Three-Fingers FBG Tactile Sensing System Based on Squeeze-and-Excitation LSTM for Object Classification
Chengang Lyu, Bo Yang, Jiachen Tian, Jie Jin, Chunfeng Ge, Jiachen Yang
Abstract
As one of the important sensing technology, tactile sense has been the focus of attention in recent years. Because of its small, light and anti-electromagnetic interference, fiber Bragg grating (FBG), as an advanced tactile sensor, can be encapsulated on any type of industrial manipulator. Based on the three-dimensional characteristics of the grabbed object, this paper designs a three-fingers FBG tactile sensing system. The structure of wavelength-swept optical coherence tomography is built to collect high sensitivity three-channels FBG tactile sensing signal. The obtained tactile signal is one-dimensional small volume data, which has fast transmission rate and occupies a small bandwidth. The system is suitable for application in any places especially in industrial with complex environment and precious bandwidth. FBG tactile signal is demodulated into a time-correlation sequence representing the grasping process, as the input of neural network. The classification results of two neural networks for processing time-correlation signal: WaveNet and LSTM are compared. For three-channels of the obtained tactile signal, Squeeze-and-Excitation module, which increases the correlation between channels, is added to the better performance LSTM model. The accuracy of classification is further improved. The Squeeze-and-Excitation LSTM (SE-LSTM) classification result shows that the classification accuracy of SE-LSTM for six types of objects with similar size and shape reaches 95.97%, which proves the effective of FBG tactile sensing technology for object classification. The time of single recognition can reach 1.2ms, which meets the requirements of real-time.