Calibration method of surgical robot and electronic device

Abstract

The application provides a calibration method and electronic equipment of a surgical robot, and the calibration method comprises the following steps: acquiring a first transformation matrix and a second transformation matrix when a mechanical arm moves to each preset pose respectively; the first transformation matrix represents the conversion relationship from the base coordinate system to the TCP coordinate system, and the second transformation matrix represents the conversion relationship from the camera coordinate system to the reference array coordinate system; according to the motion characteristics of the mechanical arm, the first translation matrix and the first rotation matrix under each preset pose, and the corresponding second translation matrix and the second rotation matrix are determined respectively, and the first homogeneous transformation equation between the third transformation matrix from the camera coordinate system to the base coordinate system and the fourth transformation matrix from the TCP coordinate system to the reference array coordinate system; the plurality of first homogeneous transformation equations are solved to determine the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix. By adopting the technical scheme, the calibration accuracy can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical devices, and particularly relates to a calibration method of a surgical robot and an electronic device. BACKGROUND

[0002] Robots have the characteristics of high positioning accuracy and repeatable operation, and are widely applied to the medical field. For example, oral implant surgery. At present, the eye-hand surgical robot refers to the process in which the camera is fixed outside the robot arm, and there is no rigid connection between the two, and the camera and the robot arm base are relatively static.

[0003] Hand-eye calibration is a calibration method in which the robot captures a series of corresponding poses of the Tool Central Point (TCP) in the robot base coordinate system and the poses of the reference array in the camera coordinate system through the robot sensor and the camera sensor through a series of movements, and then calculates the corresponding pose relationship between the camera coordinate system and the robot base coordinate system.

[0004] During the operation, since the camera and the robot arm are not rigidly connected, the pose relationship between the camera coordinate system and the robot coordinate system cannot be directly determined, and needs to be obtained through the hand-eye calibration process. The correctness of the calibration result will directly determine the positioning accuracy of the surgical robot. At the same time, the more complex the calibration process is, the longer the operation time is, and the higher the space requirement for the operating room is.

[0005] Under this background, how to provide a technical solution to improve the calibration accuracy has become a technical problem that needs to be solved by the technical personnel in the field. SUMMARY

[0006] Therefore, the present application provides a calibration method of a surgical robot and an electronic device, which can improve the calibration accuracy.

[0007] The application provides a calibration method of a surgical robot, comprising: acquiring a first transformation matrix and a second transformation matrix when a mechanical arm moves to each preset pose respectively; the first transformation matrix represents a conversion relationship from a base coordinate system to a TCP coordinate system, and the second transformation matrix represents a conversion relationship from a camera coordinate system to a reference array coordinate system; the first transformation matrix comprises a first translation matrix and a first rotation matrix, and the second transformation matrix comprises a second translation matrix and a second rotation matrix; according to the motion characteristics of the mechanical arm, the first translation matrix and the first rotation matrix at each preset pose and the corresponding second translation matrix and second rotation matrix are determined, and a first homogeneous transformation equation between a third transformation matrix from the camera coordinate system to the base coordinate system and a fourth transformation matrix from the TCP coordinate system to the reference array coordinate system; a plurality of first homogeneous transformation equations are solved to determine a first calibration result of the third transformation matrix and a second calibration result of the fourth transformation matrix.

[0008] Correspondingly, the application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor executes the calibration method of the surgical robot according to any one of the examples.

[0009] Compared with the prior art, the technical scheme of the application has the following advantages:

[0010] The calibration method of the surgical robot provided by the application can acquire the first transformation matrix and the second transformation matrix when the mechanical arm moves to each preset pose, all the transformation matrices are obtained from sensors, the application scenario is more extensive and the accuracy is higher compared with algorithms requiring standard values; and data is continuously collected during translation, the efficiency is higher, and the process complexity can be reduced. Based on the motion characteristics of the mechanical arm, the first translation matrix and the first rotation matrix at each preset pose and the corresponding second translation matrix and second rotation matrix can be determined, and a first homogeneous transformation equation between the third transformation matrix and the fourth transformation matrix, the first homogeneous transformation equation can truly reflect the transformation of the robot itself in the motion state, so that the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix can be determined by solving a plurality of first homogeneous transformation equations, the accuracy is further improved under the condition of ensuring the accuracy; and the third transformation matrix and the fourth transformation matrix are considered at the same time, so that the calibration result is more close to the true value, and the calibration accuracy can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to make the technical solutions of the present application clearer, the drawings needed to be used in the present application or the prior art description will be briefly introduced as follows. Obviously, the drawings described below are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0012] Figure 1 A structural schematic diagram of a mechanical arm in an embodiment of the present application is shown.

[0013] Figure 2 A flowchart of a calibration method of a surgical robot in an embodiment of the present application is shown.

[0014] Figure 3 A flowchart of a correction process of a calibration result in an embodiment of the present application is shown.

[0015] Figure 4 A specific flowchart of iterative processing of a calibration result in an embodiment of the present application is shown.

[0016] Figure 5 A flowchart of iterative processing of a calibration result by using a least square method in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0017] At present, the conversion relationship between the end reference array coordinate system of the mechanical arm and the end intrinsic TCP coordinate system of the mechanical arm (referred to as the end tool coordinate system conversion relationship) can be obtained by the following methods:

[0018] Solution one: the end of the mechanical arm must make a series of specified movements, for example, specified direction translation and / or rotation around a specified center, which has low flexibility and is greatly limited by the space of the operating room and the placement position of the instrument, and cannot obtain more accurate calibration results.

[0019] Solution two: compared with solution one, although the number of calibration points is reduced, the influence of noise and other factors is further amplified in the calibration process, resulting in a decrease in precision.

