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Accurate kinematic models are essential for effective control of surgicalrobots. For tendon driven robots, which is common for minimally invasivesurgery, intrinsic nonlinearities are important to consider. Traditionalanalytical methods allow to build the kinematic model of the system by makingcertain assumptions and simplifications on the nonlinearities. Machine learningtechniques, instead, allow to recover a more complex model based on theacquired data. However, analytical models are more generalisable, but can beover-simplified; data-driven models, on the other hand, can cater for morecomplex models, but are less generalisable and the result is highly affected bythe training dataset. In this paper, we present a novel approach to combininganalytical and data-driven approaches to model the kinematics of nonlineartendon-driven surgical robots. Gaussian Process Regression (GPR) is used forlearning the data-driven model and the proposed method is tested on bothsimulated data and real experimental data.

Hybrid Data-Driven and Analytical Model for Kinematic Control of a Surgical Robotic Tool