Multi-camera vision is widely used for guiding the machining robot to remove flash and burrs on complex automotive castings and forgings with arbitrary initial posture. Aiming at the problems of insufficient field of vision and regional occlusion in actual machining, a gradient-weighted multi-view calibration method (GWM-View) is proposed for the machining robot positioning based on the convergent binocular vision. Specifically, the mapping between each auxiliary camera and the main camera in the multi-view system is calculated by the inverse equation and intrinsic parameter matrix. Then, the gradient-weighted suppression algorithm is introduced to filter out the errors caused by camera angle variation. Next, the spatial coordinates of the feature points after suppression are used to correct the transformation matrix. Finally, the hand-eye calibration algorithm is implemented to transform the corrected data into the robot base coordinate system for the accurate positioning of the robot under multiple views. The experiment on the automotive engine flywheel shell indicates that the average positioning error is controlled within 1 mm under different postures. The stability and robustness of the proposed method are further improved while the positioning accuracy of the machining robot meets the requirements.

多摄像头视觉广泛应用于引导加工机器人去除具有任意初始姿态的复杂汽车铸锻件的飞边和毛刺。针对实际加工中视野不足和区域遮挡等问题，提出了一种基于会聚双目视觉的加工机器人定位梯度加权多视标定方法（GWM-View）。具体地，多视点系统中每个辅助摄像机与主摄像机之间的映射是通过逆方程和内参数矩阵计算的。然后引入梯度加权抑制算法滤除摄像机角度变化引起的误差。接着，利用抑制后的特征点的空间坐标对变换矩阵进行修正。最后，实现手眼标定算法，将校正后的数据转换到机器人底座坐标系中，实现机器人多视角下的精确定位。在汽车发动机飞轮壳上的实验表明，不同姿态下的平均定位误差控制在1 mm以内。在加工机器人定位精度满足要求的情况下，进一步提高了所提方法的稳定性和鲁棒性。