Identification of characteristics of slip signal based on fiber Bragg grating flexible sensor

  Objective   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.

  Methods   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.

  Results and Discussions   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).

  Conclusions   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.