Zero-force dragging performance evaluation method for medical operation mechanical arm

Abstract

The invention discloses a method for evaluating zero-force dragging performance of a medical surgical mechanical arm, which comprises the following steps: in the movement process of the mechanical arm, measuring external force data of an end effector in real time, and carrying out accumulation and average calculation on three-axis external force data; the total mass of an end effector of the robot is obtained by calculating the ratio of the quadratic sum of the mean value of the three-axis external force to the gravitational acceleration; the mechanical arm is controlled to be in three fixed postures, and external force and torque data of the end effector in different postures are measured and recorded; establishing a ratio relation equation set of torque and external force by using the data, and resolving the equation set to determine a coordinate value of the mass center in a three-dimensional space; and finally, comparing the calculated total mass and the mass center coordinate with a preset range to evaluate the zero-force dragging performance of the mechanical arm. Reliable data support is provided for mechanical arm performance evaluation, operation is easy, implementation is easy, evaluation cost and time are reduced, and the using effect and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical surgical robot arms, and in particular to a method for evaluating the zero-force dragging performance of a medical surgical robot arm. Background Art

[0002] In the medical field, surgical robotic arms are advanced surgical auxiliary equipment, and their performance stability and accuracy have a crucial impact on the success of the operation. Especially when performing delicate surgical operations, the zero-force drag performance of the robotic arm, that is, the ability of the robotic arm to remain stationary or move at a constant speed when not affected by external forces, is one of the key indicators to measure its performance.

[0003] However, the current evaluation method for the zero-force dragging performance of surgical manipulators is still imperfect, with problems such as complex evaluation process and inaccurate evaluation results. Traditional evaluation methods often rely on the kinematic model and dynamic model of the manipulator, but these models are often interfered by various factors in practical applications, such as the manufacturing error of the manipulator, the measurement error of the sensor, the influence of environmental factors, etc., which greatly affects the reliability and accuracy of the evaluation results. Therefore, a comprehensive and accurate evaluation method for the zero-force dragging performance of surgical manipulators is needed. Summary of the invention

[0004] The purpose of the present invention is to provide a zero-force drag performance evaluation method for a medical surgical robot arm in response to the problems existing in the prior art; it can measure and calculate the external force and torque data of the end effector of the robot arm in real time, and then accurately obtain the total mass and mass center coordinates of the end effector, providing reliable data support for the performance evaluation of the robot arm, and the operation is simple and easy to implement, reducing the evaluation cost and time, and improving the use effect and safety of the surgical robot arm.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for evaluating the zero-force dragging performance of a medical surgical robot arm comprises: in the process of the robot arm moving, using sensors to measure the external force data of the end effector along the X, Y and Z axes in real time; accumulating the three-axis external force data at each moment, calculating the cumulative value, and then obtaining the average value of the three-axis external force; obtaining the total mass of the robot end effector by calculating the ratio of the sum of the squares of the three-axis external force average values ​​to the gravitational acceleration; controlling the robot arm to be in three fixed postures respectively, measuring and recording the external force and torque data of the end effector in different postures; using the measured external force and torque data, establishing a torque-to-external force ratio relationship equation group, and solving the equation group to determine the coordinate value of the mass center in three-dimensional space; comparing the calculated total mass of the end effector with a preset mass range, and comparing the mass center coordinates with a preset coordinate range, if both are within the preset range, it is determined that the zero-force dragging performance of the robot arm is good.

[0007] During the movement of the robot arm, the three-axis external force data of the end effector of the robot arm are: the external force F along the X-axis direction x (t); external force F along the Y axis y (t); external force F along the Z axis z (t);

[0008] In the process of real-time measurement of the external force data of the end effector of the robot arm through the sensor, the nonlinear compensation algorithm is introduced to obtain the corrected three-axis external force data:

[0009] F x (t) comp =F x (t)+k nl ·f nl (F x (t));

[0010] F y (t) comp =F y (t)+k nl ·f nl (F y (t));

[0011] F z (t) comp =F z (t)+k nl ·f nl (F z (t));

[0012] Among them, f nl (X) is the sensor nonlinear compensation function, k nl is the nonlinear compensation coefficient.

