空间曲线的特征识别与高质量非均匀采样

为避免传统均匀采样方法因忽视曲线重要特征而生成不理想的采样结果,获得给定数量且由特征点和辅助点组成的采样点序列,提出基于特征识别的高质量空间曲线非均匀采样方法.首先使用抛物线插值法得到曲线上所有曲率极大值点和挠率极大值点的近似位置,经筛选后产生特征点,以更好地抓住空间曲线的轮廓特征.然后定义基于弧长、曲率和挠率加权组合的特征函数,并以此自适应地选取曲线上的辅助点.与3种主流采样方法比较的实验结果表明,该方法能够获得更高质量的采样结果且具有更好的实用性,从而进一步改善空间曲线的B样条拟合效果.     

利用控制顶点插值的光滑B样条曲线构造方法

针对非均匀采样点拟合时的光滑B样条曲线构造问题,提出一种基于已知控制顶点和节点向量求解未知控制顶点来构造光滑B样条曲线的方法.首先对所有控制顶点进行基函数极值参数化,再将已知控制顶点作为型值点进行光滑样条曲线插值,并在此曲线上通过利用参数化结果求值得到未知控制顶点.该方法通过控制顶点所在曲线的光顺性提高最终生成曲线的连...     

曲率自适应条件下特征点选取的非均匀B样条曲线插值方法

针对计算机数控编程阶段生成的海量离散刀位数据,在满足预设插值精度的条件下,提出一种基于曲率自适应选取特征点的非均匀B样条曲线插值方法.首先,采用相邻3点形成近似圆弧的方法计算各个离散刀位数据点的曲率,将曲率分段点、曲率极大值点等特征数据点作为初始插值数据点,构造生成初始非均匀B样条插值曲线;其次,建立插值误差计算模型,并用于计算所有未参与插值的数据点与非均匀B样条插值曲线间的插值误差,在超出预设插值误差的曲率段内增加新的特征点,生成新的非均匀B样条插值曲线;重复上述过程,直至所有不在非均匀B样条插值曲线上的数据点都满足插值精度条件为止.对实际加工离散刀位数据的仿真计算结果表明,该方法即便去除了大量原始离散刀位数据,也能更好地保留原始刀位数据曲线在外形和精度方面的特征,且具有迭代计算次数少、数据点去除量大等特点,在海量离散刀位数据的样条化数控编程方面具有较高的应用价值.     

基于曲率优化的机器人砂带磨削轨迹规划

针对复杂曲面零件砂带磨削编程效率低、精度差的问题,基于B样条曲线曲面重构和机器人离线编程技术,提出了一种根据关键接触点曲率值生成工业机器人磨削轨迹的方法.首先,利用零件表面上需要进行砂带磨削的关键接触点和积累弦长参数化法构造节点矢量,从而计算出磨削轨迹的B样条基函数;其次,根据控制顶点反求矩阵得到全部未知控制点和3次B样条加工曲线;然后,分析关键接触点之间的曲率变化率和弧长,对关键点细化生成符合磨削工艺要求的目标点;最后,通过求解双3次B样条插值曲面方程获得目标点的加工姿态.以水龙头磨削为例进行试验,结果表明曲率优化算法磨削的零件表面轮廓形状明显优于截面法,且其粗糙度值能稳定在0.082 μm左右,可以有效提高工件表面加工质量.     

利用弦长参数插值拟合光滑闭合B样条曲线构造方法

文章针对非均匀采样点拟合光滑B样条曲线构造问题,提出一种基于已知控制点和相邻控制点之间弦长求解控制点方程组系数矩阵来构造光滑B样条曲线的方法。该方法通过控制顶点所在曲线的光顺性提高最终生成曲线的连续性和光滑性。在此基础上,设计了闭合B样条曲线控制点的快速求解算法。首先利用所有控制顶点和相邻点间弦长建立求解系数的参数矩阵,再提出一种基于LU矩阵分解的优化算法。根据方程组系数矩阵的特点,参照追赶法的LU分解,构造了分解后的L、U矩阵结构。最后通过实例说明,采用文中方法所构造的B样条曲线具有较好的光滑性,也证明了该算法的可靠性和有效性。     

