CN112168361B - Catheter surgical robot pose prediction method capable of effectively relieving time delay influence - Google Patents

Catheter surgical robot pose prediction method capable of effectively relieving time delay influence Download PDF

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CN112168361B
CN112168361B CN202011176074.2A CN202011176074A CN112168361B CN 112168361 B CN112168361 B CN 112168361B CN 202011176074 A CN202011176074 A CN 202011176074A CN 112168361 B CN112168361 B CN 112168361B
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catheter
time
blood vessel
displacement
freedom
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CN112168361A (en
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胡陟
戴贤萍
胡文雁
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Shanghai University of Engineering Science
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/30—Surgical robots
    • A61B34/37—Master-slave robots
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/70—Manipulators specially adapted for use in surgery
    • A61B34/76—Manipulators having means for providing feel, e.g. force or tactile feedback
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/101—Computer-aided simulation of surgical operations
    • A61B2034/105—Modelling of the patient, e.g. for ligaments or bones
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/107—Visualisation of planned trajectories or target regions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/108—Computer aided selection or customisation of medical implants or cutting guides
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • A61B2034/2046—Tracking techniques
    • A61B2034/2065—Tracking using image or pattern recognition

Abstract

The invention relates to a method for predicting the pose of a catheter surgical robot for effectively relieving the influence of time delay, which is characterized in that after the total time delay, the operation time and the displacement of a master-slave surgical system are respectively obtained, the master-slave surgical system, the slave-slave surgical system and the operation time are combined to obtain the prediction result of the pose of a flexible catheter; the acquisition process of the displacement is as follows: firstly, according to the experience of expert doctors, the translation freedom displacement-time relation curve of different blood vessel lengths and blood vessel bifurcation angles during the motion of the catheter is obtained
Figure DDA0002748722210000011
Angle-time curve of sum rotational degree of freedom
Figure DDA0002748722210000012
Then fuzzy fusion is carried out to obtain translation freedom displacement-time relation curves corresponding to different vessel length grades and vessel bifurcation angle grades when the catheter moves
Figure DDA0002748722210000013
Angle-time curve of sum rotational degree of freedom
Figure DDA0002748722210000014
Finally, in the real operation, the blood vessel length and the blood vessel bifurcation angle are obtained according to the CT of the patient, and the translation freedom displacement-time relation curve when the catheter moves is obtained through fuzzy fusion
Figure DDA0002748722210000015
Angle-time curve of sum rotational degree of freedom
Figure DDA0002748722210000016
The invention can effectively alleviate the adverse effect of time delay.

Description

Catheter surgical robot pose prediction method capable of effectively relieving time delay influence
Technical Field
The invention belongs to the technical field of master-slave teleoperation force feedback control, and relates to a catheter surgical robot pose prediction method for effectively relieving time delay influence.
Background
Minimally invasive cardiovascular interventional surgery is widely used due to the advantages of reduction of postoperative pain, shortening of recovery time, small trauma and the like, wherein a catheter is used as a key interventional instrument and is required to be deeply inserted into a narrowed or blocked part of the wall of a coronary artery of a heart. The teleoperation medical robot system assists a doctor to perform an operation on a patient at a local long distance, so that the patient in remote areas or disaster areas such as Xinjiang can be diagnosed and treated in time, the operation cost and time are effectively reduced, and the doctor is protected from the harm of X-ray radiation in the operation. The teleoperation medical robot is divided into a master hand end and a slave hand end, communication is carried out by using a network, an interventional device such as a catheter is held and operated by a slave end mechanism, and a doctor controls the catheter to move at the master end.
The effect of latency on transparency has been demonstrated and is listed as part of the scholars' next study program. The pose prediction can relieve the influence of time delay on the manual tactile feedback effect of the surgical robot and improve the transparency of the force tactile feedback. With the development of the blood vessel interventional robot, urgent needs are provided for efficient force tactile feedback pose prediction.
However, even under 5G communication technology, only about 80ms is required for uploading data from the robot to the cloud in teleoperation, and data acquisition and processing, haptic rendering calculation, actuator response, and the like cause a time delay. The time delay can obviously reduce the accuracy of the prediction of the catheter pose, so how to relieve the influence of the time delay and realize the accurate prediction of the catheter pose becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a method for predicting the pose of a catheter surgical robot, which can effectively relieve the time delay influence.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for predicting the pose of a catheter surgical robot capable of effectively relieving the time delay influence comprises the steps of respectively obtaining the total time delay, the operation time and the displacement of a master-slave surgical system, and combining the master-slave surgical system, the slave-slave surgical system and the operation time to obtain a prediction result of the pose of a flexible catheter;
the total time delay of the master-slave operation system is obtained by adding the experimental data of the time delay test of each link;
the acquisition process of the displacement is as follows: firstly, the empirical data of catheter control performed by a doctor at a blood vessel bifurcation, namely a translation freedom displacement-time relation curve of the doctor operating the catheter to move under different blood vessel lengths and blood vessel bifurcation angles
Figure BDA0002748722190000011
Angle-time curve of sum rotational degree of freedom
Figure BDA0002748722190000012
Then fuzzy fusion is carried out on empirical data of catheter control of a doctor at a blood vessel bifurcation to obtain a control rule of the doctor for operating the catheter, namely a translation freedom displacement-time relation curve of the doctor for operating the catheter to move corresponding to different blood vessel length grades and blood vessel bifurcation angle grades
Figure BDA0002748722190000021
Angle-time curve of sum rotational degree of freedom
Figure BDA0002748722190000022
Finally, in a real operation, the blood vessel length and the blood vessel bifurcation angle are obtained according to the CT of a patient, and a translation freedom displacement-time relation curve when a doctor operates the catheter to move is obtained through fuzzy fusion
Figure BDA0002748722190000023
Angle-time curve of sum rotational degree of freedom
Figure BDA0002748722190000024
Namely obtaining the displacement;
the combination method comprises the following steps: adding the total time delay of the master-slave operation system and the operation time to obtain the total time e, and obtaining the total time e from the sum
Figure BDA0002748722190000025
And
Figure BDA0002748722190000026
upper intercept interval [0, e]And obtaining a prediction result of the flexible conduit pose corresponding to the curve segment.
