A method and device for adjusting the operating field of a microscopically guided ophthalmic surgery robot

Abstract

A kind of microimage guided ophthalmic surgery robot operating field adjustment method and device, it is related to medical robot technical field, the method includes: obtaining the pixel coordinates of target point in fundus image;The pixel coordinates of target point are converted into the physical coordinates under eyeball center coordinate system based on mapping coefficient;Target rotation angle of target point around the x-axis and y-axis rotation of eyeball center coordinate system is solved based on physical coordinates;Motion planning is carried out based on target rotation angle under the condition that preset virtual constraint is satisfied, generates the joint control instruction of robot, and preset virtual constraint includes that the distance between left scleral tunnel needle and eyeball center, the distance between right scleral tunnel needle and eyeball center and the distance between left scleral tunnel needle and right scleral tunnel needle all remain constant;The movement of scleral tunnel needle is driven based on joint control instruction to control robot, to drive eyeball rotation, so that target point moves to microscope field center.

Description

Technical Field

[0001] This application relates to the field of medical robot technology, and in particular to a method and apparatus for adjusting the surgical field of an ophthalmic surgical robot guided by microscopic images. Background Technology

[0002] With the development of robot-assisted fundus surgery technology, dual-arm / multi-arm surgical systems are increasingly being applied to posterior segment surgeries such as subretinal injections and epiretinal membrane peeling. In these surgeries, the surgeon uses the dominant-handed surgical arm to mount instruments for precise manipulation, while the non-dominant-handed surgical arm carries an intraocular illumination fiber optic cable for illumination. However, because the retinal pigment epithelium covers the entire fundus, the location of lesions varies significantly among patients. This necessitates that after the surgical instruments are inserted into the eye through the scleral cannula, the surgical field of view is adjusted by rotating the eyeball by dragging the cannula, ensuring that the lesion area is centered within the microscope's field of view, facilitating the surgeon's operation.

[0003] Currently, the adjustment of the surgical field of vision mainly relies on the doctor's manual operation. For example, the doctor uses the master hand manipulator to control the slave robotic arm to complete the rotation of the eyeball and the repositioning of the retinal field of vision. This method requires the doctor to pay attention to the surgical operation and the adjustment of the field of vision at the same time, which increases the cognitive load of the operation.

[0004] Therefore, how to provide a method for adjusting the surgical field of ophthalmic surgical robots guided by microscopic images to achieve automated visual field adjustment is a key research topic for those skilled in the art. Summary of the Invention

[0005] In a first aspect, this application provides a method for adjusting the surgical field of an ophthalmic surgical robot guided by a microscopic image. The method includes: acquiring the pixel coordinates of a target point in a fundus image, wherein the target point is a lesion point selected by a user on the fundus image that needs to be aligned with the center of the microscope's field of view; the fundus image is acquired by a microscope and is located in an image pixel coordinate system; converting the pixel coordinates of the target point into physical coordinates in the eye's central coordinate system based on a preset mapping coefficient between pixels and physical length; and calculating a target rotation angle around the x-axis and y-axis of the eye's central coordinate system based on the physical coordinates of the target point in the eye's central coordinate system, wherein the target rotation angle is such that the target point, after rotation, is aligned with the eye's central coordinate system. The rotation angle coincides with the z-axis, which is aligned with the optical axis of the microscope. Based on the target rotation angle, motion planning is performed under preset virtual constraints to generate joint control commands for the robot. The eye rotation is driven by the left and right scleral cannulas fixed at the scleral puncture point. The preset virtual constraints include maintaining a constant distance between the left and right scleral cannulas and the center of the eyeball, and maintaining a constant distance between the left and right scleral cannulas. Based on the joint control commands, the robot is controlled to drive the left and right scleral cannulas to rotate the eyeball, moving the target point to the center of the microscope's field of view.

[0006] The surgical field adjustment method for ophthalmic surgical robots guided by microscopic images provided in this application obtains the pixel coordinates of the target point in the fundus image, and converts the pixel coordinates into physical coordinates in the eye center coordinate system based on the mapping coefficient between pixels and physical length. Then, the target rotation angle of the target point around the x-axis and y-axis of the eye center coordinate system is solved, realizing automatic mapping from image space to physical space and automatic calculation of eye rotation angle. Users only need to click to select the target point on the fundus image, without relying on experience for manual field adjustment, which significantly reduces the surgical workload and operation threshold.

[0007] On the other hand, when performing motion planning based on the target rotation angle, this application introduces preset virtual constraints to maintain constant distances between the left scleral cannula and the center of the eyeball, constant distances between the right scleral cannula and the center of the eyeball, and constant distances between the left and right scleral cannulas. These preset virtual constraints are used as prerequisites for generating robot joint control commands. Since the above three constant distance constraints directly correspond to the relative positional relationship between the scleral puncture point and the center of the eyeball, as well as the relative positional relationship between the left and right puncture points, during eyeball rotation, embedding these constraints into the motion planning process can physically prevent the cannulas from tearing or compressing the sclera while driving eyeball rotation, fundamentally ensuring the surgical safety of the surgical field adjustment process.

[0008] On the other hand, this application transforms the task of aligning the target point with the center of the microscope's field of view into solving the target rotation angle of the target point around the x-axis and y-axis of the eye's central coordinate system. Based on this target rotation angle, it automatically generates joint control commands for the robot to drive the movement of the scleral cannula. This ensures that the angle of eye rotation precisely corresponds to the spatial deviation between the target point and the center of the field of view, realizing fully automated closed-loop control from target selection to eye rotation. This avoids inaccurate or over-adjustment caused by visual field limitations or hand tremors during manual operation, ensuring the accuracy and stability of the surgical field adjustment. At the same time, since the joint control commands are generated based on motion planning, the robot's movement is smooth and continuous, avoiding abrupt changes and oscillations during the field of view adjustment process, thus improving the smoothness and reliability of the surgical operation.

[0009] Secondly, this application also provides a surgical field adjustment device for a microscopic image-guided ophthalmic surgical robot, including a unit for performing any of the microscopic image-guided ophthalmic surgical robot surgical field adjustment methods in the first aspect.

[0010] Thirdly, this application also provides a computer storage medium that can store multiple instructions adapted for loading and execution by a processor of any of the microscopic image-guided ophthalmic surgical robot surgical field adjustment methods described in the first aspect.