Bézier样条曲线的近似弧长参数化方法

提出了Bézier样条曲线近似弧长参数化的方法及相应的算法.通过求出曲线近似二分之一弧长的点及其相应的参数值,可将曲线分割为两条Bézier样条曲线.这两条曲线的弧长近似相等,因此让它们带有相同的权1.对新生成的Bézier样条曲线不断重复上述工作,最终得到一条由多条Bézier样条曲线所构成的新的曲线.将这多条Bézier样条曲线合并为一条Bézier样条曲线,进而通过节点插入技术将其转化为B样条形式的曲线以便得到全局参数,其中各段Bézier曲线在全局参数域中所占子区间的长度与它们所具有的权成比例,这样便得到一条近似弧长参数化曲线.     

利用形状参数构造保凸插值的双曲多项式B样条曲线

把一个参数化的奇异多边形与双曲多项式B样务按某一个因子调配,可自动生成带形状参数且插值给定平面点列的C^2（或G^1）连续的双曲多项式B样条曲线．把这一曲线的曲率符号函数写为Bernstein多项式形式,并利用Bernstein多项式的非负性条件,得到形状参数的合适取值来保证样条曲线对插值点列的保凸性．此方法简单、方便,无需解方程组或迭代计算,生成的插值曲线具有较均匀的曲率．大量实例验证了算法的正确与有效．     

基于曲率单调变化的空间非均匀三次B样条曲线的构造方法

B样条曲线在目前CAD系统中得到广泛应用,针对B样条曲线的光顺问题,给出并证明了具有曲率单调变化的非均匀三次B样条曲线的构造方法.首先通过给定非均匀三次B样条曲线的中间控制边矢量及相关初始条件,然后计算初始和结尾控制边矢量,由此得到的非均匀三次B样条曲线具有单调变化的曲率.实验在Windows系统下基于VC++语言实现,相关实例验证了该构造方法的有效性及实用性.     

基于曲率调节的二次均匀B样条插值曲线

提出了一种二次均匀B样条插值曲线的构造方法,首先给定某一段曲线首点的相对曲率和该段曲线的首端切矢量的方向角,利用二次均匀B样条曲线的端点性质,求出其余各段曲线控制顶点,来生成整条插值曲线。该方法无需做反求运算,不仅保持了B样条曲线的优点,而且可以通过修改曲线首点的相对曲率和该段曲线的首端切矢量的方向角对曲线进行整体调节。     

基于给定精度的空间B样条曲线弧长分段点求取方法

对B样条等参数曲线按弧长精确分段,是沿曲线路径加工、检测中的一个重要问题。通过对B样条曲线弧长计算方法以及弧长计算误差与分段精度的关系进行分析,通过建立弧长分段点搜索区间及弧长二分法确定符合精度要求的弧长分段点。实验证明该方法是解决参数曲线弧长精确分段的有效方法。     

An error-bounded B-spline curve approximation scheme using dominant points for CNC interpolation of micro-line toolpath

This paper presents a unified framework for computing a B-spline curve to approximate the micro-line toolpath within the desired fitting accuracy. First, a bi-chord error test extended from our previous work is proposed to select the dominant points that govern the overall shape of the micro-line toolpath. It fully considers the geometric characteristics of the micro-line toolpath, i.e., the curvature, the curvature variation and the torsion, appropriately determining the distribution of the dominant points. Second, an initial B-spline curve is constructed by the dominant points in the least square sense. The fitting error is unpredictable and uncontrollable. It is classified into two types: (a) the geometric deviations between the vertices of the polygon formed by the data points and the constructed B-spline curve; (b) those between the edges of the polygon and the constructed B-spline curve. Herein, an applicable dominant point insertion is employed to keep the first geometric deviation within the specified tolerance of fitting error. A geometric deviation model extended from our previous work is developed to estimate the second geometric deviation. It can be effectively integrated into global toolpath optimization. Computational results demonstrate that the bi-chord error test applies to both the planar micro-line toolpath and the spatial micro-line toolpath, and it can greatly reduce the number of the control points. Simulation and experimental results demonstrate that the proposed B-spline approximation approach can significantly improve machining efficiency while ensuring the surface quality.     