As a preferred technical scheme:
according to the method for predicting the pose of the catheter surgical robot for effectively relieving the time delay influence, each link comprises data acquisition and processing, actuator response, communication and force and touch rendering links.
According to the method for predicting the posture of the catheter surgical robot for effectively relieving the time delay influence, the calculation formula of the surgical operation time is as follows:
MT＝a+b(IDtranslation+IDRotate)；
Figure BDA0002748722190000027
Figure BDA0002748722190000028
Wherein MT is the operation time; a and b are two empirical parameters, and the determination method is as follows: under the same environment and force feedback equipment as the real operation scene, determining difficulty coefficients ID and operation time MT corresponding to different tasks through experiments, and obtaining the relation between the ID and the MT through linear fitting, wherein the MT is a + b multiplied by the ID, namely a and b; IDTranslationIs the translation direction difficulty coefficient; IDRotateIs the rotation direction difficulty coefficient; c is a blood vessel path obtained from CT image data of a preoperative patient; s is catheter displacement and is obtained by real-time measurement of a pose sensor; w(s) is the blood vessel width, obtained from preoperative patient CT image data; theta is a rotation angle and is obtained by real-time measurement of the pose sensor; omega is a parameter influencing the width of the blood vessel and is obtained by comprehensive analysis and judgment of the pose sensor and the CT image data of the patient.
According to the method for predicting the pose of the catheter surgical robot for effectively relieving the time delay influence, the determination steps of a and b are as follows:
(1) establishing a geometric model and a dynamic model of a catheter, an aorta, a coronary artery and a branch vessel in a virtual environment of a main hand end by using a spring-proton model;
(2) determining parameters of geometric and kinetic models of catheters, aorta, coronary arteries and branch vessels: stiffness coefficient of 3X 103N/m, viscosity coefficient of 240 N.s/m, aorta internal diameter of 25mm, coronary artery internal diameter of 3.2mm, and branch vessel internal diameter of 2.3 mm;
(3) the vessel length LE was set to 100mm,200mm, and the vessel inner diameter W was set tovessel25mm,3mm and 2mm, the angle AE of the blood vessel bifurcation is 90 degrees and 200 degrees, and the outer diameter of the catheter is 0.8 mm;
(4) determining typical surgical task requirements and a difficulty coefficient ID corresponding to the typical surgical task requirements;
(5) testing the operation time MT required by different task requirements;
(6) and linearly fitting the difficulty coefficient ID and the operation time MT to obtain a and b.
According to the method for predicting the pose of the catheter surgical robot for effectively relieving the time delay influence, the displacement acquisition process is as follows:
(1) collecting the empirical data of catheter control of a doctor at a vascular bifurcation to obtain
Figure BDA0002748722190000031
And
Figure BDA0002748722190000032
(2) selecting a membership function of a blood vessel length LE, and dividing the LE into 4 grades, wherein the LE belongs to { LH (large length), LM (large length), LN (small length) and LL (small length) };
(3) selecting a membership function of a blood vessel bifurcation angle AE, and dividing the AE into 4 grades, wherein the AE belongs to { AH (large angle), AM (large angle), AN (small angle) and AL (small angle) };
(4) setting a fuzzy fusion rule, and determining output vectors U of the catheter position PO corresponding to various combinations of different LE grades and different AE grades, wherein the fuzzy fusion rule table is as follows:
Figure BDA0002748722190000033
(5) collecting the f-th U, namely UfAll of being related
Figure BDA0002748722190000034
And fitting the translation freedom degree displacement-time relation curve into a new translation freedom degree displacement-time relation curve
Figure BDA0002748722190000035
Simultaneously collecting the F-th U (namely U)fAll of being related
Figure BDA0002748722190000036
And fitting the curve into a new rotational freedom degree angle-time relation curve
Figure BDA0002748722190000037
f＝1，2，…16；
(6) LE and AE were obtained from patient CT during actual surgery;
(7) determining the LE grade of the patient and the corresponding fuzzy membership z according to the membership function of the LE in the step (2)(LE)；
