A method and system for generating precision information

Abstract

The present invention provides a method and system for generating accuracy information. The present invention realizes the dynamic association between accuracy information and working conditions by receiving working condition information representing the current working condition and combining the first calibration point pose information of the end tool of the robot under test and the second calibration point pose information of the target position. Compared with the related art that only measures the accuracy of the robot under a single or ideal working condition, the present invention can fully reflect the accuracy performance of the surgical robot under different working conditions, thereby overcoming the problem in the related art that the specific working conditions are not considered and the measurement accuracy information may be one-sided, and effectively improving the comprehensiveness and pertinence of the accuracy evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of surgical robots, and in particular to a method and system for generating precision information. Background Art

[0002] A surgical robot is a high-precision intelligent device specially designed to assist surgical operations, combining mechanical, electronic and computer technologies. It can perform delicate operations in minimally invasive surgery, such as cutting, suturing, and positioning, under the control of surgeons through robotic arms and end tools. The surgical robot can be equipped with an imaging system, a multi-degree-of-freedom robotic arm, a navigation device, and a matching tracker and control system.

[0003] In the scenario of measuring the accuracy of surgical robots, there is a technical problem that the specific working conditions of the surgical robots are not taken into consideration, resulting in incomplete measurement accuracy information. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned technical deficiencies and provide a method and system for generating accuracy information so as to solve the technical problem that in the scenario of measuring the accuracy of the surgical robot, the specific working conditions of the surgical robot are not taken into consideration, resulting in incomplete measurement accuracy information.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for generating accuracy information, the method comprising:

[0007] receiving operating condition information indicating a current operating condition;

[0008] In the current working condition, a movement instruction is sent to the robot under test, so that the robot under test performs a preset movement operation; wherein the movement operation causes the end tool of the robot under test to move to at least one target position; wherein the end tool is configured with a first calibration point, and the first calibration point is used to calibrate the posture of the end tool; wherein the target position is configured with a second calibration point, and the second calibration point is used to calibrate the posture of the target position;

[0009] Upon receiving feedback information sent by the robot under test, at least obtaining the position and posture information of the current first calibration point; wherein the feedback information is information used to indicate that the robot under test confirms that it has reached the target position;

[0010] Accuracy information is generated based on the working condition information, the position and posture information of the first calibration point, and the pre-acquired position and posture information of the second calibration point.

[0011] In a second aspect, the present invention provides a method for analyzing accuracy information, the method comprising:

[0012] Acquire accuracy information under multiple working conditions; wherein the accuracy information is generated by one of the above-mentioned accuracy information generation methods;

[0013] At least one data analysis method is executed on the accuracy information under the multiple working conditions to obtain accuracy analysis result information.

[0014] In a third aspect, the present invention provides a method for determining operating condition information, the method comprising:

[0015] receiving data indicating accuracy requirements;

[0016] Acquire accuracy information or accuracy analysis result information; wherein, the accuracy information is generated by one of the above-mentioned accuracy information generation methods, and the accuracy analysis result information is generated by one of the above-mentioned accuracy information analysis methods;

[0017] Based on the data representing the accuracy requirement, the optimal operating condition information is determined from the accuracy information or from the accuracy analysis result information.

[0018] In a fourth aspect, the present invention provides a system for generating accuracy information, comprising:

[0019] A first marking device, which is used to be connected to the end tool of the robot under test and is provided with a first calibration point; wherein the first calibration point is used to calibrate the position and posture of the end tool;

[0020] A second marking device, which is used to provide a target position to which the end tool will move when measuring the accuracy of the robot being measured, and is provided with a second calibration point; wherein the second calibration point is used to calibrate the position and posture of the target position;

[0021] A measuring device, which is used to detect the position and posture information of the first calibration point and the second calibration point;

[0022] A processing device is used to receive working condition information representing a current working condition, and generate accuracy information based on the working condition information, the position and posture information of the first calibration point, and the position and posture information of the second calibration point acquired in advance. Beneficial Effects

[0023] The present invention realizes the dynamic association between accuracy information and working conditions by receiving working condition information representing the current working condition and combining the first calibration point posture information of the end tool of the robot under test and the second calibration point posture information of the target position. Compared with the related art that only measures the robot accuracy under a single or ideal working condition, the present invention can fully reflect the accuracy performance of the surgical robot under different working conditions, thereby overcoming the problem that the related art does not consider the specific working conditions and may lead to one-sided measurement accuracy information, and effectively improves the comprehensiveness and pertinence of the accuracy evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic diagram of a scenario example of a system for generating accuracy information provided by an embodiment of the present invention;

[0025] Figure 2 It is a flowchart of a method for generating accuracy information provided by an embodiment of the present invention;

[0026] Figure 3 is a schematic structural diagram of a surgical tool end detection tooling provided by an embodiment of the present invention;

[0027] Figure 4 is a schematic diagram of the structure of the surgical tool end detection tooling provided by an embodiment of the present invention; wherein, Figure 4 (a) shows the schematic diagram of the structure of the end detection tooling of the surgical tool when the end is flat. Figure 4 (b) shows a schematic diagram of the structure of the end detection tooling of the surgical tool when the end is cylindrical;

[0028] Figure 5 is a schematic structural diagram of a precision testing tool provided by an embodiment of the present invention;

[0029] Figure 6 is a schematic structural diagram of a precision testing tool provided by an embodiment of the present invention;

[0030] Figure 7 It is a flowchart of a method for generating accuracy information provided by an embodiment of the present invention;

[0031] Figure 8 It is a flowchart of a method for generating accuracy information provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0033] In related technologies, with the rapid development of modern medical technology, surgical robots are increasingly used in minimally invasive surgery. Surgical robots are usually composed of multiple robotic arms, end tools, navigation devices, and control systems. Their core function is to complete high-precision surgical actions such as cutting, suturing, or positioning implants under the operation of doctors. In order to ensure the safety and effectiveness of the operation, the accuracy measurement of surgical robots has become a key link. This measurement process usually requires evaluating the robot's posture accuracy, path stability, and repeatability under specific conditions to verify whether its performance meets the surgical requirements.

[0034] In related technologies, the accuracy of surgical robots is often measured using standardized testing methods, such as obtaining the accuracy information of the robot's end tool under a specific motion path through fixed marking points and measuring devices. However, most of these measurement methods assume that the robot operates under ideal working conditions and do not fully consider the specific working conditions that may exist during the operation, such as the field of view limitations of the navigation device, changes in the initial posture of the robotic arm, and the impact of the patient's anatomical characteristics on path planning. This measurement method that ignores the impact of working conditions may result in the lack of comprehensiveness and applicability of the generated accuracy information in the actual surgical environment.

[0035] Specifically, changes in the navigation device's field of view may increase the positioning error of the surgical robot, and the relevant methods cannot capture this effect. Differences in the initial posture of the surgical robot may cause inconsistencies in path planning, but the relevant methods do not evaluate this. The anatomical characteristics of patients during surgery and the diversity of surgical paths put forward different requirements for robot accuracy. The measurement methods in the relevant technologies cannot dynamically adjust the evaluation criteria according to specific working conditions.

[0036] The consequence of the above problems is that although the accuracy information generated by the relevant technology performs well in standard tests, it may deviate in actual surgical scenarios, thus affecting the surgical results and even bringing potential risks. Therefore, how to comprehensively consider the specific working conditions when measuring the accuracy of surgical robots has become a technical problem that needs to be solved urgently.

[0037] Based on this, this embodiment provides a method for generating accuracy information, which generates more comprehensive accuracy information by introducing multi-dimensional parameters related to the working conditions during the operation (the field of view of the navigation device, the initial posture of the robot arm, and the configured angle of the tracker). This method can not only reflect the actual performance of the surgical robot under different working conditions, but also provide accurate data support for surgical path planning and robot optimization, thereby improving the safety and reliability of the operation.

[0038] like Figure 1 The present application provides a scenario example of a system for generating precision information. A system for generating precision information may include: a processing device, a measuring device, and a mold, and may also include a server.

[0039] In the scenario of measuring the accuracy of a surgical robot, a processing device (e.g., DSP) may receive working condition information representing the current working condition (e.g., the field of view of the navigation device of the robot under test, the configured angle of the tracker of the robot under test, and the initial posture of the robot under test). The working condition information may be directly input by the user. It is understandable that after the user sets the above working condition, before the accuracy measurement, the above working condition information is input into the processing device.

