针对目前为智能仿生体柔性皮肤领域提供支持的光纤布拉格光栅传感器研究对滑觉信号特性识别手段的不足，提出了一种通过人工学习网络对基于分布式光栅传感单元所检测的滑觉速度与滑觉载荷进行预测的方法。设计了由四支光栅构成的传感阵列，采用封装技术制成柔性传感器，并搭建实验平台对滑觉信号进行采集。给出了滑觉过程对布拉格光栅波长偏移曲线的作用原理，对经验模态分解与小波分析的去噪效果进行比较，信噪比分别达到15.99与16.15。搭建了滑觉实验系统，对采集的不同速度与载荷分度的滑觉信号的特征值设定提取标准，构建滑觉样本集，引入随机森林与神经网络两个回归模型进行训练，并对比了预测效果。实验结果指出，速度特性预测中，两种模型的R²系数分别为0.9746和0.9681，平均误差分别为5.22%和4.31%；载荷特性预测中，两种模型的R²系数分别为0.9982和0.9835，平均误差分别为1.12%和3.02%。该研究方法基本实现了对滑觉样本两种特征的准确识别，在柔性仿生皮肤传感领域对滑觉信号的研究具有一定价值。

The realization mode of robot is developing towards intelligence. In the unstructured environment, bionics need to make self-adaptive decisions on the contact physical quantities (mostly dynamic and continuously changing sliding processes) in the environment. The sliding sensor is the main means for the artificial bionics to realize the perception of the changes in the external physical quantities and make the controller conduct body feedback. Fiber Bragg Grating (FBG) sensor encapsulated by silica gel has the characteristics of high sensitivity, small size, strong anti-electromagnetic interference ability, and is an ideal model for bionic skin. At present, the relevant research at home and abroad has designed FBG flexible sensors with different numbers and array distributions, but the degree of research is still limited, and the analysis of the basic characteristics of the signal at the phenomenal level lacks the necessary quantitative support. In the aspect of feature recognition and analysis of tactile signals, even though some studies have used artificial learning networks to identify or decouple the position, load and other characteristics of tactile signals, the research level is still in the static signal of tactile sensing, and there is a lack of attempts on dynamic and continuous sliding, resulting in deficiencies in the research direction of sliding signal feature recognition. Therefore, this work designs a distributed sensor system based on FBG using PDMS material packaging, and proposes a method to predict the sliding speed and sliding load detected by the distributed grating sensor unit through artificial learning network.The sensor array is composed of four gratings (Fig.2), and is packaged into a flexible sensor. An experimental platform is built to collect the slip signal (Fig.3). The principle of FBG wavelength shift during sliding is studied. The sliding signal is compared by EMD decomposition and wavelet analysis, and the signal-to-noise ratio is 15.99 and 16.15, respectively (Tab.1). Set up the sliding experiment system, set the extraction criteria for the sliding signal characteristic values of different speed and load scales (Fig.6), build the sliding sample set, introduce two regression models of random forest and neural network to train and predict the effect.The wavelet function is more conducive to the fidelity of the eigenvalues than the EMD function. The average signal-to-noise ratio coefficient is 0.322 higher. The signal-to-noise ratio and the root-mean-square error of the EMD denoising 8 layers perform well, but the extreme point deviation is large. The SNR and RMSE coefficients perform best when the wavelet decomposition 7 layers, but the waveform still has noise, while the error coefficient and the extreme point of the EMD denoising 8 layers are relatively stable; The magnitude of the FBG center wavelength peak value is related to the pressure load and sliding speed of the slider on the sensor. When the sliding speed and load change, the center wavelength offset curve changes with the sliding characteristic value. In a certain parameter range, the sensor responds well to the change of these two characteristic values and has a relatively linear change rule (Fig.7); The difference between RF and BP regression algorithms in slip speed prediction is not large. RF regression performs better than BP regression in slip load prediction, and basically realizes accurate recognition of slip characteristics (Fig.13).The experimental results show that the R² coefficients of the two models are 0.9746 and 0.9681, respectively, and the average error is 5.22% and 4.31%, respectively; In the load characteristic prediction, the R² coefficients of the two models are 0.9982 and 0.9835, respectively, and the average error is 1.12% and 3.02%, respectively (Tab.3). In the work, a detection and recognition method for the surface slip of distributed FBG flexible sensor is proposed. Through the introduction of artificial regression model, the two characteristics of sliding speed and load of sliding samples under different sliding conditions can be effectively predicted. This research method basically realizes the accurate recognition of the two characteristics of sliding samples, and has certain value in the research of sliding signal in the field of flexible bionic skin sensing.