Neural Network based Inverse Dynamics Identification and External Force Estimation on the da Vinci Research Kit

Abstract

Most current surgical robotic systems lack the ability to sense tool/tissue interaction forces, which motivates research in methods to estimate these forces from other available measurements, primarily joint torques. These methods require the internal joint torques, due to the robot inverse dynamics, to be subtracted from the measured joint torques. This paper presents the use of neural networks to estimate the inverse dynamics of the da Vinci surgical robot, which enables estimation of the external environment forces. Experiments with motions in free space demonstrate that the neural networks can estimate the internal joint torques within 10% normalized root-mean-square error (NRMSE), which outperforms model-based approaches in the literature. Comparison with an external force sensor shows that the method is able to estimate environment forces within about 10% NRMSE.