[0040] Under the current working condition, the processing device sends a movement instruction to the robot under test, so that the robot under test performs a preset movement operation. It can be understood that the processing device and the control system of the robot under test can be connected by wire or wirelessly. The movement instruction can be generated by the processing device after the user performs a corresponding operation on the processing device, and the movement instruction is sent to the control system of the robot under test. It can be understood that the movement instruction can be a high-level control signal that does not contain specific path information or motor control details. The main function of the movement instruction is to send a "move to a certain target position" instruction to the control system of the robot under test, and the remaining control work is completed autonomously by the control system of the robot under test. Once the movement instruction is sent, it will trigger the control system of the robot under test to perform a complete closed-loop operation from posture acquisition, path generation to motion control, so that the end tool automatically moves to the target position.

[0041] When the processing device receives the feedback information sent by the robot under test, it can obtain the position and posture information of the current first calibration point. It can be understood that the first calibration point can be configured on the end tool. The position and posture information of the second calibration point can also be obtained, and the second calibration point is used to calibrate the position and posture of the target position. The target position is also the position to which the end tool needs to move. For example, one or more target positions (planning marking points) can be configured on the mold described in the figure, and the mold can be a mold of various structural forms.

[0042] The processing device can generate accuracy information based on the working condition information, the position and posture information of the first calibration point, and the pre-acquired position and posture information of the second calibration point.

[0043] Accordingly, the processing device may also send the accuracy information to the server. The server may analyze the accuracy information based on a preset analysis tool or algorithm to obtain accuracy analysis result information. Accordingly, the server may further analyze the accuracy information based on user needs to obtain optimal working condition information.

[0044] like Figure 2 As shown, this embodiment provides a method for generating accuracy information, which can be executed by a processing device and may include:

[0045] Step S12: receiving operating condition information indicating the current operating condition.

[0046] In this embodiment, the receiving action may be represented by the processing device receiving the working condition information input by the user. The working condition information may include multiple working condition parameters, for example, when measuring the robot under test, a working condition parameter representing the field of view of the navigation device, a working condition parameter representing the configured angle of the tracker of the robot under test, and a working condition parameter representing the initial posture of the robot under test.

[0047] Specifically, the robot under test may be a surgical robot with multiple degrees of freedom. The surgical robot may have a multi-degree-of-freedom robotic arm and a corresponding control system, and the end of the robotic arm has an end tool. The surgical robot may also have the navigation device and a matching tracker. The tracker may further include a patient tracker and a robot tracker. It is understandable that the patient tracker may be configured on or around the patient to indicate the position of the patient, especially the patient's affected part (e.g., knee), and the robot tracker may be configured on the robot's robotic arm to indicate the position of the end tool. The end tool may be a cylindrical end tool, a planar end tool, a conical end tool, and so on.

[0048] It can be understood that during the operation, the navigation device can continuously obtain the posture information of the patient tracker and the robot tracker, and transmit the posture information to the control system of the robot under test. The control system first uses a predetermined good transformation matrix (for example, it can be predetermined by a CT image) to convert the posture information of the patient tracker and the robot tracker into the patient's affected part, especially the posture information of the surgical point of the affected part, and the posture information of the end tool. Then, the control system can use the posture information of the surgical point of the affected part and the posture information of the end tool as input, call the preset path planning algorithm, generate a corresponding path, and then generate control signals for each motor of the multi-degree-of-freedom robotic arm based on the path, so that the robotic arm, especially the end tool, moves from the initial posture to the posture of the patient's affected part.

[0049] It is also understandable that there are often some errors in the above process, which makes it impossible for the end tool to accurately move to the position of the patient's affected part. This error may be caused by the error of the mechanical device of the robot arm. For example, the coupling error between degrees of freedom or the repeated positioning error may cause the end tool to fail to accurately reach the target position.

[0050] This error may be caused by the preset path planning algorithm. For example, the path planning algorithm may produce a suboptimal path in complex anatomical structures, resulting in the end tool failing to accurately reach the surgical site.

[0051] This error may also be caused by the incorrect position of the navigation device, which leads to problems with its field of view, and thus causes errors. For example, the navigation device's shooting angle or imaging area is limited, which may lead to inaccurate posture information of the patient tracker and robot tracker, affecting the path calculation.

[0052] This error may also be caused by an error in the predetermined transformation matrix, etc. It is understandable that the generation of the transformation matrix depends on preoperative imaging data. If the preoperative image resolution or registration accuracy is insufficient, it may cause deviations in the pose calculation.

[0053] It is understandable that the navigation device and the matching tracker of the robot under test may be based on optical measurement, electromagnetic measurement, ultrasonic measurement, etc.

[0054] In this embodiment, the processing device may be a digital signal processor (DSP), an industrial computer (IPC), a programmable logic controller (PLC), an MCU, an embedded control system (for example, an embedded control system based on ARM or FPGA), or an industrial-grade single-board computer.

[0055] Step S14: Under the current working condition, a movement instruction is sent to the robot under test, so that the robot under test performs a preset movement operation; wherein the movement operation causes the end tool of the robot under test to move to at least one target position; wherein the end tool is configured with a first calibration point, and the first calibration point is used to calibrate the posture of the end tool; wherein the target position is configured with a second calibration point, and the second calibration point is used to calibrate the posture of the target position.

[0056] In this embodiment, the processing device can send a movement instruction to the robot under test, specifically, send a movement instruction to the control system of the robot under test, so that the robot under test (for example, a multi-degree-of-freedom robotic arm) performs a preset movement operation. It can be understood that the processing device and the control system of the robot under test are connected in a wired (for example, Ethernet, serial port) or wireless (for example, Bluetooth, Wi-Fi) manner, and the control system of the robot under test (for example, DSP) can receive the instruction sent by the processing device.

[0057] In this embodiment, the sending action may be triggered by a user operation. For example, a user may input an instruction to drive the robot under test to move to a preset target position to the processing device, and accordingly, the processing device processes or directly sends the instruction to the control system of the robot under test. It is understandable that the preset target position may be one target position or multiple target positions.

[0058] In this embodiment, the movement instruction can be expressed as an instruction indicating the target position to trigger the robot under test to perform a preset movement operation. Specifically, the movement instruction can carry an identity identifier of the target position, and the identity identifier is used to indicate the specific target position. The target position can be a single position point (for example, a pre-set planned marking point), or a line segment composed of two or more position points, or an area in a plane composed of three or more position points. Specifically, the identity identifier can be the number of the pre-set planned marking point, for example, "PointA", "LineSegmentAB", or "PlaneP".

[0059] In this embodiment, after receiving the moving instruction, the robot under test can parse the identity of the target position carried in the moving instruction, and then start the process of posture acquisition, the process of path planning, and the process of control signal generation and execution based on the identity of the target position. For example, first, the control system of the robot under test controls the navigation device to collect the posture of the current patient tracker and the robot tracker. After the posture of the current patient tracker and the robot tracker is collected, the posture of the current patient tracker and the robot tracker is converted to the posture of the target position and the posture of the end tool based on the preset conversion matrix (the conversion matrix of the relative posture relationship between the patient tracker and the target position and the conversion matrix of the relative posture relationship between the robot tracker and the end tool). Then, the control system of the robot under test performs path planning with the posture of the target position and the posture of the end tool as the input of the preset path planning algorithm to obtain the target path. Then, a control signal is generated based on the target path, and the control signal can control the mechanical arm of the robot under test to perform the preset moving operation. Finally, the control system sends the control signal to the motor module (for example, a drive chip and a motor body) of the robot arm of the robot under test, so that the robot under test performs a preset movement operation.

[0060] In this embodiment, the movement instruction may include an identification mark of the target position and a trigger signal. The trigger signal can trigger the robot under test to execute the above-mentioned posture acquisition process, path planning process and control signal generation and execution process.

[0061] In this embodiment, the movement instruction may exist in the form of a data packet, which may be a standardized format such as JSON, XML, etc.

[0062] It is understandable that in this embodiment, the movement instruction can be a high-level control signal that does not contain specific path information or motor control details. The main function of the movement instruction is to send a "move to a certain target position" instruction to the control system of the robot under test, and the remaining control work is completed autonomously by the control system of the robot under test. Once the movement instruction is sent, it will trigger the control system of the robot under test to perform a complete closed-loop operation from posture acquisition, path generation to motion control, so that the end tool automatically moves to the target position.