(8) Determining the AE grade of the patient and the corresponding fuzzy membership z according to the membership function of the AE in the step (3)(AE)；
(9) Determining the U related to the blood vessel of the patient according to the fuzzy fusion rule in the step (4);
(10) the conduit pose data z is obtained by resolving the ambiguityj PI.e. by
Figure BDA0002748722190000041
The translation degree of freedom displacement corresponding to the upper moment j is obtained
Figure BDA0002748722190000042
The value interval of j is [0, XPmax]，XPmaxIs composed of
Figure BDA0002748722190000043
The corresponding maximum abscissa value, the deblurring formula is as follows:
Figure BDA0002748722190000044
wherein v is the total number of U's involved in the patient's blood vessel; u shapeiIs the i-th U involved in the patient's blood vessel; cp(Ui) Is UiCorresponding to
Figure BDA0002748722190000045
YjCp(Ui) Is UiCorresponding to
Figure BDA0002748722190000046
A translation degree of freedom displacement value corresponding to the upper moment j;
Figure BDA00027487221900000416
is UiCorresponding to z(LE)；
Figure BDA00027487221900000415
Is UiCorresponding to z(AE)；
Meanwhile, the position and orientation data z of the catheter is obtained by resolving the ambiguityw XI.e. by
Figure BDA0002748722190000047
The rotational degree of freedom degree value corresponding to the upper moment w is obtained
Figure BDA0002748722190000048
The value interval of w is [0, XXmax]，XXmaxIs composed of
Figure BDA0002748722190000049
The corresponding maximum abscissa value, the deblurring formula is as follows:
Figure BDA00027487221900000410
wherein v is the total number of U's involved in the patient's blood vessel; u shapeiIs the i-th U involved in the patient's blood vessel; cX(Ui) Is UiCorresponding to
Figure BDA00027487221900000411
YjCX(Ui) Is UiCorresponding to
Figure BDA00027487221900000412
The rotational freedom degree angle value corresponding to the upper moment w;
Figure BDA00027487221900000413
is UiCorresponding to z(LE)；
Figure BDA00027487221900000414
Is UiCorresponding to z(AE)。
According to the method for predicting the pose of the catheter surgical robot for effectively relieving the time delay influence, the fitting process in the step (5) is as follows: firstly, sampling is carried out by taking 0.1s as a sampling period, then data of each sampling point is multiplied by a weight and added, and finally fitting is carried out on the added data by using a conformal interpolation method.
Has the advantages that:
the surgical robot has time delays for data acquisition and processing, actuator response and communication, and the like. The current research on the time delay problem of the vascular interventional robot mainly focuses on the stability, and the research on the transparency is listed as the next plan by scholars. The flexible catheter guide wire is stressed to bend and twist, and the pose of the flexible catheter guide wire is difficult to accurately predict. The invention estimates the typical operation time of two-degree-of-freedom cooperative motion of the catheter based on the Fitts law, performs multi-information fuzzy fusion of catheter poses based on medical experience data, realizes the prediction of the catheter poses, relieves the time delay influence, improves the transparency of a master-slave control system of the catheter, and can solve the application problem of force-touch feedback in the vascular interventional robot.
Drawings
FIG. 1 is a schematic diagram of a teleoperation communication link based on 5G network technology;
FIG. 2 is a schematic diagram of the distance and area size of catheter movement in a blood vessel;
FIG. 3 is a schematic view of the effect of a simulated interventional procedure;
FIG. 4 is a graph of the results of linear fitting of the difficulty coefficient ID and the surgical procedure time MT;
FIG. 5 is a depiction of the input and output of vessel information;
FIG. 6 is a graph of membership function for vessel length LE;
FIG. 7 is a graph of membership function for the vascular bifurcation angle AE;
FIG. 8 is a graph showing the correspondence between the lengths LE of blood vessels, the levels LE of blood vessels, and the fuzzy membership;
FIG. 9 is a diagram showing the correspondence between the bifurcation angle AE of the blood vessel, the AE level and the fuzzy membership;
FIG. 10 is a fuzzy control predicted catheter pose curve;
fig. 11 is a comparison of real interaction force versus simulation data, wherein (a) is a translational degree of freedom displacement versus time curve and (b) is a rotational degree of freedom displacement versus time curve.
