基于ELM的航空制孔机器人定位精度补偿方法

针对航空制孔机器人绝对定位精度补偿中存在的建模复杂及运算量大的问题,提出了一种基于极限学习机的绝对定位精度补偿方法。该方法通过将机器人视为一个黑箱系统,忽略机器人的几何因素和非几何因素的影响,通过高精度的激光跟踪仪测量获得机器人的末端运动误差,采用极限学习机建立机器人误差预测模型。由机器人误差预测模型获得机器人在期望位置的位置偏差,通过修正机器人位置坐标来实现机器人的绝对定位精度补偿。最后该方法在航空制孔机器人上进行了试验,试验结果显示机器人的绝对位置误差的平均值和最大值分别降低了75.69%和78.16%。

A Calibration and Compensation Method Based on ELM for Aviation Drilling Robot

In order to solve the problems that modeling was complex and a large computation was costed for calibra-tion and compensation of aviation drilling robot, a calibration and compensation method based on extreme learning machine was proposed. The aviation drilling robot was regarded as a black-box system in this method which ignored the influence of geometric factors and non-geometric factors of robot. Then, according to robot positional errors measured by a high-accu-racy laser tracker, a robot error prediction model based on extreme learning machine was trained and established. Next, the positional error in desired position could be predicted by robot error prediction model and the robot position was compen-sated to achieve the robot calibration. Final, experimental studies were carried on an aviation drilling robot. The experimen-tal results showed that the mean and maximum positional error of robot was reduced by 75.69% and 78.16%, respectively.