[0011] Fourthly, embodiments of this application also provide a computer program product containing instructions that, when the computer program product is run on an electronic device, cause the electronic device to execute any one of the microscopic image-guided ophthalmic surgical robot surgical field adjustment methods in the first aspect.

[0012] Fifthly, embodiments of this application also provide a chip module, including a transceiver component and a chip, wherein the chip is used to execute any of the microscopic image-guided ophthalmic surgical robot surgical field adjustment methods in the first aspect.

[0013] In a sixth aspect, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method described in any one of the first aspects is executed when the processor executes the program.

[0014] It is understood that the surgical field adjustment device, computer storage medium, computer program, computer program product, chip system, and electronic device provided above for microscopic image-guided ophthalmic surgical robot are all used to execute the method shown in any implementation of the first aspect of the embodiments of this application. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding methods, and will not be repeated here. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the coordinate system settings involved in surgical field adjustment provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the positional relationship between a calibration plate and a microscope, as well as the corner point identification results, provided in an embodiment of this application. Figure 3 This is a schematic flowchart of a method for adjusting the surgical field of an ophthalmic surgical robot guided by microscopic images, provided in an embodiment of this application. Figure 4 This is a schematic flowchart of a method for converting the pixel coordinates of a target point into physical coordinates in the eye center coordinate system, provided in an embodiment of this application. Figure 5 This is a schematic flowchart of a method for solving the target rotation angle of a target point rotating around the x-axis and y-axis of the eyeball center coordinate system, provided in an embodiment of this application. Figure 6 This is a flowchart illustrating a method for generating joint control commands for a robot based on a target rotation angle, as provided in an embodiment of this application. Figure 7 This is a schematic diagram illustrating the relative positional relationship between a scleral cannula and an eyeball model provided in an embodiment of this application; Figure 8 This is a schematic diagram comparing the field of view of a microscope before and after target adjustment, as provided in an embodiment of this application. Figure 9 This is a schematic diagram of a surgical field adjustment device for a microscopic image-guided ophthalmic surgical robot provided in an embodiment of this application. Detailed Implementation

[0016] First, we will introduce an example of the coordinate system settings involved in the surgical field adjustment in this scheme.

[0017] like Figure 1 As shown, the coordinate systems involved include: Robot coordinate system {Orob}, see Figure 1 (a): Fixed to the base of the surgical robot, the x-axis is along the horizontal direction of the robot base, the y-axis is along the vertical direction of the base, and the z-axis is perpendicular to the plane of the base (vertically upward). This coordinate system serves as the reference coordinate system for the entire coordinate system and is used to describe the movement position of the robot arm.

[0018] The eyeball center coordinate system {O}, see Figure 1 (c): With the geometric center Po of the eyeball as the origin, the x-axis and y-axis are aligned with the horizontal and vertical directions of the eyeball's equatorial plane, respectively, and the z-axis is along the optical axis of the eyeball (from the retina to the cornea). The positional relationship between this coordinate system and the robot coordinate system {Orob} can be determined through hand-eye calibration experiments.

[0019] Eye coordinate system {Oe}, see Figure 1 (a): Fixed to the eyeball body, its origin coincides with the origin Po of the eyeball's central coordinate system {O}. In the initial state (before rotation), the x-axis, y-axis, and z-axis of {Oe} are parallel to the corresponding coordinate axes of {O}. When the eyeball rotates, {Oe} rotates synchronously with the eyeball, used to describe the rotational motion posture of the eyeball.

[0020] The microscope coordinate system is {Om}, see Figure 1 (a): Fixed at the center of the microscope's optical axis, the x-axis aligns with the microscope's horizontal adjustment direction, the y-axis aligns with the microscope's vertical direction, and the z-axis coincides with the microscope's optical axis. A fixed projection transformation relationship exists between the microscope coordinate system and the image pixel coordinate system. This image pixel coordinate system is fixed on the two-dimensional imaging plane of the fundus image. The fundus image is acquired by the microscope's imaging chip (such as a CMOS or CCD sensor). During surgery, the position of the microscope's imaging chip and its optical system in physical space remains fixed; therefore, the position and orientation of the image pixel coordinate system in physical space also remain fixed. The horizontal axis u of the image pixel coordinate system extends along the horizontal direction of the fundus image, corresponding to the x-axis direction in the microscope coordinate system. The vertical axis v extends along the vertical direction of the horizontal axis u, corresponding to the y-axis direction in the microscope coordinate system. The coordinate unit is a pixel. This image pixel coordinate system does not move with eye rotation.

[0021] In this application, the mapping relationship between the microscope coordinate system and the image pixel coordinate system is a two-dimensional to two-dimensional projection mapping, involving only coordinate transformations in the x and y axes. The mapping coefficient k between the pixel distance and physical length between the x and y axes of the image pixel coordinate system and the microscope coordinate system can be pre-calibrated using a checkerboard calibration plate. Specifically, the checkerboard calibration plate is placed on the focusing plane of the microscope, making the calibration plate plane perpendicular to the microscope optical axis. The checkerboard of the calibration plate has known physical dimensions. A high-resolution image of the calibration plate is acquired, and the corner coordinates of the checkerboard in the image are extracted using an image processing algorithm (such as the Harris corner detection algorithm), and the pixel distance between adjacent corner points is calculated. As an example, the positional relationship between the calibration plate and the microscope and the corner recognition results are illustrated as follows. Figure 2 As shown.

[0022] In this embodiment, the value of k is determined by the following formula: k = d_physical / d_pixel, where d_physical is the known physical dimension (in mm) between adjacent corner points of the checkerboard, and d_pixel is the pixel distance (in pixels) between the corresponding corner points in the image. To improve calibration accuracy, the k value of multiple pairs of adjacent corner points can be calculated and averaged. In subsequent adjustments, the pixel offset can be converted into a physical offset based on this mapping coefficient k.

[0023] In the initial state, this method aligns the microscope's optical axis with the center of the eyeball by adjusting its horizontal position. This horizontal position remains unchanged throughout the surgery. That is, the x and y axes of the microscope's coordinate system are parallel and aligned with the x and y axes of the eyeball's central coordinate system. Therefore, the x and y physical coordinate components obtained by converting the u and v pixel coordinates in the image pixel coordinate system using the mapping coefficient k are equivalent to the x and y physical coordinate components in both the microscope and eyeball central coordinate systems.

[0024] Based on the above equivalence relationship, when converting the pixel coordinates of the target point into physical coordinates in the eye center coordinate system, the pixel offset can be directly converted into the x-axis and y-axis physical coordinate components in the eye center coordinate system based on the mapping coefficient k, and then the z-axis physical coordinate components can be determined based on the eye radius R and the spherical equation.