Detailed Description
The invention will be further illustrated with reference to specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
A method for predicting the pose of a catheter surgical robot for effectively relieving the time delay influence comprises the following steps:
(1) acquiring the total time delay of a master-slave surgical system;
the total time delay of the master-slave surgical system is obtained by adding time delay test experimental data of data acquisition and processing, actuator response, communication and haptic rendering links; the time delay of the four links is from tens of milliseconds to hundreds of milliseconds, and the delay is inevitable for the limitation of different hardware configurations and software in the man-machine interaction; in a virtual reality environment, a small additional delay of 30 milliseconds may cause image instability and simulator discomfort, so that a user feels nausea and dizziness, and the quality of human-computer interaction is seriously reduced; FIG. 1 is a schematic diagram of a teleoperation communication link based on 5G network technology, wherein the delay of the communication link mainly occurs in the optical fiber transmission from a terminal station to a cloud;
(2) acquiring operation time;
the calculation formula of the operation time is as follows:
MT＝a+b(IDtranslation+IDRotate)；
Figure BDA0002748722190000051
Figure BDA0002748722190000052
Wherein MT is the operation time; a and b are two empirical parameters, and the determination method is as follows: under the same environment and force feedback equipment as the real operation scene, determining difficulty coefficients ID and operation time MT corresponding to different tasks through experiments, and obtaining the relation between the ID and the MT through linear fitting, wherein the MT is a + b multiplied by the ID, namely a and b; IDTranslationIs the translation direction difficulty coefficient; IDRotateIs the rotation direction difficulty coefficient; c is a blood vessel path obtained from CT image data of a preoperative patient; s is catheter displacement and is obtained by real-time measurement of a pose sensor; w(s) is the blood vessel width, obtained from preoperative patient CT image data; theta is a rotation angle and is obtained by real-time measurement of the pose sensor; omega is a parameter influencing the width of the blood vessel and is obtained by comprehensive analysis and judgment of the pose sensor and the CT image data of the patient;
specifically, the derivation process of the calculation formula is as follows:
based on the Fitts law, the invention researches the task difficulty of the translation and rotation degrees of freedom in cooperation with the surgical operation and estimates the time required by the typical surgical operation, namely the surgical operation time; the present Fitts law is mainly used for estimating the operation time based on the target distance and width of the operation, and the operation time is single degree of freedom, while the invention firstly uses the Fitts law to estimate the operation time with two degrees of freedom, concretely, the invention obtains the target distance and the area size of translation and rotation when the catheter performs the vascular bifurcation based on the typical operation task according to the CT of the patient, and uses the two degrees of freedom to cooperate with the Fitts law to predict the time required by the doctor to complete the operation;
the Fitts law is defined as follows:
MT＝a+b·ID；
wherein MT is the operation time; ID is a difficulty coefficient; a and b are empirical parameters;
ID＝log2(A/W+q)；
in the formula, q is an adjusting parameter; a is the distance between the starting position and the target object; w is the size of the target area;
as shown in fig. 2, the difficulty coefficient ID of the task of the blood vessel with a bifurcation has a positive correlation function with translation and rotation, and the invention improves the fits law in the vascular intervention operation as follows:
MT＝a+b(IDtranslation+IDRotate)；
Integrating the blood vessel path c, wherein the difficulty coefficient of the translation direction is as follows:
Figure BDA0002748722190000061
for a blood vessel path with a bifurcation angle, determining the path width according to the rotation angle as follows: w (θ) ═ θ + ω)3Where d (θ) ═ 3(θ + ω)2The difficulty coefficient of the rotation direction is as follows:
Figure BDA0002748722190000062
specifically, the steps of determining a and b are as follows:
(I) establishing a geometric model and a dynamic model of a catheter, an aorta, a coronary artery and a branch vessel in a virtual environment of a main hand end by using a spring-proton model, and simulating the effect of an interventional operation as shown in figure 3;
(II) determining parameters of geometric and kinetic models of catheters, aorta, coronary arteries and branch vessels: stiffness coefficient of 3X 103N/m, viscosity coefficient of 240 N.s/m, aorta internal diameter of 25mm, coronary artery internal diameter of 3.2mm, and branch vessel internal diameter of 2.3 mm;
(III) setting the blood vessel length LE to 100mm,200mm, and the blood vessel inner diameter Wvessel25mm,3mm,2mm, vessel bifurcation angle AE 90 °,200 °, catheter outer diameter 0.8mm, all distances between objects are expressed in common units and physical units, although the virtual environment is scaled;
(IV) determining typical surgical task requirements and their corresponding difficulty factors ID; the invention divides the process of leading the guide wire to enter the branch vessel along the aorta through the coronary artery into 3 stages: the guide wire enters an aorta, the guide wire enters a coronary artery from the aorta, and the guide wire enters a branch vessel from the coronary artery; respectively defining the Fitt's law as grades of different difficulty coefficients, wherein the completion time of different tasks is different, and the larger the difficulty coefficient is, the longer the completion time is; predicting a remote interventional procedure having a time delay according to a completion time of each level; the completion time can be greatly shortened after the operator is trained for many times, which indicates that the proficiency of the operator can also influence the completion time, but the difficulty coefficients of virtual reality equipment at different operation stages are mainly researched, so that human factors are out of consideration; the corresponding relation between the difficulty coefficient and the task requirement is specifically shown in table 1;
