Fault-Tolerant Six-Axis FBG Force/Moment Sensing for Robotic Interventions

A fiber Bragg grating (FBG) based six-axis force/moment (F/M) tactile sensor with the capability of nonlinear decoupling, fault-tolerant, and temperature compensation (TC) is developed for robot-assisted minimally invasive surgery. Eight tightly suspended FBGs have been fixed inside a small three-dimensional printed flexure with a diameter of 10 mm to form the force-sensitive unit of the sensor. Considering the suspended FBG fracture risk, an optimized back propagation neural network (BPNN) algorithm has been proposed to eliminate the nonlinear crosstalk effect of the six-axis F/M outputs, diagnose and modify the fault response of the sensor under several FBGs fractures and eliminate the temperature-induce errors. Experimental results indicate that the Type I and Type II errors are within 5% using the BPNN nonlinear decoupling model. The force resolutions can reach 2.73 mN, 2.21 mN, and 28.39 mN within +/- 4 N, and the moment resolutions can reach 0.36 mN center dot mm, 0.96 mN center dot mm, and 47.5 mN center dot mm within +/- 20 N center dot mm. Moreover, the Type I errors in the six-axis can be modified within 8%, the Type II errors can be modified within 10%, even with one and two FBGs fractures. After TC, the maximum temperature-induced errors can be modified within 0.061 N and 0.127 N center dot mm for force and moment components, respectively. Combining the robot-assisted scanning and the BPNN, the positions of the buried vessels in a phantom can be effectively identified by the designed sensor, even two FBGs fracture. Such merits validate the dependability and robustness of the designed sensor with FBGs fracture.

Automated summary

A six-axis force/moment tactile sensor based on fiber Bragg grating (FBG) has been developed for robot-assisted minimally invasive surgery, with the capabilities of nonlinear decoupling, fault-tolerant, and temperature compensation. The sensor consists of eight tightly suspended FBGs fixed inside a small three-dimensional printed flexure. An optimized back propagation neural network (BPNN) algorithm has been proposed to eliminate nonlinear crosstalk, diagnose and modify fault response, and eliminate temperature-induced errors.