[0063] In this embodiment, the target position may be one or more than one. For example, multiple target positions may be integrated on one mold, and the mold may have multiple planning marking points, and one planning marking point corresponds to one target position.

[0064] In this embodiment, the position and posture of the first calibration point and the second calibration point can be directly collected by a measuring device, and the measuring device can be pre-calibrated and has high accuracy. The measuring device can detect the position and posture of the first calibration point and the second calibration point by optical measurement. It can also be based on electromagnetic measurement or ultrasonic measurement to detect the position and posture of the first calibration point and the second calibration point.

[0065] In this embodiment, the first calibration point can be further set on a first marking device, and the first marking device can be connected to the end tool. The first marking device can be a surgical tool end detection tool.

[0066] like Figure 3 and Figure 4 As shown, in a possible and specific embodiment, the surgical tool end detection tooling can adopt the following structural form:

[0067] One end of the surgical tool end detection tool is designed with a connection interface for connecting with the end of the surgical instrument, and the other end is designed with a traceable marker. The connection interface is designed in the form of a cylinder and a plane groove.

[0068] Specifically, for point and line accuracy, the end tool of the robot arm is cylindrical, and the cylindrical end tool is installed in the cylindrical hole of the detection tool, so as to measure the position of the cylindrical end surgical tool based on the tracking marker. For surface accuracy, the end tool of the robot arm is planar, and the planar end tool is installed in the planar groove of the detection tool, so as to measure the position of the planar end surgical tool based on the tracking marker.

[0069] In this embodiment, the second calibration point can be directly set at the target position to calibrate the position and posture of the target position. The second calibration point can also be set near the target position at a certain distance. For example, in the case where there are multiple target positions, the multiple target positions can be integrated on a mold, and the mold can have multiple planning marking points, and one planning marking point corresponds to one target position. Accordingly, the mold can provide an area for setting the second calibration point. The mold can be of various shapes. The target position can also be of various shapes, such as being used to simulate the bony features of the spine, acetabulum and knee joint. The mold can also be configured with an installation interface for a patient tracker.

[0070] It is understandable that if the second calibration point is directly set at the target position, the processing device does not need to pre-configure the conversion matrix between the target position and the second calibration point (because in this case, the position and posture of the second calibration point is the position of the target position, no conversion is required, and the second calibration point and the measuring device are compatible). However, for the control system of the robot under test, it is still necessary to pre-configure the conversion matrix between the target position and the patient tracker (because in many cases, the navigation device of the robot under test may not be able to directly detect the position and posture of the second calibration point, for example, the position and posture of the second calibration point is collected by the measuring device in an electromagnetic way, and the navigation device of the robot under test is based on an optical measurement method).

[0071] It is understandable that if the second calibration point is set near the target position at a certain distance. It is necessary to pre-configure the transformation matrix between the second calibration point and the target position in the processing device. It is also understandable that the second calibration point is set near the target position, maintaining a certain distance. The advantage of doing so is that physical conflict with tools or other components at the target position can be avoided, especially when the target position itself has a complex shape. In the case where there are multiple target positions, these multiple target positions can be integrated into the same mold. The mold is used to provide a benchmark for all target positions and supports multiple measurements and comparisons. Multiple planning marking points can be set on the mold, and each planning marking point corresponds to a target position. These planning marking points provide accurate posture data for each target position through calibration.

[0072] In this embodiment, the target position can be designed into a variety of shapes to accommodate surgical simulations of different bones. For example, it can be a shape used to simulate the spine, and can be a curved structure with multiple different protrusions to simulate the actual spinal anatomical morphology. It can be used to simulate the complex groove structure of the acetabulum to facilitate the accuracy test of hip replacement surgery. It can also be designed to conform to the shape of the knee joint anatomy to simulate the needs of knee joint surgery.

[0073] like Figure 5 and Figure 6 As shown, in a possible and specific implementation scheme, the target position can be provided by presetting a precision test fixture:

[0074] For example, a precision test fixture may be provided. The precision test fixture includes a planning simulation module and a reference module. The planning simulation module is responsible for simulating bone features and planning, and has fixed bone features, including the simulation of bone features of the spine, acetabulum, and knee joint.

[0075] The planning simulation module also has fixed standard points for planning For points, lines and surfaces, there are:

[0076] Point planning: from standard point Choose any one as ;

[0077] Line planning: from standard points Choose any two, one of which is ; The direction of the line connecting the two standard points is ;

[0078] Surface planning: From the standard point of view Choose any three, one of which is , the normal vector of the plane formed by the three standard points is .

[0079] The reference module is provided with a tracker installation interface for installing the patient tracker of the surgical robot system under test, and a trackable marker to form a reference tracker for confirming the spatial position of the planned marker point. The precision test fixture can be positioned by 3-ball-V-shaped surface, wherein a V-shaped surface is designed in the reference module, and the corresponding position on the simulation planning module is set as a steel ball, and positioning is achieved through the contact between the 3 balls and the V-shaped surface. At the same time, a magnet is designed in the middle of the planning simulation module and the reference module to achieve fixation between the two.

[0080] Furthermore, before conducting the test, the user may install the patient tracker in the tracker installation interface, and then use CT, MRI or intraoperative X-ray image acquisition device to collect image data of the accuracy test tooling.

[0081] Next, the pose of the trackable marker, the pose of the patient tracker, and the pose of each planning marker point in the image data can be determined respectively.

[0082] After determining the positions of the above-mentioned trackable markers, the position of the patient tracker, and the position of each planning marker point, the corresponding transformation matrix can be generated. Specifically, the transformation matrix between the trackable marker and each planning marker point, and the transformation matrix between the patient tracker and each planning marker point.

[0083] It is to be understood that the transformation matrix is ​​configured in the processing device and the control system of the robot under test before the formal accuracy test is performed.

[0084] In a possible and specific implementation, the user can select a target mark point on a model (which can be a three-dimensional model) of the precision test fixture through an interactive interface (e.g., a display screen) provided by the processing device, and can select one or more target mark points to achieve the specified target position. The identity of the target position (specific planning mark point) will be carried in the movement instruction and sent to the control system of the robot under test.

[0085] After receiving the movement instruction, the control system of the robot under test performs parsing to extract the identity of the target position, thereby determining the specific planning mark point. After determining the specific planning mark point, the control system of the robot under test can obtain the position and posture of the current patient tracker through the navigation device of the robot under test, and then call the preset conversion matrix to convert the position and posture of the current patient tracker into the position and posture of the specific planning mark point. Correspondingly, the navigation device of the robot under test can obtain the position and posture of the robot tracker, and convert the position and posture of the robot tracker into the position and posture of the end tool through the corresponding conversion matrix (the conversion matrix between the robot tracker position and the end tool, which is also pre-configured in the control system of the robot under test). After determining the position and posture of the specific planning mark point (the position and posture of the target position) and the position and posture of the end tool, the control system of the robot under test can call the preset path planning algorithm to generate the corresponding target path, and finally generate the corresponding control signal based on the target path, and the control signal can control each motor of the robot under test to perform the corresponding movement.

[0086] Step S16: upon receiving feedback information sent by the robot under test, obtaining at least the position and posture information of the current first calibration point; wherein the feedback information is information indicating that the robot under test has confirmed reaching the target position.

[0087] In this embodiment, when the robot under test completes the moving operation, the processing device receives feedback information from the control system of the robot under test. The feedback information is generated by the control system of the robot under test, and is used to confirm that the robot has reached the predetermined target position and indicates that it is currently ready to accept the next operation. The feedback information confirms that the robot under test has moved as planned and the end tool has reached the indicated target position, triggering the subsequent precision measurement process.

[0088] In this embodiment, the feedback information can be represented as a data packet generated by the control system of the robot under test and sent to the processing device provided in this embodiment. The feedback information is used to confirm that the robot under test has executed and completed the predetermined movement operation, especially to confirm that the end tool has moved to the target position. The feedback information can trigger the processing device to perform the next step of the precision measurement operation and provide a synchronization signal for the precision measurement.

[0089] In this embodiment, the feedback information can be transmitted in a format such as JSON or XML. The feedback information can have multiple fields, such as a "confirmation in place field", which can be used to indicate the type of the feedback information, making it clear that this is a feedback signal confirming that the robot end tool has reached the preset target position. The "target position identity" field can be used to indicate the position where the current end tool has reached, so as to match it with the preset target position and ensure the consistency of the operation. "Path status information", this field can be used to indicate the success status of path planning.