[0013] The three-axis external force data at each moment are accumulated and the cumulative value is calculated as follows:

[0014]

[0015] The average value of the three-axis external force is calculated as:

[0016]

[0017] After calculating the average value of the three-axis external force, the Kalman filter is used to filter the average force, and the average value of the three-axis external force after filtering is obtained as follows:

[0018]

[0019] in, is the state estimate at the current moment, K k is the Kalman gain, z k is the measurement value, and H is the measurement matrix.

[0020] After calculating the average value of the three-axis external force, the average value is corrected for error, and the corrected average value of the three-axis external force is obtained as follows:

[0021]

[0022] Among them, k e is the error correction coefficient, e x is the current measurement error.

[0023] By calculating the ratio of the sum of the squares of the average values ​​of the three-axis external forces to the gravitational acceleration, the total mass of the robot end effector is obtained as:

[0024]

[0025] Where g is the acceleration due to gravity.

[0026] The external force and torque data of the end effector measured at a fixed posture are:

[0027] The external force and torque data of the end effector measured under fixed posture 2 are:

[0028] The external force and torque data of the end effector measured at three fixed postures are:

[0029] The external force and torque data of the end effector are measured at each posture, and the ratio relationship equations of torque and external force are established, including:

[0030] For fixed posture 1, establish the equation system:

[0031]

[0032] For fixed posture 2, establish the equation system:

[0033]

[0034] For fixed posture three, establish the equation system:

[0035]

[0036]

[0037] Among them, x, y, and z are the coordinates of the mass center of the end effector.

[0038] Multi-threading technology is used to concurrently execute the motion control and external force measurement tasks of the robot arm; among them, the motion control task of the robot arm controls the robot arm to move according to a predetermined joint position sequence, and the external force measurement task obtains the external force and torque data of the end effector in real time.

[0039] A zero-force drag performance evaluation system for a surgical robot arm, comprising:

[0040] The data acquisition module is used to measure the external force data of the end effector along the X, Y, and Z axes in real time through sensors during the movement of the robotic arm; measure and record the external force and torque data of the end effector in different fixed postures;

[0041] The total mass calculation module is used to accumulate the three-axis external force data at each moment, calculate the cumulative value, and then obtain the average value of the three-axis external force; by calculating the ratio of the sum of the squares of the average values ​​of the three-axis external forces to the gravitational acceleration, the total mass of the robot end effector is obtained;

[0042] The posture control module controls the robot arm to be in three fixed postures;

[0043] The mass center calculation module uses the measured external force and moment data to establish a ratio relationship equation group between moment and external force, and solves the equation group to determine the coordinate value of the mass center in three-dimensional space;

[0044] Performance evaluation module: compare the calculated total mass of the end effector with the preset mass range, and compare the mass center coordinates with the preset coordinate range. If both are within the preset range, it is determined that the zero-force dragging performance of the robot arm is good.

[0045] A computer-readable storage medium stores a computer program, wherein the computer program implements the above method steps when executed by a processor.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] A comprehensive and accurate means of evaluating the zero-force dragging performance of a surgical robot arm is provided. Through real-time measurement and calculation, the mass and mass center coordinates of the end effector can be accurately obtained, providing data support for the performance evaluation of the robot arm. At the same time, the method is simple to operate and easy to implement, greatly reducing the evaluation cost and time. In addition, the method can timely discover problems with the robot arm, provide guidance for subsequent maintenance and optimization, and help improve the use effect and safety of the surgical robot arm. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 Detailed description of the invention Figure 1 is an overall flow chart of the evaluation method in one embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] In a first aspect of the present application, a method for evaluating the zero-force drag performance of a medical surgical robot arm is provided, comprising the following steps:

[0052] During the movement of the robot arm, the external force data of the end effector along the X, Y, and Z axes are measured in real time through sensors;

[0053] Accumulate the three-axis external force data at each moment, calculate the cumulative value, and then obtain the average value of the three-axis external force;

[0054] The total mass of the robot end effector is obtained by calculating the ratio of the sum of the squares of the average values ​​of the three-axis external forces to the gravitational acceleration;

[0055] The robot arm is controlled to be in three fixed postures, and the external force and torque data of the end effector in different postures are measured and recorded;

[0056] Using the measured external force and moment data, establish a system of equations for the ratio of moment to external force, and solve the system of equations to determine the coordinates of the center of mass in three-dimensional space;

[0057] The calculated total mass of the end effector is compared with the preset mass range, and the coordinates of the mass center are compared with the preset coordinate range. If both are within the predetermined range, it is determined that the zero-force dragging performance of the robot arm is good.