[0020] To address the aforementioned technical problems, this invention provides a calibration method for a surgical robot. This method acquires the first and second transformation matrices when the robotic arm moves to various preset poses. All transformation matrices are obtained from sensors, offering wider application scenarios and higher accuracy compared to algorithms requiring standard values. Furthermore, continuous data acquisition during translation increases efficiency and reduces process complexity. Based on the robotic arm's motion characteristics, the first translation and first rotation matrices, as well as the corresponding second translation and second rotation matrices, can be determined for each preset pose. A first homogeneous transformation equation is then established between these matrices and the third and fourth transformation matrices. This first homogeneous transformation equation accurately reflects the robot's transformations during motion. Solving multiple first homogeneous transformation equations yields the first calibration result for the third transformation matrix and the second calibration result for the fourth transformation matrix, further improving accuracy while maintaining overall precision. Simultaneously considering both the third and fourth transformation matrices ensures the calibration results approximate true values, thus enhancing calibration accuracy.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be described by way of example below with reference to the accompanying drawings.

[0022] See Figure 1 , Figure 1 A schematic diagram of a robotic arm according to an embodiment of the present invention is shown. Figure 1 As shown, the robotic arm includes a base coordinate system C. base TCP coordinate system (i.e., end flange coordinate system) C flange Camera coordinate system C camera and the reference array coordinate system C endRF .

[0023] Among them, the base coordinate system C base It is the starting point of the robotic arm coordinate system, the base coordinate system C. base This is used to describe information such as the initial position, movement path, and ending position of the robotic arm 100. In the robotic arm's control system, the base coordinate system C... base It is typically used to determine the posture and position of the robotic arm 100 to ensure that the robotic arm 100 can move along a predetermined path.

[0024] TCP coordinate system C flange This refers to the coordinate system of the center point of the end effector of a robotic arm (such as a gripper or welding torch). TCP coordinate system C flange It forms the basis for the programming and motion control of the robotic arm 100, defining the position and orientation of the end effector.

[0025] Camera coordinate system C cameraThis refers to the coordinate system used by the positioning camera 101 on the end effector of the robotic arm. In the integrated application of the robotic arm and the positioning camera 101, the camera coordinate system C... camera Typically associated with the position and orientation of the end effector of the robotic arm, it is used to describe the specific position and orientation of the positioning camera 101 on the robotic arm.

[0026] Reference array coordinate system C endRF The positioning camera 101 can identify four factory-installed reflective spheres within its field of view. These four reflective spheres are fixed in different relative positions, forming a reference array coordinate system C. endRF For a detailed implementation of the reference array, please refer to existing examples.

[0027] When the robotic arm moves to any target pose, the positioning camera 101 obtains the target pose in the camera coordinate system C. camera The pose information, and then based on the camera coordinate system C camera With TCP coordinate system C flange The transformation relationship between them determines the target pose in the TCP coordinate system C. flange The pose information within the coordinate system, and then based on the TCP coordinate system C flange With base coordinate system C base The transformation relationship between them is used to determine the target pose in the base coordinate system C. base The pose information is then used to determine the target pose in the base coordinate system C. base The pose information inside the robot arm controls the robot arm 100 to move to the corresponding position of the target pose.

[0028] In some embodiments, when the robotic arm moves to any target pose, the base coordinate system C is determined by sensors inherent to the robotic arm. base To TCP coordinate system C flange The first transformation matrix F1 between the two transformations can include: a first rotation matrix. First translation matrix .

[0029] Similarly, when the robotic arm needs to move to any target pose, it directly obtains the camera coordinate system C through the positioning camera 101. camera To the reference array coordinate system C endRF The second transformation matrix F2 between the two transformations can include: a second rotation matrix. Second translation matrix .

[0030] In some embodiments, during the movement of the robotic arm, the base coordinate system C base With camera coordinate system C camera Keeping relatively stationary, camera coordinate system Ccamera To the base coordinate system C base There is a third transformation matrix between them, where the third transformation matrix F3 includes: a third rotation matrix. Third translation matrix .

[0031] Similarly, TCP coordinate system C flange With reference array coordinate system C endRF The connection is rigid and remains relatively stationary during calibration; the TCP coordinate system C... flange To the reference array coordinate system C endRF There is a fourth transformation matrix between them, where the fourth transformation matrix F4 includes: a fourth rotation matrix. Fourth translation matrix .

[0032] The first transformation matrix F1 and the second transformation matrix F2 can be directly obtained from the control system of the robotic arm 100, while the third transformation matrix F3 and the fourth transformation matrix F4 are unknowns. That is, the third transformation matrix F3 and the fourth transformation matrix F4 need to be obtained through calibration.

[0033] It should be noted that in this scheme, all transformation matrices are homogeneous transformation matrices, and can be in the form of: Where a and b represent coordinate systems, for example, a is the TCP coordinate system C. flange b is the reference array coordinate system C endRF .

[0034] Accordingly, see Figure 2 The flowchart shown is a calibration method for a surgical robot according to an embodiment of the present invention, as follows: Figure 2 As shown, the following steps can be performed:

[0035] S201, respectively obtain the first transformation matrix and the second transformation matrix when the robotic arm moves to each preset pose.

[0036] In some embodiments, during the calibration process, the robotic arm can be driven to move. During this movement, the transformation relationship between the base coordinate system and the TCP coordinate system, as well as the transformation relationship between the camera coordinate system and the reference array coordinate system, can be obtained. This allows for the acquisition of a first transformation matrix F1 and a second transformation matrix F2 under different preset poses. That is, one preset pose corresponds to one first transformation matrix and one second transformation matrix, and the first and second transformation matrices have a corresponding relationship. Specifically, the robotic arm automatically moves to move the target registration point from the initial pose to a preset number of first poses.

[0037] The first transformation matrix F1 can be obtained through the intrinsic sensors of the robotic arm, and the second transformation matrix F2 can be obtained through the positioning camera.

[0038] In other words, when step S201 is performed, the first transformation matrix representing the transformation relationship from the base coordinate system to the TCP coordinate system, and the second transformation matrix representing the transformation relationship from the camera coordinate system to the reference array coordinate system can be obtained.

