Yongchen Guo

TL;DR: In this article, a deep learning-based method is proposed to measure the instrument grip force without mounting additional sensors in a robot-assisted minimally invasive surgery (RMIS), and the results of the ablation study tell that the master-slave trajectory has the highest accuracy, the optimized input data frame can reduce the error by 17%, and each component of CAM-FoC can enhance the measurement accuracy.

TL;DR: A novel hand–eye calibration algorithm is proposed to tackle the problem which relies purely on surgical instrument already in the operating scenario for robot-assisted minimally invasive surgery (RMIS), formed by the geometry information of the surgical instrument and the remote center-of-motion (RCM) constraint.

TL;DR: A grip force perception method based on denoising AutoEncoder Neural Network (dAENN) is proposed, which shows adequate expressive capability of the extracted coding as well as the superior grip force Perception performance over several popular data-based methods under the same dataset.

TL;DR: This article utilizes the dynamic analysis of the cable-driven system to conduct feature engineering and add prior knowledge to the Gaussian process regression (GPR), and illustrates the significance to integrate the system’s characteristics when introducing machine learning techniques into robot systems.

TL;DR: In this paper , a learning-based method ACAM-FoC was proposed to measure the grip force in mass-produced surgical instruments, taking the difference in motion hysteresis and mechanism friction among mass-manufactured surgical instruments into account.