A Six-Axis FBG Force/Moment Sensor With Nonlinear Decoupling and Fault Tolerance for Laparoscopic Instruments
Tianliang Li, Pingan Huang, Shasha Wang, Changsheng Li, Liang Qiu, Chwee Ming Lim, Hongliang Ren
Abstract
In this article, a six-axis fiber Bragg grating (FBG) force/moment (F/M) sensor is created and integrated into laparoscopic forceps to retrieve interactive force feedback for surgery. This sensor consists of a 3-D-printed ellipsoidal hollow elastomer and six Stewart-like suspended FBGs in the elastomer, leading to a compact size and high sensitivity. An algorithm based on the seagull optimization algorithm and extreme learning machine (SOA-ELM) is proposed to depress the nonlinear crosstalk effect of six-axis F/M output and realize fault tolerance of FBG fractures. Compared with the backpropagation neural network and extreme learning machine method, the experiment results show that the nonlinear decoupling performance based on SOA-ELM harvests an excellent accuracy with a small error of less than 6%, as well as the excellent fault-tolerance effect with an error below 10% while one FBG fractures. The maximum dynamic error of the designed sensor is within 10%. The feasibility and effectiveness of the designed sensor for real-time force feedback in laparoscopic surgery are demonstrated through simulation tasks of threading, suturing, cutting the ex vivo tissues, and operation in the oral cavity. Such merits show the great potential of the designed sensor to provide force feedback in surgery.