基于支持向量回归机的机器人定位精度提高

Method for improving positioning accuracy of robot based on support vector regression

为了进一步提升机器人的绝对定位精度,提出了一种通过支持向量回归机(Support Vector Regression,SVR)实现误差预测的方法。采用MDH(Modified Denavit-Hartenberg)模型建立机器人运动模型,并利用SVR建立机器人转角与位置误差的预测模型。通过空间精度控制网格划分,并对采样点与校准精度之间的关系进行分析,以确立合适的区域划分方式。最后,用激光跟踪仪测量机器人末端实际位置坐标与机器人理论值做比较,获得转角与位置误差样本集用于SVR模型的训练,以实现机器人单点位置误差的补偿。实验结果表明,机器人在中心位置和边缘位置的算术平均误差分别由2.107 mm和2.182 mm减少到0.103 mm和0.123 mm,验证了采用SVR对机器人的绝对定位误差进行补偿的正确性和有效性。

To further improve the absolute positioning accuracy of a robot,a method for realizing the error prediction based on support vector regression(SVR)was proposed.First,an MDH model was used to establish a kinematic robot model,and SVR was used to establish the prediction model of the rotation angle and position error of a robot.Second,the grid division was controlled based on the spatial accuracy,and the relationship between the sampling points and the calibration accuracy was analyzed to establish an appropriate mode for the area division.Finally,the differences between the values of the theoretical and real position coordinates of the robot measured with a laser tracker were used to train the SVR model and compensate the single-point position errors.The experimental results indicate that the arithmetic mean error of the robot at the center,and the edge positions,are reduced from 2.107 mm and 2.182 mm to 0.103 mm and 0.123 mm,respectively.The correctness and effectiveness of the SVR for the absolute positioning error compensation of a robot are also verified.