Robot Arm Touch Detection by Signal Separation with Higher Order Statistics and Compliance Control

通过具有高阶统计和合规控制的信号分离进行机器人手臂触摸检测

• 批准号: 18560436
• 负责人: SUGIMOTO Kenji
• 金额: $ 2.39万
• 依托单位: Nara Institute of Science and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2006
• 资助国家: 日本
• 起止时间: 2006 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18560436/
• 关键词: Signal Separation; Robot Arm; Independent Component Analysis; Control; Touch Detection

The objectives of this research are two-folds:1) To complete an analysis method for control systems by means of signal separation with higher order statistics. A novel method in statistical learning, named independent component analysis, enables us to analyze signals based on higher order statistics such as Kurtosis. Thereby we can separate independent components from the mixture of signals and recover the source signal. In this research, we modify this method and develop a method for analysis and synthesis of control systems.2) To derive a mechanism for touch detection of a robot arm to some obstacle. By real-time detection we can stop or relax the arm motion. Furthermore, we aim at developing a compliance control technique.Concerning 1),we have completed the method and applied it to some problems in control. Then, by reversing the idea of separation with inverse operation of observed signals, we have developed multivariable version of what is called Feedback Error Learning (FEL), where a feedforward controller for command inputs is adjusted by some learning law to obtain higher accuracy of the motion. For the former result, we have obtained a best paper award from SICE (our main academic society), and for the latter result, we have obtained a best presentation award from SI division of SICE.On the other hand, we have not yet succeeded in the item2), partly because we have concentrated on the above topics. But we have succeeded in writing one-stroke characters by a robot arm, which is a side-effect from 2). We are continuing efforts on this issue.

本研究的目的有二：1）利用高阶统计量的信号分离，完成控制系统的分析方法。一种新的统计学习方法，称为独立分量分析，使我们能够分析信号的高阶统计量，如峰度。从而可以从混合信号中分离出独立分量，恢复出源信号。在本研究中，我们将此方法加以修正，并发展出一种控制系统分析与综合的方法。2）推导出一种机械手臂对障碍物的触碰侦测机制。通过实时检测，我们可以停止或放松手臂运动。针对1），我们已经完成了该方法，并将其应用于控制中的一些问题。然后，通过反转的想法与观测信号的逆操作的分离，我们已经开发了所谓的反馈误差学习（FEL）的多变量版本，其中命令输入的前馈控制器通过一些学习律进行调整，以获得更高的运动精度。前一项成果获得了SICE（主要学术团体）的最佳论文奖，后一项成果获得了SICE的SI部门的最佳演讲奖。另一方面，由于我们集中精力研究上述课题，所以在第2项中还没有成功。但是我们已经成功地用机器人手臂写了一笔字，这是2）的副作用。我们正在继续就这一问题作出努力。

项目成果

Generalization of Feedback Error Learning (FEL) to Strictly Proper MIMO systems

将反馈误差学习 (FEL) 推广到严格正确的 MIMO 系统

• 发表时间: 2006
• 作者: Basel;Alali;Kentaro;Hirata;Kenji;Sugimoto
• 通讯作者: Sugimoto

独立成分分析に基づく多入力多出力システムのブラインド同定

基于独立分量分析的多输入多输出系统盲辨识

• 发表时间: 2007
• 期刊: 電子情報通信学会和文論文誌(A) J90-A
• 作者: 新田益大;杉本謙二
• 通讯作者: 杉本謙二

Blind identification of polynomial matrix fraction via independent component analysis

• DOI: 10.1002/rnc.1130
• 发表时间: 2007-05
• 期刊: International Journal of Robust and Nonlinear Control
• 影响因子: 3.9
• 作者: M. Nitta;Kenji Sugimoto

独立成分分析に基づく外乱抑制制御系設計

基于独立分量分析的扰动抑制控制系统设计

• 发表时间: 2006
• 期刊: 計測自動制御学会論文集 42
• 作者: 新田益大;杉本謙二
• 通讯作者: 杉本謙二

ICA Based Blind Identification via Exact Parameterization

通过精确参数化进行基于 ICA 的盲识别

• 发表时间: 2006
• 期刊: 17th International Symposium on Mathematical Theory of Networks and Systems (国際会議論文)
• 作者: M.Nitta;K.Sugimoto
• 通讯作者: K.Sugimoto