Evaluation of torque measurement surrogates as applied to grip torque and jaw angle estimation of robotic surgical tools

John J. O'Neill, Trevor K. Stephens, Timothy M Kowalewski

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The estimation of grip force for surgical tools such as the da Vinci has been shown to be valuable in possible applications such as haptics, tissue identification, and surgical training. Successful estimation attempts have been previously demonstrated, but utilize customized sensors; this letter aims to provide an estimate considering only typical sensor streams already present in commercially available surgical robots. The objective of this letter is to evaluate three proximal-end torque surrogate methods in their abilities to estimate distal-end states. The estimates are compared with previously reported results found in literature and the percent difference between the customized sensor approach and previous standards is reported. The most effective surrogates for proximal-end torque were commanded motor current and measured motor current. The jaw angle estimate resulted in 0.37 degree root mean square error, and the distal-end torque estimate resulted in 4.42 mNm RMSE, which compares favorably to existing literature approaches.

Original languageEnglish (US)
Pages (from-to)3027-3034
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume3
Issue number4
DOIs
StatePublished - Oct 1 2018

Fingerprint

Torque measurement
Torque
Robotics
Angle
Sensors
Evaluation
Estimate
Sensor
Mean square error
Tissue
Haptics
Percent
Robot
Roots
Evaluate

Keywords

  • AI-based methods
  • Surgical robotics: Laparoscopy
  • medical robots and systems
  • perception for grasping and manipulation

Cite this

Evaluation of torque measurement surrogates as applied to grip torque and jaw angle estimation of robotic surgical tools. / O'Neill, John J.; Stephens, Trevor K.; Kowalewski, Timothy M.

In: IEEE Robotics and Automation Letters, Vol. 3, No. 4, 01.10.2018, p. 3027-3034.

Research output: Contribution to journalArticle

@article{8e1dea6796674fe4904290442f0f16de,
title = "Evaluation of torque measurement surrogates as applied to grip torque and jaw angle estimation of robotic surgical tools",
abstract = "The estimation of grip force for surgical tools such as the da Vinci has been shown to be valuable in possible applications such as haptics, tissue identification, and surgical training. Successful estimation attempts have been previously demonstrated, but utilize customized sensors; this letter aims to provide an estimate considering only typical sensor streams already present in commercially available surgical robots. The objective of this letter is to evaluate three proximal-end torque surrogate methods in their abilities to estimate distal-end states. The estimates are compared with previously reported results found in literature and the percent difference between the customized sensor approach and previous standards is reported. The most effective surrogates for proximal-end torque were commanded motor current and measured motor current. The jaw angle estimate resulted in 0.37 degree root mean square error, and the distal-end torque estimate resulted in 4.42 mNm RMSE, which compares favorably to existing literature approaches.",
keywords = "AI-based methods, Surgical robotics: Laparoscopy, medical robots and systems, perception for grasping and manipulation",
author = "O'Neill, {John J.} and Stephens, {Trevor K.} and Kowalewski, {Timothy M}",
year = "2018",
month = "10",
day = "1",
doi = "10.1109/LRA.2018.2849862",
language = "English (US)",
volume = "3",
pages = "3027--3034",
journal = "IEEE Robotics and Automation Letters",
issn = "2377-3766",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - Evaluation of torque measurement surrogates as applied to grip torque and jaw angle estimation of robotic surgical tools

AU - O'Neill, John J.

AU - Stephens, Trevor K.

AU - Kowalewski, Timothy M

PY - 2018/10/1

Y1 - 2018/10/1

N2 - The estimation of grip force for surgical tools such as the da Vinci has been shown to be valuable in possible applications such as haptics, tissue identification, and surgical training. Successful estimation attempts have been previously demonstrated, but utilize customized sensors; this letter aims to provide an estimate considering only typical sensor streams already present in commercially available surgical robots. The objective of this letter is to evaluate three proximal-end torque surrogate methods in their abilities to estimate distal-end states. The estimates are compared with previously reported results found in literature and the percent difference between the customized sensor approach and previous standards is reported. The most effective surrogates for proximal-end torque were commanded motor current and measured motor current. The jaw angle estimate resulted in 0.37 degree root mean square error, and the distal-end torque estimate resulted in 4.42 mNm RMSE, which compares favorably to existing literature approaches.

AB - The estimation of grip force for surgical tools such as the da Vinci has been shown to be valuable in possible applications such as haptics, tissue identification, and surgical training. Successful estimation attempts have been previously demonstrated, but utilize customized sensors; this letter aims to provide an estimate considering only typical sensor streams already present in commercially available surgical robots. The objective of this letter is to evaluate three proximal-end torque surrogate methods in their abilities to estimate distal-end states. The estimates are compared with previously reported results found in literature and the percent difference between the customized sensor approach and previous standards is reported. The most effective surrogates for proximal-end torque were commanded motor current and measured motor current. The jaw angle estimate resulted in 0.37 degree root mean square error, and the distal-end torque estimate resulted in 4.42 mNm RMSE, which compares favorably to existing literature approaches.

KW - AI-based methods

KW - Surgical robotics: Laparoscopy

KW - medical robots and systems

KW - perception for grasping and manipulation

UR - http://www.scopus.com/inward/record.url?scp=85059953208&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059953208&partnerID=8YFLogxK

U2 - 10.1109/LRA.2018.2849862

DO - 10.1109/LRA.2018.2849862

M3 - Article

AN - SCOPUS:85059953208

VL - 3

SP - 3027

EP - 3034

JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

SN - 2377-3766

IS - 4

ER -