[0025] The following describes the transformation relationships between coordinate systems: Transformation between the robot coordinate system {Orob} and the eyeball center coordinate system {O}: A checkerboard-based hand-eye calibration method is adopted. The checkerboard calibration plate is fixed on the surface of the eyeball model. The robot arm is controlled to move the calibration plate in different postures. At the same time, microscope images are acquired. By solving the hand-eye transformation matrix (including translation vector and rotation matrix), the coordinate transformation relationship between {Orob} and {O} is established.

[0026] In another possible implementation, the electronic device employs a markerless hand-eye calibration method based on intraoperative OCT. Specifically, using depth information provided by intraoperative OCT (iOCT) combined with two-dimensional images from a microscope, the positions of the eye surface and the cannula in three-dimensional space are obtained through three-dimensional reconstruction technology. The electronic device then registers the reconstructed three-dimensional point cloud with the robot's kinematic model using a registration algorithm, solving for the transformation relationship between the robot coordinate system and the eye center coordinate system. This method eliminates the need to fix additional calibration objects on the eye surface.

[0027] Transformation between the eye's central coordinate system {O} and the microscope coordinate system {Om}: After fixing the eye's position, first adjust the microscope's horizontal position (x, y directions) so that the center of the microscope image coincides with the origin Po of the eye's central coordinate system {O} on the image. The microscope's horizontal position is not changed during the subsequent surgery. The relative position of the microscope {Om} and {O} is changed only by adjusting the focal length in the vertical direction (z direction). That is, by adjusting the vertical distance between the microscope and the eye's surface, the target area is clearly imaged. At this point, the transformation relationship between {O} and {Om} can be directly determined by the focal length value. In other words, the physical coordinate components of the x and y axes in the microscope coordinate system are equal to those in the eye's central coordinate system. The z-axis transformation relationship between the microscope coordinate system and the eye's central coordinate system is determined by the microscope's focal length value, which characterizes the vertical distance of the microscope relative to the eye's surface.

[0028] The transformation relationship between the image pixel coordinate system and the microscope coordinate system is based on the mapping coefficient between pixels and physical length, and is used to convert the pixel coordinates of the target point into the x-axis and y-axis physical coordinate components in the microscope coordinate system.

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described below in conjunction with the accompanying drawings.

[0030] It should be noted that this application embodiment uses an electronic device as an example to illustrate the execution subject of the surgical field adjustment method for ophthalmic surgical robots guided by microscopic images provided in this application. This electronic device can also be understood as the surgical field adjustment device for ophthalmic surgical robots guided by microscopic images shown in this application embodiment. In this application embodiment, the electronic device can be a microprocessor or computer for executing program code, etc. Any electronic device that can be used to execute the method provided in this application embodiment is within the protection scope of this application embodiment, and this application does not impose any limitations. For example, the electronic device can be an industrial personal computer, an embedded ARM processor, an FPGA-based processing system, or a digital signal processor, etc., and this application embodiment does not limit this.

[0031] Please see Figure 3 , Figure 3 This is a flowchart illustrating a surgical field adjustment method for a microscopic image-guided ophthalmic surgical robot, as provided in an embodiment of this application. Figure 3 As shown, the surgical field adjustment method of the ophthalmic surgical robot guided by microscopic images includes the following steps: S301, The electronic device acquires the pixel coordinates of the target point in the fundus image.

[0032] In this embodiment, the fundus image is an image acquired in real time by a microscope and displayed on a human-computer interaction interface. The target point is the lesion point that the user clicks on the fundus image to align with the center of the microscope's field of view. In response to the user's click operation, the electronic device records the pixel coordinates (u, v) of the target point in the image pixel coordinate system, where u is the horizontal coordinate and v is the vertical coordinate.

[0033] In this embodiment of the application, the fundus image is located in the image pixel coordinate system.

[0034] The center of the microscope's field of view corresponds to the center of the fundus image, and its coordinates (u_c, v_c) in the image pixel coordinate system are fixed values ​​(usually u_c = W / 2, v_c = H / 2, where W and H are the width and height of the fundus image, respectively, and the unit is pixels).

[0035] When the target point (lesion) moves on the image as the eye rotates, its pixel coordinates (u, v) in the image pixel coordinate system change, while the coordinates (u_c, v_c) of the center of the microscope field of view remain unchanged. Based on this, the pixel offset (u - u_c, v - v_c) of the target point relative to the center of the field of view can be calculated for subsequent coordinate transformation and angle calculation.

[0036] In one possible implementation, the user can click on the target point on the fundus image using a mouse or touch screen. The electronic device obtains the relative coordinates of the click position in the image control through the click event response mechanism of the graphical user interface (GUI), and maps the click position to pixel coordinates in the image pixel coordinate system by combining the ratio between the size of the image control and the actual pixel size of the image.

[0037] In another possible implementation, the user selects a rectangular region containing the lesion on a fundus image using a touchscreen or graphics tablet. The electronic device automatically segments and detects the lesion within this region. Image processing algorithms (such as thresholding, edge detection, or a pre-trained deep learning object detection model) automatically identify the precise location of the lesion and use this location as the pixel coordinates of the target point. This method reduces the positioning error caused by manual clicking and improves the accuracy of target point selection. The deep learning object detection model can employ object detection networks such as the YOLO series or Faster R-CNN, and is pre-trained using a dataset of fundus images labeled with lesion locations.

[0038] S302, the electronic device converts the pixel coordinates of the target point into physical coordinates in the eye center coordinate system based on a preset mapping coefficient between pixels and physical length.

[0039] For example, such as Figure 4 As shown, the electronic device converts the pixel coordinates of the target point into physical coordinates in the eye's central coordinate system, specifically including the following sub-steps: S3021, Electronic device acquires the pixel coordinates of the center of the microscope's field of view.

[0040] In this embodiment, the pixel coordinates of the current field of view center of the microscope refer to the pixel position in the fundus image corresponding to the intersection of the microscope's optical axis and the imaging plane. Before the surgical field adjustment begins, the electronic device has completed the initial alignment of the microscope and the eyeball: adjusting the horizontal position of the microscope (x, y directions) so that the center of the microscope image coincides with the center of the eyeball on the image, and the horizontal position of the microscope remains unchanged during the surgery. Therefore, the pixel coordinates of the current field of view center of the microscope are a fixed value during the surgery.

