A hybrid learning-based hysteresis compensation strategy for surgical robots.

Abstract

The hysteretic forces arising from the electric cables that externally run along the robotic joints are the main disturbance to the precise parameter estimation of gravity compensation model, for the Master Tool Manipulator (MTM) of the da Vinci Research Kit (dVRK). Because such nonlinear disturbance forces and the gravitational forces are often hybrid and in the same magnitude. A strategy is proposed to separate these two hybrid forces, and model them by individual learning-based algorithms. A specially designed Elastic Hysteresis Neural Network model is employed to capture the hysteresis nature of disturbance forces. The experimental results show that our proposed strategy has higher compensation accuracy (78.64%-93.32%), and fewer real samples are required for model estimation (100 samples for each joint). Our proposed gravity compensation strategy for the MTM of the dVRK shows great improvement over existing state-of-the-arts methods through conducted comparative experiments.