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

Robotica ◽  
2021 ◽  
pp. 1-22
Author(s):  
Xinyi Yu ◽  
Zhaoying Cheng ◽  
Yikai Zhang ◽  
Linlin Ou
Keyword(s):  
Trajectory Planning ◽  
Coating Thickness ◽  
Point Cloud ◽  
Cloud Model ◽  
Free Form ◽  
Spray Painting ◽  
Painting Robot ◽  
Point Cloud Model ◽  
Free Form Surface ◽  
Cloud Modeling

Abstract 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.

scholarly journals Trajectory Planning for Spray Painting Robot Based on Point Cloud Slicing Technique

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 908 ◽  
Author(s):  
Wei Chen ◽  
Xu Li ◽  
Huilin Ge ◽  
Lei Wang ◽  
Yuhang Zhang
Keyword(s):  
Trajectory Planning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Cloud Model ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Spray Painting ◽  
Cloud Data ◽  
Painting Robot ◽  
Point Cloud Model

In this paper, aiming at the problem of poor quality and low spraying efficiency of irregular for complex freeform surfaces, a new spray painting robot trajectory planning method based on point cloud slicing technology is proposed. Firstly, the point cloud data of the workpiece to be sprayed is obtained by laser scanning. The point cloud data is processed to obtain the point cloud model of the sprayed workpiece. Then the section polysemy line is obtained after slice acquisition and section data processing of the point cloud model. The section polysemy line is sampled on average, and the normal vector of all sampling points is estimated. Finally, interpolation algorithm is used to connect the data points to obtain the space trajectory of spraying robot. In addition, the droplet trajectory model for electrostatic spray painting is established. The experimental results show that the method fully meets the requirements of coating thickness and improves the spraying efficiency and uniformity of coating.

Trajectory Generation of Spray Painting Robot Using Point Cloud Slicing

2010 ◽  
Vol 44-47 ◽  
pp. 1290-1294 ◽  
Author(s):  
Ming Zhu Li ◽  
Zhang Ping Lu ◽  
Chun Fa Sha ◽  
Li Qing Huang
Keyword(s):  
Point Cloud ◽  
Planning Process ◽  
Cloud Model ◽  
Trajectory Generation ◽  
Spray Painting ◽  
Cloud Data ◽  
Sampling Points ◽  
Painting Robot ◽  
Normal Vectors ◽  
Spray Gun

In the trajectory planning process of spray painting robot, an approach to automatic trajectory generation of spray gun using point cloud slicing is presented. Firstly, the point cloud data is obtained by scanning the surface of the workpiece. After the uniform slicing of point cloud model, the spraying position is determined by the average sampling of cross-section contours. Then the normal vectors of the sampling points are estimated. Finally the trajectory of spray gun is generated by offsetting the sampling points along their normal vectors. Experimental results show that the method has good feasibility and effectiveness. The spraying trajectory, direction and distance of spray gun can be controlled accurately, thus the spraying quality and efficiency are improved.

Research on Digital Modeling about Free-Form Surface with Holes Based on Coordinate Measuring Machine

2011 ◽  
Vol 287-290 ◽  
pp. 2805-2809
Author(s):  
Ming Yu Huang ◽  
Xiu Juan Wu ◽  
Zhong Shi Jia ◽  
Hong Jun Ni ◽  
Jing Jing Lv ◽  
...  
Keyword(s):  
Point Cloud ◽  
Coordinate Measuring Machine ◽  
Free Form ◽  
Model Reconstruction ◽  
Contact Measurement ◽  
Digital Modeling ◽  
Measuring Machine ◽  
Fast Reconstruction ◽  
Free Form Surface ◽  
Free Form Surfaces

Data acquisition and model reconstruction of free-form surfaces with holes were been studied, based on coordinate measuring machines. First, the structural process of the parts was analyzed, the method of combinate contact measurement with non-contact measurement were used to get point cloud; Then the point cloud were been preprocessed, feature curve extracted and solid modeled; Finally, the restructure model was been quality assessed and accuracy assessed. Using the measurement of combinated contact and non-contact can also meet both the precision requirement of key part and the fast reconstruction requirement of non-critical part, which has great significance on that part to fast and accurate reconstruction.

scholarly journals GEOREFERENCING AND REPROJECTION ERROR INVESTIGATION ON IMAGE BASED 3D DIGITIZATION AND MAPPING OF HISTORICAL BUILDINGS

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 71-75
Author(s):  
C. Altuntas
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Low Cost ◽  
Three Dimensional ◽  
Control Points ◽  
Historical Buildings ◽  
Reprojection Error ◽  
Dense Point ◽  
Point Cloud Model

