An Adaptive Modeling Approach Based on ESN for Robotic Belt Grinding

摘要：

Robotic belt grinding system has good prospect to release hand-grinder from their dirty and noisy working environment. However, as a kind of non-rigid processing system, it is a challenge to model its processes precisely for free-form surface because its performance is unstable due to a variety of factors, such as belt wear and belt replacement.In order to adapt to the variability, an adaptive modeling approach based on echo state network (ESN) is presented, whose majoridea is to exhaust information from new data by using sliding window technique to select training samples. With machine learning paradigm this approach is more flexible than traditional ones which often base on formula and experimental curves. Experimental results of grinding turbine blades demonstrate this approach is workable and effective.

关键词： Robotic Belt Grinding Adaptive Modeling Echo State Network Samples Selection

作者: Hongbo Lv Yixu Song Peifa jia Zhongxue Gan Lizhe Qi

作者单位: State Key Laboratory of Intelligent Technology and System,Tsinghua National Laboratory for Informati InterSmart Robotic Systems Co.,Ltd.,Langfang Hebei 065001,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-6

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）