Tactile recognition technology based on Multi-channel fiber optical sensing system

In order to achieve efficient and high-accuracy tactile sensing recognition of objects in complex industrial environments, this paper builds a multi-channel fiber optic tactile sensing system which is based on recurrence plot(RP) and VGG network. We design and build a bionic three-fingers tactile sensing system with fiber bragg grating(FBG) for acquiring signals. Three fiber Bragg gratings are pasted on the inside of an industrial three-finger flexible mechanical claw using an epoxy resin. After test,the packaged FBG still has good linear sensing characteristics, and its strain sensitivity is 0.51 pm/με. For solving the fusion problem of three-finger tactile signals, this paper proposes an improved RP algorithm to encode multi-channel one-dimensional tactile sensing signals into images, which not only reveals deeper temporal correlation of sensing signals, but also has higher robustness to noise. We use the VGG network to accurately classify the grasped objects. Six visually similar classes of fruits and vegetables are selected for tactile sensing. The results show that the accuracy of the system can reach 99.204%, the recognition time of each object can be as low as 0.29 s, which almost meets the industry standard of real-time sorting.