[0058] Specifically, the method of this embodiment uses sensors to measure the external force data of the end effector of the robot arm along the X, Y, and Z axes during the movement, and then calculates the average value of the three-axis external force; the total mass of the end effector can be inferred by using the ratio of these average values ​​to the gravitational acceleration; then, by controlling the robot arm to be in different fixed postures, and measuring and recording the external force and torque data of the end effector in these postures, a group of equations for the ratio of torque to external force can be established; by solving this group of equations, the coordinate value of the center of mass in three-dimensional space can be determined; finally, the calculated mass and mass center coordinates are compared with a preset range to evaluate the zero-force dragging performance of the robot arm.

[0059] This method provides a comprehensive and accurate means of evaluating the zero-force dragging performance of a surgical robot arm. Through real-time measurement and calculation, the mass and mass center coordinates of the end effector can be accurately obtained, providing data support for the performance evaluation of the robot arm. At the same time, the method is simple to operate and easy to implement, greatly reducing the evaluation cost and time. In addition, this method can timely discover problems with the robot arm, provide guidance for subsequent maintenance and optimization, and help improve the use effect and safety of the surgical robot arm.

[0060] In some embodiments, during the movement of the robot arm, the three-axis external force data of the end effector of the robot arm are: the external force F along the X-axis direction x (t); external force F along the Y axis y (t); external force F along the Z axis z (t); In the process of real-time measurement of the external force data of the end effector of the robot arm through the sensor, the nonlinear compensation algorithm is introduced to obtain the corrected three-axis external force data:

[0061] F x (t) comp =F x (t)+k nl ·f nl (F x (t));

[0062] F y (t) comp =F y (t)+k nl ·f nl (F y (t));

[0063] F z(t) comp =F z (t)+k nl ·f nl (F z (t));

[0064] Among them, f nl (X) is the sensor nonlinear compensation function, k nl is the nonlinear compensation coefficient.

[0065] Specifically, in order to improve the accuracy of the measurement data, a nonlinear compensation algorithm is introduced. The algorithm corrects the original measurement data through a nonlinear compensation function and a corresponding nonlinear compensation coefficient, thereby obtaining more accurate three-axis external force data; it helps to eliminate the influence of the nonlinear characteristics of the sensor itself on the measurement data and improve the reliability and accuracy of the data.

[0066] In some embodiments, the three-axis external force data at each moment is accumulated, and the accumulated value is calculated as:

[0067]

[0068] The average value of the three-axis external force is calculated as:

[0069]

[0070] Specifically, this embodiment can effectively reduce random errors and fluctuations in measurement data by adopting the method of data accumulation and average calculation, improve the accuracy and reliability of external force data, and help to more accurately calculate the total mass of the end effector of the robot arm.

[0071] In some embodiments, after calculating the average value of the three-axis external force, the average force is filtered using Kalman filtering, and the filtered average value of the three-axis external force is obtained as:

[0072]

[0073] in, is the state estimate at the current moment, K k is the Kalman gain, z k is the measurement value, and H is the measurement matrix.

[0074] Specifically, in this embodiment, after the average value of the three-axis external force is calculated, the Kalman filter algorithm is further used to filter the average force; the Kalman filter algorithm is a highly efficient recursive filter, which uses the estimated value at the previous moment and the measured value at the current moment to update the state estimate at the current moment by calculating the Kalman gain; through Kalman filtering, the noise and interference in the measurement data can be effectively reduced, and a more accurate and stable average value of the three-axis external force can be obtained.

[0075] In some embodiments, after calculating the average value of the three-axis external force, the average value is error corrected to obtain the corrected average value of the three-axis external force:

[0076]

[0077] Among them, k e is the error correction coefficient, e x is the current measurement error.

[0078] Specifically, in this embodiment, after the three-axis external force average value is calculated, the error correction is further performed on the average value by introducing an error correction coefficient and a current measurement error. Specifically, the error correction coefficient is multiplied by the current measurement error, and the result is subtracted from the original three-axis external force average value to obtain a corrected three-axis external force average value. This method can effectively reduce the systematic errors and random errors in the measurement data and improve the accuracy and reliability of the external force data.