[0090] In this embodiment, after the processing device receives the feedback information sent by the control system of the robot under test, it can obtain the current posture information of the first calibration point (end tool), and accordingly, it can also simultaneously obtain the posture information of the second calibration point (target position).

[0091] It can be understood that in this embodiment, because the posture of the target position often does not change in the scenario of measuring the accuracy of the robot under test, the posture of the target position can be detected before this step S16, when the end tool of the robot under test has not yet moved to the target position.

[0092] In this embodiment, the acquisition action can be represented by the processing device issuing corresponding instructions to a preset measuring device, so that the measuring device detects the posture information of the current first calibration point, or simultaneously detects the posture information of the current first calibration point and the posture information of the second calibration point, and then receives the detection result returned by the measuring device (this is done to ensure that there is no offset or error caused by external force in the target position).

[0093] In this embodiment, the measuring device is another device that can measure posture, which is different from the navigation device of the robot under test. And the measuring device can be calibrated in advance. In other words, the navigation device is a part of the robot under test and is the object to be measured. Although the navigation device of the robot under test is used for positioning and path planning, its measurement accuracy may be affected by some factors (such as changes in ambient light, equipment aging, etc.), while the measuring device provided in this embodiment has been specially calibrated for accuracy and can provide more stable posture data. Therefore, the measuring device is the main body that performs the accuracy measurement of the robot under test in this embodiment.

[0094] In this embodiment, the measuring device may be a high-precision optical measuring device. Accordingly, in this case, the first calibration point and the second calibration point may be traceable optical markers, such as reflective marker balls or active luminous markers.

[0095] In this embodiment, the measuring device may be a laser scanning device, and accordingly, in this case, the first calibration point and the second calibration point may be marking points with obvious geometric features, such as laser reflective targets.

[0096] In this embodiment, the measuring device may be an electromagnetic tracking device. Accordingly, in this case, the first calibration point and the second calibration point may be electromagnetic sensors or coil-type electromagnetic markers.

[0097] Step S18: Generate accuracy information based on the working condition information, the position and posture information of the first calibration point, and the pre-acquired position and posture information of the second calibration point.

[0098] In this embodiment, the pre-acquired position and posture information of the second calibration point may be acquired by the processing device passively sending instructions to the measuring device in step S16 when the processing device receives the feedback information sent by the measured robot. Accordingly, the pre-acquired position and posture information of the second calibration point may also be acquired autonomously by a preset instruction before step S18 or even before step S16, without being triggered by the feedback information sent by the measured robot (it can be understood that the target position often does not change in this scenario).

[0099] In this embodiment, the working condition information is obtained in the previous steps, such as the field of view of the navigation device, the initial posture of the robot arm, the tracker angle, etc. These working condition information will affect the accuracy of the end tool of the robot under test, and therefore can be used as an important input for accuracy generation.

[0100] In this embodiment, the first calibration point may be a marking point located on the end tool of the robot under test. In step S16, when the robot under test completes the moving operation, the actual position information of the first calibration point may be detected and obtained by the measuring device. This information may represent the real position and direction of the end tool in space.

[0101] In this embodiment, the accuracy information may include an index and an index value. The working condition information may be used as an index of the accuracy information. The index value may be difference information, which may be further generated by the pose information of the first calibration point and the pose information of the second calibration point acquired in advance.

[0102] In this embodiment, the action of generating the accuracy information can be represented as first calculating the difference information, which can be used to quantify the deviation between the position of the end tool of the robot under test and the target position. Then, the difference information is associated with the working condition information to facilitate subsequent analysis. For example, the working condition information can be used as an index and the difference information can be used as an index value.

[0103] In this embodiment, the difference information may be a position difference. For example, the processing device may compare the position information of the first calibration point (actual position of the end tool) with the position information of the second calibration point (target position) to calculate the spatial position difference between the two. The position difference may be expressed as a three-dimensional coordinate offset in space.

[0104] In this embodiment, the difference information may also be a direction difference. For example, the direction data (e.g., rotation matrix or quaternion) of the first calibration point and the second calibration point are compared to calculate the angle difference between the directions. The direction difference can be described by Euler angle or axis angle representation, indicating the deviation in direction between the end tool and the target position.

[0105] In this embodiment, the difference information can be represented in the form of a table or a key-value pair, and each working condition corresponds to a set of difference information (ie, a pair of index and index value), which is convenient for subsequent data analysis and accuracy evaluation.

[0106] In this embodiment, by receiving the working condition information representing the current working condition, and combining the first calibration point pose information of the end tool of the robot under test and the second calibration point pose information of the target position, the dynamic association between the accuracy information and the working condition is achieved. Compared with the related art that only measures the robot accuracy under a single or ideal working condition, this embodiment can fully reflect the accuracy performance of the surgical robot under different working conditions, thereby overcoming the problem that the related art does not consider the specific working conditions and may lead to one-sided measurement accuracy information, effectively improving the comprehensiveness and pertinence of the accuracy evaluation, and providing a basis for the performance optimization and safety assurance of surgical robots under complex working conditions.

[0107] like Figure 7 As shown, in some embodiments, the step of obtaining at least the pose information of the current first calibration point upon receiving the feedback information sent by the robot under test includes:

[0108] Step S162: Upon receiving feedback information sent by the robot under test, a first measurement instruction is sent to a preset measuring device; wherein the first measurement instruction is an instruction for triggering the measuring device to detect the current first calibration point and second calibration point posture information.

[0109] In this embodiment, when the processing device receives the feedback information sent by the control system of the robot under test, it can send the first measurement instruction to the preset measurement device.

[0110] It can be understood that when the processing device receives feedback information sent by the control system of the robot under test, it indicates that the end tool has reached the predetermined target position and is ready to proceed to the next step of posture measurement.

[0111] In this embodiment, the first measurement instruction can be transmitted by wire (Ethernet, serial port) or wireless (Wi-Fi, Bluetooth) to ensure that the measuring device can receive and understand the instruction. The logic of the processing device is to monitor the feedback of the control system of the robot under test, and once the conditions are met (that is, the end tool reaches the target position), the measurement instruction is immediately sent to ensure that the detection of the measuring device and the posture state are in the same timing to avoid unnecessary delays.

[0112] In this embodiment, after the measuring device receives the first measurement instruction, the measuring device starts to detect the position and posture information of the current first calibration point and the second calibration point according to the instruction. The measuring device (for example, an optical measuring device, a laser scanning device, or an electromagnetic tracking device) will collect the three-dimensional spatial position information and posture information of the two calibration points to form the posture information. In the case of optical measurement, the measuring device can use optical markers to determine the position and posture of the calibration points. In the case of laser scanning or electromagnetic tracking, the measuring device can emit corresponding signals and determine the position and posture based on the reflected or induced signals.

[0113] Step S164: receiving the position and posture information of the current first calibration point and the second calibration point returned by the measuring device.

[0114] In this embodiment, after the measuring device detects the positions and postures of the two calibration points, the data can be packaged into a measurement result and returned to the processing device through a communication link. The measurement result can include the current position and posture information of the first calibration point and the second calibration point.

[0115] In this embodiment, it is understood that when there are multiple target positions, in other words, when the end tool of the robot under test needs to move to multiple positions (that is, multiple target positions), the robot under test can send the feedback information to the processing device each time it moves to a target position. After the processing device receives the feedback information, it can execute the above process to obtain the pose information of the first calibration point (end tool) corresponding to the current target position. It is understood that when there are multiple target positions, the pose information of multiple first calibration points (end tool) can be obtained.

[0116] In other words, when the end tool of the robot under test needs to move to multiple target positions, the entire precision measurement process will be repeatedly executed, and a complete posture measurement and feedback process will be performed every time a new target position is reached. The robot under test needs to move to multiple planned target positions according to the measurement requirements. These target positions can be configured according to different surgical points, simulated bone positions, or other specified areas. The robot under test moves from one target position to the next one step by step according to the instructions of its own control system. Whenever the end tool reaches a target position, the control system of the robot under test sends a feedback message to the processing device, indicating that the target position has been reached and the end tool has stabilized. After receiving the feedback message, the processing device immediately triggers the aforementioned measurement process. Each target position represents a different measurement point. Combined with the precision measurement of multiple target positions, the precision performance of the robot under test in the entire working range can be comprehensively evaluated. By measuring at multiple target positions, the processing device can establish a precision analysis model containing multiple sets of data to determine the precision consistency of the robot under test at different positions. In addition, multiple measurements help to understand the error distribution characteristics of the robot under test. For example, by analyzing the error differences at different positions, possible deviations of the robot arm in certain directions or angles can be identified, and weak points or areas for improvement under specific working conditions of the robot can be found.

