Maintenance Reduction of Medical Robotic Manipulators through Automatic Data-Driven Updates of Feedforward Control

Abstract

The paper presents a new method for data-driven feedforward compensation of static and quasi-static forces acting on a multi-axis medical robotic manipulator. The proposed approach uses a look-up current calibration table (CCT) and an adaptive algorithm updating the CCT to ensure that the manipulator maintains accurate, fast, and safe performance over time. The key aspect of our control strategy is called data assimilation step, which involves modelling the CCT using an approximating function. We use the NURBS (Non-uniform rational basis spline) technique, which has desirable properties such as high accuracy and flexibility in approximating and even interpolating complex functions. The technique allows the manipulator to compensate for external disturbances such as gravity, friction and gear or cabling resistance. This can improve the precision and reduce the downtime of the manipulator due to periodic feedforward recalibration.