Micro robotic end rotation positioning self-calibration method, system and medium

Abstract

The application relates to the field of robot technology and precision instruments, and discloses a microscopic robot end rotating positioning self-calibration method, a system and a medium.The application is to solve the problem that an existing kinematic model cannot comprehensively describe the coupling effect of rotating and linear motion, and the model parameters depend on external vision continuous calibration, and a self-calibration framework that can run offline after parameter convergence is not established.The application constructs a motion chain of an end effector according to a microscopic robot control system, the motion chain couples rotating and linear motion of the end effector; the end effector is driven to perform rotating and feeding motion in the motion chain by using the microscopic robot control system, and motion parameters are collected in a motion track of a tip of the end effector; based on the motion parameters, a self-adaptive feedforward controller is used to adjust tip position error of vision feedback of the microscopic robot control system to convergence, and motion control coefficients are obtained, which are used for positioning control of the end effector.

Description

Technical Field

[0001] This application belongs to the field of robotics and precision instruments, and in particular relates to a method, system and medium for self-calibration of end-effector rotational positioning of a microrobot. Background Technology

[0002] In fields such as biology, electronics, and materials science, robotic manipulators integrated into microscope systems have become key tools for performing tasks such as microinjection, multi-directional imaging, and microstructure assembly. These tasks can be broadly categorized into linear positioning and rotational positioning. Linear positioning is relatively mature due to its simple kinematics and high static accuracy, while high-precision rotational positioning still faces significant challenges, but it is indispensable in applications such as low-damage cell puncture, multi-angle in-situ observation, and torsion testing.

[0003] Currently, rotational positioning techniques in micromanipulation are mainly divided into three categories:

[0004] The first type is a static compensation method based on the RPP (rotation-translation-translation) kinematic chain. This method compensates for axial offset by pre-calibration. However, since the manufacturing and assembly of the end effector inevitably introduces angular deviations between the physical rotation axis and the desired rotation axis, and the static compensation model fails to consider the amplification effect of this angular error along the kinematic chain (for example, a deviation of 0.05° at a 150mm arm length will result in an end effector error of about 130μm), it cannot handle the positioning error introduced by the angular deviation, nor does it support real-time positioning during axial movement.

[0005] The second category is dynamic adaptive control methods based on PPR (translation-translation-rotation) kinematic chains. This method improves robustness when changing end effectors by compensating for angular deviations online through an adaptive controller. However, this type of method heavily relies on continuous, real-time visual feedback to maintain control accuracy. Once the field of view is obstructed, the target is lost, or image processing fails, visual tracking is interrupted, and the system cannot maintain stable operation. At the same time, its control model is mainly designed for scenarios with fixed endpoints, making it difficult to handle complex operation tasks where the end effector performs both rotation and axial translation simultaneously.

[0006] The third type is the RPP-type method that introduces axial feed. This method attempts to support endpoint movement scenarios, but it simply superimposes axial motion without modeling the comprehensive kinematic compensation required for each axis, and lacks active focusing tracking along the microscope optical axis. Due to the inherent narrow field of view and shallow depth of field of the microscope system, the end point is very likely to deviate from the focal plane during the rotational operation of the endpoint movement, resulting in the target being out of focus, which in turn affects the operation accuracy and visual feedback quality.

[0007] In summary, existing technologies generally suffer from the following technical problems: First, high precision and versatility are difficult to achieve simultaneously; complex and time-consuming methods are unsuitable for scenarios requiring frequent end-effector replacement, while general-purpose methods have limited accuracy. Second, most methods only support rotation with a fixed end-effector position, failing to support free axial translation during rotation. Third, all dynamic compensation methods rely excessively on continuous visual feedback, failing once visual input is interrupted, resulting in poor robustness. Fourth, there is a lack of integrated depth compensation mechanisms, making it difficult to simultaneously maintain the microscope's focal plane during rotation-translation composite motion. The root cause of these problems lies in the fact that existing kinematic models fail to fully describe the coupling effect of rotation and translation, and the model parameters rely on continuous external visual calibration, failing to establish a self-calibration framework that can run offline after parameter convergence. Therefore, there is an urgent need for a high-precision rotational positioning method that can simultaneously support free end-effector movement and active focusing tracking without relying on continuous visual feedback. Summary of the Invention

[0008] This application aims to address the problems that existing kinematic models fail to fully describe the coupling effect of rotation and translation, and that model parameters rely on continuous external visual calibration, thus failing to establish a self-calibration framework that can run offline after parameter convergence. It provides a self-calibration method, system, and medium for rotational positioning of the end effector of a microrobot.