[0117] In this embodiment, after receiving the feedback information sent by the robot under test, the processing device can actively send a first measurement instruction to the preset measuring device to trigger the measuring device to detect the posture information of the current first calibration point (end tool) and the second calibration point (target position), and obtain the detection results in time. This method can ensure the timing of precision measurement and the validity of data, that is, when the robot under test reaches the target position, the measuring device immediately performs posture detection, thereby reducing errors caused by waiting or multiple measurements. In addition, by simultaneously acquiring the posture information of the first calibration point and the second calibration point, the difference between the end tool and the target position can be evaluated more directly and accurately under the same working conditions, thereby improving the overall efficiency and reliability of precision measurement. This method avoids the accumulation of measurement errors that may be caused by the different acquisition of posture information at the same time, helps to improve the consistency and accuracy of precision evaluation, and thus provides more reliable data support for robot control and optimization.

[0118] like Figure 8 As shown, in some embodiments, between the step of sending a movement instruction to the robot under test so that the robot under test performs a preset movement operation and the step of obtaining at least the position and posture information of the current first calibration point, the method further includes:

[0119] Step S151: sending a second measurement instruction to a preset measurement device; wherein the second measurement instruction is an instruction for triggering the measurement device to detect the position and posture information of a second calibration point.

[0120] Step S152: Obtain the position and posture information of the second calibration point.

[0121] This implementation adds the step of sending a second measurement instruction to the measuring device and obtaining the position and posture information of the second calibration point before executing the movement operation of the robot under test. This implementation can obtain the position and posture information of the second calibration point in advance and provide accurate reference data for subsequent operations. On the one hand, this can reduce unnecessary waiting caused by missing or delayed information in the subsequent calibration process and improve the operating efficiency of the system; on the other hand, it can improve measurement accuracy and operational reliability, avoid error accumulation or operational deviation caused by inaccurate dynamic calibration data, and effectively optimize the stability and real-time performance of the calibration process. It is suitable for robot calibration scenarios with high accuracy requirements.

[0122] In some implementations, the step of generating accuracy information based on the working condition information, the position and posture information of the first calibration point, and the pre-acquired position and posture information of the second calibration point includes:

[0123] Step S182: Based on the posture information of the first calibration point and the posture information of the second calibration point acquired in advance, generate at least one of the first difference information, the second difference information and the third difference information; wherein, the first difference information is used to characterize the spatial position deviation between the target position and the end tool when the end tool moves to a certain target position; the second difference information is used to characterize the deviation between the motion path of the end tool and the target path when the end tool moves to at least two target positions and the motion path is a straight line, and the target path is determined by the at least two target positions; the third difference information is used to characterize the deviation between the actual plane formed by the movement of the end tool and the target plane when the end tool moves to at least three target positions, and the target plane is determined by the at least three target positions.

[0124] In this embodiment, the position and posture information of the first calibration point can be first converted into the actual position and posture information of the end tool through a preset transformation matrix (for example, the transformation matrix can be obtained by performing image registration in advance).

[0125] In some cases, the second calibration point may also be set near the target position at a certain distance. For example, when there are multiple target positions, the multiple target positions may be integrated on a mold, and the mold may have multiple planning marking points, one planning marking point corresponding to one target position. Therefore, in this case, it is necessary to convert the pose information of the second calibration point into the actual pose information of the target position through a preset transformation matrix (it is understandable that the transformation matrix between the second calibration point and each target position can also be obtained in advance through image registration).

[0126] Therefore, the pose information of the second calibration point can then be converted into actual pose information of the target position through a transformation matrix.

[0127] In some other cases, the second calibration point may coincide with the target position. In other words, the second calibration point may be directly set at the target position. In this case, there is no need to perform conversion, and the pose information of the second calibration point is the pose information of the target position.

[0128] Finally, the first difference information, the second difference information and the third difference information mentioned above can be generated respectively based on the actual posture information of the end tool and the actual posture information of the target position.

[0129] Specifically, for the first difference information, the Euclidean distance between the actual position of the end tool and the target position may be calculated based on the actual position information of the end tool and the actual position information of the target position.

[0130] For the second difference information, the Euclidean distance between the actual position of the end tool and the target position, that is, the distance deviation (multiple actual positions of the end tool and the corresponding multiple target positions), can also be calculated. The angle deviation can also be further calculated. That is, based on the actual posture information (multiple) of the end tool, its rotation matrix direction component is extracted. And based on the actual posture information (multiple) of the target position, the target path direction is extracted to calculate the angle between the movement direction of the end tool and the target path direction. The distance deviation in this case describes the deviation between the position of a point on the actual movement path of the end tool and the point on the target path. The angle deviation in this case describes the deviation between the direction of the movement path of the end tool and the direction of the target path, reflecting the path accuracy.

[0131] For the third difference information, the Euclidean distance between the actual position of the end tool and the target position, that is, the distance deviation (multiple actual positions of the end tool and the corresponding multiple target positions), can also be calculated. The angle deviation can also be further calculated. That is, based on the actual posture information (multiple) of the end tool, the normal vector of the actual plane formed by the movement of the end tool is extracted, and based on the actual posture information (multiple) of the target position, the normal vector of the target plane is extracted. Thereby, the angle between the actual plane of the end tool and the target plane is calculated. The distance deviation in this case describes the position deviation between the point on the actual movement plane of the end tool and the point on the target plane. The angle deviation in this case describes the deviation between the normal vector directions of the actual movement plane of the end tool and the target plane, reflecting the plane accuracy.

[0132] In a possible and specific implementation, the first difference information may be calculated by the following formula:

[0133]

[0134] In the formula, The identity of the target location can be expressed as In the case of , the target position is different from the spatial position deviation of the corresponding end tool. The spatial position deviation can be the Euclidean distance. Represents the actual pose matrix of the end tool, represents the actual pose matrix of the target position, and It can be represented as the identity of the target position (planning point) in the case of point planning. It is understandable that when the target positions are distributed on a mold or workpiece, for example, in orthopedic surgery simulation, the target positions can be bone feature points, such as anatomical landmarks of the spine, acetabulum or knee joint. Therefore, the mold may contain multiple target positions (the end tool needs to move multiple times), which need to be distinguished by the identity.

[0135] It is also understandable that in some specific implementation schemes, the target position can be provided by presetting a precision test fixture:

[0136] For example, a precision test fixture may be provided. The precision test fixture includes a planning simulation module and a reference module. The planning simulation module is responsible for simulating bone features and planning, and has fixed bone features, including the simulation of bone features of the spine, acetabulum, and knee joint.

[0137] The planning simulation module also has fixed standard points for planning .

[0138] For points, lines and surfaces, there are:

[0139] Point planning: from standard point Choose any one as ;

[0140] Line planning: from standard points Choose any two, one of which is , the direction of the line connecting the two standard points is ;

[0141] Surface planning: From the standard point of view Choose any three, one of which is , the normal vector of the plane formed by the three standard points is .

[0142] In a possible and specific implementation, the second difference information may be calculated by the following formula:

[0143]

[0144] In the formula, Represents the actual pose matrix of the end tool, Represented as the actual pose matrix of the target position, It can be expressed as the identity of the target location (planning point) in the case of online planning. It is represented as the actual pose matrix of the end tool under the reference of the precision test fixture. represents the normal vector of the surface planning, Indicates the angle between the calculated attitude and the normal vector.

[0145] In a possible and specific implementation, the third difference information may be calculated by the following formula:

[0146]

[0147] In the formula, Represents the actual pose matrix of the end tool, Represented as the actual pose matrix of the target position, It can be expressed as the identity of the target location (planning point) in the case of surface planning. It is represented as the actual pose matrix of the end tool under the reference of the precision test fixture. represents the normal vector of the surface line planning, Indicates the angle between the calculated attitude and the normal vector.

[0148] Step S184: Generate accuracy information by taking the operating condition information as an index and at least one of the first difference information, the second difference information and the third difference information as an indexed value.

