Study on Reconstruction Property of Revolved Surface Based on B-Spline

2014 ◽  
Vol 644-650 ◽  
pp. 1674-1677
Author(s):  
Ruo Can Sun ◽  
Dan Liu ◽  
Ji Qing Zhao ◽  
Ying Ping Qian ◽  
Guo Feng Yi
Keyword(s):  
Surface Reconstruction ◽  
Cross Section ◽  
Point Cloud ◽  
Control Points ◽  
Surface Point ◽  
Scanning Angle ◽  
Factors Affecting ◽  
B Spline ◽  
Spline Curve ◽  
Cloud Density

B-spline curve parameters were estimated by the least squares. The influences of the scanning angle to point cloud density was discussed. In the case of scanning distance and horizontal being sure, the smaller the vertical scanning angle, the larger point cloud density, it can reflect the characteristics of the surface cross-section more accurately. The factors affecting the accuracy of the surface reconstruction were discussed. The surface fitting order has a greater degree of influence on the accuracy of surface reconstruction. When the surface order was 4 and the number of control points was 75, the surface-point cloud deviation was 0.188mm by the segmented fitting.

scholarly journals Approximation of Vehicle Trajectory with B-Spline Curve

2016 ◽  
Vol 19 (1) ◽  
pp. 1-5
Author(s):  
Dušan Páleš ◽  
Veronika Váliková ◽  
Ján Antl ◽  
František Tóth
Keyword(s):  
Basis Function ◽  
Extreme Case ◽  
Bézier Curve ◽  
Control Points ◽  
B Spline ◽  
Spline Curve ◽  
Planar Curve ◽  
Vehicle Trajectory ◽  
The Given ◽  
Real Vehicle

In this contribution, we present the description of a B-spline curve. We deal with creation of its basis function as well as with creation of the curve itself from entered control points. Following the literature, we formed an algorithm for B-spline modelling and we used it for the planar and spatial curve. The planar curve is made of chosen points. The spatial curve approximates the trajectory of a real vehicle, which trajectory was obtained by the set of measured points. The modelled curve very exactly describes the polygon created from the linked control points. With the lowering degree of the curve, this one is more clamping to the given polygon and for the extreme case it is transformed to the polygon itself. The advantage of the B-spline curve use is, for example in comparison with a Bézier curve, high adaptability, which is expressed in its parameters - besides entered control points, these are knots generated on the curve and degree of the curve.

Research on Algorithm for Space Weld Reconstruction and Torch Pose Simulation

2014 ◽  
Vol 1049-1050 ◽  
pp. 833-837
Author(s):  
Peng Yang ◽  
Dong Xing Hui ◽  
Zheng Kai ◽  
Li Shu Tian
Keyword(s):  
Coordinate Transformation ◽  
Spline Interpolation ◽  
Three Dimensional ◽  
Automatic Welding ◽  
Control Points ◽  
B Spline ◽  
Spline Curve ◽  
Planning Algorithm ◽  
Control Terminal ◽  
Path Planning Algorithm

A path planning algorithm based on B-spline interpolation techniques was constructed for automatic welding system.The system used a B-spline curve to reconstruct the weld,it was achieved by reversing the control points of B-spline curve through the prescribed date points. The weld posture model was then obtained from the osculating plane and normal plane of B-spline curve. By taking a series coordinate transformation to the weld posture model, the torch posture model based on control terminal was provided.Experiments show that the new algorithm can readily be used for various three-dimensional welding tasks.

T-B Spline Curves with a Shape Parameter

2012 ◽  
Vol 241-244 ◽  
pp. 2144-2148
Author(s):  
Li Juan Chen ◽  
Ming Zhu Li
Keyword(s):  
Shape Parameter ◽  
Simple Structure ◽  
Control Points ◽  
Curve Segment ◽  
Closed Curves ◽  
B Spline ◽  
Spline Curve ◽  
Spline Curves

A T-B spline curves with a shape parameter λ is presented in this paper, which has simple structure and can be used to design curves. Analogous to the four B-spline curves, each curve segment is generated by five consecutive control points. For equidistant knots, the curves are C^2 continuous, but when the shape parameter λ equals to 0 , the curves are C^3 continuous. Moreover, this spline curve can be used to construct open and closed curves and can express ellipses conveniently.

On the Modeling and Analysis of an Improved CNC Interpolation Algorithm

2009 ◽  
Vol 626-627 ◽  
pp. 459-464 ◽  
Author(s):  
Lei Luo ◽  
L. Wang ◽  
Jun Hu
Keyword(s):  
Interpolation Method ◽  
Control Points ◽  
Interpolation Algorithm ◽  
Modeling And Analysis ◽  
B Spline ◽  
Spline Curve ◽  
Spline Surface ◽  
Cnc Interpolation ◽  
Data Points ◽  
Back Calculation

An improved interpolation method is presented based on B-spline curve back calculation which regards data points as control points. First, a B-spline surface reconstruction is done, and a favorable condition for real-time interpolation can be provided for NC machining. Then, by prejudging the trajectory feedrate, the tangent vectors of spline curve junction can be calculated, which can be used to establish the spline curve equations based on time. At last, with the equations mentioned above, the trajectory and feedrate profile can be generated simultaneously by the improved interpolation algorithm. An error analysis is also discussed and the feasibility of the improved algorithm is verified by the simulation results.

B-Spline Curve Fitting Based on Adaptive Particle Swarm Optimization Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1299-1304 ◽  
Author(s):  
Yue Hong Sun ◽  
Zhao Ling Tao ◽  
Jian Xiang Wei ◽  
De Shen Xia
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Control Points ◽  
Swarm Optimization ◽  
B Spline ◽  
Spline Curve

For fitting of ordered plane data by B-spline curve with the least squares, the genetic algorithm is generally used, accompanying the optimization on both the data parameter values and the knots to result in good robust, but easy to fall into local optimum, and without improved fitting precision by increasing the control points of the curve. So what we have done are: combining the particle swarm optimization algorithm into the B-spline curve fitting, taking full advantage of the distribution characteristic for the data, associating the data parameters with the knots, coding simultaneously the ordered data parameter and the number of the control points of the B-spline curve, proposing a new fitness function, dynamically adjusting the number of the control points for the B-spline curve. Experiments show the proposed particle swarm optimization method is able to adaptively reach the optimum curve much faster with much better accuracy accompanied less control points and less evolution times than the genetic algorithm.

Highly Anti-Noise and Adaptive B-Spline Curve Reconstruction Based on ICT Slice Images

2013 ◽  
Vol 397-400 ◽  
pp. 1083-1087
Author(s):  
Guang Shuai Liu ◽  
Bai Lin Li
Keyword(s):  
Point Cloud ◽  
Adaptive Method ◽  
Point Cloud Data ◽  
Curve Reconstruction ◽  
B Spline ◽  
Cloud Data ◽  
Spline Curve ◽  
Multi Scale ◽  
Key Issues ◽  
Selection Of

Obtaining effective value points is one of key issues in cubic B-spline curve reconstruction. Since it is unfavorable for the selection of value points through curvature methods and the point cloud data acquired from ICT slice images is characterized with large volume of data, high noise and density, a baseline adaptive method is presented to get value points for curve reconstruction, baseline and scale threshold determined by wavelet multi-scale, in which the value points is obtained and curve is reconstructed automatically. Hausdorff distance is adopted to calculate the error of cubic B-spline curve reconstruction. Comparative analysis with existing methods proves that our method can effectively restrain noise and quickly reconstruct contour curves.

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