A Multifunction Sensing Forceps Based on Fiber Bragg Gratings for Membrane Peeling

Abstract

Robot-assisted membrane peeling provides several benefits, including improvement of manipulation precision and safety. A forceps, which is capable of sensing peeling force, scleral force, and insertion depth, is the basis of developing robot control method, but such forceps still lacks. To relieve these challenges, a multifunction sensing forceps based on fiber Bragg gratings (FBGs) is proposed. Six FBG sensors are divided into three groups and attached along the grooves of a 23G forceps. The first FBG group aims to measure the peeling force, while the other two FBG groups measure the scleral force and insertion depth. The calculation algorithm is built. Both linear calibration method and radial basis function (RBF) neural network calibration method are proposed to improve the sensing accuracy. Experimental results show that the precision of the RBF neural network is approximately 76.28% higher than that of linear calibration method. Verification experimental results show that the average root mean square (rms) errors of the proposed forceps are 0.28 mN (peeling force), 0.37 mN (scleral force), and 0.29 mm (insertion depth), respectively. The tested sensing ranges are 30 mN (peeling force), 100 mN (scleral force), and 10 mm (insertion depth), respectively.