[0149] In this embodiment, this step uses the acquired working condition information (such as the field of view of the navigation device, the initial posture of the robot arm, the tracker angle, etc.) as an index, and uses the generated difference information (including the first difference information, the second difference information and the third difference information) as the indexed value to generate comprehensive accuracy information, reflecting the accuracy performance of the end tool of the robot under test under different working conditions.

[0150] It is understandable that the working condition information is collected and recorded in the previous steps and is an important condition that affects the accuracy of the end tool of the robot being tested. For example, the field of view of the navigation device (affects the positioning accuracy of the end tool), the initial posture of the robot arm (affects the execution error of the path), and the tracker angle (affects the detection accuracy of the built-in navigation device). Therefore, different working conditions may lead to changes in the accuracy of the end tool. The working condition information serves as an index to help associate different accuracy performances with their corresponding environmental conditions.

[0151] In this embodiment, the working condition information recorded in the previous step, such as the field of view of the navigation device, the initial posture of the robot arm, and the tracker angle, can be first called as the core data of the current index. Then, the working condition information is structured, for example, a unique identifier is configured for different field of view, initial posture, and angle. Then, the generated difference information is matched with the working condition information to ensure that each set of difference information can be uniquely identified by its corresponding working condition information. Finally, an index relationship is established to generate the accuracy information.

[0152] In this embodiment, the accuracy information may be stored in the form of a table or other structured data.

[0153] This embodiment generates multi-dimensional difference information (including single-point position deviation, straight line path deviation and plane posture deviation) that characterizes the end tool motion accuracy by combining the first calibration point and the pre-acquired second calibration point's position and posture information, and further constructs the difference information into accuracy information with the working condition information as an index. In this way, not only can the positioning accuracy, path accuracy and spatial attitude accuracy of the end tool in different motion scenarios be fully reflected, but also the accuracy information can be structured for management, which is convenient for subsequent accuracy analysis and performance optimization. This method provides an efficient and accurate technical means for robot calibration and accuracy evaluation under complex working conditions, which helps to improve the reliability and operational stability of the system.

[0154] This embodiment provides a method for analyzing accuracy information. The execution subject of the method may be a server. The method includes:

[0155] Step S22: Acquire accuracy information under multiple working conditions; wherein the accuracy information is generated by a method for generating accuracy information provided in the above-mentioned embodiment.

[0156] In this embodiment, the acquisition action may be represented as the server acquiring the accuracy information (real-time data stream) under multiple working conditions from the processing device in real time. For example, the server may maintain a real-time connection with the processing device and periodically capture the accuracy information through an interface.

[0157] In this embodiment, the acquisition action may also be represented as the server extracting accuracy information (stored historical information) under multiple historical working conditions from a database.

[0158] In this embodiment, the action obtained by the server can be triggered by the user's operation. The user creates a new analysis task through a graphical operation interface (GUI) or a command line tool. The preset task creation interface allows the user to enter or select the following key information: data range, analysis target, and output result format.

[0159] Specifically, the data range can further include working conditions: the specific range of parameter values, the time range, and the data source type (real-time data or historical data). The analysis goal can be to analyze the accuracy change trend under different working conditions, or to find out the key reasons for the reduction of accuracy under certain specific working conditions. The output result format can be the output form of the accuracy analysis results selected by the user, such as a report, a chart, or a raw data file.

[0160] Step S22: Execute at least one data analysis method on the accuracy information under the multiple working conditions to obtain accuracy analysis result information.

[0161] In this embodiment, the data analysis method may be a statistical analysis method. Accordingly, the accuracy analysis result information obtained may be the average deviation, maximum deviation and minimum deviation under a certain working condition and the degree of dispersion (standard deviation) of the accuracy data.

[0162] In this embodiment, the data analysis method may be a trend analysis method. For example, the trend analysis of the change of time or working condition may be performed on the accuracy information. It may be a time series analysis, or a trend model may be established based on methods such as polynomial regression and exponential fitting. Accordingly, the accuracy analysis result information obtained may be the trend of the accuracy information changing with the working condition parameters, the changing law of the influence of key parameters on the accuracy, and the accuracy mutation information corresponding to the working condition switching point, etc.

[0163] In this embodiment, the data analysis method may be a cluster analysis method.

[0164] In this embodiment, the data analysis method may be a correlation analysis method.

[0165] In this embodiment, the data analysis method may also be an anomaly detection method.

[0166] In this embodiment, the data analysis method may also be a regression analysis method.

[0167] The accuracy information analysis method provided in this embodiment can mine the key factors affecting accuracy from a large amount of complex working condition data and generate accuracy analysis result information by obtaining accuracy information under multiple working conditions and performing data analysis. On the one hand, by comparing and analyzing the accuracy performance under different working conditions, this method can help quickly identify the positive or negative impact of specific working conditions on the accuracy of the end tool, and provide a scientific basis for subsequent equipment optimization and parameter adjustment; on the other hand, the flexibility of the data analysis method (statistical analysis, cluster analysis or regression analysis) can adapt to a variety of application scenarios, generate multi-dimensional accuracy analysis results, and enhance data insight. In addition, this embodiment can be executed by a server, has high computing efficiency and processing power, is suitable for real-time or offline analysis of large-scale data, and provides important support for robot accuracy optimization of complex tasks.

[0168] In some embodiments, the working conditions include at least the field of view of the navigation device of the robot under test, the configured angle of the tracker of the robot under test, and the initial posture of the robot under test;

[0169] The step of executing at least one data analysis method on the accuracy information under the multiple working conditions to obtain accuracy analysis result information includes:

[0170] Step S222: Based on preset filtering conditions, traverse the accuracy information under the multiple working conditions to determine the accuracy information under the target working condition; wherein the filtering conditions are conditions for filtering the boundary field of the navigation device, the boundary configuration angle of the tracker, and the boundary initial posture of the robot being measured.

[0171] In this embodiment, the screening condition may specifically be a navigation device field of view screening condition. The field of view angle of the navigation device may be limited to a certain range, such as 30°-120°. The operating condition information of the navigation device at the minimum field of view angle (such as 30°) or the maximum field of view angle (such as 120°) may be screened to analyze the influence of the field of view boundary of the navigation device on the accuracy. The operating condition information of the navigation device having a partially blocked area (such as a blocked area greater than 20%) in the field of view may also be screened.

[0172] In this embodiment, the screening condition may specifically be a screening condition for the configuration angle of the tracker. The configuration angle range of the tracker may be limited, for example, 10°-90°. The operating condition information of the tracker at an extreme configuration angle (such as 10° or 90°) may be screened to analyze the impact of the configuration angle boundary on the accuracy. The operating condition information may also be screened according to the orientation of the tracker (such as horizontal, vertical or tilt angle).

[0173] In this embodiment, the screening condition may specifically be a screening condition for the initial posture of the robot being tested. The working condition information in which the initial position of the robot is in certain specific areas (x, y, z coordinate range) may be screened. The working condition information in which the initial posture of the robot is in an unbalanced position (inclination angle greater than 15°) may be screened.

[0174] In this embodiment, the screening condition may be a combination screening condition. It may be a specific condition that satisfies a combination of multiple parameters such as the field of view of the navigation device, the tracker configuration angle, and the initial posture. For example, when the field of view of the navigation device is 90°, the tracker configuration angle is 45°, and the robot's initial posture tilt angle is 20°.

[0175] In this embodiment, the screening condition may specifically be an extreme working condition, and the working condition information when the navigation device, the tracker and the robot posture are all at the boundary value (such as the combination of the minimum field of view of the navigation device, the maximum configuration angle of the tracker, and the maximum tilt angle of the robot initial posture) may be screened.

[0176] Step S224: performing standardization processing on the accuracy information under the target working condition to obtain accuracy analysis result information.

[0177] In this embodiment, the standardization process may be a minimum-maximum normalization process, and the numerical value of the precision information may be linearly mapped to a fixed range to eliminate dimension and range differences.

[0178] In this implementation, the standardization process may specifically be a mean normalization process.

[0179] This embodiment introduces screening conditions and standardized processing to accurately locate the accuracy performance under key working conditions in multiple working conditions, thereby improving the pertinence and consistency of data analysis. First, based on the preset screening conditions, step S222 can effectively screen out the accuracy information of specific boundary conditions (such as the boundary field of view of the navigation device, the boundary configuration angle of the tracker, and the boundary initial posture of the robot under test), so as to focus on the accuracy analysis under complex or extreme conditions, which is convenient for discovering potential problems or optimization directions. Secondly, step S224 can eliminate the dimensional and range differences of different working condition parameters by standardizing the accuracy information under the screened target working conditions, making the comparison of accuracy data more intuitive and unified. This method not only improves the scientificity and credibility of the accuracy analysis results, but also provides more valuable reference accuracy analysis results for robot performance evaluation under complex working conditions, thereby supporting more refined working condition optimization and equipment improvement.