[0041] In one possible implementation, after initial alignment, the electronic device records and stores the current image center position (u_c, v_c) as the pixel coordinates of the microscope's field of view center, where u_c = W / 2, v_c = H / 2, and W and H are the width and height of the fundus image (in pixels), respectively. This method is suitable for cases where the microscope image center completely coincides with the physical center of the image.

[0042] In another possible implementation, considering minor distortions in the microscope's optical system or installation deviations in the image acquisition equipment, the electronic equipment determines the precise position of the field of view center in the image pixel coordinate system through a pre-calibrated procedure. Specifically, during the calibration phase, a standard target with a known center position (such as a crosshair target or a checkerboard pattern with a center mark) is placed in the microscope's field of view, aligning the target's center with the microscope's optical axis. The pixel coordinates of the target's center in the image are extracted using an image processing algorithm, and these coordinates are used as the pixel coordinates (u_c, v_c) of the field of view center and stored. This method can compensate for system deviations caused by the optical system and image acquisition equipment.

[0043] S3022, the electronic device calculates the pixel offset between the pixel coordinates of the target point and the pixel coordinates of the current field of view center of the microscope.

[0044] Specifically, the electronic device calculates the pixel offsets of the target point relative to the center of the field of view in the horizontal and vertical directions: u' = u - u_c, v' = v - v_c. Where u' is the horizontal pixel offset (positive value means the target point is to the right of the center of the field of view, negative value means to the left), and v' is the vertical pixel offset (positive value means the target point is below the center of the field of view, negative value means above).

[0045] S3023, the electronic device converts the pixel offset into physical offsets in the x-axis and y-axis directions in the eye center coordinate system based on a preset mapping coefficient between pixels and physical length, thereby obtaining the x-axis and y-axis physical coordinate components of the target point in the eye center coordinate system.

[0046] In this embodiment, the mapping coefficient k (unit: mm / pixel) between pixels and physical length is obtained in advance through calibration. The electronic device multiplies the pixel offset by the mapping coefficient k to obtain the physical coordinate components x_o and y_o of the x-axis and y-axis in the eye center coordinate system, specifically: x_o = k·u', y_o = k·v'.

[0047] S3024, the electronic device determines the z-axis physical coordinate component of the target point in the eye center coordinate system based on the eye radius and the x-axis and y-axis physical coordinate components of the target point in the eye center coordinate system through the spherical equation.

[0048] In this embodiment, since the target point is located on the inner surface of the eyeball (on the retina), the eyeball can be approximated as a sphere with radius R (R is the radius of the eyeball, unit: mm). The target point coordinates (x_o, y_o, z_o) satisfy the spherical equation: x_o 2 + y_o 2 +z_o 2 = R 2Therefore, electronic devices can calculate the z-axis physical coordinate components using the following formula: z_o = -√(R 2 - x_o 2 - y_o 2 A negative value indicates that the target point is located behind the center of the eyeball (i.e., on the retinal side, opposite to the positive direction of the z-axis).

[0049] In one possible implementation, the eyeball can be a model, and the eyeball radius R can be determined based on measurement or calibration data. In other possible implementations, the eyeball is a real human eyeball, in which case the radius R can be a standard human eye anatomy statistical value, or the eyeball radius R can also be obtained by preoperative medical imaging (such as optical coherence tomography, ultrasound biomicroscopy, or magnetic resonance imaging), which is not limited in this article.

[0050] In other possible implementations, the electronic device does not directly use the spherical equation to calculate the z-axis coordinates. Instead, it acquires the depth information of the target point in real time through intraoperative OCT (iOCT). While acquiring fundus images under a microscope, the electronic device simultaneously acquires iOCT images of the same field of view. The retinal surface depth value at the target point's location is extracted from the iOCT images and used as the physical z-axis coordinate component of the target point in the microscope coordinate system. Then, based on the z-axis transformation relationship between the microscope coordinate system and the central eye coordinate system, the z-axis coordinate component of the target point in the central eye coordinate system is determined. This method does not rely on preset values ​​for the eyeball radius or preoperative measurements, and can adapt to individual differences among patients and subtle changes in intraoperative eyeball morphology.

[0051] Regarding the calibration method for the mapping coefficients: Prior to S302, electronic devices used a checkerboard calibration plate to pre-calibrate the mapping coefficient k between pixels and physical lengths. Specifically, the checkerboard calibration plate was placed on the focusing plane of the microscope, with the calibration plate plane perpendicular to the microscope's optical axis. A high-resolution image of the calibration plate was acquired, and the corner coordinates of the checkerboard in the image were extracted using an image processing algorithm (such as the Harris corner detection algorithm). The pixel distances between adjacent corner points in the image (such as the pixel differences between horizontal and vertical adjacent corner points) were calculated and combined with the actual physical dimensions of the checkerboard. To improve calibration accuracy, the mapping coefficients of multiple corner point pairs could be calculated and averaged.

[0052] In another possible implementation, the electronic device is calibrated using a reference object of known physical dimensions. This reference object can be an instrument of known size used in surgery (such as a cannula or retinal forceps with a known diameter). The reference object is placed in the microscope's field of view, an image of the reference object is acquired, and the pixel dimensions of the reference object in the image are extracted using image processing algorithms. The mapping coefficient k is then calculated based on the known physical dimensions of the reference object. This method eliminates the need for an additional calibration plate and allows calibration to be performed directly using surgical instruments during the surgical preparation phase.

[0053] In some other possible implementations, the mapping coefficient k can also be directly calculated from the microscope's magnification parameter. The electronic device reads the microscope's current optical magnification M and the pixel size p (unit: mm / pixel) of the image acquisition device, and calculates the mapping coefficient k = p / M. This method eliminates the need for image acquisition and corner detection, resulting in high calibration efficiency, but its accuracy is limited by the calibration precision of the microscope's magnification.

[0054] S303, the electronic device calculates the target rotation angle of the target point around the x-axis and y-axis of the eye center coordinate system based on the physical coordinates of the target point in the eye center coordinate system.

[0055] In this embodiment, the target realignment problem can be transformed into: solving the target rotation angle Δx around the x-axis of the eyeball center coordinate system and the target rotation angle Δy around the y-axis, so that the target point coincides with the z-axis (i.e., the direction of the microscope optical axis) of the eyeball center coordinate system after rotation.