[0009] The first aspect of this application provides a method for self-calibration of end-effector rotational positioning of a microrobot, including:

[0010] The kinematic chain of the end effector is constructed according to the microrobot control system. The kinematic chain couples the rotation and linear motion of the end effector. The kinematic chain includes three translational degrees of freedom, one feed degree of freedom and one rotational degree of freedom. The three translational degrees of freedom are perpendicular to each other.

[0011] The microrobot control system drives the end effector to perform rotational and feed movements in the kinematic chain, and collects motion parameters in the motion trajectory of the end effector tip.

[0012] Based on the motion parameters, an adaptive feedforward controller is used to adjust the tip position error of the visual feedback of the microrobot control system until it converges, thereby obtaining motion control coefficients for positioning control of the end effector.

[0013] In one possible design, the microrobot control system includes:

[0014] Visual feedback unit: used to acquire the actual position of the tip of the end effector;

[0015] Operation unit: used to drive the end effector to perform rotational and feed motions in the kinematic chain.

[0016] In one possible design, the acquisition of motion parameters from the motion trajectory of the end effector tip includes:

[0017] The end effector is driven to perform at least one rotational motion, and the coordinates of multiple projection points of the end effector tip in the microscope image plane coordinate system are collected and fitted into an elliptical trajectory.

[0018] The motion parameters are calculated based on the major and minor axis radii and direction angles of the elliptical trajectory. The motion parameters include: rotation axis tilt angle, angular deviation, and spatial circular trajectory radius.

[0019] In one possible design, the process of adjusting the tip position error of the visual feedback from the microscopic robot control system to convergence using an adaptive feedforward controller based on the motion parameters to obtain motion control coefficients includes:

[0020] The motion control coefficient estimates are updated using an adaptive rate update law, so that the position error between the actual position and the desired position of the end effector tip converges to 0.

[0021] In one possible design, the expression for the adaptive feedforward controller is:

[0022] ,

[0023] in, To control the input, , and These represent the velocities of the end effector tip in the three translational degrees of freedom. for The first derivative, The desired position of the tip of the end effector. This is the motion control coefficient estimation vector. This refers to the angular displacement of the end effector about its rotation axis. and These represent the velocities of the end effector tip in the feed and rotational degrees of freedom, respectively. For positional error, For proportional gain, and These are the components of the Jacobian matrix with respect to the feed and rotational degrees of freedom, respectively.

[0024] In one possible design, the expression for the adaptive rate update law is:

[0025] ,

[0026] ,

[0027] in, For adaptive rate update law, Represents the projection operator. Indicates intermediate variables. The learning rate matrix, This is the gain matrix;

[0028] The expression for the gain matrix is:

[0029] .

[0030] In one possible design, the Jacobian matrix is ​​expressed as:

[0031] ,

[0032] This represents the motion control coefficient vector. The tilt angle of the rotation axis. This refers to angular deviation.

[0033] In one possible design, the expression for the motion control coefficient vector is:

[0034] ,

[0035] ,

[0036] ,

[0037] ,

[0038] ,

[0039] ,

[0040] in, Let the radius of the spatial circular trajectory be . The initial angle of the end effector.

[0041] The second aspect of this application provides a self-calibration system for the end-effector rotational positioning of a microrobot, comprising:

[0042] Modeling unit: used to construct the kinematics of the end effector according to the microrobot control system. The kinematics couples the rotation and linear motion of the end effector. The kinematics includes three translational degrees of freedom, one feed degree of freedom, and one rotational degree of freedom. The three translational degrees of freedom are perpendicular to each other.