TABLE 1
Figure BDA0002748722190000071
(V) testing the surgical procedure time MT required for different task requirements; 8 persons with the operation experience of the hand controller respectively complete the operation through Omega-7, and the time for taking up the guide wire from the test subject from the starting point to the end point is recorded, and the result is shown in a table 2;
TABLE 2
Figure BDA0002748722190000072
Figure BDA0002748722190000081
(VI) linearly fitting the difficulty coefficient ID and the operation time MT to obtain a and b; the results of line fitting the data of table 2 are shown in fig. 4, with two empirical parameters a, b of-0.89 and 3.5, respectively;
(3) obtaining displacement;
the current pose prediction adopts extrapolation prediction based on early state quantity, the feedback control of the pose prediction of the surgical robot is carried out according to the possible operation points of a doctor, and the track prediction is carried out based on a stress model; unlike the above situation, the catheter is stressed, bent and twisted in the vascular intervention operation, and the transformation rule is relatively complex; the invention uses multivariate information fuzzy fusion to carry out the prediction control of the catheter deformation pose for the first time, in particular, the invention is based on a large amount of medical experience data, carries out effective fusion on multivariate information of blood vessels, classifies the blood vessels with different characteristics after fuzzifying the characteristics of the blood vessels, thereby obtaining the pose change curve of the blood vessels and storing the classified data in a knowledge base, and when facing a specific patient, the invention can predict the pose change of the catheter in an interventional operation through a plurality of blood vessel fork characteristics and the knowledge base;
fuzzy control is control based on knowledge building rules of relevant experts, and an accurate mathematical model controlled by a controlled object does not need to be built in design, so that the fuzzy control is very suitable for objects with dynamic characteristics which are difficult to master or very obvious in change;
the length of a blood vessel bifurcation directly determines the linear displacement of a catheter, the angle of the blood vessel of the bifurcation also has important influence on the rotation angular displacement of the catheter, and in order to convert the position and posture change of the catheter in the blood vessel interventional operation into a fusion algorithm described by an algorithm language which can be accepted by a computer, the invention quantifies two major factors of the blood vessel bifurcation into a fuzzy catheter position and posture state by a fuzzy inference model, and fuses the fuzzy catheter position and posture state into a unified catheter position and posture state according to a certain rule;
in order to obtain the pose change of the catheter at the typical blood vessel bifurcation, as shown in fig. 5, the invention takes the bifurcation blood vessel length and the blood vessel bifurcation angle as the input of a fuzzy controller, uses LE to represent the linguistic variable of the bifurcation blood vessel length, uses AE to represent the linguistic variable of the blood vessel bifurcation angle, uses the catheter pose as the unique output quantity of the system, and uses PO to represent the linguistic variables;
the specific steps for obtaining the displacement are as follows:
(3.1) collecting the empirical data of catheter control performed by the doctor at the vascular bifurcation, namely the translational freedom displacement-time relation curve of the catheter operated by the doctor under different vascular lengths and vascular bifurcation angles
Figure BDA0002748722190000082
Angle-time curve of sum rotational degree of freedom
Figure BDA0002748722190000083
(3.2) selecting a membership function of the length LE of the blood vessel (as shown in FIG. 6), and dividing the LE into 4 levels, wherein the LE belongs to { LH (large length), LM (large length), LN (small length) and LL (small length) };
(3.3) selecting a membership function of a blood vessel bifurcation angle AE (as shown in FIG. 7), and dividing the AE into 4 grades, wherein the AE belongs to { AH (large angle), AM (large angle), AN (small angle) and AL (small angle) };
(3.4) setting a fuzzy fusion rule, and determining output vectors U of the catheter pose PO corresponding to different LE grades and various combinations of different AE grades, wherein the fuzzy fusion rule table is as follows:
Figure BDA0002748722190000091
(3.5) Collection with item fIs UfAll of being related
Figure BDA0002748722190000092
And fitting the translation freedom degree displacement-time relation curve into a new translation freedom degree displacement-time relation curve
Figure BDA0002748722190000093
Simultaneously collecting the F-th U (namely U)fAll of being related
Figure BDA0002748722190000094
And fitting the curve into a new rotational freedom degree angle- time relation curve
Figure BDA0002748722190000095
Figure BDA0002748722190000095
Figure BDA0002748722190000095
1,2,. 16; the fitting process is as follows: firstly, sampling is carried out by taking 0.1s as a sampling period, then data of each sampling point is multiplied by a weight and added, and finally, fitting is carried out on the added data by using a shape-preserving interpolation method;
(3.6) obtaining LE and AE from patient CT in real surgery;
(3.7) determining the LE grade of the patient and the corresponding fuzzy membership z according to the membership function of the LE in the step (3.2)(LE)；
(3.8) determining the AE grade of the patient according to the membership function of the AE in the step (3.3) and the corresponding fuzzy membership z(AE)；
(3.9) determining the U involved in the blood vessel of the patient according to the fuzzy fusion rule in the step (3.4);
(3.10) deblurring to obtain catheter pose data zj PThe displacement of the desired doctor's operating catheter in translation freedom versus time
Figure BDA0002748722190000096
The displacement value of the translation freedom degree corresponding to the upper moment j is obtained
Figure BDA0002748722190000097
The value interval of j is [0, XPmax]，XPmaxIs composed of