[0056] Specifically, such as Figure 5 As shown, the electronic device performs the following sub-steps to determine the target rotation angle: S3031, Electronic equipment determines the coordinate transformation equations of the target point after rotation around the x-axis and rotation around the y-axis.

[0057] Let the physical coordinates of the target point before rotation be ( , , (i.e., (x_o, y_o, z_o) obtained in S202), the rotated coordinates are ( .

[0058] Based on rotation matrix theory, rotation about the x-axis rotation matrix and rotation about the y-axis rotation matrix They are respectively:

[0059]

[0060] After the target point is rotated Δy around the y-axis and then Δx around the x-axis (the rotation order can be adjusted according to the specific implementation method), the coordinate transformation equations are as follows:

[0061] Since it needs to coincide with the z-axis after rotation, it is necessary to ,and .

[0062] S3032, electronic devices ,and To solve the constraints, the target rotation angle is used. and For the variables to be solved, the first two equations in the coordinate transformation equation system (i.e., ...) are solved using Newton's iteration method. and Solve the corresponding equation to obtain the target rotation angle. and .

[0063] It should be noted that, in addition to Newton's iteration method, analytical methods can be used to solve the problem directly, or methods such as rotation vectors and quaternions can be used to replace Euler angles to solve for the target rotation angle.

[0064] During the solution process, The third equation in the coordinate transformation equation set is automatically determined after the target rotation angle is determined, and it is not involved in the constraint conditions for solving the target rotation angle. That is, the electronic equipment, after obtaining... and Afterwards, and Substitution expression calculation The value of is determined, but this value does not affect the angle solution result.

[0065] S304, the electronic device performs motion planning based on the target rotation angle and under the condition of satisfying preset virtual constraints, and generates joint control commands for the robot.

[0066] In this embodiment, eye rotation is jointly driven by the left and right scleral cannulas fixed at the scleral puncture point. Preset virtual constraints include: maintaining a constant distance between the left and right scleral cannulas and the center of the eyeball, maintaining a constant distance between the left and right scleral cannulas, and maintaining a constant distance between the left and right scleral cannulas. These constraints ensure that the cannulas do not tear or compress the sclera during eye rotation, thus ensuring surgical safety.

[0067] Specifically, such as Figure 6 As shown, the electronic device performs the following sub-steps to generate joint control commands for the robot based on the target rotation angle: S3041, Electronic equipment acquires the current position of the left scleral cannula in the robot coordinate system. Current position of the right scleral cannula in the robot coordinate system And the position of the eyeball center in the robot coordinate system. .

[0068] In this embodiment, the robot coordinate system is fixed to the base of the surgical robot and is used to describe the robot's movement position. This position information can be obtained through the robot's forward kinematics combined with the pose sensor of the end effector.

[0069] For example, the electronic device reads the angle values ​​of each joint through the robot's joint angle encoder, and performs forward kinematics calculations in conjunction with the robot's DH parameter model to obtain the pose of the robotic arm end effector (i.e., the end effector holding the scleral cannula) in the robot coordinate system, thereby determining the position of the scleral cannula. and The center of the eyeball The coordinates (origin) of the eyeball center in the eyeball center coordinate system are then transformed to the robot coordinate system using the transformation matrix obtained from the preoperative hand-eye calibration.

[0070] As an example, the relative positional relationship between the scleral cannula and the eyeball model is as follows: Figure 7 As shown, left and right scleral cannulas are inserted into the scleral surface, with their tips located at the puncture points. The eyeball is connected to the end of the robotic arm through the cannulas. The robotic arm drives the eyeball to rotate around its center by pushing and pulling the end of the cannulas that protrudes from the sclera. Rotate.

[0071] S3042, The electronic device determines the left scleral scleral cannula needle based on preset virtual constraints. Right scleral cannula position With the center of the eyeball The three distance equality constraints between them.

[0072] The three distance equality constraints are:

[0073] in, and These are the fixed distances from the left and right scleral cannula needles to the center of the eyeball (i.e., the distances from the left and right puncture points to the center of the eyeball). This is the fixed distance between the left and right scleral cannulas. These two distances remain constant throughout the procedure. · ' represents the magnitude of a vector, i.e., the straight-line distance between two points.

[0074] In some possible implementations, the x-coordinate of the left scleral endoscopic needle can be constrained to be less than 0, while the x-coordinate of the right scleral endoscopic needle can be greater than 0, to avoid obtaining a solution that satisfies the distance constraint but results in the left and right scleral endoscopic needles intersecting. In other possible implementations, various safety strategies such as time-varying potential functions, velocity limits, and torque limits can also be set.

[0075] In one possible implementation, , and The values ​​are obtained through preoperative measurements or robotic teaching. Specifically, after the cannula is inserted into the sclera, the electronic device records the current cannula position and the center position of the eyeball, calculates and stores them. , and A fixed value. It should be noted that, generally speaking... , Equal, for example, both are In this application, , They can be equal or they can have some differences; this article does not impose any restrictions on this.

[0076] In another possible implementation , and The value is determined during the surgical planning phase. The electronic device reconstructs a three-dimensional model of the eyeball based on the patient's preoperative medical images (such as OCT or CT), plans the optimal puncture point location on the model, and calculates the distance from the puncture point to the center of the eyeball and the distance between the left and right puncture points, as... , And the default value of d_LR.

[0077] S3043, Electronic equipment applies three distance equality constraints with respect to joint variables. Find the partial derivatives and construct the constrained Jacobian matrix. .

[0078] In this embodiment, the electronic device applies the above three distance equation constraints to the joint variables respectively. Find the partial derivatives and construct the constrained Jacobian matrix. . Establish joint velocity The linear mapping relationship between the three distance change rates constrains the Jacobian matrix. Each row corresponds to the rate of change of distance from the left scleral cannula to the center of the eyeball, the rate of change of distance from the right scleral cannula to the center of the eyeball, and the rate of change of distance between the left and right scleral cannulas.