Contour curve reconstruction from cloud data for rapid prototyping

In this study, a method for generation of sectional contour curves directly from cloud point data is given. This method computes contour curves for rapid prototyping model generation via adaptive slicing, data points reducing and B-spline curve fitting. In this approach, first a cloud point data set is segmented along the component building direction to a number of layers. The points are projected to the mid-plane of the layer to form a 2-dimensional (2D) band of scattered points. These points are then utilized to construct a boundary curve. A number of points are picked up along the band and a B-spline curve is fitted. Then points are selected on the B-spline curve based on its discrete curvature. These are the points used as centers for generation of circles with a user-define radius to capture a piece of the scattered band. The geometric center of the points lying within these circles is treated as a control point for a B-spline curve fitting that represents a boundary contour curve. The advantage of this method is simplicity and insensitivity to common small inaccuracies. Two experimental results are included to demonstrate the effectiveness and applicability of the proposed method.     

Data reduction using cubic rational B-splines

A geometric method for fitting rational cubic B-spline curves to data representing smooth curves, such as intersection curves or silhouette lines, is presented. The algorithm relies on the convex hull and on the variation diminishing properties of Bezier/B-spline curves. It is shown that the algorithm delivers fitting curves that approximate the data with high accuracy even in cases with large tolerances. The ways in which the algorithm computes the end tangent magnitudes and inner control points, fits cubic curves through intermediate points, checks the approximate error, obtains optimal segmentation using binary search, and obtains appropriate final curve form are discussed     

基于Freeman链码的B样条曲线轮廓拟合

提出了一种Freeman链码与B样条曲线误差控制相结合实现轮廓拟合的算法,首先利用Freeman链码法进行边界跟踪,根据相邻像素点间的不同的链码变化关系,排除伪特征点,提取出轮廓中绝大多数特征点,然后结合基于误差控制的B样条曲线法,取得能够精确表示轮廓信息的特征点。本文算法即避免了使用曲率来进行求取特征点的复杂计算,提高了特征点检测速度,又提取出能够精确拟合轮廓的局部支撑点,实现了基于误差控制的轮廓曲线拟合。实验结果证明了本文算法的正确性。     

带法向约束的隐式T样条曲线重构

目的 隐式曲线能够描述复杂的几何形状和拓扑结构,而传统的隐式B样条曲线的控制网格需要大量多余的控制点满足拓扑约束。有些情况下,获取的数据点不仅包含坐标信息,还包含相应的法向约束条件。针对这个问题,提出了一种带法向约束的隐式T样条曲线重建算法。方法 结合曲率自适应地调整采样点的疏密,利用二叉树及其细分过程从散乱数据点集构造2维T网格;基于隐式T样条函数提出了一种有效的曲线拟合模型。通过加入偏移数据点和光滑项消除额外零水平集,同时加入法向项减小曲线的法向误差,并依据最优化原理将问题转化为线性方程组求解得到控制系数,从而实现隐式曲线的重构。在误差较大的区域进行T网格局部细分,提高重建隐式曲线的精度。结果 实验在3个数据集上与两种方法进行比较,实验结果表明,本文算法的法向误差显著减小,法向平均误差由10^-3数量级缩小为10^-4数量级,法向最大误差由10^-2数量级缩小为10^-3数量级。在重构曲线质量上,消除了额外零水平集。与隐式B样条控制网格相比,3个数据集的T网格的控制点数量只有B样条网格的55.88%、39.80%和47.06%。结论 本文算法能在保证数据点精度的前提下,有效降低法向误差,消除了额外的零水平集。与隐式B样条曲线相比,本文方法减少了控制系数的数量,提高了运算速度。     