[0039] In some embodiments, the first transformation matrix can include a first translation matrix and a first rotation matrix, where the first rotation matrix represents the rotation relationship of the base coordinate system relative to the TCP coordinate system, indicating how to align the direction of each coordinate axis of the base coordinate system with the coordinate axis direction of the TCP coordinate system without moving the origin; the first translation matrix represents the position of the origin of the base coordinate system relative to the origin of the TCP coordinate system. After the rotation step, the point after rotation needs to be translated by this vector to finally express the point in the TCP coordinate system (both align the direction and align the origin).

[0040] Similarly, the second transformation matrix can include a second translation matrix and a second rotation matrix.

[0041] In some embodiments, when the robot arm is in a first pose (i.e., an initial pose) P0, the following are recorded: Thus, the first transformation matrix and the second transformation matrix corresponding to the first target pose P0 can be obtained.

[0042] The robot arm is controlled to move to a second pose P1 by any motion, and the following are recorded: Thus, the first transformation matrix and the second transformation matrix corresponding to the second pose P1 can be obtained.

[0043] Then, the robot arm is controlled to move to a third pose P2 by any motion, and the following are recorded: Thus, the first transformation matrix and the second transformation matrix corresponding to the third pose P2 can be obtained.

[0044] Then, the pose of the robot arm (which can be one of the determined poses) is kept unchanged, and the robot arm is controlled to move along the X-axis, Y-axis, and Z-axis directions of the TCP coordinate system C flange respectively by any length, and the first transformation matrix and the second transformation matrix corresponding to the fourth pose P3, the fifth pose P4, and the sixth pose P5 respectively are obtained.

[0045] For example, in this scheme, the first transformation matrix obtained when the robot arm moves to each preset pose includes:

[0046] Multiple initial translation matrices and their corresponding initial rotation matrices are obtained when the robotic arm reaches each preset pose. The three-dimensional vectors of the multiple initial translation matrices are sorted according to their respective dimensions to obtain the median of each dimension vector. After combination, the first translation matrix is ​​obtained. The parameters in the multiple initial rotation matrices are converted into axis angles to obtain the rotation angle and three-axis components of each initial rotation matrix. The rotation angles and three-axis vectors of the multiple initial rotation matrices are sorted according to their respective dimensions to obtain the median of each dimension vector. After combination, an inverse transformation is performed to obtain the first rotation matrix.

[0047] In some embodiments, when the robotic arm moves to a preset pose according to preset motion rules, the end effector of the robotic arm remains stationary while continuously collecting sensor information. j Second (of which, j The value can be 21, meaning 21 data points can be retrieved. j The first initial translation matrix and j By using a first initial rotation matrix and then performing median filtering on these data, the first translation matrix and the first rotation matrix can be determined respectively.

[0048] For example, corresponding to the first translation matrix, it has the same properties as the three-dimensional vector. corresponding Components, of which, i Indicates the number of preset poses. j This indicates the number of initial translation matrices.

[0049] Then, sort the three-dimensional vectors according to their respective dimensions to obtain the median of each dimension. and obtain .

[0050] Correspondingly, for the first rotation matrix, which is a 3x3 matrix, simply extracting the median from its elements is not feasible, as this would cause the first rotation matrix to lose its orthogonality. Therefore, it can be... Convert to axis angle, get .

[0051] After arranging the angular vectors along their respective dimensions, and then taking the median of each, we obtain... , After transforming back into the rotation matrix, we can obtain the final result. .

[0052] That is, after reaching the preset pose, the collected data is filtered by median filtering to obtain the final sensor value. By filtering noise, the sensor returns a result closer to the true value, further improving the accuracy of traditional least squares and SVD decomposition, and the calibration accuracy of the first transformation matrix is higher.

[0053] It should be noted that for the second transformation matrix, the above method can also be used for median filtering to determine the final second transformation matrix.

[0054] S202, according to the motion characteristics of the robot arm, respectively determine the first translation matrix and the first rotation matrix in each preset pose, and the corresponding second translation matrix and the second rotation matrix, and the first homogeneous transformation equation between the third transformation matrix from the camera coordinate system to the base coordinate system and the fourth transformation matrix from the TCP coordinate system to the reference array coordinate system.

[0055] In some embodiments, during the movement of the robot arm, the robot arm system satisfies some motion relationships. For example, the base coordinate system C base The camera coordinate system C camera The TCP coordinate system C flange The reference array coordinate system C endRF are rigidly connected, so that the relative relationship between the first transformation matrix, the second transformation matrix, the third transformation matrix and the fourth transformation matrix can be determined to obtain the first homogeneous transformation equation.

[0056] In some embodiments, when the robot arm moves to a plurality of different poses according to the preset motion strategy, the first homogeneous transformation equation corresponding thereto can be established. Based on a plurality of first homogeneous transformation equations, the calibration results of the third transformation matrix and the fourth transformation matrix can be determined.

[0057] In some embodiments, the third transformation matrix includes a third translation matrix and a third rotation matrix, and the fourth transformation matrix includes a fourth translation matrix and a fourth rotation matrix.

[0058] In this way, in step S202, the relative relationship between the rotation matrix and / or the translation matrix can be determined.

[0059] For example, according to the motion characteristics of the robot arm, the first rotation matrix in each first transformation matrix, the third rotation matrix, the fourth rotation matrix, and the corresponding second rotation matrix are constructed into the first homogeneous equation.

[0060] Specifically, during the movement of the robot arm, there is a transmission relationship between the rotation matrices. Based on the transmission relationship, the first homogeneous equation in each pose can be determined.

[0061] Exemplarily, the first homogeneous equation can be: wherein, denotes the third rotation matrix, denotes the first rotation matrix, denotes the fourth rotation matrix (to be solved), denotes the second rotation matrix (to be solved).