[0043] Acquisition unit: used to drive the end effector to perform rotational and feed movements in the kinematic chain using the microrobot control system, and to acquire motion parameters in the motion trajectory of the end effector tip;

[0044] Control unit: Based on the motion parameters, it uses an adaptive feedforward controller to adjust the tip position error of the visual feedback of the microrobot control system until convergence, and obtains motion control coefficients for positioning control of the end effector.

[0045] A third aspect of this application provides a microrobot end-effector rotational positioning self-calibration device, which includes a processor and a memory. The memory stores at least one instruction, which is loaded and executed by the processor to implement the microrobot end-effector rotational positioning self-calibration method described above.

[0046] A fourth aspect of this application provides a computer storage medium storing at least one instruction, which is loaded and executed by a processor to implement the microrobot end-effector rotational positioning self-calibration method described above.

[0047] The beneficial effects of this application are:

[0048] 1. High positioning accuracy: Micrometer-level (<2μm) rotational positioning accuracy is achieved even with free movement of the endpoints.

[0049] 2. Strong anti-interference and robustness: Through self-calibration and adaptive feedforward control, the reliance on continuous high-precision visual feedback is reduced. Even if visual tracking is temporarily interrupted (such as by occlusion), the system can still maintain stable operation based on the calibrated model.

[0050] 3. Wide applicability: The proposed universal kinematic framework and two-step calibration strategy enable it to adapt to different end effectors, different micromanipulators (mechanical or piezoelectric), and different microscope magnifications.

[0051] 4. Depth compensation based on 2D feedback: No additional depth sensor is required; it is achieved directly using a kinematic model. The active focus tracking of the axis effectively solves the problem of defocusing during rotation operations in shallow depth of field.

[0052] 5. Wide range of applications: In complex biological micromanipulation, such as organoid rotational shearing injection, high precision and anti-obstruction capability can significantly improve the injection success rate; for example, it allows the tip of a micropipette to capture and hold tiny units (such as cells, workpieces) for multi-angle rotational imaging; and for example, the rotational positioning operation with end-point convergence allows for low-damage, in-situ rotational wrapping biopsy sampling of soft tissues (such as cell clusters, organoids). Attached Figure Description

[0053] Figure 1 This is a schematic diagram of a PPR type kinetic chain;

[0054] Figure 2 A schematic diagram of the kinematic model of a PPR-type micromanipulator;

[0055] Figure 3 This is a block diagram of the control principle.

[0056] Figure 4 This is a schematic diagram illustrating the model verification of an example embodiment;

[0057] Figure 5 A schematic diagram of the end point trajectory under rotation-feed for different controllers. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0059] Specific Implementation Method 1: The microrobot end-effector rotational positioning self-calibration method described in this implementation method includes:

[0060] The kinematic chain of the end effector is constructed according to the microrobot control system. The kinematic chain couples the rotation and linear motion of the end effector. The kinematic chain includes three translational degrees of freedom, one feed degree of freedom and one rotational degree of freedom. The three translational degrees of freedom are perpendicular to each other.

[0061] The microrobot control system drives the end effector to perform rotational and feed movements in the kinematic chain, and collects motion parameters in the motion trajectory of the end effector tip.

[0062] Based on the motion parameters, an adaptive feedforward controller is used to adjust the tip position error of the visual feedback of the microrobot control system until it converges, thereby obtaining motion control coefficients. These coefficients are used to perform positioning control on the end effector. In other words, before obtaining the motion parameters, the motion control is vision-dependent online, but after obtaining the motion control coefficients, offline control can be performed directly using the motion parameters.

[0063] In one embodiment, the microscopic robot control system includes:

[0064] Visual feedback unit: used to acquire the actual position of the tip of the end effector;

[0065] Operation unit: used to drive the end effector to perform rotational and feed motions in the kinematic chain.

[0066] In one embodiment, acquiring motion parameters from the motion trajectory of the end effector tip includes:

[0067] The end effector is driven to perform at least one rotational motion, and the coordinates of multiple projection points of the end effector tip in the microscope image plane coordinate system are collected and fitted into an elliptical trajectory.

