标题: 利用学习法则于机器人之整体性精密度校正
A GLOBAL CALIBRATION SCHEME BASED ON A LEARNING ALGORITHM
作者: 王嘉庆
Chia-Ching Wang
杨谷洋
Dr. Kuu-Young Young
电控工程研究所
关键字: 误差参数,量测空间分析,整体性校正,模糊小脑模型算数计数器;Error Parameter,Measurement Space Analysis,Global Calibration, FCMAC
公开日期: 1993
摘要: 本论文提出一整体性精密度校正的方法来解决计算机辅助设计系统与机机
器人之座标等效问题。目前机器人精密度校正的研究大都着重于某些局部
区域,也就是说仅要求机器人在某些工作区域的误差参数能达到所需的精
密度要求。这主要是由于不精确的误差结果不能完全被模式化和限制辨别
误差参数所需量测点的数目。为了去克服此仅是区域性准确的问题,我们
首先提出利用量测空间分析来合理分割工作区域成数个小区域和选择在此
小区域的具代表性的误差参数,并利用此有限的误差参数经小脑模型算数
计数器或模糊小脑模型算数计数器神经网路的学习法则 来产生整个工作
区域的合理的误差参数。最后,我们透过模拟及实验来验证此整体性精密
度校正方法的可行。
A global calibration scheme is proposed to resolve the
coordinates' equivalence problem in integrating the CAD system
and Robots. Current robot calibration schemes are inevitably
with certain locality, i.e., the calibrated error parameters
(CEP) will solicit the demanded accuracy only in certain region
of the robot workspace. It s mainly due to that the errors
resulting in the imprecision are not completely modeled and
only limited number of measured data are available for
identifying the CEPs. To tackle this locality problem, we
propose first performing the measurement space analysis to
appropriately divide the workspace into local regions and
select the representative set of CEPs from each local region.
Learning algorithms based on both the CMAC or FCMAC neural
networks are then employed to generate appropriate sets of CEPs
for the whole workspace based on the derived finite sets of
CEPs above. Simulation and experiment are executed to verify
the proposed global calibration scheme.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT820327060
http://hdl.handle.net/11536/57780
显示于类别:Thesis