Wednesday, May 19, 2021

A Hybrid Learning‐based Hysteresis Compensation Strategy for Surgical Robots

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Abstract

Background

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.

Methods

A strategy is proposed to separate these two hybrid forces, and model them by individual learning-based. A specially designed Elastic Hysteresis Neural Network (EHNN) model, is employed to capture the hysteresis nature of disturbance forces.

Results

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).

Conclusions

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.

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