[0062] According to the motion characteristics of the mechanical arm, a second homogeneous equation between each first transformation matrix, the third transformation matrix, the fourth translation matrix, and the second translation matrix is constructed.

[0063] Specifically, in the motion process of the mechanical arm, there is a transmission relationship between the translation matrix and the rotation matrix, and based on the transmission relationship, the second homogeneous equation in the respective poses can be determined.

[0064] Exemplarily, the second homogeneous equation can be: wherein, denotes the third translation matrix, denotes the first translation matrix, denotes the fourth translation matrix, denotes the second translation matrix in the preset pose.

[0065] Therefore, by constructing the first homogeneous equation and the second homogeneous equation, the first homogeneous transformation equation can be obtained.

[0066] S203, performing solving operation on the plurality of first homogeneous transformation equations to determine the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix.

[0067] In some embodiments, by performing steps S201 and S202, the first homogeneous transformation equation corresponding to each target pose can be determined, and then based on the plurality of first homogeneous transformation equations, the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix can be determined.

[0068] That is, in the present scheme, the constraint relationship of the third transformation matrix and the fourth transformation matrix is considered at the same time, and the third transformation matrix and the fourth transformation matrix are calculated at the same time in the same calibration process, so that a calibration result which more truly reflects the real transformation relationship can be obtained.

[0069] In some embodiments, step S203 can include:

[0070] A1) selecting first homogeneous equations corresponding to at least two adjacent preset poses, and performing difference processing to obtain a first homogeneous difference equation; A2) selecting second homogeneous equations corresponding to the at least two adjacent preset poses, and performing difference processing to obtain a second homogeneous difference equation.

[0071] In some embodiments, for the same preset pose, there is a corresponding set of first to fourth transformation matrices. Based on the motion characteristics during the motion of the robot arm, the first homogeneous transformation equation corresponding to two preset poses can be selected, and difference processing is performed to obtain the first homogeneous difference equation and the second homogeneous difference equation.

[0072] For example, the first homogeneous difference equation is: Wherein, The meaning is that the difference value obtained by subtracting the matrices with the same meaning but in different poses.

[0073] The second homogeneous difference equation is:

[0074] A3) selecting first transformation matrices and second transformation matrices corresponding to at least four preset poses from a plurality of preset poses, and substituting them into the second homogeneous equation respectively, and performing difference processing to obtain a first result, wherein the at least four preset poses selected satisfy: at least three preset poses are poses obtained by moving one of the preset poses along three axes of the TCP coordinate system.

[0075] Specifically, in the at least four preset poses selected, only the translation direction displacement of the end of the robot arm is changed, and the attitude of the TCP of the robot arm is unchanged, so the difference value of the first rotation matrix in different poses is a zero matrix.

[0076] For example, the preset poses P3, P4 and P5 are poses obtained by moving the preset pose P1 along the xyz axes. Correspondingly, by substituting the data corresponding to the preset poses P3, P4 and P5, and the initial pose P1 into the second homogeneous equation, a first result is obtained Wherein, O represents a zero matrix.

[0077] A4) respectively substituting the first result into the first homogeneous difference equation and the second homogeneous difference equation to obtain a third homogeneous difference equation corresponding to the second homogeneous difference equation.

[0078] In some embodiments, since the first result value is a zero matrix, when it is substituted into the first homogeneous difference equation and the second homogeneous difference equation respectively, the first homogeneous difference equation is a zero matrix, and the second homogeneous difference equation becomes the third homogeneous difference equation, wherein the third homogeneous difference equation is: A5) calculating a first difference value of a first transformation matrix corresponding to the one of the at least three preset poses and a second difference value between a second transformation matrix corresponding to the one of the at least three preset poses, respectively.

[0079] In some embodiments, based on formula (5), two translation matrices are involved, and thus the difference values between the translation matrices can be calculated respectively.

[0080] Specifically, if the selected preset poses are P1, P3, P4 and P5, the following equations are obtained: In other words, P0 to P5 obtained in the scheme satisfy that P0, P1 and P2 have linearly independent attitude changes, the attitudes of P1, P3, P4 and P5 are the same, and only the positions are different.

[0081] A6) bringing the first difference value and the second difference value into the third homogeneous difference equation, and determining the calibration result of the third rotation matrix according to the reversible characteristic of the rotation matrix.

[0082] In some embodiments, during the movement of the robot arm, the preset poses P3, P4 and P5 are obtained by translating P1 along the X, Y and Z axes, respectively, and thus linearly independent, linearly independent.

[0083] Thus, the following is obtained:

[0084] are reversible.

[0085] Thus, the following is obtained: A7) bringing the third rotation matrix into the first homogeneous equation, and determining the calibration result of the fourth rotation matrix in the fourth transformation matrix.

[0086] It should be noted that since there are multiple groups of data, the solution obtained from each group of data can be converted into an axis-angle form, and the average value is obtained and then converted back to the final solution.

[0087] Thus, the calibration results of the third rotation matrix and the fourth rotation matrix can be determined by using the above steps A1) to A7).

[0088] Correspondingly, the scheme also provides an example of determining the calibration results of the third translation matrix and the fourth translation matrix.

[0089] For example, based on the reversible characteristics of the rotation matrix, the second homogeneous equation is rewritten to obtain a fourth homogeneous difference equation; the fourth homogeneous difference equation is rewritten according to the first transformation matrix and the second transformation matrix corresponding to the selected at least three preset poses, and the least square method is used to determine the calibration results of the third translation matrix and the fourth translation matrix, wherein the selected at least three preset poses have linearly independent attitude changes.

[0090] For example, for the formula (2) of the second homogeneous equation, based on the reversible characteristics of the rotation matrix, the following is obtained after transformation: Further simplification is obtained: After the data is brought in: It meets the requirements of the least square method, and the least square method is used to obtain: Therefore, the following can be obtained: , denoted as .

[0091] In summary, the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix can be determined according to the above content.