[0068] The motion parameters are calculated based on the major and minor axis radii and direction angles of the elliptical trajectory. The motion parameters include: rotation axis tilt angle, angular deviation, and spatial circular trajectory radius.

[0069] In one implementation, the step of adjusting the tip position error of the visual feedback of the microscopic robot control system to convergence using an adaptive feedforward controller based on the motion parameters to obtain motion control coefficients includes:

[0070] The motion control coefficient estimates are updated using an adaptive rate update law, so that the position error between the actual position and the desired position of the end effector tip converges to 0.

[0071] To further illustrate the implementation scheme of this application, the following embodiments are provided. Each step is described in detail below:

[0072] I. System Composition

[0073] An inverted microscope equipped with 4x, 10x, and 20x electric microscopes, an electric stage, and an electric focusing device was used as the main visual feedback unit.

[0074] The operating unit consists of a commercial 4-axis micromanipulator and a hollow rotary motor integrated thereon, forming a PPR (translation-translation-rotation) kinematic chain. The micromanipulator provides three translational degrees of freedom (…). , , ) and a feed degree of freedom ( The hollow rotary motor provides the key rotational degree of freedom. The end effector (such as a micro-suction tube) is held in a specially designed gripper, such as... Figure 1 As shown.

[0075] The system also includes a hydraulic pump driven by a stepper motor to provide operating pressure.

[0076] On the control side, a PC is responsible for image processing and the user interface.

[0077] A custom multi-axis controller based on the CAN bus (with an STM32 MCU as its core) is responsible for achieving high-speed, real-time motion control and directly driving each motor.

[0078] II. Kinematic Modeling of PPR-type Robotic Arm

[0079] To describe the three-dimensional trajectory of the end effector under the coupling of rotation and linear motion, and to achieve high-precision rotational positioning under arbitrary end-point motion states, a PPR-type kinematic chain is constructed, such as... Figure 2 The following integrated kinematic model was established.

[0080] 1. Definition of coordinate system

[0081] Node coordinate system {N}: An introduced intermediate coordinate system used to connect the base coordinate system and the pixel coordinate system. The three axes of the node coordinate system are represented as follows: , , The origin is ,origin along After feeding, the point is q. This indicates that point q is at The projection point of the plane. This indicates that the end point p (the tip of the end effector) is at... The projection point of the plane;

[0082] Base coordinate system {B}: The coordinate system of the micromanipulator itself, with the three axes represented as follows: , , The origin is ;

[0083] Pixel coordinate system {I}: The plane coordinate system of the microscope image, whose two orthogonal coordinate axes are represented as follows: and .

[0084] Figure 2 middle, This represents the circular trajectory of the end effector (i.e., the micropipette in the figure) in the nodal coordinate system {N}. for along The trajectory after feeding, express exist The projection trajectory of a plane.

[0085] 2. Position model of the endpoint in three-dimensional space

[0086] The coordinates of the endpoint p in the nodal coordinate system {N} can be expressed as:

[0087] , ,

[0088] in, For the end point p along the inclined axis Displacement; first intermediate variable , The initial angle of the end effector; For the end effector about the rotation axis angular displacement; Due to angular deviation The radius of the resulting spatial circular trajectory; This refers to the angular deviation, i.e., the rotation axis relative to the base coordinate system. The tilt angle of the axis.

[0089] The endpoint p is represented in the base coordinate system {B} as:

[0090] ,

[0091] in, Let be the rotation transformation matrix. Indicates circling Axis rotation , Indicates circling Axis rotation , It is a translation vector.

[0092] Analysis shows that the trajectory of the endpoint p on the projection plane is a semi-major axis. semi-short shaft An ellipse with a semi-minor axis and The included angle of the axis is , For the axis of rotation The angle of inclination in a plane.

