
}

Download the bib file

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