Proposal for a New Method to Improve the Trajectory Generation of a Robotic Arm Using a Distribution Function

2020 ◽  
pp. 213-231
Author(s):  
Yadira Quiñonez ◽  
Oscar Zatarain ◽  
Carmen Lizarraga ◽  
Jezreel Mejía
Keyword(s):  
Distribution Function ◽  
Trajectory Generation ◽  
New Method ◽  
Robotic Arm

New method for the restoration of the continuous distribution function of hyperfine fields andits possibilities

1993 ◽  
Vol 78 (1-4) ◽  
pp. 391-396 ◽  
Author(s):  
S. D. Antipov ◽  
G. E. Gorjunov ◽  
A. F. Djura ◽  
Ju. M. Polukarov ◽  
D. V. Sadkov ◽  
...  
Keyword(s):  
Distribution Function ◽  
Continuous Distribution ◽  
New Method ◽  
Continuous Distribution Function ◽  
Hyperfine Fields

Vision-Based Trajectory Generation for a Two-Link Robotic Arm Using Quadtree Decomposition and Curve Smoothing

2010 ◽  
Vol 108-111 ◽  
pp. 1439-1445
Author(s):  
Shahed Shojaeipour ◽  
Sallehuddin Mohamed Haris ◽  
Ehsan Eftekhari ◽  
Ali Shojaeipour ◽  
Ronak Daghigh
Keyword(s):  
Image Processing ◽  
A Priori ◽  
Trajectory Generation ◽  
Robotic Arm ◽  
Generation System ◽  
End Effector ◽  
Quadtree Decomposition ◽  
Practical Applications ◽  
Robot Trajectory ◽  
Curve Smoothing

In this article, the development of an autonomous robot trajectory generation system based on a single eye-in-hand webcam, where the workspace map is not known a priori, is described. The system makes use of image processing methods to identify locations of obstacles within the workspace and the Quadtree Decomposition algorithm to generate collision free paths. The shortest path is then automatically chosen as the path to be traversed by the robot end-effector. The method was implemented using MATLAB running on a PC and tested on a two-link SCARA robotic arm. The tests were successful and indicate that the method could be feasibly implemented on many practical applications.

A New Method to Identify the Preisach Distribution Function of Hysteresis

2005 ◽  
pp. 2107-2110
Author(s):  
Fan Li ◽  
Jian Qin Mao ◽  
Hai Shan Ding ◽  
Wen Bo Zhang ◽  
Hui Bin Xu ◽  
...  
Keyword(s):  
Distribution Function ◽  
New Method

Application of Nonparametric Kernel Density Estimation in Hongkong Stock Market

2011 ◽  
Vol 55-57 ◽  
pp. 209-214
Author(s):  
Yu Ling Wang ◽  
Jing Wang
Keyword(s):  
Distribution Function ◽  
Stock Returns ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
New Method ◽  
Fat Tails ◽  
Nonparametric Kernel Density ◽  
Nonparametric Kernel ◽  
Better Than

A new method, non-parametric kernel density, is used to research the distribution function of HangSeng index returns. The new method can not only depict the character of peak and fat tails of stock returns, but also capture the market risk better than normal distribution. Further more, more accurate conclusions are concluded.

scholarly journals New method for reducing parton distribution function uncertainties in the high-mass Drell-Yan spectrum

2019 ◽  
Vol 99 (5) ◽  
Author(s):  
C. G. Willis ◽  
R. Brock ◽  
D. Hayden ◽  
T.-J. Hou ◽  
J. Isaacson ◽  
...  
Keyword(s):  
Distribution Function ◽  
Parton Distribution ◽  
Parton Distribution Function ◽  
New Method ◽  
High Mass

Trajectory Generation for Glazing Spray Gun Based on STL Model

2014 ◽  
Vol 487 ◽  
pp. 617-620
Author(s):  
Hai Peng Pan ◽  
Hao Feng ◽  
Li Gang Cao
Keyword(s):  
Ceramic Industry ◽  
Trajectory Generation ◽  
New Method ◽  
Freeform Surface ◽  
Ceramic Product ◽  
Manual Work ◽  
Stl Model ◽  
Product Surface ◽  
Spray Gun

Glazing is a key procedure in ceramic product surface decorating processing. Recently, robots are gradually utilized to fulfill the glazing work instead of manual work. However, the researches on freeform surface glazing technology lag behind the research on robotics technology, thus limits the application of glazing robot in ceramic industry. In order to improve the quality and efficiency of robot glazing, this paper presents a new method which is base on STL model to generate trajectory for glazing spray gun. The whole procedures of this method have been discussed explicitly.

scholarly journals The New Method Using Shannon Entropy to Decide the Power Exponents on JMAK Equation †

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 28
Author(s):  
Hirokazu Maruoka
Keyword(s):  
Distribution Function ◽  
Shannon Entropy ◽  
Fractional Power ◽  
Step Function ◽  
New Method ◽  
Kolmogorov Equation ◽  
Geometrical Factor ◽  
Power Exponent ◽  
Integral Power ◽  
Insight Into

The JMAK (Johnson–Mehl–Avrami–Kolmogorov) equation is exponential equation inserted power-law behavior on the parameter, and is widely utilized to describe the relaxation process, the nucleation process, the deformation of materials and so on. Theoretically the power exponent is occasionally associated with the geometrical factor of the nucleus, which gives the integral power exponent. However, non-integral power exponents occasionally appear and they are sometimes considered as phenomenological in the experiment. On the other hand, the power exponent decides the distribution of step time when the equation is considered as the superposition of the step function. This work intends to extend the interpretation of the power exponent by the new method associating Shannon entropy of distribution of step time with the method of Lagrange multiplier in which cumulants or moments obtained from the distribution function are preserved. This method intends to decide the distribution of step time through the power exponent, in which certain statistical values are fixed. The Shannon entropy to which the second cumulant is introduced gives fractional power exponents that reveal the symmetrical distribution function that can be compared with the experimental results. Various power exponents in which another statistical value is fixed are discussed with physical interpretation. This work gives new insight into the JMAK function and the method of Shannon entropy in general.

A New Method to Identify the Preisach Distribution Function of Hysteresis

2005 ◽  
Vol 475-479 ◽  
pp. 2107-2110 ◽  
Author(s):  
Fan Li ◽  
Jian Qin Mao ◽  
Hai Shan Ding ◽  
Wen Bo Zhang ◽  
Hui Bin Xu ◽  
...  
Keyword(s):  
Distribution Function ◽  
Least Squares ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Least Squares Method ◽  
Input Sequence ◽  
Least Square Method ◽  
Least Square ◽  
New Method ◽  
Inference System

In this paper, a new method which combines the least square method with Tree-Structured fuzzy inference system is presented to approximate the Preisach distribution function. Firstly, by devising the input sequence and measure the output, discrete Preisach measure can be identified by the use of the least squares method. Then, the Preisach function can be obtained with Tree-Structured fuzzy inference system without any special smoothing means. So, this new method is not sensitive to noise, and is a universal approximator of the Preisach function. It collect the merit and overcome the deficiency of the existing methods.

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