[0093] Taking the derivative of the above equation with respect to time yields the kinematic model for the rotational positioning of the end effector:

[0094] ,

[0095] in, Let p be the velocity of the endpoint in the base coordinate system; For velocity vectors, , , , and They are respectively , , , and The speed of the shaft; The Jacobian matrix of the system:

[0096] ,

[0097] The coefficient is defined as follows:

[0098] ,

[0099] ,

[0100] ,

[0101] ,

[0102] ,

[0103] ,

[0104] The above coefficients are constants and are determined by the system parameters. Composition, in which It can be obtained by identifying the ellipse parameters in the pixel coordinate system. Determined by adaptive control parameters.

[0105] III. Adaptive Feedforward Control

[0106] To facilitate controller design, the system kinematic model was rewritten as follows:

[0107] ,

[0108] in, and These are the components of the Jacobian matrix with respect to the feed and rotation degrees of freedom, respectively, and the vectors. .

[0109] make To control the input, the control problem can be reformulated as unknown system parameters. and known disturbances The rotational positioning problem is addressed. Therefore, an adaptive feedforward controller is designed to compensate for end-effector positioning errors.

[0110] Define position error :

[0111] ,

[0112] in, Let p be the expected location of the endpoint. This represents the actual position of the endpoint p. , and These are the expected values ​​for the three translational degrees of freedom components.

[0113] The adaptive feedforward controller is designed as follows:

[0114] ,

[0115] in, for The first derivative; For proportional gain; For the coefficient estimation vector, , , , , , They are respectively , , , , , The estimated value, coefficient estimation vector The following adaptive rate update law applies. :

[0116] ,

[0117] Second intermediate variable ,

[0118] in, The learning rate matrix, Here is the gain matrix:

[0119] , , , , , , They are respectively , , , , , Adaptive gain;

[0120] ,

[0121] Projection operator Used to display parameter boundaries:

[0122] .

[0123] In summary, the system control process can be summarized as follows: Figure 3 The block diagram is shown below. Figure 3 As shown, the system obtains the actual position of the end effector tip through visual feedback, and then calculates the error through coordinate transformation. An adaptive law drives the model coefficients to converge, and proportional control is used for compensation. The self-calibration process (combining pre-calibration and online calibration), shown by the dashed line, ultimately determines all kinematic parameters, enabling the system to... Focused tracking is performed during axis motion, and estimation is based solely on 2D visual feedback. Shaft error.

[0124] Figure 3 middle, and These are the actual coordinates and target coordinates of the endpoint p in the pixel coordinate system, respectively. This is the scaling factor from pixel coordinates to actual coordinates; This is the homogeneous transformation matrix from pixel coordinates to the base coordinate system; For the endpoint p along The speed that the microscope focusing motor should compensate for during axis feed; For the end point p, wrap around robotic arm when the axis rotates The shaft should compensate for the speed. , and These are the actual values ​​of the three translational degrees of freedom components.

[0125] In this embodiment, a micropipette of random size (6cm~9cm in length, 25μm~50μm in tip size) is mounted on... Figure 1 The system shown is used to verify the accuracy of the proposed general kinematic model. The specific steps are as follows:

[0126] After the system starts, the microrobot executes rotational and axial feed movements according to instructions. The microscope's vision module acquires the actual position of the robot's end effector. The actual position is compared with the pre-set target position to calculate the position error. Position error It is the starting point for all actions of the entire controller.

[0127] By fitting ellipse parameters to the pixel plane, the parameters of a 3D tilted circle in space are obtained. Perform pre-identification, such as Figure 4 As shown.

[0128] Fixed angle rotation The shaft drives the rotation after each rotation. , , The micropipette tip was positioned to the center of the field of view and the focal plane by axial repositioning, and the coordinates of the robotic arm were recorded. After completing a 360° rotation, the original trajectory measured in the experiment was obtained.

[0129] Then along Feed the axis 250μm, repeat the above steps and record the robot arm coordinates to obtain the trajectory after feeding.

[0130] The proposed adaptive feedforward controller is used to adjust the model parameters. Identification is performed, combined with pre-calibrated parameters. Once a defined system model is obtained, the trajectory of the micropipette tip before and after feeding can be predicted using the model.

[0131] like Figure 4 The results of one experiment are shown, and they are consistent with the model's predictions, demonstrating the accuracy of the proposed model.