[0079] In some embodiments, by calculating the ratio of the sum of the squares of the average values ​​of the three-axis external forces to the gravitational acceleration, the total mass of the robot end effector is obtained as:

[0080]

[0081] Where g is the acceleration due to gravity.

[0082] Specifically, the sum of the squares of the average values ​​of the three-axis external forces is first calculated, and then the ratio operation is performed on this sum of squares and the gravitational acceleration. Since the gravitational acceleration is a known constant, the total mass of the robot end effector can be obtained through this ratio operation. The method of this embodiment utilizes the force condition of the robot end effector under the action of gravity, and indirectly infers the total mass by measuring and calculating the external force data, which has the advantages of simple principle and convenient operation.

[0083] In some embodiments, the external force and torque data of the end effector measured at a fixed posture are: The external force and torque data of the end effector measured under fixed posture 2 are: The external force and torque data of the end effector measured at three fixed postures are:

[0084] The external force and torque data of the end effector are measured at each posture, and the ratio relationship equations of torque and external force are established, including:

[0085] For fixed posture 1, establish the equation system:

[0086]

[0087] For fixed posture 2, establish the equation system:

[0088]

[0089] For fixed posture three, establish the equation system:

[0090]

[0091] Among them, x, y, and z are the coordinates of the mass center of the end effector.

[0092] Specifically, in this embodiment, the external forces and moments of the end effector in the three axes are measured in each posture, and a set of equations containing three equations is established based on this. The unknowns in these sets of equations are the coordinates x, y, and z of the center of mass of the end effector; by solving these sets of equations, the specific coordinate values ​​of the center of mass in three-dimensional space can be determined.

[0093] In some embodiments, multi-threading technology is used to concurrently execute the motion control and external force measurement tasks of the robotic arm; wherein, the motion control task of the robotic arm controls the robotic arm to move according to a predetermined joint position sequence, and the external force measurement task obtains the external force and torque data exerted on the end effector in real time.

[0094] Specifically, one thread focuses on controlling the movement of the robotic arm according to a predetermined joint position sequence to ensure the accuracy of the robotic arm's movement; while the other thread obtains the external force and torque data of the end effector in real time to provide data support for subsequent performance evaluation. The two tasks are carried out in parallel, improving the overall performance and response speed of the surgical robotic arm.

[0095] In a second aspect of the present application, a zero-force drag performance evaluation system for a surgical robot arm is provided, comprising:

[0096] The data acquisition module is used to measure the external force data of the end effector along the X, Y, and Z axes in real time through sensors during the movement of the robotic arm; measure and record the external force and torque data of the end effector in different fixed postures;

[0097] The total mass calculation module is used to accumulate the three-axis external force data at each moment, calculate the cumulative value, and then obtain the average value of the three-axis external force; by calculating the ratio of the sum of the squares of the average values ​​of the three-axis external forces to the gravitational acceleration, the total mass of the robot end effector is obtained;

[0098] The posture control module controls the robot arm to be in three fixed postures;

[0099] The mass center calculation module uses the measured external force and moment data to establish a ratio relationship equation group between moment and external force, and solves the equation group to determine the coordinate value of the mass center in three-dimensional space;

[0100] Performance evaluation module: compare the calculated total mass of the end effector with the preset mass range, and compare the mass center coordinates with the preset coordinate range. If both are within the preset range, it is determined that the zero-force dragging performance of the robot arm is good.

[0101] According to a third aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program implements the above-mentioned method steps when executed by a processor.

[0102] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0103] In the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish descriptions and cannot be understood as indicating or suggesting relative importance. It should also be understood that although the terms "first", "second", etc. are used to describe various elements in some embodiments of the present application in the text, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first table can be named as the second table, and similarly, the second table can be named as the first table without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0104] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0105] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the zero-force drag performance of a medical surgical robot arm, characterized in that: include: During the movement of the robot arm, the external force data of the end effector along the X, Y, and Z axes are measured in real time through sensors; Accumulate the three-axis external force data at each moment, calculate the cumulative value, and then obtain the average value of the three-axis external force; The total mass of the robot end effector is obtained by calculating the ratio of the sum of the squares of the average values ​​of the three-axis external forces to the gravitational acceleration; The robot arm is controlled to be in three fixed postures, and the external force and torque data of the end effector in different postures are measured and recorded; Using the measured external force and moment data, establish a system of equations for the ratio of moment to external force, and solve the system of equations to determine the coordinates of the center of mass in three-dimensional space; The calculated total mass of the end effector is compared with the preset mass range, and the coordinates of the mass center are compared with the preset coordinate range. If both are within the predetermined range, it is determined that the zero-force dragging performance of the robot arm is good.

