A Deep Learning Approach to Intrinsic Force Sensing on the da Vinci Surgical Robot

被引：11

作者：

Tran, Nam ^{[1
,2
]}

Wu, Jie Ying ^{[1
]}

Deguet, Anton ^{[1
]}

Kazanzides, Peter ^{[1
]}

机构：

[1] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA

[2] Santa Clara Univ, Dept Comp Sci & Engn, Santa Clara, CA 95053 USA

来源：

2020 FOURTH IEEE INTERNATIONAL CONFERENCE ON ROBOTIC COMPUTING (IRC 2020) | 2020年

基金：

美国国家科学基金会;

关键词：

deep learning; neural networks; force estimation; surgical robotics; dVRK; SURGERY;

D O I：

10.1109/IRC.2020.00011

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In robot-assisted minimally-invasive surgery (RAMIS), force estimation remains a challenging issue. We seek to estimate external forces based on available measurements from the joint encoders and motor currents. To this end, we propose a deep learning approach for end-to-cud force estimation on the da Vinci Surgical System that is trained using data collected by both moving an instrument in free space and by palpating a tissue phantom that has an embedded force sensor for ground truth. The trained neural network provides reasonable force estimates (within about IN to 2N precision given a full range of 10N) and is generalizable to other regions of the robot workspace. We further show that our proposed system can provide useful haptic feedback in a pilot study to differentiate stiffness in various tissue phantoms.

引用

页码：25 / 32

页数：8

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