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.

scholarly journals Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Ant Colony Optimization

2020 ◽  
Vol 38 (3A) ◽  
pp. 343-351
Author(s):  
Mohammed I. Abdulakareem ◽  
Firas A. Raheem
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Planning Problem ◽  
B Spline ◽  
Spline Curve ◽  
Probabilistic Roadmap ◽  
Planning Algorithm ◽  
Feasible Path ◽  
Path Planning Algorithm

n this paper, a unique combination among probabilistic roadmap, ant colony optimization, and third order B-spline curve has been proposed to solve path-planning problem in complex and very complex environments. This proposed method can be divided into three stages. First stage is to construct a random map depending on the environment complexity using probabilistic roadmap algorithm. This could be done by sampling N nodes randomly in complex and very complex static environments, then connecting these nodes together according to some criteria or conditions. The constructed roadmap contains huge number of possible random paths that may connect the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Ant colony optimization is selected to find or to search the best path between start and goal points. Finally, the third stage uses B-spline curve to smooth and reduce total length of the found path in the previous stage where path’s length has been reduced by 1% in first environment and by 15% in second environment. The results of the proposed approach ensure feasible path between start and goal points in complex and very complex environment. In addition, the path is guaranteed to be shortest, smooth, continues and safe.

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.

Kinematics of a Novel Single-Loop Under-Actuated Wrist

2012 ◽  
Author(s):  
Raffaele Di Gregorio
Keyword(s):  
Path Planning ◽  
Closed Form ◽  
Nonholonomic Constraint ◽  
Three Dimensional ◽  
Single Loop ◽  
Closed Form Solutions ◽  
Position Analysis ◽  
Tracking Tasks ◽  
Planning Algorithm ◽  
Path Planning Algorithm

A novel type of parallel wrist (PW) is proposed which, differently from previously presented PWs, features a single-loop architecture and only one nonholonomic constraint. Due to the presence of a nonholonomic constraint, the proposed PW type is under-actuated, that is, it is able to control the platform orientation in a three-dimensional workspace by employing only two actuated pairs, one prismatic (P) and the other revolute (R); and it cannot perform tracking tasks. Position analysis and path planning of this novel PW are studied. In particular, all the relevant position analysis problems are solved in closed form, and, based on these closed-form solutions, a path-planning algorithm is built.

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.

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