A hybrid learning‐based hysteresis compensation strategy for surgical robots. Issue 4 (1st June 2021)

• Record Type: Journal Article
• Authors: Gao, Qian; Tan, Ning; Sun, Zhenglong
• Is Part Of: International journal of medical robotics and computer assisted surgery. Volume 17:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-01
• Subjects: hysteresis compensation -- learning‐based control -- preisach theory -- surgical manipulator; Robotics in medicine -- Periodicals; Surgery -- Technological innovations -- Periodicals; Imaging systems in medicine -- Periodicals; 617.90285
• DOI: 10.1002/rcs.2275
• Languages: English
• ISSNs: 1478-5951

Abstract

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 algorithms. A specially designed Elastic Hysteresis Neural Network 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.