<p><strong>Abstract.</strong> Image based dense point cloud creation is easy and low-cost application for three dimensional digitization of small and large scale objects and surfaces. It is especially attractive method for cultural heritage documentation. Reprojection error on conjugate keypoints indicates accuracy of the model and keypoint localisation in this method. In addition, sequential registration of the images from large scale historical buildings creates big cumulative registration error. Thus, accuracy of the model should be increased with the control points or loop close imaging. The registration of point point cloud model into the georeference system is performed using control points. In this study historical Sultan Selim Mosque that was built in sixteen century by Great Architect Sinan was modelled via photogrammetric dense point cloud. The reprojection error and number of keypoints were evaluated for different base/length ratio. In addition, georeferencing accuracy was evaluated with many configuration of control points with loop and without loop closure imaging.</p>

scholarly journals VISUALIZATION OF POINT CLOUD MODELS IN MOBILE AUGMENTED REALITY USING CONTINUOUS LEVEL OF DETAIL METHOD

2020 ◽  
Vol XLIV-4/W1-2020 ◽  
pp. 167-170
Author(s):  
L. Zhang ◽  
P. van Oosterom ◽  
H. Liu
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Cloud Model ◽  
Point Clouds ◽  
Level Of Detail ◽  
Mobile Augmented Reality ◽  
Cloud Models ◽  
Point Cloud Model ◽  
Visual Artifacts ◽  
Mobile Ar

Abstract. Point clouds have become one of the most popular sources of data in geospatial fields due to their availability and flexibility. However, because of the large amount of data and the limited resources of mobile devices, the use of point clouds in mobile Augmented Reality applications is still quite limited. Many current mobile AR applications of point clouds lack fluent interactions with users. In our paper, a cLoD (continuous level-of-detail) method is introduced to filter the number of points to be rendered considerably, together with an adaptive point size rendering strategy, thus improve the rendering performance and remove visual artifacts of mobile AR point cloud applications. Our method uses a cLoD model that has an ideal distribution over LoDs, with which can remove unnecessary points without sudden changes in density as present in the commonly used discrete level-of-detail approaches. Besides, camera position, orientation and distance from the camera to point cloud model is taken into consideration as well. With our method, good interactive visualization of point clouds can be realized in the mobile AR environment, with both nice visual quality and proper resource consumption.

scholarly journals Research on the Detection Method of Implicit Self Symmetry in a High-Level Semantic Model

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 28 ◽  
Author(s):  
Chao Wang
Keyword(s):  
Point Cloud ◽  
Detection Method ◽  
Cloud Model ◽  
High Reliability ◽  
Semantic Model ◽  
Symmetry Detection ◽  
Model Surface ◽  
Point Cloud Model ◽  
High Level ◽  
Skeleton Point

In order to improve the accuracy of semantic model intrinsic detection, a skeleton-based high-level semantic model intrinsic self-symmetry detection method is proposed. The semantic analysis of the model set is realized by the uniform segmentation of the model within the same style, the component correspondence of the model between different styles, and the shape content clustering. Based on the results of clustering analysis, for a given three-dimensional (3D) point cloud model, according to the curve skeleton, the skeleton point pairs reflecting the symmetry between the model surface points are obtained by the election method, and the symmetry is extended to the model surface vertices according to these skeleton point pairs. With the help of skeleton, the symmetry of the point cloud model is obtained, and then the symmetry region of point cloud model is obtained by the symmetric correspondence matrix and spectrum method, so as to realize the intrinsic symmetry detection of the model. The experimental results show that the proposed method has the advantages of less time, high accuracy, and high reliability.

scholarly journals Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 774 ◽  
Author(s):  
Sun ◽  
Wang ◽  
Ding ◽  
Lu ◽  
Sun
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Imaging System ◽  
Cloud Model ◽  
Tree Canopy ◽  
Coefficient Of Determination ◽  
Remote Measurement ◽  
Probability Density Estimation ◽  
Point Cloud Model ◽  
Aerial Vehicle

Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based on the probability density estimation and rapid segmentation of the point-cloud data for each apple tree, through which the tree canopy height, H, width, W, and volume, V, were determined for remote orchard canopy evaluation. When the ground sampling distance (GSD) was in the range of 2.13 to 6.69 cm/px, the orchard point-cloud model had a measurement accuracy of 100.00% for the rows and 90.86% to 98.20% for the columns. The coefficient of determination, R2, was in the range of 0.8497 to 0.9376, 0.8103 to 0.9492, and 0.8032 to 0.9148, respectively, and the average relative error was in the range of 1.72% to 3.42%, 2.18% to 4.92%, and 7.90% to 13.69%, respectively, among the H, W, and V values measured manually and by UAV photogrammetry. The results showed that UAV visual imaging is suitable for 3D morphological remote canopy evaluations, facilitates orchard canopy informatization, and contributes substantially to efficient management and control of modern standard orchards.

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