Qiu Haitao, Huang Dan, Zhang Bo, Wang Ming
School of Electric Power Engineering, South China University of Technology, Guangzhou, China.
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Front Neurosci. 2023 Jul 14;17:1221740. doi: 10.3389/fnins.2023.1221740. eCollection 2023.
This study proposes a multidimensional uncalibrated technique for tracking and grasping dynamic targets by a robotic arm in the eye-in-hand mode. This method avoids complex and cumbersome calibration processes, enabling machine vision tasks to be adaptively applied in a variety of complex environments, which solved the problem of traditional calibration methods being unstable in complex environments. The specific method used in this study is first, in the eye-in-hand mode, the robotic arm moves along the , and axes in sequence, and images are taken before and after each movement. Thereafter, the image Jacobian matrix is calculated from the three (or more) sets of images collected. Finally, the robotic arm converts the target coordinates in the real-time captured images by the camera into coordinates in the robotic arm coordinate system through the image Jacobian matrix and performs real-time tracking. This study tests the dynamic quasi-Newton method for estimating the Jacobian matrix and optimizes the initialization coupling problem using the orthogonal moving method. This optimization scheme significantly shortens the iteration process, making the uncalibrated technology more fully applied in the field of dynamic object tracking. In addition, this study proposes a servo control algorithm with predictive compensation to mitigate or even eliminate the systematic error caused by time delay in dynamic target tracking in robot visual servo systems.
本研究提出了一种用于机器人手臂在手眼模式下跟踪和抓取动态目标的多维未校准技术。该方法避免了复杂繁琐的校准过程，使机器视觉任务能够在各种复杂环境中自适应应用，解决了传统校准方法在复杂环境中不稳定的问题。本研究使用的具体方法是，首先在手眼模式下，机器人手臂依次沿x、y和z轴移动，每次移动前后拍摄图像。此后，根据收集到的三组（或更多）图像计算图像雅可比矩阵。最后，机器人手臂通过图像雅可比矩阵将相机实时捕获图像中的目标坐标转换为机器人手臂坐标系中的坐标，并进行实时跟踪。本研究测试了用于估计雅可比矩阵的动态拟牛顿法，并使用正交移动法优化初始化耦合问题。这种优化方案显著缩短了迭代过程，使未校准技术更充分地应用于动态目标跟踪领域。此外，本研究提出了一种具有预测补偿的伺服控制算法，以减轻甚至消除机器人视觉伺服系统中动态目标跟踪时由时间延迟引起的系统误差。