[0092] In the actual calibration process, the inventors further found that although the median filtering has been performed to reduce the influence of noise, the precision loss caused by noise, camera error and robot arm error cannot be ignored.

[0093] Based on this, the nonlinear least square method (for example, Levenberg-Marquardt algorithm) is introduced in the present scheme, that is, step S204, using the nonlinear least square method, and according to the plurality of first transformation matrices between any two adjacent preset poses and the corresponding second transformation matrices, the first calibration result and the second calibration result are iteratively optimized to further improve the accuracy of the calibration result.

[0094] In some embodiments, referring to the flowchart of the calibration result correction process in an embodiment of the present application shown in Figure 3 , as shown in Figure 3 , step S204 specifically includes:

[0095] S2041, obtaining a plurality of first transformation matrices of the robot arm between any two adjacent preset poses and corresponding second transformation matrices.

[0096] In some embodiments, during the movement of the robot arm, parameter information between any two adjacent preset poses can also be obtained.

[0097] For example, in the process of controlling the end of the robot arm to move from the initial pose P0 to the second pose P1 (for example, the end needs to have a pose transformation, and can have no position transformation), the corresponding frequency (for example, 60 Hz) is recorded .

[0098] Correspondingly, in the process of controlling the end of the robot arm to move from the second pose P1 to the third pose P2, the frequency is recorded .

[0099] Then, in the process of moving the robot arm in the subsequent adjacent poses, the pose of the end of the robot arm is kept unchanged, and the robot arm is controlled to move along the X, Y, and Z axes of the robot arm by any length, to determine the corresponding . .

[0100] In some embodiments, the plurality of first transformation matrices between any two adjacent preset poses, and the corresponding second transformation matrices also have a timestamp, and through the timestamp, the first transformation matrix and the second transformation matrix having the corresponding relationship can be determined.

[0101] For example, if the difference between the timestamps of the first transformation matrix and the second transformation matrix is within a preset time difference (for example, 5 ns), it can be considered that the first transformation matrix and the second transformation matrix correspond in time sequence.

[0102] S2042, according to the motion characteristics of the robot arm, determining at least one of the first transformation matrix and the second transformation matrix between any two adjacent preset poses, and the second homogeneous transformation equation between the corresponding third transformation matrix and the fourth transformation matrix, and constructing a target function according to the second homogeneous transformation equation.

[0103] In some embodiments, for different types of matrices, their corresponding second homogeneous transformation equations are different, which are described below by examples.

[0104] Example one, for the correction of the rotation matrix, step S2042 can include:

[0105] B1) According to the motion characteristics of the robot arm, determining the first rotation matrix and the second rotation matrix between any two adjacent preset poses, and the fifth homogeneous equation between the third rotation matrix in the third transformation matrix and the fourth rotation matrix in the fourth transformation matrix.

[0106] Wherein, the fifth homogeneous equation can be: B2) Convert the first rotation matrix, the second rotation matrix, the third rotation matrix, and the fourth rotation matrix in the fifth homogeneous equation to obtain a quaternion, and obtain a quaternion transformation equation.

[0107] In some embodiments, for formula (10), the inventors find that if the iterative process is directly used, there are 18 unknown constants to be solved, and the orthogonality of the rotation matrix needs to be continuously corrected in the iterative process, which occupies resources and is slow, and the accuracy is poor. Therefore, it is proposed to convert the rotation matrix to a quaternion for calculation.

[0108] The conversion of the rotation matrix to the quaternion can be referred to the existing examples.

[0109] Correspondingly, each rotation matrix is converted to a quaternion and used The obtained result is as follows: B3) rewriting the quaternion transformation equation, and taking the rewritten equation as the objective function; wherein the objective function is a function of the quaternion corresponding to the first transformation matrix as the independent variable, the quaternion corresponding to the third transformation matrix and the quaternion corresponding to the fourth transformation matrix as the iterative parameter, and the quaternion corresponding to the second transformation matrix as the dependent variable.

[0110] In some embodiments, by rewriting the quaternion transformation equation, an optimization function can be obtained, and then the objective function is determined: .

[0111] In example two, the translation matrix is corrected, and step S2042 can include:

[0112] C1) determining a first translation matrix between any two adjacent preset poses, a third translation matrix, a fourth translation matrix, a third rotation matrix, and a sixth homogeneous equation between the second translation matrix according to the motion characteristics of the mechanical arm.

[0113] C2) rewriting the sixth homogeneous equation to obtain the objective function.

[0114] The objective function is as follows: As can be seen, for different matrices, the objective functions constructed are different.

[0115] S2043, using at least one of the first transformation matrix and the second transformation matrix between any two adjacent preset poses, and the calibration results of the third transformation matrix and the fourth transformation matrix, the objective function is iteratively processed to obtain the first target calibration result of the third transformation matrix and the second target calibration result of the fourth transformation matrix.

[0116] In some embodiments, the nonlinear least squares method can be Levenberg-Marquardt method. It is an algorithm for finding the maximum (minimum) value using gradient, which is a kind of "climbing mountain" method. It has the advantages of gradient method and Newton method. When When very small, the step size is equal to the step size of the Newton method, when When very large, the step size is approximately equal to the step size of the gradient descent method. In the hand-eye calibration scenario, the precision can be effectively improved by iterative implementation.

[0117] In some embodiments, when determining the type of nonlinear least squares method, the corresponding input data can be configured, and then iterative processing is performed according to the data obtained.

[0118] In some embodiments, if the rotation matrix is iteratively processed, see Figure 4 , the following steps can be performed:

[0119] S401, based on the type of nonlinear least squares method, configuring the corresponding input data.

[0120] In some embodiments, the nonlinear least squares method is the Levenberg-Marquardt method, and the corresponding input data includes: iteration parameter q, independent variable x i dependent variable y i sequence, and the configured number of iteration steps It , iteration coefficient l and preset residual , and target function .

