Improving robot calibration results using modeling optimization
Juan Manuel Mauricio, José Maurício Santos Torres da Motta, R.S. McMaster
- 发表年份
- 2002
- 引用次数
- 6
摘要
This work describes techniques for planning, optimizing and simulating the calibration processes of robots using offline programming. The identification of geometric parameters of the nominal kinematic model is optimized using techniques of numerical optimization of the mathematical model. The simulation of the actual robot and the measurement system is achieved by introducing random errors representing their physical behaviour similar to its statistical repeatability. An evaluation of the corrected nominal kinematic model brings about a clear perception of the influence of distinct variables involved in the process for suitable planning, and indicates a considerable accuracy improvement when the optimized model is compared to the nonoptimized one.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002