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Research Papers

Sensorless Force Sensing for Minimally Invasive Surgery

[+] Author and Article Information
Baoliang Zhao

Department of Mechanical
and Materials Engineering,
University of Nebraska–Lincoln,
Lincoln, NE 68508
e-mail: baoliang.zhao2@gmail.com

Carl A. Nelson

Department of Mechanical
and Materials Engineering,
University of Nebraska–Lincoln,
Lincoln, NE 68508
e-mail: cnelson5@unl.edu

Manuscript received January 22, 2015; final manuscript received July 28, 2015; published online October 15, 2015. Assoc. Editor: Venketesh Dubey.

J. Med. Devices 9(4), 041012 (Oct 15, 2015) (14 pages) Paper No: MED-15-1015; doi: 10.1115/1.4031282 History: Received January 22, 2015; Revised July 28, 2015

Robotic minimally invasive surgery (R-MIS) has achieved success in various procedures; however, the lack of haptic feedback is considered by some to be a limiting factor. The typical method to acquire tool–tissue reaction forces is attaching force sensors on surgical tools, but this complicates sterilization and makes the tool bulky. This paper explores the feasibility of using motor current to estimate tool-tissue forces and demonstrates acceptable results in terms of time delay and accuracy. This sensorless force estimation method sheds new light on the possibility of equipping existing robotic surgical systems with haptic interfaces that require no sensors and are compatible with existing sterilization methods.

Copyright © 2015 by ASME
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References

Figures

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Fig. 1

A decoupled cable-driven grasper [18]

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Fig. 2

Cables driving one jaw [18]

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Fig. 3

Cable deformation model

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Fig. 4

The 3DOF surgical grasper prototype

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Fig. 6

Motion comparison between prototype and computer-aided design (CAD) model

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Fig. 7

Force decoupling between grasp and yaw motion (* zero yaw angle is the position where the whole tool tip is in line with the tool shaft)

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Fig. 9

Linear relation between clamp force and cable tension [19]

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Fig. 10

Linear relation between motor current and motor torque

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Fig. 11

The 3DOF surgical grasper prototype and master control

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Fig. 12

Experiment setting of force estimation on (a) grasp DOF, (b) pitch DOF, and (c) yaw DOF [20]

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Fig. 13

Force estimation on grasp DOF for long steady input

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Fig. 14

Force estimation on grasp DOF for short steady input

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Fig. 15

Force estimation on grasp DOF for periodic input [20]

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Fig. 16

Influence of (a) pitch and (b) yaw motion on grasp force

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Fig. 17

The actual sized prototype

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Fig. 18

The force estimation fit before calibration on grasp DOF

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Fig. 19

The calibrated result for long input on grasp DOF

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Fig. 20

The calibrated result for short input on grasp DOF

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Fig. 21

The calibrated result for periodic input on grasp DOF

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Fig. 22

Stiffness differentiation

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Fig. 23

Grasp force control (* every grid line on the x axis represents four seconds)

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Fig. 24

Animal tissue with tumor embedded

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Fig. 25

Grasp forces at locations (a) with tumor and (b) without tumor

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