Hybrid Data-Driven and Analytical Model for Kinematic Control of a Surgical Robotic Tool
This addresses the challenge of improving control accuracy for surgical robots, which is incremental as it builds on existing methods by integrating them.
The paper tackles the problem of accurately modeling the kinematics of tendon-driven surgical robots by combining analytical and data-driven approaches, resulting in a hybrid method tested on simulated and real data.
Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which is common for minimally invasive surgery, intrinsic nonlinearities are important to consider. Traditional analytical methods allow to build the kinematic model of the system by making certain assumptions and simplifications on the nonlinearities. Machine learning techniques, instead, allow to recover a more complex model based on the acquired data. However, analytical models are more generalisable, but can be over-simplified; data-driven models, on the other hand, can cater for more complex models, but are less generalisable and the result is highly affected by the training dataset. In this paper, we present a novel approach to combining analytical and data-driven approaches to model the kinematics of nonlinear tendon-driven surgical robots. Gaussian Process Regression (GPR) is used for learning the data-driven model and the proposed method is tested on both simulated data and real experimental data.