Abstract:In minimally invasive surgical robot system, the implementation of the force feedback function can increase the flexibility of the surgeon during surgery and reduce the risk of damage to the tissues and organs of the patient. In order to achieve the force detection during surgical process, a multidimensional force sensor based on fiber Bragg grating (FBG) is designed for minimally invasive surgical robot and its decoupling methods are studied. The multidimensional force sensor is composed of three fiber gratings spaced 120° apart that are attached to the end of the surgical tool rod along the axial direction. Firstly, based on the stress analysis of the sensor, the least squares method is used for decoupling. However, the sensor has a nonlinear relationship between inputs and outputs due to the factors such as assembly and etc., so the feedforward neural network is used to carry out the nonlinear decoupling of the multidimensional force sensor. Then, the effects of the trocar translation on the decoupled force are analyzed theoretically and experimentally. The experiment results show that the feedforward neural network has good decoupling effects for the multidimensional force sensor, and the average errors in three mutually perpendicular directions are 005, 007 and 018 N， respectively. The maximum average error of the force detection after the trocar translation is 0036 N, which is negligible. The designed sensor, the decoupling method and the analysis of trocar translation effects are proved to have strong practicality.