基于工业机器人的未知曲面测量算法研究

在装甲装备维修保养作业中,为实现对车辆底盘部分零部件的自动装卸载,首先应解决CAD模型未知的零部件自动扫描测量和模数重建的问题。提出基于工业机器人的非接触自适应测量曲面重建方法。在测量过程中,将激光测距传感器安装于工业机器人法兰并转换坐标,根据被测零部件上的已测点,由提出的曲线圆弧预测自适应算法,得到下一个预测点矢量及测头轨迹点矢量,拟合成B样条曲线,规划合理的测量路径,进而指导工业机器人自动采集被测零部件上的真实三维点。通过仿真和试验结果表明,该方法能够根据此类零部件曲面特征获取采样点,减小了由测量入射角的存在和算法本身缺陷而导致的精度损失,有效地提高了CAD模型未知曲面数字化采样的精度,为模数重建提供了良好的点云数据。

Research on measurement algorithm for unknown free-form surfacebased on industrial robot

In the operations of maintence for armored equipment, to achieve an automatic loading and unloading of some components of the vehicle chassis, the problem of components whose CAD moedel is unknown scanning and model reconstruction for free-form surfaces need to be solved. In this study, a surface reconstruction approach based on industrial robot non-contact adaptive measurement is proposed. In the process, a laser displacement sensor is installed on the flange of robot end-effector,and a coordinate transformation model is established. Then, based on the measured point on the component and the adaptive measurement algorithm of curve arc length extrapolation prediction, the next prediction vector is calculated and a B-spline fitting curve is generated. Ultimately, with rational planning of measurement path, this method can be applied for guiding industrial robotic arm to automatically collect real three-dimensional points on the component under test. The results of the simulation and experiment indicate that with such an approach, the sampling points can be determined according to the surface features of components,and the detriment of precision due to the existence of incident angle as well as defects of the algorithm can be reduced. Thus, this approch improves the sampling accuracy of unknown free-form surface ,and provides better point cloud for model reconstruction.