[0079] In this embodiment of the application, the constraint Jacobian matrix is ​​used. It is used to describe the joint speed of a robot. A matrix representing the linear mapping relationship between the distance change rates of the three virtual constraints. Specifically, It is a 3*n matrix, where n is the number of joints in the robot; each row corresponds to a virtual constraint—the distance from the left scleral cannula to the center of the eyeball. The distance from the right scleral cannula needle to the center of the eyeball and the distance between the left and right scleral cannulas. Each column corresponds to a robot joint, and the matrix element J_c[i,j] represents the contribution of the velocity of the j-th joint to the rate of change of the distance of the i-th virtual constraint. In constructing... In this case, a numerical differentiation method can be used. Specifically, the finite difference method is used to apply a small perturbation Δq_j to each joint variable, while keeping other joint variables constant. The change in the distance of the three virtual constraints before and after the perturbation, Δd_i, is calculated. Then, J_c[i,j] = Δd_i / Δq_j. After traversing all n joints, the complete constraint Jacobian matrix can be obtained. .because This reflects the instantaneous impact of the joint movements on the safety distance under the current robot posture, and is then substituted into the constraints of the quadratic programming optimization model. This ensures that the optimized joint velocities are obtained. The cannula is driven to move under the premise of satisfying virtual constraints (constant distance). Simultaneously, a relaxation variable 's' is introduced to absorb small residuals when the constraints cannot be precisely satisfied (i.e., allowing extremely small stretching or compression of the three distances), ensuring that the optimization problem is always solvable. The electronic device transforms abstract safety constraints into specific, real-time calculable joint velocity constraints, thereby rapidly solving for the optimal joint velocity command within the servo control cycle to achieve safe and smooth eye rotation. It should be noted that in some other possible implementations, the constraints can also be directly set as... .

[0080] S3044, Electronic equipment based on target rotation angle and And a quadratic programming optimization model, with joint speed Let slack variable s be the optimization variable, and solve for the joint velocity that satisfies the constraints. .

[0081] In this embodiment of the application, the objective function and constraints of the quadratic programming optimization (QP optimization) model are as follows:

[0082] Here, min represents the objective function, and the part following st(subject to) represents the constraints. H is the weight matrix (positive definite matrix), α and β are weight coefficients (positive real numbers), and slack variables s are used to apply the constraints. When the objective function cannot be precisely satisfied, the residuals are absorbed to ensure that the quadratic programming optimization model remains solvable even in numerically singular cases. [(1 / 2)· H +α 2 The [item] is used to penalize the magnitude of joint velocity, making the movement smoother; The term is used to penalize the introduction of slack variables s, so that the pre-defined virtual constraints are satisfied as precisely as possible.

[0083] In one possible implementation, the electronic device rotates the target by an angle. and This is converted into the desired cannula tip speed, which serves as a reference input for QP optimization. Specifically, the electronic device is based on... and The desired position of the cannula tip at each moment during eye rotation is calculated, the desired velocity is obtained through difference, and then the desired joint velocity is calculated through inverse kinematics. _ref, will _ref is added as a tracking term for the objective function, meaning the objective function becomes min { , s} (1 / 2) H(·q - _ref) +α 2 + β s 2 This approach allows QP optimization to track the desired motion trajectory while satisfying virtual constraints.

[0084] Optionally, regarding the solver for the quadratic programming optimization model, the electronic device can employ various QP solvers to solve the aforementioned optimization problem. For example, the electronic device can use a QP solver based on the efficient set method (such as QuadProg, qpOASES, etc.). The efficient set method iteratively identifies the effective inequality constraints, transforming the original problem into QP subproblems with equality constraints, making it suitable for small to medium-sized QP problems. Alternatively, the electronic device can also use a QP solver based on the interior-point method (such as OSQP, GUROBI, etc.). The interior-point method, through iterative search within the feasible region, can efficiently handle large-scale QP problems and has good robustness to ill-conditioned conditions. Alternatively, the electronic device can also use a QP solver based on the alternating direction multiplier method (ADMM). ADMM decomposes the original problem into several subproblems for separate solving, achieving global convergence through alternating updates and multiplier updates, making it suitable for distributed computing and embedded real-time control scenarios.

[0085] S3045, Electronic equipment based on the solved joint velocities This generates joint control commands for the robot.

[0086] In this embodiment of the application, the electronic device will calculate the joint velocity. As a speed command sent to the robot, the joint controller drives the joint motors to move based on the speed command, thus achieving smooth rotation of the eyeball.

[0087] In one possible implementation, the electronic device performs QP optimization once per control cycle (e.g., 1ms or 5ms), and applies the solution obtained in the current cycle... The joint speed command for that cycle is sent to the lower-level controller, which then tracks the speed using PID or feedforward control. This method achieves real-time closed-loop control, enabling a rapid response to external disturbances.

[0088] In another possible implementation, the electronic device uses trajectory interpolation to generate joint control commands. Specifically, after solving for a series of discrete joint velocities or joint position points, the electronic device generates a smooth joint spatial trajectory through spline interpolation (such as cubic splines or B-splines). The trajectory is then sent to the joint controller in the form of a position-time series, and the joint controller performs trajectory tracking according to a predetermined schedule. This method is suitable for scenarios requiring high trajectory smoothness and can reduce abrupt acceleration changes during motion.

[0089] The S305 electronic device, based on joint control commands, controls the robot to drive the left and right scleral cannula needles to rotate the eyeball and move the target point to the center of the microscope's field of view.

[0090] In this embodiment, the electronic device sends the generated joint control commands to the robot's joint actuators, driving the robotic arm to move according to the planned motion trajectory. The end effector of the robotic arm holds the exposed ends of the left and right scleral cannulas, and by pushing and pulling the cannulas, it causes the eyeball to rotate around its geometric center.

[0091] For example, the electronic device drives the robotic arm using a position control mode. The electronic device sends the planned joint position sequence as instructions to the joint servo driver, which uses position closed-loop control to ensure that each joint accurately reaches the target position. This method offers high control precision and is suitable for scenarios with strict positioning accuracy requirements.

[0092] During eye rotation, the preset virtual constraints ensure that the distances from the left scleral cannula to the center of the eyeball, the right scleral cannula to the center of the eyeball, and the distances between the left and right cannulas remain constant or undergo only extremely minor changes. Therefore, the cannulas will not tear or compress the sclera, effectively ensuring surgical safety.

[0093] After the eyeball has rotated, the target point moves to the center of the microscope's field of view (i.e., the microscope's optical axis coincides with the z-axis of the eye's central coordinate system). The surgeon can then clearly observe the lesion area from the center of the microscope's field of view and perform subsequent delicate surgical procedures. For example, such as... Figure 8 As shown, the field of view of the microscope is compared before and after target point adjustment, where the target point is marked as follows: After adjustment, it is marked as .