[0180] In some embodiments, the step of performing at least one data analysis method on the accuracy information under the multiple working conditions to obtain accuracy analysis result information includes:

[0181] With respect to the accuracy information under the multiple working conditions, the overall distribution and confidence boundary of the accuracy information are determined.

[0182] In this embodiment, the server can determine the overall distribution of the accuracy information by calculating basic statistics such as the mean, standard deviation, variance, maximum and minimum values ​​of the data. Accordingly, the confidence boundary can be calculated based on the mean and standard deviation of the data.

[0183] Alternatively, with respect to the accuracy information under the multiple operating conditions, principal component analysis is performed based on preset operating condition parameters to determine the priority ranking of the operating condition parameters, wherein the operating condition parameters are specific parameters representing the operating conditions.

[0184] In this embodiment, the working condition parameters may be the field of view angle of the navigation device, the configuration angle of the tracker, and the initial posture of the robot under test.

[0185] In this embodiment, the covariance matrix of the operating parameter data matrix can be calculated, and the covariance matrix can represent the correlation between different operating parameters. Then, the covariance matrix is ​​subjected to eigenvalue decomposition to obtain each eigenvalue and its corresponding eigenvector. The eigenvalue can represent the contribution of the corresponding principal component to the overall data variance. Then, according to the eigenvalue sorting, the first several principal components (such as the first 2-3) with a cumulative variance contribution rate of more than 90% are selected. Finally, according to the eigenvector of each principal component, the contribution weight of each operating parameter to the principal component is analyzed to obtain the parameter weight. According to the size of the parameter weight in the principal component, the operating parameters are sorted to obtain a priority list that affects the accuracy.

[0186] Alternatively, for the accuracy information under the multiple operating conditions, based on preset operating condition parameters, a trend of the accuracy information changing with the operating condition parameters is determined.

[0187] In this embodiment, the working condition parameters may be the field of view angle of the navigation device, the configuration angle of the tracker, and the initial posture of the robot under test.

[0188] In this embodiment, the preset operating condition parameters may be used to filter the target data.

[0189] In this embodiment, a regression analysis method may be used to determine the trend of target data changing with operating parameters.

[0190] In this embodiment, a trend line fitting method may be used to determine the trend of target data changing with operating condition parameters.

[0191] In this embodiment, a correlation analysis method may be used to determine the trend of target data changing with operating condition parameters.

[0192] Alternatively, for the accuracy information under the multiple operating conditions, based on preset operating condition parameters, the distribution and confidence boundary of the accuracy information under the preset operating condition parameters are determined.

[0193] In this embodiment, the working condition parameters may be the field of view angle of the navigation device, the configuration angle of the tracker, and the initial posture of the robot under test.

[0194] In this embodiment, the preset operating condition parameters may be used to filter the target data.

[0195] In this embodiment, the distribution and confidence boundary of the target data can be determined. For example, the overall distribution of the accuracy information can be determined by calculating basic statistics such as the mean, standard deviation, variance, maximum value and minimum value of the target data. Accordingly, the confidence boundary can be calculated based on the mean and standard deviation of the data.

[0196] This implementation method uses a variety of data analysis methods to deeply process the accuracy information under multiple working conditions, which can fully reveal the influence of working conditions on accuracy performance and improve the scientificity and pertinence of data analysis. By analyzing the overall distribution and confidence boundaries of accuracy information, the global characteristics of accuracy performance can be quickly evaluated to help identify extreme values ​​and their occurrence ranges; prioritizing operating parameters based on principal component analysis can effectively extract key parameters that have the greatest impact on accuracy, providing a clear direction for operating condition optimization; analyzing the trend of accuracy information changing with operating condition parameters can reveal the dynamic impact of complex parameter interactions on accuracy and provide refined optimization strategies; evaluating the distribution and confidence boundaries of accuracy information under specific operating condition parameters further enhances the pertinence of the analysis and helps to deeply understand the accuracy performance under boundary conditions. This method combines multi-dimensional analysis methods to provide a scientific basis for accuracy optimization and reliability improvement under complex operating conditions.

[0197] This embodiment provides a method for determining working condition information. The execution subject of the method may be a server. The method may include:

[0198] Step S32: Receive data indicating accuracy requirements.

[0199] In this embodiment, the data indicating the accuracy requirement may be an accuracy value or an accuracy interval value. For example, the end tool positioning error is required to be less than 0.5 mm. between.

[0200] In this embodiment, the data representing the accuracy requirement may be basic information such as the surgical procedure, image, gender, etc. of the target patient.

[0201] Step S34: Obtain accuracy information or accuracy analysis result information; wherein, the accuracy information is generated by one of the above-mentioned accuracy information generation methods, and the accuracy analysis result information is generated by one of the above-mentioned accuracy information analysis methods.

[0202] Step S36: Based on the data representing the accuracy requirement, determine the optimal operating condition information from the accuracy information or from the accuracy analysis result information.

[0203] In this embodiment, the optimal working condition information can be determined from the accuracy information. For example, the accuracy information can be directly matched with the data representing the accuracy requirements to select the working condition information that meets the requirements. Specifically, first, according to the data representing the accuracy requirements (such as the target accuracy value or the accuracy range), the accuracy information that meets the requirements is screened out. For example, if the accuracy requirement is "the end tool positioning error ≤ 0.5mm", the working condition information with a positioning error ≤ 0.5mm is screened out from the accuracy information. Then, among the working condition information that meets the accuracy requirements, the optimal value is selected. For example, among all the working conditions that meet the accuracy requirements, the working condition with the smallest positioning error is selected. Or the working condition with the best comprehensive performance (such as positioning error, path deviation) of multiple working conditions is selected. Finally, the selected optimal working condition information is output, including its corresponding working condition parameters (navigation device field of view angle, tracker configuration angle, etc.).

[0204] In this embodiment, the optimal operating condition information may also be determined from the accuracy analysis result information. For example, data indicating the accuracy requirement may be used to filter the accuracy analysis result information and select the operating condition information that meets the requirement.

[0205] This embodiment provides a method for determining working condition information, which intelligently determines the optimal working condition information by combining the data with accuracy requirements, accuracy information and accuracy analysis result information. First, this method can accurately respond to the user's personalized needs for accuracy, ensuring that the determined working condition information has good adaptability while meeting the accuracy requirements. Secondly, by making optimization decisions based on the accuracy analysis result information, the uncertainty caused by parameter interaction in complex working conditions can be effectively reduced, thereby improving the accuracy performance and stability of the robot under test in actual application scenarios.

[0206] In some embodiments, the data representing the accuracy requirement includes intraoperative imaging data of the target patient, the type of surgery, and the safety factor of the target patient, and the step of determining the optimal operating condition information from the accuracy information or from the accuracy analysis result information based on the data representing the accuracy requirement includes:

[0207] Step S362: Generate a motion path of the end tool of the robot under test based on the intraoperative image data and the type of surgery.

[0208] In this embodiment, the intraoperative image data may be CT image data, MRI image data, X-ray image data or real-time ultrasound image data.

[0209] In this embodiment, the type of surgery may be spinal surgery, joint replacement surgery, tumor resection, and the like.

[0210] In this embodiment, the server can first extract anatomical features based on intraoperative image data. For example, the boundaries of target anatomical structures (such as bones and organs) can be extracted through image segmentation algorithms (such as threshold segmentation and deep learning segmentation). A three-dimensional point cloud or boundary contour of the target area can be generated. The server can then calibrate the target points in the anatomical structure based on the type of surgery. For example, vertebral positioning points in spinal surgery and bone screw implantation points in fracture repair, etc. Then, the server can call a preset path planning algorithm to generate the motion path of the end tool. The motion path can be represented in a three-dimensional point sequence or in a parameterized form.

[0211] Step S364: Based on the motion path, calculate the distance parameter between the end tool and the boundary of the anatomical structure represented in the intraoperative image data.

