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Research Papers

Adaptive Control of a Robot-Assisted Tele-Surgery in Interaction With Hybrid Tissues

[+] Author and Article Information
Hamidreza Kolbari

Mechanical Engineering Department,
Amirkabir University of Technology
(Tehran Polytechnic),
Tehran 158754413, Iran
e-mail: hamid-glb@aut.ac.ir

Soroush Sadeghnejad

Mechanical Engineering Department,
Amirkabir University of Technology
(Tehran Polytechnic),
Tehran 158754413, Iran
e-mail: s.sadeghnejad@aut.ac.ir

Mohsen Bahrami

Mechanical Engineering Department,
Amirkabir University of Technology
(Tehran Polytechnic),
Tehran 158754413, Iran
e-mail: mbahrami@aut.ac.ir

Kamali E. Ali

Mechanical Engineering Department,
Amirkabir University of Technology
(Tehran Polytechnic),
Tehran 158754413, Iran
e-mail: alikamalie@aut.ac.ir

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received March 29, 2018; final manuscript received July 7, 2018; published online August 1, 2018. Assoc. Editor: Dumitru I. Caruntu.

J. Dyn. Sys., Meas., Control 140(12), 121012 (Aug 01, 2018) (12 pages) Paper No: DS-17-1528; doi: 10.1115/1.4040818 History: Received March 29, 2018; Revised July 07, 2018

In a haptic teleoperation system, which interacts with unknown and hybrid environments, it is important to achieve stability and transparency. In medical usages, the utilization of knowledge on the tissues behavior in a controller design can improve the performance of the surgery in a robot-assisted telesurgery. Simultaneous interaction with hard and soft tissues makes it difficult to achieve stability and transparency. To deal with this difficulty, two controller schemes are designed. At first, a nonlinear mathematical model (inspired by the Hunt-Crossley (HC) model), which has the properties of soft and hard tissues, is combined with the slave side dynamic. In the second approach, the reaction force applied by hybrid tissues during the transition between tissues of different properties is modeled as an unknown force acting on the slave side. In a four-channel (4-CH) architecture, nonlinear adaptive controllers are designed without any knowledge about the parameters of the master, the slave robot, and the environment. For both control schemes, Lyapunov candidate functions provide a way to ensure the stability and transparency in the presence of uncertainties. The testbed comprises two Novint Falcon robots functioning as master and slave robots. Moreover, the experiments are performed on various objects, including a soft cube, a hard cube, and a phantom tissue. This paper rigorously evaluates the performances of the proposed methods, comparing them with each other and other previous schemes. Experimental and numerical results demonstrate the effectiveness of the proposed control schemes.

Copyright © 2018 by ASME
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Figures

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Fig. 1

A master–slave surgical teleoperation system

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Fig. 2

The 4-CH teleoperation architecture

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Fig. 3

The diagram of a teleoperation system in the scheme B

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Fig. 4

The diagram of a teleoperation system in the scheme C

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Fig. 5

Block diagram of both control schemes

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Fig. 6

System setup. The right-side (the master robot) Novint Falcon is constrained in 1DOF.

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Fig. 7

The master–slave block diagram and its experimental setup

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Fig. 8

The phantom tissue made for simulating the soft and hard tissues

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Fig. 9

Three different materials used as the environment through the experiments. From left to right: the hard cube, soft cube, and phantom tissue.

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Fig. 10

The master and slave position trajectories in interaction with the hard cube

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Fig. 11

The environment and human operator forces in interaction with the hard cube

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Fig. 12

The PTE and FTE generated by the scheme A, B, and C controllers in contact with the hard cube

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Fig. 13

The master and slave position tracking in interaction with the soft cube

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Fig. 14

The force exerted to the environment and the human operator's hand during contact with the soft cube

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Fig. 15

The PTE and FTE generated by the scheme A, B, and C controllers in contact with the soft cube

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Fig. 16

The master and slave robots position trajectories while interaction with the phantom tissue

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Fig. 17

The exerted forces to the environment and the human operator hand while interaction with the phantom tissue

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Fig. 18

The PTE and FTE generated by the scheme A, B, and C controllers in contact with phantom tissue

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