关键点选取的最小二乘渐进迭代逼近

目的 最小二乘渐进迭代逼近（LSPIA）方法多以均匀参数化或弦长参数化的形式均匀地确定初始控制点,虽然取得了良好效果,但在处理复杂曲线时,迭代速度相对较慢且误差精度不一定能达到预期设定值。为了进一步提高迭代效率和误差精度,本文提出了基于关键点（局部曲率最大点和极端曲率点）的最小二乘渐进迭代逼近方法。方法 首先计算所有数据点的离散曲率,筛选出局部曲率最大点;接着设定初始的曲率下限,筛选出极端曲率点;然后将关键点与均匀选取的控制点按参数顺序化,并将其作为迭代的初始控制点;最后利用LSPIA方法对数据点进行拟合。结果 对同一组数据点,分别采用LSPIA方法和基于关键点的LSPIA方法,本文方法较好地提高了收敛速度;在相同的控制点数目下,与LSPIA算法相比,本文方法的误差精度较小。结论 本文方法适合于比较复杂的曲线,基于曲率分布的关键点的选取,可以更好地反映曲线的几何信息。数值实例表明,结合关键点筛选策略的LSPIA算法提高了计算效率,取得了更好的拟合效果。     

Shape modeling based on specifying the initial B-spline curve and scaled BFGS optimization method

In this paper, we consider the problem of fitting the B-spline curves to a set of ordered points, by finding the control points and the location parameters. The presented method takes two main steps: specifying initial B-spline curve and optimization. The method determines the number and the position of control points such that the initial B-spline curve is very close to the target curve. The proposed method introduces a length parameter in which this allows us to adjust the number of the control points and increases the precision of the initial B-spline curve. Afterwards, the scaled BFGS algorithm is used to optimize the control points and the foot points simultaneously and generates the final curve. Furthermore, we present a new procedure to insert a new control point and repeat the optimization method, if it is necessary to modify the fitting accuracy of the generated B-spline fitting curve. Associated examples are also offered to show that the proposed approach performs accurately for complex shapes with a large number of data points and is able to generate a precise fitting curve with a high degree of approximation.     

基于有向距离场的代数B-样条曲线重建

提出了一种以代数B-样条曲线为表达形式、基于有向距离场的隐式曲线重建方法.首先给定一个表示封闭曲线、可能带有噪音且分布不均匀的平面点云,采用移动最小平方(moving least square,简称MLS)方法对点云去噪、重采样,得到一个低噪音、分布均匀的"线状"点云,再通过Level Set方法建立该"线状"点云的离散几何距离场,最后用一个代数B-样条函数光顺拟合该离散距离场,代数函数的零点集即为重建曲线.曲线重建过程可以归结为求解线性方程组问题.这种重建方法不仅可以得到高质量的重建曲线,还可以得到曲线周围的距离场信息.同时,避免了隐式曲线重建中经常出现的多余分支问题.     

Least square geometric iterative fitting method for generalized B-spline curves with two different kinds of weights

Generalized B-spline bases are generated by monotone increasing and continuous “core” functions; thus generalized B-spline curves and surfaces not only hold almost the same perfect properties which classical B-splines hold but also show more flexibility in practical applications. Geometric iterative method (also known as progressive iterative approximation method) has good adaptability and stability and is popular due to its straight geometric meaning. However, in classical geometric iterative method, the number of control points is the same as that of data points. It is not suitable when large numbers of data points need to be fitted. In order to combine the advantages of generalized B-splines with those of geometric iterative method, a fresh least square geometric iterative fitting method for generalized B-splines is given, and two different kinds of weights are also introduced. The fitting method develops a series of fitting curves by adjusting control points iteratively, and the limit curve is weighted least square fitting result to the given large data points. Detailed discussion about choosing of core functions and two kinds of weights are also given. Plentiful numerical examples are also presented to show the effectiveness of the method.