基于信号特征分析和多小波变换的机械手滑动觉感知研究

Research on sliding perception of manipulators based on signal feature analysis and multi wavelet transform

针对机械手抓取目标过程中滑移特征信号辨识困难问题,提出了多小波变换滑动觉特征检测方法。首先,研究基于FBG传感的柔性触滑觉感知机理,设计非对称梁式双层“十字”型分布传感单元结构,分析了搭载该触滑觉传感器的机械手多阶段动态抓握信号特征;其次,构建触滑觉感知实验平台,开展了动态抓握过程的触滑觉感知实验;然后,基于db10小波降噪方法对滑动觉感知信号降噪处理;最后,提出Mexican hat连续小波和一阶Haar离散小波的滑动觉信号特征分离和感知方法,并进行了相关实验研究。实验结果表明,在小波细节系数检测阈值±2×10^(-4)作用下,不同抓握力的滑动检测平均准确率可达98.88%,可以精确识别被机械手抓取目标的滑移状态。

A multi wavelet transform sliding feature detection method is proposed to address the difficulty in identifying sliding feature signals during the process of robotic arm grasping targets.Firstly,the mechanism of flexible tactile slip sensing based on FBG sensing is studied,and a double-layer“cross”type distributed sensing unit based on FBG is designed.Secondly,a tactile perception experimental platform is established and tactile perception experiments are implemented on the dynamic grasping process.Then,based on the db10 wavelet denoising method,the sliding perception signal is denoised.Finally,a sliding signal feature separation and perception method using the Mexican hat continuous wavelet and the first-order Haar discrete wavelet is proposed,and relevant experimental research is conducted.The experimental results show that the detection threshold of wavelet detail coefficients is±2×10^(-4),and the average accuracy of sliding detection with different grip forces can reach 98.88%,which can accurately identify the sliding state of the target being grasped by the robotic arm.