Marquardt algorithm based force feedback model construction method for radial-basis meshless soft tissue data

A technology of model modeling and gridless method, which is applied in special data processing applications, electrical digital data processing, calculation, etc., can solve the problems of high calculation accuracy, large calculation amount, low calculation efficiency, etc., and achieve simplified calculation amount, Efficient modeling and convenient calculation

Active Publication Date: 2016-11-23
NANCHANG UNIV
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Problems solved by technology

However, due to the problem of low computational efficiency in gridless, it is not very ideal in terms of real-time performance.
[0004] To sum up, it can be seen that the mass-spring model (MS) based on the network structure has a simple structure and good real-time performance, but it has local properties in deformation and is not accurate enough
The f...

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  • Marquardt algorithm based force feedback model construction method for radial-basis meshless soft tissue data
  • Marquardt algorithm based force feedback model construction method for radial-basis meshless soft tissue data
  • Marquardt algorithm based force feedback model construction method for radial-basis meshless soft tissue data

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Embodiment Construction

[0027] The invention will be further illustrated by the following examples.

[0028] biological soft tissue h αβ Nodes have the following systems:

[0029]

[0030] Take the force point O as the origin of the coordinates, where x, y, and z are h αβ The displacement of the node in the horizontal axis direction, the vertical axis direction and the vertical horizontal plane direction, F is the feedback force of the force point O, is a random error term.

[0031] The radial basis meshless method is used to obtain the soft tissue output force F(k) and input displacement x(k), y(k), z( k) data. (In actual operation, the tissue diameter range of biological tissue deformed by force is about 1-4 cm).

[0032] α×β=H

[0033] Considering the characteristics of biological soft tissue force and deformation, the system can be written as:

[0034] make

[0035] The original system function can be written as

[0036]

[0037] where x, y, and z are h αβ The displaceme...

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Abstract

The invention provides a Marquardt algorithm based fitting method for radial-basis meshless soft tissue data. The method aims at overcoming defects of complicated structure, large calculated amount and poor real-time performance of a conventional physical model of biological soft tissue, anisotropic characteristics of the soft tissue are taken into consideration, stress displacement data of a finite number of equally spaced points on the surface with a soft tissue stress point as a center are obtained once with a radial-basis meshless method, a soft tissue node force displacement model is fitted with a Marquardt algorithm, and the biological soft tissue model is constructed by forming lines with points and forming planes with lines. The method has the advantages as follows: the structure is concise, the calculation is convenient, precision of the radial-basis meshless method is reserved, meanwhile, the calculated amount is greatly reduced, high real-time performance of virtual interaction is guaranteed, and efficient construction of a soft tissue force feedback model of a virtual surgery system is realized.

Description

technical field [0001] The invention relates to modeling of a soft tissue force feedback model in virtual surgery, which is used to realize accurate real-time interaction between a biological soft tissue force feedback model and an operator in virtual surgery. Background technique [0002] An ideal biological soft tissue model can accurately feed back the displacement and force of the soft tissue to the operator in real time, which requires the biological soft tissue model to be highly accurate and real-time. In virtual surgery, the establishment of an efficient physical model of soft tissue force feedback plays a vital role in the success of the virtual surgery system. [0003] At present, the physical-driven soft tissue models are becoming more and more mature, and can be roughly divided into two categories: one is based on the network structure, mainly including the mass-spring model and the finite element model. The advantages of the mass-spring model (MS) are simple pr...

Claims

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Application Information

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IPC IPC(8): G06F19/12
CPCG16B5/00
Inventor 李春泉罗族周建勇索婧雯张浩林凡超熊辉
Owner NANCHANG UNIV
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