Application of Neural Network to Nonlinear Static Decoupling of Robot Wrist Force Sensor

Abstract

The static coupling of wrist force sensor is a major influencing factor to its measuring precision. Aiming at resolving the disadvantages such as low decoupling precision of the traditional method, we put forward a nonlinear decoupling method based on neural network. The major idea applied is to use the BP network to realize the mapping from input to output of the sensor. Owing to BP network's good nonlinear mapping ability, the decoupling result can reach an arbitrary precision theoretically. The effectiveness of this method was verified in the calibration of wrist force sensor of a force sensing system for an underwater robot gripper. The decoupling results demonstrate the validation of neural network method