[0094] The surgical field adjustment method for ophthalmic surgical robots guided by microscopic images provided in this application involves an electronic device acquiring the pixel coordinates of the target point in the fundus image and converting them into physical coordinates in the eye's central coordinate system based on a preset mapping coefficient between pixels and physical length. This achieves automatic mapping from image space to physical space, eliminating the need for doctors to manually measure or estimate the spatial position of the target point. Furthermore, the electronic device automatically calculates the target rotation angle of the target point around the x-axis and y-axis of the eye's central coordinate system based on these physical coordinates. This allows doctors to automatically obtain the direction and amplitude parameters of eye rotation simply by clicking to select the target point on the fundus image, significantly reducing the cognitive load of the surgical procedure. Surgical field alignment can be completed without relying on extensive experience in visual field accommodation, thus lowering the operational threshold.

[0095] On the other hand, this application introduces preset virtual constraints in motion planning to maintain constant distances between the left scleral cannula and the center of the eyeball, constant distances between the right scleral cannula and the center of the eyeball, and constant distances between the left and right scleral cannulas. These preset virtual constraints are used as necessary conditions for generating robot joint control commands. Since the above three constant distance constraints directly correspond to the relative positional relationship between the scleral puncture point and the center of the eyeball, as well as the relative positional relationship between the left and right puncture points during eyeball rotation, embedding these constraints into the motion planning process ensures that the robot does not change the relative position between the cannulas and the eyeball tissues during eyeball rotation. This fundamentally avoids tearing, compression, or dislodgement of the cannulas from the sclera, ensuring surgical safety while achieving automated visual field adjustment and overcoming the safety risks caused by improper control of pushing and pulling force and direction during manual operation.

[0096] Furthermore, this application solves for joint velocities using a quadratic programming optimization model in motion planning, with the objective function constructed as a weighted combination of three terms. The first term [(1 / 2)· H The first term, α, sets a joint weight matrix H, enabling the system to differentiate the allocation of joint motion velocities based on each joint's safety requirements, range of motion, or mechanical characteristics. Joints of higher importance or closer to the eyeball are assigned greater weights, thus bearing smaller velocity components during movement and effectively reducing the risk of these critical joints exceeding limits. 2 A global penalty is applied to the sum of squares of all joint velocities, making the overall joint velocities obtained from the solution tend to be smooth, avoiding excessively rapid movement in any joint, thereby ensuring the smoothness and stability of eyeball rotation, avoiding abrupt changes, oscillations, or pauses during visual field adjustment, and improving the smoothness and safety of the surgical procedure; the third term By setting a large weighting coefficient β to strongly penalize the slack variable s, s is driven to approach zero as much as possible, thereby ensuring the virtual constraint. The optimization objectives are satisfied with maximum precision—allowing only micrometer-level deviations even in extreme cases such as numerical singularities—ensuring that the optimization problem is always solvable while keeping safety deviations within clinically acceptable limits. Through the synergistic effect of these three optimization objectives, this application achieves an optimal balance between differentiated protection of key joints, overall motion smoothness, and stringent safety constraints.

[0097] This application also provides a surgical field adjustment device for a microscopic image-guided ophthalmic surgical robot, including a unit for performing any of the microscopic image-guided ophthalmic surgical robot surgical field adjustment methods described in the above method embodiments.

[0098] Please refer to Figure 9 This is a schematic diagram of the surgical field adjustment device for a microscopic image-guided ophthalmic surgical robot provided in an embodiment of this application. Figure 9 As shown, the device may include: The target acquisition unit 901 is used to acquire the pixel coordinates of the target in the fundus image. The target is the lesion point selected by the user on the fundus image that needs to be aligned with the center of the microscope's field of view. The coordinate transformation unit 902 is used to convert the pixel coordinates of the target point into physical coordinates in the eye center coordinate system based on a preset mapping coefficient between pixels and physical length. Angle solving unit 903 is used to solve the target rotation angle of the target point around the x-axis and y-axis of the eye center coordinate system based on the physical coordinates of the target point in the eye center coordinate system; The motion planning unit 904 is used to perform motion planning based on the target rotation angle under the condition of satisfying preset virtual constraints, and generate joint control commands for the robot. The control execution unit 905 is used to control the robot to drive the movement of the scleral cannula needle based on joint control commands, so as to rotate the eyeball.

[0099] In some possible implementations, the above-mentioned device further includes: a calibration unit for pre-calibrating the mapping coefficients between pixels and physical lengths, and for pre-establishing the transformation relationships between various coordinate systems.

[0100] In the embodiments of this application, any of the implementation methods mentioned in the method embodiments are also applicable to the surgical field adjustment device of the ophthalmic surgical robot guided by microscopic images provided in this application. For specific execution steps, please refer to the description of the foregoing method embodiments, which will not be detailed here.

[0101] This application also provides a computer storage medium that can store multiple instructions. These instructions are adapted to be loaded and executed by a processor. The specific execution process can be found in the detailed description of the above-described method embodiments, and will not be elaborated here.

[0102] This application also provides a computer program product containing instructions that, when run on an electronic device, cause the electronic device to execute the method steps of the method embodiments shown above.

[0103] This application also provides a chip module, including a transceiver component and a chip, wherein the chip is used to execute the method steps of the above-described method embodiments.

[0104] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in any of the above-described method embodiments.

[0105] It is understood that the surgical field adjustment device, computer storage medium, computer program, computer program product, chip system, and electronic device provided above for microscopic image-guided ophthalmic surgical robot are all used to execute the method shown in any implementation of the corresponding aspect of the embodiments of this application. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding method, and will not be detailed here.

[0106] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes the processes of the embodiments of the above methods.

[0107] The terms “including” and “having” mentioned above, and any variations thereof, are intended to cover non-exclusive inclusion.

[0108] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for adjusting the surgical field of an ophthalmic surgical robot guided by microscopic images, characterized in that, The method includes: Obtain the pixel coordinates of the target point in the fundus image. The target point is the lesion point selected by the user on the fundus image that needs to be aligned with the center of the microscope's field of view. The fundus image is acquired by a microscope and is located in the image pixel coordinate system. Based on a preset mapping coefficient between pixels and physical length, the pixel coordinates of the target point are converted into physical coordinates in the eye center coordinate system; Based on the physical coordinates of the target point in the eye center coordinate system, the target rotation angle of the target point around the x-axis and y-axis of the eye center coordinate system is calculated. The target rotation angle is the rotation angle that makes the target point coincide with the z-axis of the eye center coordinate system after rotation. The z-axis is consistent with the optical axis of the microscope. Based on the target rotation angle, motion planning is performed under the condition of satisfying preset virtual constraints to generate joint control commands for the robot; wherein, the rotation of the eyeball is jointly driven by the left and right scleral cannulas fixed at the scleral puncture point, and the preset virtual constraints include keeping the distance between the left scleral cannulas and the center of the eyeball constant, keeping the distance between the right scleral cannulas and the center of the eyeball constant, and keeping the distance between the left and right scleral cannulas constant; Based on the joint control commands, the robot is controlled to drive the left and right scleral cannulas to rotate the eyeball and move the target point to the center of the microscope's field of view.

