A Hybrid Learning‐based Hysteresis Compensation Strategy for Surgical Robots

xlomafota13 shared this article with you from Inoreader A Hybrid Learning‐based Hysteresis Compensation Strategy for Surgical Robots Via The International Journal of Medical Robotics and Computer Assisted Surgery 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. This article is protected by copyright. All rights reserved. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.