Figure BDA0002748722190000098
The corresponding maximum abscissa value, the deblurring formula is as follows:
Figure BDA0002748722190000099
wherein v is the total number of U's involved in the patient's blood vessel; u shapeiIs the i-th U involved in the patient's blood vessel; cp(Ui) Is UiCorresponding to
Figure BDA00027487221900000910
YjCp(Ui) Is UiCorresponding to
Figure BDA00027487221900000911
A translation degree of freedom displacement value corresponding to the upper moment j;
Figure BDA00027487221900000912
is UiCorresponding to z(LE)；
Figure BDA00027487221900000913
Is UiCorresponding to z(AE)；
Meanwhile, the position and orientation data z of the catheter is obtained by resolving the ambiguityw XI.e. the desired angular-time dependence of the rotational degree of freedom
Figure BDA00027487221900000914
The rotational degree of freedom degree value corresponding to the upper moment w is obtained
Figure BDA0002748722190000101
The value interval of w is [0, XXmax]，XXmaxIs composed of
Figure BDA0002748722190000102
The corresponding maximum abscissa value, the deblurring formula is as follows:
Figure BDA0002748722190000103
wherein v is the total number of U's involved in the patient's blood vessel; u shapeiIs the i-th U involved in the patient's blood vessel; cX(Ui) Is UiCorresponding to
Figure BDA0002748722190000104
YjCX(Ui) Is UiCorresponding to
Figure BDA0002748722190000105
The rotational freedom degree angle value corresponding to the upper moment w;
Figure BDA0002748722190000106
is UiCorresponding to z(LE)；
Figure BDA0002748722190000107
Is UiCorresponding to z(AE)；
The procedure of obtaining the displacement according to the CT of the patient, i.e., the above steps (3.6) to (3.10), will now be described with reference to specific cases:
first, the blood vessel length LE (8.8cm) and the blood vessel bifurcation angle AE (41 °) are obtained from the patient CT;
then, the LE rank of the patient and its corresponding fuzzy membership z are determined according to the LE membership function as shown in FIG. 8(LE)LE levels are LL and LN, corresponding fuzzy membership z(LE)0.8 and 0.2 respectively;
next, the patient's AE rating and its corresponding fuzzy membership z are determined from the membership function of AE as shown in FIG. 9(AE)AE grades AL and AN, corresponding fuzzy membership z(AE)0.7 and 0.3 respectively;
further, U relating to the blood vessel of the patient is determined by the fuzzy fusion rule, and it is known from the fuzzy fusion rule that:
IF LE＝LL，AE＝AL，Then U＝U1；
IF LE＝LN，AE＝AL，Then U＝U2；
IF LE＝LL，AE＝AN，Then U＝U5；
IF LE＝LN，AE＝AN，Then U＝U6；
i.e. the U involved in the patient's blood vessel is U1、U2、U5、U6；
Finally, the conduit pose data z is obtained by resolving the ambiguityj PI.e. by
Figure BDA0002748722190000108
The displacement value of the translation freedom degree corresponding to the upper moment j is obtained
Figure BDA0002748722190000109
The deblurring formula is as follows:
Figure BDA00027487221900001010
wherein v is the total number of U involved in the blood vessel of the patient, and the value is 4; u shapeiIs the i-th U involved in the patient's blood vessel; 1, UiIs U1；i＝2，UiIs U2；i＝3，UiIs U5；i＝4，UiIs U6；Cp(Ui) Is UiCorresponding to
Figure BDA00027487221900001011
YjCp(Ui) Is composed of
Figure BDA00027487221900001012
A translation degree of freedom displacement value corresponding to the upper moment j;
Figure BDA00027487221900001013
is UiCorresponding to z(LE)；
Figure BDA00027487221900001014
Is UiCorresponding to z(AE)；
In particular, zj P＝0.8×0.7×YjCp(U1)+0.2×0.7×YjCp(U2)+0.8×0.3×YjCp(U5)+0.2×0.3×YjCp(U6)；
As can be seen from the above-mentioned formula,
Figure BDA0002748722190000111
the shift value of the translational freedom degree corresponding to the last certain moment is 0.8 multiplied by 0.7 multiplied by U1Corresponding to
Figure BDA0002748722190000112
The corresponding translational degree of freedom displacement value at that time +0.2 × 0.7 × U2Corresponding to
Figure BDA0002748722190000113
The corresponding translational degree of freedom displacement value at that time +0.8 × 0.3 × U5Corresponding to
Figure BDA0002748722190000114
The corresponding translational degree of freedom displacement value at that time +0.2 × 0.3 × U6Corresponding to
Figure BDA0002748722190000115
The corresponding translation freedom displacement value at the moment;
meanwhile, the position and orientation data z of the catheter is obtained by resolving the ambiguityw XI.e. by
Figure BDA0002748722190000116
The rotational degree of freedom degree value corresponding to the upper moment w is obtained
Figure BDA0002748722190000117
The deblurring formula is as follows:
Figure BDA0002748722190000118
wherein v is the total number of U involved in the blood vessel of the patient, and the value is 4; u shapeiIs the i-th U involved in the patient's blood vessel; 1, UiIs U1；i＝2，UiIs U2；i＝3，UiIs U5；i＝4，UiIs U6；CX(Ui) Is UiCorresponding to
Figure BDA0002748722190000119
YwCX(Ui) Is composed of
Figure BDA00027487221900001110
The rotational freedom degree angle value corresponding to the upper moment w;
Figure BDA00027487221900001111
is UiCorresponding to z(LE)；
Figure BDA00027487221900001112
Is UiCorresponding to z(AE)；
In particular, zw X＝0.8×0.7×YwCX(U1)+0.2×0.7×YwCX(U2)+0.8×0.3×YwCX(U5)+0.2×0.3×YwCX(U6)；
As can be seen from the above-mentioned formula,
Figure BDA00027487221900001113
the rotational degree of freedom corresponding to the last moment is 0.8 × 0.7 × U1Corresponding to
Figure BDA00027487221900001114
The corresponding rotational degree of freedom at that time is +0.2 × 0.7 × U2Corresponding to
Figure BDA00027487221900001115
The corresponding rotational freedom angle value at that moment is +0.8 × 0.3 × U5Corresponding to
Figure BDA00027487221900001116
The corresponding rotational degree of freedom at that time is +0.2 × 0.3 × U6Corresponding to
Figure BDA00027487221900001117
The corresponding rotational freedom angle value at the moment;
(4) the total time delay of the master-slave operation system, the operation time and the displacement are combined to obtain a prediction result of the pose of the flexible catheter;
adding the total time delay of the master-slave operation system and the operation time to obtain the total time e, and obtaining the total time e from the sum
Figure BDA00027487221900001118
And
Figure BDA00027487221900001119
upper intercept interval [0, e]And obtaining a prediction result of the flexible conduit pose corresponding to the curve segment.