2. The method as described in claim 1, characterized in that, The process of converting the pixel coordinates of the target point into physical coordinates in the eye center coordinate system based on a preset mapping coefficient between pixels and physical length includes: Obtain the pixel coordinates of the center of the microscope's field of view; Calculate the pixel offset between the pixel coordinates of the target point and the pixel coordinates of the center of the microscope's field of view; Based on the preset mapping coefficient between pixel distance and physical length, the pixel offset is converted into physical offsets in the x-axis and y-axis directions in the eye center coordinate system, and the x-axis and y-axis physical coordinate components of the target point in the eye center coordinate system are obtained. Based on the eyeball radius and the x-axis and y-axis physical coordinate components of the target point in the eyeball center coordinate system, the z-axis physical coordinate components of the target point in the eyeball center coordinate system are determined by the spherical equation.

3. The method as described in claim 2, characterized in that, The mapping coefficients are pre-calibrated in the following manner: The checkerboard calibration plate is placed on the focusing plane of the microscope, and the checkerboard of the calibration plate has known physical dimensions. The image of the chessboard calibration board is acquired, the coordinates of the corner points of the chessboard in the image are extracted, and the pixel distance between adjacent corner points is calculated. The mapping coefficient is determined based on the ratio of the known physical size between adjacent corner points to the pixel distance.

4. The method according to any one of claims 1 to 3, characterized in that, The calculation of the target rotation angle around the x-axis and y-axis of the eyeball center coordinate system based on the physical coordinates of the target point in the eyeball center coordinate system includes: Determine the set of coordinate transformation equations for the target point after rotation around the x-axis and rotation around the y-axis, wherein the physical coordinates of the target point before rotation are ( , , The target coordinates after rotation are ( The coordinate transformation equations are as follows: Let x be the target rotation angle about the x-axis of the coordinate system at the center of the eyeball. The target rotation angle is the rotation around the y-axis of the coordinate system at the center of the eyeball. by ,and To solve the constraints, with and For the variables to be solved, Newton's iteration method is used to solve the coordinate transformation equations. and Solving the two equations yields the target rotation angle. and ; in, The third equation in the coordinate transformation equation set is automatically determined after the target rotation angle is determined, and it does not participate in the solution of the target rotation angle.

5. The method as described in claim 4, characterized in that, The process of performing motion planning based on the target rotation angle, under the condition of satisfying preset virtual constraints, and generating joint control commands for the robot includes: Obtain the current position of the left scleral cannula in the robot coordinate system. and the current position of the right scleral cannula in the robot coordinate system And the position of the eyeball center in the robot coordinate system. ; Based on the preset virtual constraints, determine the The above With the The three distance equality constraints between them are: in, and These are the fixed distances from the left and right scleral cannula needles to the center of the eyeball, respectively. The fixed distance between the left scleral endoscopic cannula and the right scleral endoscopic cannula; The three distance equality constraints are respectively related to the joint variables. Find the partial derivatives and construct the constrained Jacobian matrix. The constrained Jacobian matrix Used to represent the joint velocity A linear mapping relationship between the three distance change rates; Based on the target rotation angle and And a quadratic programming optimization model, with the joint speed Let slack variable s be the optimization variable, and solve for the joint velocity that satisfies the constraints. The objective function and constraints of the quadratic programming optimization model are as follows: Wherein, min is the objective function, st is the constraint condition, H is the weight matrix, α and β are weight coefficients, and the slack variable s is used to apply the constraint condition. When the objective function cannot be precisely satisfied (=0), the residuals are absorbed to ensure that the quadratic programming optimization model remains solvable even in numerically singular cases. The term is used to penalize the introduction of the slack variable s; Based on the joint velocity obtained by the solution This generates joint control commands for the robot.

6. The method as described in claim 5, characterized in that, Before obtaining the pixel coordinates of the target point in the fundus image, the method further includes: Determine the following coordinate system: A robot coordinate system, fixed to the base of the surgical robot, is used to describe the robot's movement position; The microscope coordinate system is fixed at the center of the microscope optical axis. There is a projection transformation relationship between the microscope coordinate system and the image pixel coordinate system. The image pixel coordinate system is a coordinate system fixed on the two-dimensional imaging plane of the microscope fundus image. The eyeball-centered coordinate system has its origin at the geometric center of the eyeball. An eyeball coordinate system, fixed to the eyeball body, is used to describe the rotational attitude of the eyeball. The eyeball coordinate system coincides with the eyeball center coordinate system when the eyeball is not rotating. Establish the following coordinate system transformation relationships: The transformation relationship between the robot coordinate system and the eyeball center coordinate system is determined by the hand-eye calibration method; The transformation relationship between the image pixel coordinate system and the microscope coordinate system is determined based on the mapping coefficient between pixels and physical length, and is used to convert the pixel coordinates of the target point into the x-axis and y-axis physical coordinate components in the microscope coordinate system. The transformation relationship between the microscope coordinate system and the eyeball center coordinate system is such that the physical coordinate components of the x-axis and y-axis in the microscope coordinate system are equal to the physical coordinate components of the x-axis and y-axis in the eyeball center coordinate system, and the z-axis transformation relationship between the microscope coordinate system and the eyeball center coordinate system is determined by the focal length value of the microscope.

7. The method as described in claim 6, characterized in that, The hand-eye calibration method includes: Fix the checkerboard calibration plate to the surface of the eyeball model; The robot is controlled to move the chessboard calibration plate in different postures, while simultaneously acquiring microscope images. Based on the collected robot pose data and corresponding image data, the hand-eye transformation matrix is ​​solved. The hand-eye transformation matrix includes translation vectors and rotation matrices, which are used to establish the coordinate transformation relationship between the robot coordinate system and the eyeball center coordinate system.

8. A surgical field adjustment device for a microscopic image-guided ophthalmic surgical robot, characterized in that, Includes a unit for performing the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which, when executed, performs the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method of any one of claims 1 to 7 is performed.

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