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  [essays and dissertation][Other Subjects][Computing and Mathematics]A Relialble Approach to Compute the Forward Kinematics of Robot with Uncertain Geometric Parameters论文



论文编号: lw200708061140494387
论文属性: essay
论文语言:English
论文国家:China
登出日期: 2007-08-06  
字数: 15077
源程序: 无
价格: 免费论文
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论文大纲,目录
关键词搜索:Relialble Approach   RobotUncertain Geometric Parameters   
 
d-effector envelop can be determined, which is essential for the robot off-line programming, obstacle autonomous avoidance, etc.

In most cases, the error distribution should be identified, that is, with known end-effector position error to determine the robot kinematic parameter deviation. It is important for the robot calibration and robot production. Using the interval theory to solve this problem inversely is ongoing.

  

ACKNOWLEDGEMENTS

  

The research reported in this paper was supported by China Postdoctoral Science Foundation and the National Nature Science Foundation of China (10072014).

  

Reference

  

1         Xu W L. Monte Carlo technique for workspace analysis of robot manipulator. Journal of Southeast University, 1990, 20(1): 1-8.

2         Gong C H. Nongeometric error identification and compensation for robotic system by inverse calibration, International Journal of Machine Tools & Manufacturing, 2000,40: 2119-2137.

3         Wang D G. Nonlinear inversion algorithms and their applications (Ph. D Dissertation). Dalian: Dalian university of technology, 2001. (in Chinese)

4         Chen S H, Yang X W. Interval finite element method for beam structures. Finite elements in analysis and design, 2000, 34:75-88.

5         Zhang J Y, Shen S F. Interval analysis method for determining kinematic solutions of mechanisms. Chinese Journal of Mechanical Engineering, 1991,27(2):75-99. (in Chinese)

6         Alefeld G, Herzber英语论文网 【http://www.51lunwen.org】ger J. Introduction to interval computations. Academic Press, New York. 1983.

7         Alefeld G, Claudio D. The basic properties of interval arithmetic, its software realizations and some applications. Computers and Structures, 1998, 67:3-8.

8         Goldberg D E. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley, 1989.

9         Michalewicz Z. Genetic Algorithm+Data Structures=Evolution Programs. Springer-Verlag, Berlin Heidelberg. 1996.



  

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