Design of hybrid visual servo control system of mechanical arm based on image and position

Cao, Xuhang; Zhang, Qingyong; Ni, Zhixian

doi:10.1109/icrae.2017.8291407

Cited by 2 publications

(1 citation statement)

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“…Chakraborty et al 30 discussed the 2.5D visual servo control in terms of state and input delays, and a robust controller based on partial differential equations is proposed. An image- and position-based hybrid vision servo control system for robotic arms is proposed, 31 where the horizontal rotation and vertical swing of the robotic arm rely on IBVS control and the depth contraction relies on PBVS control, and the control system is experimentally shown to accurately capture the motion target with high stability. Rastegarpanah et al 32 proposed an optimised hybrid visual servo method that can provide more efficient optimised trajectories and shorter camera paths for robots with stronger robustness than traditional servo methods.…”

Section: Introductionmentioning

confidence: 99%

Image-based visual servoing with Kalman filter and swarm intelligence optimisation algorithm

Dong,

Li,

Wang

2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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The article proposes a new Kalman depth estimation and an improved swarm intelligence optimisation algorithm for adaptive tuning of servo gain for image-based visual servo control. First, a Kalman depth estimation model is established from the principle of image-based visual servoing, and two state equations are designed for depth estimation based on the number of state quantities. Second, the improved sparrow search algorithm is proposed to tune the servo gain adaptively to improve the convergence speed and stability. To verify the effectiveness of the proposed method, the conventional image-based visual servoing and conventional Kalman estimation are reproduced and compared with the proposed method, and the simulation is completed on the Simulink simulation platform for verification. Finally, the experiments are completed in the robotic arm experimental platform. Both the simulation and experimental results show the effectiveness of the proposed method, which reduces the redundancy of the camera and shortens the convergence time.

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Section: Introductionmentioning

confidence: 99%