Simulation study example
(1) An interactive device is used for obtaining interventional operation interactive force through a measurement experiment, the length of a blood vessel in the experiment is 8.8cm, the bifurcation angle of the blood vessel is 41 degrees, and a catheter translation freedom degree displacement-time relation curve and a catheter rotation freedom degree displacement-time relation curve are measured in advance through a fuzzy fusion controller, as shown in figure 10;
(2) the virtual simulation sets that the length of a blood vessel is 8.8cm, the branch angle of the blood vessel is 41 degrees, pose change data obtained after fuzzy fusion is brought into a blood vessel model based on a spring-proton, and output simulation feedback force F is obtained through force touch rendering calculation, wherein the calculation formula is as follows:
Figure BDA0002748722190000121
wherein x is the displacement deformation in the blood vessel model,
Figure BDA0002748722190000122
is bloodThe speed of displacement change in the pipe model,
Figure BDA0002748722190000123
the rigidity coefficient K of the human body model blood vessel is 2 multiplied by 10 for the displacement variation acceleration in the blood vessel model5N/M, viscosity coefficient B is 20 N.s/M, and mass coefficient M is 1;
(3) as shown in fig. 11, the solid line is the interventional operation interaction force measured by the interaction device, the dotted line is the force of fuzzy fusion according to the human body blood vessel data in the virtual simulation, and the simulation data and the force in the experiment have the same variation trend, which indicates that the feedback force can be effectively provided for the master hand by performing the offline fuzzy fusion on the catheter pose based on the Fitts law and the medical experience multivariate information.

Claims (5)

1. A catheter surgical robot capable of effectively relieving time delay influence to predict pose is characterized by comprising a data processing module and a multivariate information fuzzy fusion module;
the data processing module is used for acquiring the total time delay and the operation time of the master-slave operation system;
the total time delay of the master-slave operation system is obtained by adding the experimental data of the time delay test of each link;
the multivariate information fuzzy fusion module is used for acquiring displacement;
the acquisition process of the displacement is as follows:
(1) firstly, the empirical data of catheter control performed by a doctor at a blood vessel bifurcation, namely a translation freedom displacement-time relation curve of the doctor operating the catheter to move under different blood vessel lengths and blood vessel bifurcation angles
Figure FDA0003244923870000011
Angle-time curve of sum rotational degree of freedom
Figure FDA0003244923870000012
(2) Selecting a membership function of a blood vessel length LE, and dividing the LE into 4 grades, wherein the LE belongs to { LH (large length), LM (large length), LN (small length) and LL (small length) };
(3) selecting a membership function of a blood vessel bifurcation angle AE, and dividing the AE into 4 grades, wherein the AE belongs to { AH (large angle), AM (large angle), AN (small angle) and AL (small angle) };
(4) setting a fuzzy fusion rule, and determining output vectors U of the catheter position PO corresponding to different LE grades and various combinations of different AE grades;
(5) collecting the f-th U, namely UfAll of being related
Figure FDA0003244923870000013
And fitting the translation freedom degree displacement-time relation curve into a new translation freedom degree displacement-time relation curve
Figure FDA0003244923870000014
Simultaneously collecting the F-th U (namely U)fAll of being related
Figure FDA0003244923870000015
And fitting the curve into a new rotational freedom degree angle-time relation curve
Figure FDA0003244923870000016
(6) LE and AE were obtained from patient CT during actual surgery;
(7) determining the LE grade of the patient and the corresponding fuzzy membership z according to the membership function of the LE in the step (2)(LE)；
(8) Determining the AE grade of the patient and the corresponding fuzzy membership z according to the membership function of the AE in the step (3)(AE)；
(9) Determining the U related to the blood vessel of the patient according to the fuzzy fusion rule in the step (4);
(10) the conduit pose data z is obtained by resolving the ambiguityj PI.e. translation freedom displacement versus time curve for the doctor operating the catheter in motion
Figure FDA0003244923870000017
Translation corresponding to the upper time jDisplacement in degrees of freedom, and then obtaining
Figure FDA0003244923870000018
The value interval of j is [0, XPmax]，XPmaxIs composed of
Figure FDA0003244923870000019
The corresponding maximum abscissa value, the deblurring formula is as follows:
Figure FDA0003244923870000021
wherein v is the total number of U's involved in the patient's blood vessel; u shapeiIs the i-th U involved in the patient's blood vessel; cp(Ui) Is UiCorresponding to
Figure FDA0003244923870000022
YjCp(Ui) Is UiCorresponding to
Figure FDA0003244923870000023
