The Research Of The Generation Of The Spraying Robot Trajectory Based On The Point Cloud Slicing

In the international and domestic mid lacquer and surface lacquer painting lines, Painting Robot has been used in auto-body's inner and outer painting,but Painting Robot's painting process is very complicated,and multifold factors can influence the painting effects.So it become a focus that how to advance better trajectory optimization arithmetic and control strategies in the recent years.In this paper,the research is about the generation of the spraying robot trajectory based on the point cloud slicing and the related key technologies are discussed in this dissertation.The main research contents are as follows:Firstly,the significance and background of trajectory optimization on Painting Robot are expatiated,and the off-line programming system of Painting Robot is studied elementarily.Secondly,the preprocessing methods of point cloud are discussed.The relative algorithms of point cloud slicing are deeply researched,including the determination of slicing thickness,the choice of slicing direction,the calculation of slicing data,the reconstruction of poly-lines and etc.,so that the uniform slicing of point cloud is completed.Then,the preprocessing methods of cross-sectional data are studied, including data reduction,discrete curvature estimation and convex hull calculation.Thirdly,introduce the mathematical model of the spray gun trajectory and the obtain methods of the spray gun trajectory and path.Including cross-section slice of the point cloud data points for the average effective sample obtained the spray gun spray path.Calculation the offset of sample data points in the normal direction,the information contained in the offset data points is the trajectory parameters(position and orientation) of the spray gun.Finally,the prototype system of the algorithm is designed to verify its feasibility and validity.The process of the Data acquisition and preprocessing,point cloud slicing and the generation of the spraying robot trajectory is demonstrated by an example.