[0132] Table 1. Model parameters from 5 different experiments

[0133] .

[0134] Characterization data and effect data of the examples and comparative products.

[0135] Based on the proposed system and control method, a comparative experiment on end-point trajectory control was conducted, with the rotational shaft speed set. , The rotational positioning control performance of the controller without a controller, the PD controller, and the adaptive feedforward controller of this embodiment were compared under different magnifications. Under a 4x objective lens, the PD controller parameters were set to a proportional gain of 3.8 and a derivative gain of 1.2; under a 20x objective lens, the PD controller parameters were set to a proportional gain of 0.8 and a derivative gain of 0.5. The feedback gain of the adaptive controller was 4.8 under all magnifications, and the learning rate for each coefficient was 0.03. The feedback information is 2D scattered data (projection), but the spatial relative position of the trajectory can be reconstructed using model parameters, such as... Figure 5 As shown in the figure, NC (4×) represents a 4x scope without compensation, PD (4×) and PD (20×) represent the proportional differential controllers of the 4x scope and the 20x scope, respectively, and AFC (4×) and AFC (20×) represent the adaptive feedforward controllers of the 4x scope and the 20x scope, respectively.

[0136] This embodiment further quantifies the end-point estimation error by setting the rotational shaft speed. , The scatter plot error was evaluated for two revolutions (approximately 80 data points) using different controllers. The scatter plot data was used... To represent, define the error:

[0137] .

[0138] Based on a review of relevant literature and a summary of experimental results, this embodiment summarizes the rotational positioning errors under different control methods in the following table:

[0139] Table 2 Comparison of rotational positioning errors under different control methods

[0140] .

[0141] In summary, this embodiment establishes a comprehensive kinematic model applicable to any endpoint motion state. This model accurately describes the coupled influence of rotational and translational motion on the position of the end effector, providing a theoretical basis for compensation control.

[0142] A two-step self-calibration strategy based on adaptive feedforward control is proposed: First, key installation parameters are determined through image geometric estimation. Then, residual parameters of the kinematic model are obtained through adaptive online learning. Once the parameters converge, the system can calculate and perform accurate motion compensation based on the model without continuous visual feedback. Furthermore, using the identified model parameters, feedforward calculation and compensation along the microscope optical axis can be directly performed. The movement of the axis actively maintains focus during the rotation of the endpoint, thus solving the problem of defocusing in shallow depth of field.

[0143] Specific Implementation Method Two: The microscopic robot end-effector rotational positioning self-calibration system described in this implementation method includes:

[0144] Modeling unit: used to construct the kinematics of the end effector according to the microrobot control system. The kinematics couples the rotation and linear motion of the end effector. The kinematics includes three translational degrees of freedom, one feed degree of freedom, and one rotational degree of freedom. The three translational degrees of freedom are perpendicular to each other.

[0145] Acquisition unit: used to drive the end effector to perform rotational and feed movements in the kinematic chain using the microrobot control system, and to acquire motion parameters in the motion trajectory of the end effector tip;

[0146] Control unit: Based on the motion parameters, it uses an adaptive feedforward controller to adjust the tip position error of the visual feedback of the microrobot control system until convergence, and obtains motion control coefficients for positioning control of the end effector.

[0147] Specific Implementation Method 3: The microrobot end-effector rotational positioning self-calibration device described in this embodiment includes a processor and a memory. The memory stores at least one instruction, which is loaded and executed by the processor to implement the microrobot end-effector rotational positioning self-calibration method as described in Specific Implementation Method 1.

[0148] Specific Implementation Method Four: A computer storage medium as described in this embodiment stores at least one instruction, which is loaded and executed by a processor to implement the microrobot end-effector rotation positioning self-calibration method as described in Specific Implementation Method One.