[0121] Exemplarily,

[0122]

[0123]

[0124] S402, when the iteration parameter is the fourth quaternion corresponding to the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix, the initial residual matrix composed of the iteration parameter, the independent variable and the dependent variable sequence is determined, and the first residual corresponding to the initial residual matrix is determined.

[0125] In some embodiments, during the first iteration process, so that wherein, wherein, is the fourth quaternion of the first calibration result of the third transformation matrix, is the fourth quaternion corresponding to the second calibration result of the fourth transformation matrix.

[0126] In some embodiments, the initial residual matrix Correspondingly, the first residual :

[0127] S403, performing an iteration process, wherein, for any iteration process, comprising:

[0128] S4031, performing Jacobian matrix operation on the initial residual matrix to determine an iteration step length, and determining a modified iteration parameter according to the iteration step length and the iteration parameter.

[0129] In some embodiments, wherein, Regarding the Jacobian matrix, the element is: , so that .

[0130] S4032, bringing the modified iteration parameter into the initial residual matrix, and determining a corresponding second residual, and updating the iteration step number.

[0131] In some embodiments, the second residual :

[0132] It should be pointed out that when performing step S4032, a normalization operation needs to be performed, that is:

[0133] S4033, when determining that the second residual is not greater than a preset residual or the iteration step number reaches a maximum value, taking the corresponding modified iteration parameter at this time as a target iteration parameter to determine a first target calibration result of the third transformation matrix and a second target calibration result of the fourth transformation matrix.

[0134] In some embodiments, the size between the second residual , or the size between , and when the second residual is not greater than a preset residual or the iteration step number reaches a maximum value, performing step S4037 to output , that is, obtaining the final solution. For example, based on the quaternions of the third rotation matrix and the fourth rotation matrix can be determined, and through inverse transformation, the target calibration results of the third rotation matrix and the fourth rotation matrix are obtained.

[0135] S4034, when determining that the second residual is greater than a preset residual and the iteration step number has not reached a maximum value, when determining that the second residual is less than or equal to the first residual, reducing the iteration coefficient value, and taking the second residual as a new residual value for iteration, that is, performing S4035; when determining that the second residual is greater than the first residual, increasing the iteration coefficient value, that is., performing S4036 and continuing to take the iteration parameter as a new iteration parameter for iteration.

[0136] Correspondingly, if the translation matrix is iteratively processed, the following steps can be performed:

[0137] Based on the type of the nonlinear least squares method, corresponding input data is configured, wherein the input data includes the iteration parameter, the first translation matrix between adjacent preset poses, the third translation matrix, the fourth translation matrix, the third rotation matrix, and the corresponding objective function, and the iteration step number, the iteration coefficient, and the preset residual error are configured.

[0138] In some embodiments, the nonlinear least squares method is the Levenberg-Marquardt method, and the corresponding input data includes the iteration parameter q, the independent variable x i and the dependent variable y i sequence, and the configured iteration step number It , iteration coefficient l, and preset residual error , and objective function .

[0139] When the iteration parameter is the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix, an initial residual matrix composed of the iteration parameter, the first translation matrix between adjacent preset poses, the third translation matrix, the fourth translation matrix, the third rotation matrix, and the corresponding objective function is determined, and the third residual corresponding to the initial residual matrix is determined.

[0140] An iteration process is performed, wherein for any iteration process, the following steps are included: Jacobian matrix operation is performed on the initial residual matrix, the iteration step is determined, and the modified iteration parameter is determined according to the iteration step and the iteration parameter; the modified iteration parameter is brought into the initial residual matrix, the corresponding fourth residual is determined, and the iteration step number is updated; when it is determined that the fourth residual is not greater than the preset residual error or the iteration step number reaches the maximum value, the corresponding modified iteration parameter at this time is taken as the target iteration parameter to determine the first target calibration result of the third transformation matrix and the second target calibration result of the fourth transformation matrix; when it is determined that the fourth residual is greater than the preset residual error and the iteration step number has not reached the maximum value, when it is determined that the fourth residual is less than or equal to the first residual, the iteration coefficient value is reduced, and the fourth residual is taken as a new residual value for iteration; when it is determined that the fourth residual is greater than the third residual, the iteration coefficient value is increased, and the iteration parameter is continuously taken as a new iteration parameter for iteration.

[0141] In other words, the difference between the translation matrix and the rotation matrix lies in the difference in the objective function, and other aspects can be referred to the description of the correction process of the rotation matrix.

[0142] In conclusion, on the basis of traditional least square and SVD decomposition, the Levenberg-Marquardt iteration process is added, so that the accuracy can be ensured and further improved under the premise of small range, small amplitude and less translation times, and the calibration result is more close to the true value.

[0143] In some embodiments, the plurality of first transformation matrices between any two adjacent preset poses, and the corresponding second transformation matrices also have timestamps;

[0144] Before constructing the target function, the first transformation matrix and the second transformation matrix having the corresponding relationship are determined according to the timestamps.

[0145] The application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor executes the calibration method of the surgical robot according to any one of the preceding examples when running the computer program.

[0146] The electronic device comprises a central processing unit (CPU) which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) or loaded from a storage section into a random access memory (RAM), such as the method described in the above embodiments. Various programs and data required for system operation are also stored in the RAM. The CPU, the ROM and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0147] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, etc.; an output section including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface as necessary. A removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive as necessary, so that a computer program read out therefrom is installed into the storage section as necessary.

[0148] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing computer programs for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by a communication section, and / or installed from a detachable medium. When the computer program is executed by a central processing unit (CPU), various functions defined in the system of the present application are executed.

[0149] It should be noted that the computer readable medium shown in embodiments of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0150] In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable computer programs. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit programs for use by or in connection with an instruction execution system, apparatus or device. The computer programs contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination of the above.

