解耦(概率)
光纤布拉格光栅
非线性系统
计算机科学
算法
人工智能
物理
拓扑(电路)
组合数学
数学
光学
工程类
控制工程
光纤
量子力学
作者
Tianliang Li,Jinxiu Guo,Han Zheng,Shasha Wang,Liang Qiu,Hongliang Ren
出处
期刊:IEEE-ASME Transactions on Mechatronics
[Institute of Electrical and Electronics Engineers]
日期:2023-06-05
卷期号:28 (6): 3537-3550
被引量:33
标识
DOI:10.1109/tmech.2023.3268077
摘要
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-axisF/Moutputs, 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·mm, 0.96 mN·mm, and 47.5 mN·mm within ±20 N·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·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.
科研通智能强力驱动
Strongly Powered by AbleSci AI