Optimization of the penetrative path during grommet insertion in a robotic ear surgery

Abstract

The Ventilation Tube Applicator is an ear surgical device to treat otitis media with effusion by inserting a grommet into tympanic membrane (TM). This grommet insertion process requires very delicate control to minimise trauma to the TM and to ensure that the grommet is successfully inserted. This paper introduces a method to optimise the trajectory of the grommet insertion path by implementing neural network-based pattern classification, path planning algorithm, and precision motion control. A modified adaptive S-curve path was designed and optimised for four types of mock membranes. Experimental results showed that the pattern classification achieved 97% identification success, force on TM is reduced by up to 13%, TM deformation is decreased by up to 12%, and procedure’s success rate is improved by 100% for one type of mock membrane. The novel use of the Hunt–Crossley contact force model provided a good prediction of the force on the TM during the insertion process. This predicted force was used to reject the disturbance in the motion control scheme and resulted in tremendous improvement in precision and accuracy. The maximum absolute tracking error of the motion controller was reduced by up to 29%, and the root mean square error was reduced by up to 43%. This method has been proven to work well and can be considered for other surgeries requiring a penetrative motion and placement accuracy.