[0151] The computer readable storage medium can be a tangible storage medium (e.g., any type of disk including floppy disks, optical disks, CD- ROMs, and magnetic- or optical storage disks) encoded with the computer program, docket # 30699.2 PCT

[0152] The units described in the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described may

[0153] According to an aspect of the present application, a computer program product or computer program is provided, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method provided in the various possible implementation manners described above.

[0154] As another aspect, the present application also provides a computer readable medium, which can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, enable the electronic device to implement the method described in the above embodiments.

[0155] It should be noted that although several modules or units for the device for action execution are mentioned in the above detailed description, the division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into a plurality of modules or units.

[0156] Through the above description of the embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.

[0157] Although the present application is disclosed as above, the present application is not limited to this. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, and therefore the protection scope of the present application should be subject to the scope defined by the claims.

Claims

1. A calibration method of a surgical robot characterized by, The method comprises the following steps: respectively acquiring first transformation matrices and second transformation matrices when the robot arm moves to each preset pose; The first transformation matrix represents the conversion relationship between the base coordinate system and the TCP coordinate system, and the second transformation matrix represents the conversion relationship between the camera coordinate system and the reference array coordinate system. The first transformation matrix comprises a first translation matrix and a first rotation matrix, and the second transformation matrix comprises a second translation matrix and a second rotation matrix. The first transformation matrix is acquired by acquiring a plurality of first initial translation matrices and the corresponding first initial rotation matrices when the robot arm moves to each preset pose, respectively. The three-dimensional vectors of the plurality of first initial translation matrices are sorted according to their respective dimensions to obtain the median of each dimension, and the first translation matrix is obtained by combining the medians. The parameters in the plurality of first initial rotation matrices are converted into axis angles to obtain the rotation angle and the three-axis component corresponding to each first initial rotation matrix. The rotation angle and the three-axis vector of the plurality of first initial rotation matrices are sorted according to their respective dimensions to obtain the median of each dimension, and the first rotation matrix is obtained by combining the medians and performing inverse transformation. According to the motion characteristics of the robot arm, the first translation matrix and the first rotation matrix at each preset pose, and the corresponding second translation matrix and second rotation matrix, the first homogeneous transformation equation between the camera coordinate system and the base coordinate system, and the fourth transformation matrix between the TCP coordinate system and the reference array coordinate system are determined. The first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix are determined by solving a plurality of first homogeneous transformation equations.

2. The calibration method of a surgical robot according to claim 1, characterized by, The third transformation matrix comprises a third translation matrix and a third rotation matrix, and the fourth transformation matrix comprises a fourth translation matrix and a fourth rotation matrix. The first homogeneous transformation equation between the camera coordinate system and the base coordinate system, and the fourth transformation matrix between the TCP coordinate system and the reference array coordinate system are determined according to the motion characteristics of the robot arm, which comprises: According to the motion characteristics of the robot arm, the first rotation matrix in each first transformation matrix, the third rotation matrix, the fourth rotation matrix, and the corresponding second rotation matrix are constructed to form a first homogeneous equation. According to the motion characteristics of the robot arm, the first transformation matrix, the third transformation matrix, and the fourth translation matrix are constructed to form a second homogeneous equation.

3. The calibration method of a surgical robot according to claim 2, characterized by, The first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix are determined by solving a plurality of first homogeneous transformation equations. At least two adjacent preset poses corresponding to the first homogeneous equation are selected, and difference processing is performed to obtain a first homogeneous difference equation. Select the second homogeneous equation corresponding to the at least two adjacent preset poses, and perform difference processing to obtain a second homogeneous difference equation; Select the first transformation matrix and the second transformation matrix corresponding to at least four preset poses from the plurality of preset poses, and substitute them into the second homogeneous equation to obtain a first result when performing difference processing, wherein the at least four preset poses satisfy that at least three preset poses are poses obtained by moving one of the preset poses along three axes of the TCP coordinate system; Respectively, the first result is brought into the first homogeneous difference equation and the second homogeneous difference equation to obtain a third homogeneous difference equation corresponding to the second homogeneous difference equation; Respectively, the first difference value between the first transformation matrix corresponding to at least three preset poses in the at least four selected preset poses and the one of the preset poses, and the second difference value between the corresponding second transformation matrix are calculated; The first difference value and the second difference value are brought into the third homogeneous difference equation, and the calibration result of the third rotation matrix is determined according to the reversible characteristic of the rotation matrix; The third rotation matrix is brought into the first homogeneous equation to determine the calibration result of the fourth rotation matrix in the fourth transformation matrix.

4. The calibration method of a surgical robot according to claim 3, characterized by, The solving operation of the plurality of first homogeneous transformation equations to determine the first calibration result of the third transformation matrix and the second calibration result of the fourth rotation matrix includes: Based on the reversible characteristic of the rotation matrix, the second homogeneous equation is rewritten to obtain a fourth homogeneous difference equation; According to the first transformation matrix and the second transformation matrix corresponding to the at least three selected preset poses, the fourth homogeneous difference equation is rewritten, and the calibration results of the third translation matrix and the fourth translation matrix are determined by using the least square method, wherein the at least three selected preset poses have linearly independent attitude changes.

5. The calibration method of a surgical robot according to claim 1, wherein Further comprising: Obtaining a plurality of first transformation matrices and corresponding second transformation matrices of the robot arm between any two adjacent preset poses; According to the motion characteristics of the robot arm, determining the second homogeneous transformation equation between at least one of the first transformation matrix and the second transformation matrix of any two adjacent preset poses and the corresponding third transformation matrix and fourth transformation matrix, and constructing an objective function according to the second homogeneous transformation equation; Using the calibration results of at least one of the first transformation matrix and the second transformation matrix between any two adjacent preset poses and the third transformation matrix and the fourth transformation matrix, the objective function is iteratively processed by using a nonlinear least square method to obtain the first target calibration result of the third transformation matrix and the second target calibration result of the fourth transformation matrix.

