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Research Papers

Estimating Tool–Tissue Forces Using a 3-Degree-of-Freedom Robotic Surgical Tool

[+] Author and Article Information
Baoliang Zhao

Department of Mechanical
and Materials Engineering,
University of Nebraska-Lincoln,
Lincoln, NE 68588
e-mail: baoliang.zhao@yahoo.com

Carl A. Nelson

Department of Mechanical
and Materials Engineering,
University of Nebraska-Lincoln,
Lincoln, NE 68588;
Department of Surgery,
University of Nebraska Medical Center,
Omaha, NE 68198
e-mail: cnelson5@unl.edu

1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Manuscript received September 8, 2015; final manuscript received December 10, 2015; published online May 4, 2016. Assoc. Editor: Venkat Krovi.

J. Mechanisms Robotics 8(5), 051015 (May 04, 2016) (10 pages) Paper No: JMR-15-1244; doi: 10.1115/1.4032591 History: Received September 08, 2015; Revised December 10, 2015

Robot-assisted minimally invasive surgery (MIS) has gained popularity due to its high dexterity and reduced invasiveness to the patient; however, due to the loss of direct touch of the surgical site, surgeons may be prone to exert larger forces and cause tissue damage. To quantify tool–tissue interaction forces, researchers have tried to attach different kinds of sensors on the surgical tools. This sensor attachment generally makes the tools bulky and/or unduly expensive and may hinder the normal function of the tools; it is also unlikely that these sensors can survive harsh sterilization processes. This paper investigates an alternative method by estimating tool–tissue interaction forces using driving motors' current, and validates this sensorless force estimation method on a 3-degree-of-freedom (DOF) robotic surgical grasper prototype. The results show that the performance of this method is acceptable with regard to latency and accuracy. With this tool–tissue interaction force estimation method, it is possible to implement force feedback on existing robotic surgical systems without any sensors. This may allow a haptic surgical robot which is compatible with existing sterilization methods and surgical procedures, so that the surgeon can obtain tool–tissue interaction forces in real time, thereby increasing surgical efficiency and safety.

Copyright © 2016 by ASME
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References

Figures

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Fig. 1

Tool tip motion coupling on the EndoWrist

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Fig. 2

Motion coupling between the jaw position and yaw motion

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Fig. 3

Kinematics of decoupling

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Fig. 4

A compact, decoupled surgical grasper design

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Fig. 5

Dynamic modeling of a single DOF

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Fig. 6

The first version prototype

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Fig. 7

The second version prototype

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Fig. 8

The third version prototype

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Fig. 9

3-DOF master control equipped with potentiometer-based joint encoders

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Fig. 10

Force estimation experiment setup on (a) grasp DOF, (b) pitch DOF, and (c) yaw DOF

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Fig. 11

(a) Steady-state estimation error is no longer constant as in Ref. [21] and (b) calibration between the force estimation and the force measurement

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Fig. 12

Calibrated force estimation result for long steady input on grasp DOF: (a) comparison between the force estimation and the force measurement versus time and (b) the repeated testing results

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Fig. 13

The calibrated experiment result for short steady input on grasp DOF: (a) comparison between the force estimation and the force measurement versus time and (b) repeated testing results

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Fig. 14

The calibrated experiment result for periodic input on grasp DOF: (a) comparison between the force estimation and the force measurement versus time and (b) two input cycles shown in detail

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Fig. 15

Stiffness differentiation on grasp DOF: (a) experiment setup and (b) result

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Fig. 16

Tumor detection: (a) porcine liver with tumor imbedded and (b) stiffness mapping along the edge of the liver

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