2. A method for evaluating the zero-force drag performance of a medical surgical robot according to claim 1, characterized in that: During the movement of the robot arm, the three-axis external force data of the end effector of the robot arm are: the external force F along the X-axis direction x (t); external force F along the Y axis y (t); external force F along the Z axis z (t); In the process of real-time measurement of the external force data of the end effector of the robot arm through the sensor, the nonlinear compensation algorithm is introduced to obtain the corrected three-axis external force data: F x (t) comp =F x (t)+k nl ·f nl (F x (t)); F y (t) comp =F y (t)+k nl ·f nl (F y (t)); F z (t) comp =F z (t)+k nl ·f nl (F z (t)); Among them, f nl (X) is the sensor nonlinear compensation function, k nl is the nonlinear compensation coefficient.

3. A method for evaluating the zero-force drag performance of a medical surgical robot according to claim 2, characterized in that: The three-axis external force data at each moment are accumulated and the cumulative value is calculated as follows: The average value of the three-axis external force is calculated as:

4. A method for evaluating the zero-force dragging performance of a medical surgical robot according to claim 3, characterized in that: After calculating the average value of the three-axis external force, the Kalman filter is used to filter the average force, and the average value of the three-axis external force after filtering is obtained as follows: in, is the state estimate at the current moment, K k is the Kalman gain, z k is the measurement value, and H is the measurement matrix.

5. The method for evaluating the zero-force drag performance of a medical surgical robot arm according to claim 3, characterized in that: After calculating the average value of the three-axis external force, the average value is corrected for error, and the corrected average value of the three-axis external force is obtained as follows: Among them, k e is the error correction coefficient, e x is the current measurement error.

6. A method for evaluating the zero-force dragging performance of a medical surgical robot according to claim 5, characterized in that: By calculating the ratio of the sum of the squares of the average values ​​of the three-axis external forces to the gravitational acceleration, the total mass of the robot end effector is obtained as: Where g is the acceleration due to gravity.

7. The method for evaluating the zero-force drag performance of a medical surgical robot according to claim 1, characterized in that: The external force and torque data of the end effector measured at a fixed posture are: The external force and torque data of the end effector measured under fixed posture 2 are: The external force and torque data of the end effector measured at three fixed postures are: The external force and torque data of the end effector are measured at each posture, and the ratio relationship equations of torque and external force are established, including: For fixed posture 1, establish the equation system: For fixed posture 2, establish the equation system: For fixed posture three, establish the equation system: Among them, x, y, and z are the coordinates of the mass center of the end effector.

8. The method for evaluating the zero-force drag performance of a medical surgical robot arm according to claim 1, characterized in that: Multi-threading technology is used to concurrently execute the motion control and external force measurement tasks of the robot arm; among them, the motion control task of the robot arm controls the robot arm to move according to a predetermined joint position sequence, and the external force measurement task obtains the external force and torque data of the end effector in real time.

9. A zero-force drag performance evaluation system for a surgical robot arm, characterized in that: include: The data acquisition module is used to measure the external force data of the end effector along the X, Y and Z axes in real time through sensors during the movement of the robot arm; Measure and record the external force and torque data of the end effector in different fixed postures; The total mass calculation module is used to accumulate the three-axis external force data at each moment, calculate the cumulative value, and then obtain the average value of the three-axis external force; by calculating the ratio of the sum of the squares of the average values ​​of the three-axis external forces to the gravitational acceleration, the total mass of the robot end effector is obtained; The posture control module controls the robot arm to be in three fixed postures; The mass center calculation module uses the measured external force and moment data to establish a ratio relationship equation group between moment and external force, and solves the equation group to determine the coordinate value of the mass center in three-dimensional space; Performance evaluation module: compare the calculated total mass of the end effector with the preset mass range, and compare the mass center coordinates with the preset coordinate range. If both are within the preset range, it is determined that the zero-force dragging performance of the robot arm is good.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the method steps of any one of claims 1 to 8 when executed by a processor.

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