A Low-Cost Robotic Assembly Method for Large Planar Workpieces Using Laser Displacement Sensors and Monocular Vision
Abstract
To address the challenges associated with large planar workpieces, such as the side window glass of a train, this paper proposes a practical, intelligent robotic assembly method that utilizes laser displacement sensors (LDSs) and monocular vision. The laser point cloud data is fitted to the equation of the plane of the side window optimally, allowing for the calculation of its plane coefficients and unit normal vector (UNV) using the Lagrange multiplier method. The robot's end flange is adjusted to ensure the camera imaging plane is along with the window plane at a specified distance. Monocular vision is employed to capture the pose features of the upper right corner of the window, facilitating a compensation method for precise assembly. The experimental results show that the robot achieved a positioning accuracy of less than 0.5mm and an orientation accuracy within 1 degree, confirming the effectiveness of the proposed method. In the experiment, the maximum displacement deviation in the X direction is about ± 0.4mm, the maximum offset in the Y and Z directions is about ± 0.3mm, the maximum rotation deviation in the X axis is about 0.6 °, and the minimum rotation deviation in the Y axis is about 0.8 °. All deviations meet the requirements of ± 1 ° attitude correction and ± 0.5mm displacement accuracy, and have good reliability. This method solves the practical problem of assembling large planar workpieces with integrated LDS and monocular vision. This technology involves using laser point cloud data to fit the precise plane equation of the window, determining alignment parameters through Lagrange multiplier method, and adjusting the posture of the end flange of the robot. Monocular vision further assists in extracting positional features, achieving precise realtime posture correction and alignment. This system proposes an effective and low-cost automation that reduces manual intervention and alleviates typical problems of remote processing of large workpieces.
DOI:
https://doi.org/10.31449/inf.v49i17.7633Downloads
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