A translation degree of freedom displacement value corresponding to the upper moment j;
Figure FDA0003244923870000024
is UiCorresponding to z(LE)；
Figure FDA0003244923870000025
Is UiCorresponding to z(AE)；
Meanwhile, the position and orientation data z of the catheter is obtained by resolving the ambiguityw XI.e. the angle-time relationship curve of the rotational degree of freedom when the doctor operates the catheter to move
Figure FDA0003244923870000026
The rotational degree of freedom degree value corresponding to the upper moment w is obtained
Figure FDA0003244923870000027
Namely obtaining the displacement, wherein the value interval of w is [0, XXmax]，XXmaxIs composed of
Figure FDA0003244923870000028
The corresponding maximum abscissa value, the deblurring formula is as follows:
Figure FDA0003244923870000029
wherein v is the total number of U's involved in the patient's blood vessel; u shapeiIs the i-th U involved in the patient's blood vessel; cX(Ui) Is UiCorresponding to
Figure FDA00032449238700000210
YjCX(Ui) Is UiCorresponding to
Figure FDA00032449238700000211
The rotational freedom degree angle value corresponding to the upper moment w;
Figure FDA00032449238700000212
is UiCorresponding to z(LE)；
Figure FDA00032449238700000213
Is UiCorresponding to z(AE)；
Respectively obtaining the total time delay, the operation time and the displacement of a master-slave operation system, and then combining the master-slave operation system, the operation time and the displacement to obtain a prediction result of the pose of the flexible catheter;
the combination method comprises the following steps: adding the total time delay of the master-slave operation system and the operation time to obtain the total time e, and obtaining the total time e from the sum
Figure FDA00032449238700000214
And
Figure FDA00032449238700000215
upper intercept interval [0, e]And obtaining a prediction result of the flexible conduit pose corresponding to the curve segment.
2. The catheter surgical robot capable of effectively alleviating the time delay influence for pose prediction according to claim 1, wherein each link comprises data acquisition and processing, actuator response, communication and haptic rendering.
3. The catheter surgical robot capable of effectively alleviating the time delay influence for pose prediction according to claim 1, wherein the calculation formula of the operation time is as follows:
MT＝a+b(IDtranslation+IDRotate)；
Figure FDA00032449238700000216
Figure FDA00032449238700000217
Wherein MT is the operation time; a and b are two empirical parameters, and the determination method is as follows: under the same environment and force feedback equipment as the real operation scene, determining difficulty coefficients ID and operation time MT corresponding to different tasks through experiments, and obtaining the relation between the ID and the MT through linear fitting, wherein the MT is a + b multiplied by the ID, namely a and b; IDTranslationIs the translation direction difficulty coefficient; IDRotateIs the rotation direction difficulty coefficient; c is a blood vessel path obtained from CT image data of a preoperative patient; s is catheter displacement and is obtained by real-time measurement of a pose sensor; w(s) is the blood vessel width, obtained from preoperative patient CT image data; theta is a rotation angle and is obtained by real-time measurement of the pose sensor; omega is a parameter influencing the width of the blood vessel and is obtained by comprehensive analysis and judgment of the pose sensor and the CT image data of the patient.
4. The catheter surgical robot capable of effectively alleviating the time delay influence for pose prediction according to claim 3, wherein the determination steps of a and b are as follows:
(1) establishing a geometric model and a dynamic model of a catheter, an aorta, a coronary artery and a branch vessel in a virtual environment of a main hand end by using a spring-proton model;
(2) determining parameters of geometric and kinetic models of catheters, aorta, coronary arteries and branch vessels: stiffness coefficient of 3X 103N/m, viscosity coefficient of 240 N.s/m, aorta internal diameter of 25mm, coronary artery internal diameter of 3.2mm, and branch vessel internal diameter of 2.3 mm;
(3) the vessel length LE was set to 100mm,200mm, and the vessel inner diameter W was set tovessel25mm,3mm and 2mm, the angle AE of the blood vessel bifurcation is 90 degrees and 200 degrees, and the outer diameter of the catheter is 0.8 mm;
(4) determining typical surgical task requirements and a difficulty coefficient ID corresponding to the typical surgical task requirements;
(5) testing the operation time MT required by different task requirements;
(6) and linearly fitting the difficulty coefficient ID and the operation time MT to obtain a and b.
5. The catheter surgical robot capable of effectively alleviating the time delay influence for pose prediction according to claim 1, wherein the fitting process in the step (5) is as follows: firstly, sampling is carried out by taking 0.1s as a sampling period, then data of each sampling point is multiplied by a weight and added, and finally fitting is carried out on the added data by using a conformal interpolation method.
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