Research Papers

Density-Convex Model Based Robust Optimization to Key Components of Surgical Robot

[+] Author and Article Information
Shan Jiang

Centre for Advanced Mechanisms and Robotics,
School of Mechanical Engineering,
Tianjin University,
Tianjin 300072, China
e-mail: shanjmri@tju.edu.cn

Xuesheng Gao

School of Mechanical Engineering,
Tianjin University,
Tianjin 300072, China
e-mail: gaoxues@tju.edu.cn

Jun Liu

e-mail: cjr.liujun@vip.163.com

Jun Yang

e-mail: yeyang_918@sina.com
Department of Imaging,
Tianjin Union Medicine Center,
Tianjin 300121, China

Yan Yu

Division of Medical Physics,
Department of Radiation Oncology,
Thomas Jefferson University,
Kimmel Cancer Center,
111 South 11th Street,
Philadelphia, PA 19107
e-mail: yan.yu@jefferson.edu

1Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANISMS AND ROBOTICS. Manuscript received November 18, 2012; final manuscript received July 7, 2013; published online October 1, 2013. Assoc. Editor: Philippe Wenger.

J. Mechanisms Robotics 5(4), 041012 (Oct 01, 2013) (11 pages) Paper No: JMR-12-1195; doi: 10.1115/1.4025174 History: Received November 18, 2012; Revised July 07, 2013

This paper investigates a new robust optimization framework based on density-convex reliability model and applies it to the dimensional optimization of magnetic resonance (MR) compatible surgical robot. As a justified tool for assessing reliability, the density-convex model is proposed on account of the reality that available data information is always insufficient. Based on the density-convex model, reliability functions of structure are constructed and taken as constraint conditions. The Euclidean norm of the sensitivity Jacobian matrix is selected as robust index and stated as the ultimate objective function. By using finite element method and artificial neural network (FEM–ANN) method, the explicit functions of mechanical response are achieved effectively. The optimization is solved by a gradient-based optimization algorithm in the framework. As an application of the above optimization framework, a prototype robot is designed and manufactured. Finally, a test experiment verifies the high reliability of the robot and further proves the validity and effectiveness of this proposed method.

Copyright © 2013 by ASME
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Fig. 1

ECM for two-dimensional problem

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Fig. 2

ECM for three-dimensional problem

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Fig. 5

3D Solid model of the surgical robot

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Fig. 3

Relation curves between reliability and reliability index β in 2D

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Fig. 4

Curves of differences between proposed DCM and PM, ECM

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Fig. 8

FEM analysis of the surgical robot

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Fig. 9

Simplified pitch/lift module

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Fig. 6

Setup of needle insertion experiment

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Fig. 7

Curves for needle insertion into and removal from soft tissue

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Fig. 10

Maximum equivalent stress on Long-rod1 versus positions of sliders

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Fig. 11

Flowchart of the optimization

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Fig. 17

Process of training ANN and error analysis to the response surface of maximum stress

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Fig. 18

Flowchart of the optimization process: In DCM, sm denotes the MPP. In FDA, I={i|θi(u,v)=0} is the subscript set of effective constrains, SUP{·} denotes supremum function. Minimization problems (a), (b), and (c) are all linear programming problems which can be easily resolved. ‖v(k+1)-v(k)‖≤10-3 is taken as convergence condition of this optimization.

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Fig. 12

Loading and boundary conditions of FEM analysis

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Fig. 13

Schematic representation of BP-ANN architecture

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Fig. 14

Response surface of maximum deformation at l = 150 mm

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Fig. 15

Process of training ANN and error analysis to the response surface of maximum deformation

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Fig. 16

Response surface of maximum stress at l = 150 mm

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Fig. 19

Experimental platform

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Fig. 20

Experimental robot based on optimization

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Fig. 21

Comparison between measured strains and target strain




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