6. The calibration method of the surgical robot according to claim 5, wherein According to the motion characteristics of the robot arm, determining the second homogeneous transformation equation between at least one of the first transformation matrix and the second transformation matrix of any two adjacent preset poses and the corresponding third transformation matrix and fourth transformation matrix, and constructing an objective function according to the second homogeneous transformation equation, includes: determining a first rotation matrix and a second rotation matrix between any two adjacent preset poses according to motion characteristics of the mechanical arm, and a fifth homogeneous equation between a third rotation matrix in the corresponding third transformation matrix and a fourth rotation matrix in the fourth transformation matrix; converting the first rotation matrix, the second rotation matrix, the third rotation matrix and the fourth rotation matrix in the fifth homogeneous equation into quaternions to obtain a quaternion transformation equation; rewriting the quaternion transformation equation, and taking the rewritten equation as an objective function; wherein the objective function is a function of the quaternion corresponding to the first transformation matrix as an independent variable, the quaternion corresponding to the third transformation matrix and the quaternion corresponding to the fourth transformation matrix as iterative parameters, and the quaternion corresponding to the second transformation matrix as a dependent variable; the nonlinear least squares method is used to obtain the first target calibration result of the third transformation matrix and the second target calibration result of the fourth transformation matrix by iteratively processing the objective function using at least one of the first transformation matrix and the second transformation matrix between any two adjacent preset poses, and the calibration results of the third transformation matrix and the fourth transformation matrix, including: based on the type of the nonlinear least squares method, configuring corresponding input data; wherein the input data includes: iterative parameters, independent variables and dependent variable sequences, and configured iteration step number, iteration coefficient and preset residual error; when the iterative parameters are the quaternions corresponding to the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix, an initial residual matrix composed of the iterative parameters, the independent variables and the dependent variable sequences is determined, and a first residual corresponding to the initial residual matrix is determined; performing an iteration process, wherein for any iteration process, it includes: performing a Jacobian matrix operation on the initial residual matrix, determining an iteration step, and determining a modified iterative parameter according to the iteration step and the iterative parameter; the modified iterative parameter is brought into the initial residual matrix, and a corresponding second residual is determined, and the iteration step number is updated; when it is determined that the second residual is not greater than the preset residual error or the iteration step number reaches the maximum value, the corresponding modified iterative parameter at this time is taken as a target iterative parameter to determine the first target calibration result of the third transformation matrix and the second target calibration result of the fourth transformation matrix; when it is determined that the second residual is greater than the preset residual error and the iteration step number has not reached the maximum value, when it is determined that the second residual is less than or equal to the first residual, the iteration coefficient value is reduced, and the second residual is taken as a new residual value for iteration; when it is determined that the second residual is greater than the first residual, the iteration coefficient value is increased, and the iterative parameter is taken as a new iterative parameter for iteration.

7. The calibration method of a surgical robot according to claim 6, wherein the second homogeneous transformation equation between the first transformation matrix and the second transformation matrix between any two adjacent preset poses according to the motion characteristics of the mechanical arm, and the third transformation matrix and the fourth transformation matrix, and constructing an objective function according to the second homogeneous transformation equation, including: According to the motion characteristics of the mechanical arm, a first translation matrix between any two adjacent preset poses, a third translation matrix, a fourth translation matrix, a third rotation matrix, and a sixth homogeneous equation between the second translation matrix are determined; The sixth homogeneous equation is rewritten to obtain a target function; The nonlinear least squares method is used to iteratively process the target function by using at least one of the first transformation matrix and the second transformation matrix between any two adjacent preset poses, and the calibration results of the third transformation matrix and the fourth transformation matrix, to obtain a first target calibration result of the third transformation matrix and a second target calibration result of the fourth transformation matrix, including: Based on the type of the nonlinear least squares method, corresponding input data is configured, wherein the input data includes the iteration parameters, the first translation matrix between adjacent preset poses, the third translation matrix, the fourth translation matrix, the third rotation matrix, and the corresponding target function, and the iteration step number, the iteration coefficient, and the preset residual error are configured; When the iteration parameters are the first calibration result of the third transformation matrix and the second calibration result of the fourth transformation matrix, an initial residual error matrix is determined by the iteration parameters, the first translation matrix between adjacent preset poses, the third translation matrix, the fourth translation matrix, the third rotation matrix, and the corresponding target function, and a third residual error corresponding to the initial residual error matrix is determined; An iteration process is performed, wherein for any iteration process, it includes: performing a Jacobian matrix operation on the initial residual error matrix, determining an iteration step length, and determining a modified iteration parameter according to the iteration step length and the iteration parameter; the modified iteration parameter is brought into the initial residual error matrix, a corresponding fourth residual error is determined, and the iteration step number is updated; when the fourth residual error is determined to be not greater than the preset residual error or the iteration step number reaches a maximum value, the corresponding modified iteration parameter at this time is taken as a target iteration parameter to determine the first target calibration result of the third transformation matrix and the second target calibration result of the fourth transformation matrix; when the fourth residual error is determined to be greater than the preset residual error and the iteration step number has not reached the maximum value, when the fourth residual error is determined to be less than or equal to the first residual error, the iteration coefficient value is reduced, and the fourth residual error is taken as a new residual error value for iteration; when the fourth residual error is determined to be greater than the third residual error, the iteration coefficient value is increased, and the iteration parameter is continuously taken as a new iteration parameter for iteration.

8. The calibration method of the surgical robot according to claim 5, wherein The plurality of first transformation matrices between any two adjacent preset poses and the corresponding second transformation matrices also have timestamps; Before constructing the target function, the first transformation matrix and the second transformation matrix having a corresponding relationship are determined according to the timestamps.

9. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program operable to run on said processor, characterized in that, The processor runs the computer program to perform the calibration method of the surgical robot in any one of claims 1 to 8. The processor runs the computer program to perform the calibration method of the surgical robot in any one of claims 1 to 8.

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