A Spray Path Planning Algorithm based on 3D Point Cloud

Ning, Tangyuan; Kong, Minxiu; Xu, Shengrun

doi:10.1109/imcec46724.2019.8983905

Cited by 3 publications

(1 citation statement)

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“…Spraying trajectory planning based on section polysemy line and point cloud slicing technique [30] was proposed. A spray path planning algorithm was proposed based on 3D point cloud [31]. However, since the point cloud slicing method generally adopts equidistant slicing in the spraying field, the uniformity of coating thickness is difficult to be guaranteed when it is employed to the free-form surface spraying.…”

Section: Introductionmentioning

confidence: 99%

Point cloud modeling and slicing algorithm for trajectory planning of spray painting robot

Cheng

Zhang

et al. 2021

Robotica

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To improve the uniformity of coating thickness and spraying efficiency, new point cloud modeling and slicing algorithm are proposed to deal with free-form surfaces for the spray painting robot in this paper. In the process of point cloud modeling, the edge preservation algorithm is firstly presented to avoid damaging the edge characteristic of the point cloud model. For the spraying gun, the coating deposition model on the free-form surface is determined on the basis of the elliptic double $\beta $ distribution model. Then, the grid projection algorithm is proposed to obtain grid points between adjacent slices on the free-form surface. Based on this, the analytical solution for calculating the coating thickness at each grid point is obtained. The cross-section contour points are obtained by intercepting the point cloud model with several parallel slices, which is important for the trajectory planning of the spray painting robot. Finally, the uniformity of coating thickness is optimized in terms of the moving speed of the spraying gun and the slice thickness. The simulation and numerical experiment results show that the uniformity of coating thickness and spraying efficiency are improved using the proposed point cloud modeling and slicing algorithm.

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Section: Introductionmentioning

confidence: 99%