木板抓取机器人手眼标定方法

针对视觉抓取木板机器人的Eye-in-Hand视觉与机器人本体之间关联的手眼标定问题，提出了基于标定方程的求解优化。首先通过机器人带动相机以多个不同位姿观测标定板，得到多个标定方程，采集一次数据建立一个标定方程，再对所有标定方程运用Kronecker product算法和最小二乘法求解。为避免误差传递，将得到的解作为优化初始值，建立雅可比矩阵、误差函数，并采用Levenberg-Marquardt算法对初始值优化，得到精确解。在ROS系统中搭建仿真试验平台，通过3D可视化工具Rviz验证了标定结果的有效性。标定结果的精度分析表明，相同采集图像数量、不同噪声水平下，本文标定方法位置解精度比传统标定方法平均提高了30%；同一噪声水平、不同采集图像数量下，本文标定方法位置解精度比传统标定方法平均提高了31.1%。木板抓取试验结果表明，视觉系统抓取定位精度比传统标定方法平均提高了39.2%，抓取成功率为96.2%。

Method of Hand-Eye Calibration for Picking Board Robot

Aiming at the hand-eye calibration problem related to Eye-in-Hand vision and robot body of picking board, an optimization problem based on AX=ZB calibration equation was proposed. Firstly, the camera was driven by the robot to observe the calibration plate in multiple positions, and multiple calibration equations were obtained. One calibration equation AX=ZB was established by collecting data once. Kronecker product algorithm and the least square method were used to solve the calibration rotation matrix, and then the translation vector was solved according to rotation matrix and the least square method. In order to avoid the error transfer problem, the Jacobian matrix, the error function and Levenberg-Marquardt algorithm were established to optimize the attitude and position of the initial value simultaneously. Then, a simulation experiment platform was built in the ROS system, and the validity of calibration results was verified by the 3D visualization tool Rviz. The accuracy analysis of the calibration results showed that the accuracy of the new calibration method was increased by 30% on average compared with the traditional calibration method under different noise levels, and the accuracy of the new calibration method was increased by 31.1% on average compared with the traditional calibration method under different noise levels. Finally, the results of the grab test showed that the accuracy of the visual system was 39.2% higher than that of the transmission calibration method, and the success rate of the grab was 96.2%.