[0149] While specific embodiments of this application have been described herein with reference to them, it should be understood that these embodiments are merely examples of the principles and applications of this application. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of this application as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A self-calibration method for end-effector rotational positioning of a microrobot, characterized in that, include: The kinematic chain of the end effector is constructed according to the microrobot control system. The kinematic chain couples the rotation and linear motion of the end effector. The kinematic chain includes three translational degrees of freedom, one feed degree of freedom and one rotational degree of freedom. The three translational degrees of freedom are perpendicular to each other. The microrobot control system drives the end effector to perform rotational and feed movements in the kinematic chain, and collects motion parameters in the motion trajectory of the end effector tip. Based on the motion parameters, an adaptive feedforward controller is used to adjust the tip position error of the visual feedback of the microrobot control system until it converges, thereby obtaining motion control coefficients for positioning control of the end effector.

2. The microrobot end-effector rotation positioning self-calibration method according to claim 1, characterized in that, The microscopic robot control system includes: Visual feedback unit: used to acquire the actual position of the tip of the end effector; Operation unit: used to drive the end effector to perform rotational and feed motions in the kinematic chain.

3. The microrobot end-effector rotation positioning self-calibration method according to claim 1, characterized in that, The acquisition of motion parameters from the motion trajectory at the tip of the end effector includes: The end effector is driven to perform at least one rotational motion, and the coordinates of multiple projection points of the end effector tip in the microscope image plane coordinate system are collected and fitted into an elliptical trajectory. The motion parameters are calculated based on the major and minor axis radii and direction angles of the elliptical trajectory. The motion parameters include: rotation axis tilt angle, angular deviation, and spatial circular trajectory radius.

4. The microrobot end-effector rotation positioning self-calibration method according to claim 1, characterized in that, Based on the motion parameters, the adaptive feedforward controller is used to adjust the tip position error of the visual feedback of the microscopic robot control system until convergence, thereby obtaining motion control coefficients, including: The motion control coefficient estimates are updated using an adaptive rate update law, so that the position error between the actual position and the desired position of the end effector tip converges to 0.

5. The microrobot end-effector rotation positioning self-calibration method according to claim 4, characterized in that, The expression for the adaptive feedforward controller is: ， in, To control the input, , and These represent the velocities of the end effector tip in the three translational degrees of freedom. for The first derivative, The desired position of the tip of the end effector. This is the motion control coefficient estimation vector. This refers to the angular displacement of the end effector about its rotation axis. and These represent the velocities of the end effector tip in the feed and rotational degrees of freedom, respectively. For positional error, For proportional gain, and These are the components of the Jacobian matrix with respect to the feed and rotational degrees of freedom, respectively.

6. The microrobot end-effector rotation positioning self-calibration method according to claim 5, characterized in that, The expression for the adaptive rate update law is: ， ， in, For adaptive rate update law, Represents the projection operator. Indicates intermediate variables. The learning rate matrix, This is the gain matrix; The expression for the gain matrix is: 。 7. The microrobot end-effector rotation positioning self-calibration method according to claim 5, characterized in that, The Jacobian matrix expression is as follows: ， This represents the motion control coefficient vector. The tilt angle of the rotation axis. This refers to angular deviation.

8. The microrobot end-effector rotation positioning self-calibration method according to claim 7, characterized in that, The expression for the motion control coefficient vector is: ， ， ， ， ， ， in, Let the radius of the spatial circular trajectory be . The initial angle of the end effector.

9. A self-calibration system for end-effector rotation positioning of a microrobot, characterized in that, include: Modeling unit: used to construct the kinematics of the end effector according to the microrobot control system. The kinematics couples the rotation and linear motion of the end effector. The kinematics includes three translational degrees of freedom, one feed degree of freedom, and one rotational degree of freedom. The three translational degrees of freedom are perpendicular to each other. Acquisition unit: used to drive the end effector to perform rotational and feed movements in the kinematic chain using the microrobot control system, and to acquire motion parameters in the motion trajectory of the end effector tip; Control unit: Based on the motion parameters, it uses an adaptive feedforward controller to adjust the tip position error of the visual feedback of the microrobot control system until convergence, and obtains motion control coefficients for positioning control of the end effector.

10. A computer storage medium, characterized in that, The computer storage medium stores at least one instruction, which is loaded and executed by a processor to implement the microrobot end-effector rotational positioning self-calibration method as described in any one of claims 1 to 8.

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