Liu Qifang, Mao Jianliang, Han Linyan, Zhang Chuanlin, Yang Jun
IEEE Trans Cybern. 2025 May;55(5):2424-2436. doi: 10.1109/TCYB.2025.3546800. Epub 2025 Apr 23.
This article simultaneously addresses the dual-rate and view constraints issues for the image-based visual servoing (IBVS) system of robot manipulators. Considering the low sampling bandwidth of the camera, potentially diminishing the efficiency of the robotic controller in updating low-level servoing control commands, a predictive observer (PO) is initially designed to forecast the system output during the high-level sampling intervals. Moreover, by leveraging a mixture of soft-sensing and real-measured signals, a dual-rate integral-based prescribed performance control (DRIPPC) approach is devised. The benefit lies in that the proposed control method samples the low-frequency state signal while generating a relatively high-frequency control action, ensuring rapid response of the robot manipulator while maintaining strict adherence to field-of-view (FOV) constraints. Finally, the effectiveness of the proposed control approach is validated through a series of experiments conducted on a Universal Robots 5 (UR5) manipulator.
本文同时解决了机器人操纵器基于图像的视觉伺服（IBVS）系统的双速率和视野约束问题。考虑到相机的低采样带宽可能会降低机器人控制器更新低级伺服控制命令的效率，首先设计了一种预测观测器（PO）来预测高级采样间隔期间的系统输出。此外，通过利用软测量信号和实际测量信号的混合，设计了一种基于双速率积分的规定性能控制（DRIPPC）方法。其优点在于，所提出的控制方法对低频状态信号进行采样，同时生成相对高频的控制动作，确保机器人操纵器快速响应，同时严格遵守视野（FOV）约束。最后，通过在通用机器人5（UR5）操纵器上进行的一系列实验验证了所提出控制方法的有效性。
