scholarly journals Control point adjustment for B-spline curve approximation

2004 ◽  
Vol 36 (7) ◽  
pp. 639-652 ◽  
Author(s):  
Huaiping Yang ◽  
Wenping Wang ◽  
Jiaguang Sun
2019 ◽  
Vol 103 (1) ◽  
pp. 003685041988011
Author(s):  
Jiangping Mei ◽  
Fan Zhang ◽  
Jiawei Zang ◽  
Yanqin Zhao ◽  
Han Yan

According to the problem that the existing high-speed parallel robot cannot satisfy the operation requirements of non-planar industrial production line, a 6-degrees-of-freedom high-speed parallel robot is proposed to carry out the kinematic and dynamic analyses. Combining with the door-type trajectory commonly used by the parallel robot, it adopts 3-, 5-, and 7-time B-spline curve motion law to conduct the trajectory planning in operation space. Taking the average cumulative effect of joint jerky as the optimization target, a trajectory optimization method is proposed to improve the smoothness of robot end-effector motion with the selected motion law. Furthermore, to solve the deformation problem of the horizontal motion stage of the trajectory, a mapping model between the control point subset of B-spline and the motion point subset of trajectory is established. Based on the main diagonally dominant characteristic of the coefficient matrix, the trajectory deformation evaluation index is constructed to optimize the smoothness and minimum deformation of the robot motion trajectory. Finally, compared to without the optimization, the maximum robot joint jerk decreases by 69.4% and 72.3%, respectively, and the maximum torque decreases by 51.4% and 38.9%, respectively, under a suitable trajectory deformation.


2019 ◽  
Vol 13 (4) ◽  
pp. 317-328
Author(s):  
Johannes Bureick ◽  
Hamza Alkhatib ◽  
Ingo Neumann

Abstract B-spline curve approximation is a crucial task in many applications and disciplines. The most challenging part of B-spline curve approximation is the determination of a suitable knot vector. The finding of a solution for this multimodal and multivariate continuous nonlinear optimization problem, known as knot adjustment problem, gets even more complicated when data gaps occur. We present a new approach in this paper called an elitist genetic algorithm, which solves the knot adjustment problem in a faster and more precise manner than existing approaches. We demonstrate the performance of our elitist genetic algorithm by applying it to two challenging test functions and a real data set. We demonstrate that our algorithm is more efficient and robust against data gaps than existing approaches.


2013 ◽  
Vol 397-400 ◽  
pp. 1093-1098
Author(s):  
Xian Guo Cheng

This paper addresses the problem of B-spline curve approximating to a set of dense and ordered points. We choose local curvature maximum points based on the curvature information. The points and the two end points are viewed as initial feature points, constructing a B-spline curve approximating to the feature points by the least-squares method, refining the feature points according to the shape information of the curve, and updating the curve. This process is repeated until the maximum error is less than the given error bound. The approach adaptively placed fewer knots at flat regions but more at complex regions. Under the same error bound, experimental results showed that our approach can reduce more control points than Parks approach,Piegls approach and Lis approach. The numbers of control points of the curve is equal to that of the feature points after refinement.


2020 ◽  
Vol 35 (6) ◽  
pp. 431-440
Author(s):  
謟kan inik ◽  
Erkan 躭ker ◽  
ismail Ko�

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