The design of underwater tactile force sensor with differential pressure structure and backpropagation neural network calibration

Abstract

The underwater tactile force measurement was prone to cross-sensitivity, causing the difficulty in distinguishing tactile force signal with the underwater complex environment of water pressure influence. For this problem, an underwater tactile force sensor whose sensing core was based on Microelectromechanical Systems (MEMS) was designed with differential pressure typed structure. The hollow hemispherical flexible contacts located at the upper and lower end, and the hollow cylindrical shell in the middle part composed the structure of the capsule-shaped sensor. The upper flexible contact could sense the compound signal composed of water pressure and tactile force, at the same time, the lower flexible contact could measure the water pressure information. The deformation signal of the upper and lower flexible contacts could be transformed to the force sensor core’s upper and lower surfaces with silicon oil filled in the inner hollow part of the sensor. The tactile force signal could be obtained with water pressure eliminated through vector superposition method under the influence of static pressure of water. The structure and manufacture technology were introduced, and the Backpropagation (BP) neural network data regression algorithm was designed for the cross sensitivity. The experiments are conducted to demonstrate the effectiveness of the differential pressure structure in eliminating the influence of water static pressure. The results indicated that the BP neural network data regression algorithm successfully produced real tactile force signals, which is highly beneficial for the intelligent operation of underwater dexterous hand. Additionally, the sensor has an accuracy of 5%.