[0212] In this embodiment, the motion path can be represented by a three-dimensional point sequence or a parameterized form. For example, it can be represented by a three-dimensional coordinate sequence of path points. The three-dimensional boundary information of the anatomical structure, the anatomical boundary point cloud or the mesh model, etc. can be obtained through the image processing of the above steps. Therefore, the distance parameter between the motion path of the end tool and the anatomical boundary can be calculated. For example, for each path point, its Euclidean distance to all points in the anatomical boundary point cloud can be calculated. For each path point, its distance to each triangular facet can be calculated (if the boundary is a triangular mesh).

[0213] In this embodiment, the distance parameters may include the minimum distance from a point to a boundary, the maximum distance from a point to a boundary, the average distance of the entire path, the minimum distance of the entire path, the maximum distance of the entire path, and the like.

[0214] Step S366: Generate accuracy requirement data based on the safety factor and the distance parameter.

[0215] In this embodiment, the safety factor may be a preset parameter, indicating the requirement for safety margin during operation.

[0216] In this embodiment, the safety factor may be multiplied by the distance parameter to generate the accuracy requirement data.

[0217] Step S368: Based on the accuracy requirement data, determine the optimal operating condition information from the accuracy information or from the accuracy analysis result information.

[0218] This embodiment intelligently generates optimal working condition information by combining key information such as intraoperative imaging data, surgery type and safety factor of the target patient. First, the motion path of the end tool is generated by intraoperative imaging data and surgery type, which can effectively provide accurate operation path planning for individualized anatomical features and surgical needs, ensuring the accuracy and safety of the surgical process. Secondly, based on the distance parameters between the motion path and the boundary of the anatomical structure, the safe distance between the end tool and the key anatomical structure can be monitored in real time, thereby avoiding the risk of accidental contact and potential injury. In addition, the accuracy requirement data is calculated in combination with the safety factor, so that the accuracy assessment is more in line with the actual surgical needs, and the reliability and applicability of the results are improved. Finally, the optimal working condition information is screened by the accuracy requirement data, and the accurate matching of the working condition parameters and the personalized surgical needs is achieved, which not only optimizes the accuracy and efficiency of the robot operation, but also improves the success rate of the operation and the safety of the patient. This optimization method based on multi-dimensional data has important application value in complex surgical scenarios.

[0219] This embodiment provides a system for generating accuracy information, including:

[0220] The first marking device is used to be connected to the end tool of the robot under test and is provided with a first calibration point; wherein the first calibration point is used to calibrate the position and posture of the end tool.

[0221] The second marking device is used to provide a target position to which the end tool will move when measuring the accuracy of the robot being measured, and is provided with a second calibration point; wherein the second calibration point is used to calibrate the posture of the target position.

[0222] A measuring device is used to detect the position and posture information of the first calibration point and the second calibration point.

[0223] A processing device is used to receive working condition information representing a current working condition, and generate accuracy information based on the working condition information, the position and posture information of the first calibration point, and the position and posture information of the second calibration point acquired in advance.

[0224] The accuracy information generation system provided in this embodiment calibrates the posture of the end tool of the robot under test by the first marking device, and calibrates the posture of the target position by the second marking device, and uses the measuring device to accurately detect the posture information of both. The system dynamically generates accuracy information by combining the current working condition information with the processing device, which can realize the accuracy evaluation of the robot under test under specific working conditions.

[0225] According to an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a computer, the computer executes the method described in any of the above embodiments.

[0226] According to an embodiment of the present invention, a computer program product including instructions is also provided. When the instructions are executed by a computer, the computer executes a method in any one of the above embodiments.

[0227] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0228] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0229] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0230] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the protection scope of the present application.

Claims

1. A method for generating accuracy information, characterized in that: The method comprises: receiving operating condition information indicating a current operating condition; In the current working condition, a movement instruction is sent to the robot under test, so that the robot under test performs a preset movement operation; wherein the movement operation causes the end tool of the robot under test to move to at least one target position; wherein the end tool is configured with a first calibration point, and the first calibration point is used to calibrate the posture of the end tool; wherein the target position is configured with a second calibration point, and the second calibration point is used to calibrate the posture of the target position; Upon receiving feedback information sent by the robot under test, at least obtaining the position and posture information of the current first calibration point; wherein the feedback information is information used to indicate that the robot under test confirms that it has reached the target position; Based on the working condition information, the position and posture information of the first calibration point and the position and posture information of the second calibration point acquired in advance, the accuracy information is generated; wherein, the step of generating the accuracy information based on the working condition information, the position and posture information of the first calibration point and the position and posture information of the second calibration point acquired in advance comprises: generating at least one difference information among the first difference information, the second difference information and the third difference information based on the position and posture information of the first calibration point and the position and posture information of the second calibration point acquired in advance; wherein, the first difference information is used to characterize the deviation of the spatial position of the target position and the end tool when the end tool moves to a certain target position; the second difference information is used to characterize the deviation of the movement path of the end tool and the target path when the end tool moves to at least two target positions and the movement path is a straight line, and the target path is determined by the at least two target positions; the third difference information is used to characterize the deviation of the actual plane formed by the movement of the end tool and the target plane when the end tool moves to at least three target positions, and the target plane is determined by the at least three target positions; The operating condition information is used as an index and at least one of the first difference information, the second difference information and the third difference information is used as an indexed value to generate accuracy information.

2. The method according to claim 1, characterized in that The step of obtaining at least the position and posture information of the current first calibration point upon receiving the feedback information sent by the robot under test comprises: Upon receiving feedback information sent by the robot under test, a first measurement instruction is sent to a preset measurement device; wherein the first measurement instruction is an instruction for triggering the measurement device to detect the current position and posture information of the first calibration point and the second calibration point; Receive the position and posture information of the current first calibration point and the second calibration point returned by the measuring device.

3. The method according to claim 1, characterized in that Between the step of sending a movement instruction to the robot under test so that the robot under test performs a preset movement operation and the step of obtaining at least the position and posture information of the current first calibration point, the method further includes: Sending a second measurement instruction to a preset measurement device; wherein the second measurement instruction is an instruction for triggering the measurement device to detect the position and posture information of the second calibration point; Get the pose information of the second calibration point.

4. A method for analyzing accuracy information, characterized in that: The method comprises: Obtaining accuracy information under multiple working conditions; wherein the accuracy information is generated by any method described in claims 1-3; At least one data analysis method is executed on the accuracy information under the multiple working conditions to obtain accuracy analysis result information.

5. The method according to claim 4, characterized in that The working conditions at least include the field of view of the navigation device of the robot under test, the configured angle of the tracker of the robot under test, and the initial posture of the robot under test; The step of executing at least one data analysis method on the accuracy information under the multiple working conditions to obtain accuracy analysis result information includes: Based on the preset screening conditions, the accuracy information under the multiple working conditions is traversed to determine the accuracy information under the target working condition; wherein the screening conditions are conditions for screening the boundary field of the navigation device, the boundary configuration angle of the tracker, and the boundary initial posture of the robot under test; Standardization processing is performed on the accuracy information under the target working condition to obtain accuracy analysis result information.

6. The method according to claim 4, characterized in that The step of executing at least one data analysis method on the accuracy information under the multiple working conditions to obtain accuracy analysis result information includes: For the accuracy information under the multiple working conditions, determine the overall distribution and confidence boundary of the accuracy information; or, For the accuracy information under the multiple working conditions, based on preset working condition parameters, principal component analysis is performed to determine the priority ranking of the working condition parameters, wherein the working condition parameters are specific parameters representing the working conditions; or, For the accuracy information under the multiple working conditions, based on preset working condition parameters, determine the trend of the accuracy information changing with the working condition parameters; or, For the accuracy information under the multiple operating conditions, based on preset operating condition parameters, the distribution and confidence boundary of the accuracy information under the preset operating condition parameters are determined.

7. A method for determining operating condition information, characterized in that: The method comprises: receiving data indicating accuracy requirements; Obtaining accuracy information or accuracy analysis result information; wherein the accuracy information is generated by the method described in any one of claims 1 to 3, and the accuracy analysis result information is generated by the method described in any one of claims 4 to 6; Based on the data representing the accuracy requirement, the optimal operating condition information is determined from the accuracy information or from the accuracy analysis result information.

8. The determination method according to claim 7, characterized in that: The data indicating the accuracy requirement includes intraoperative imaging data of the target patient, the type of surgery, and the safety factor of the target patient. The step of determining the optimal operating condition information from the accuracy information or from the accuracy analysis result information based on the data indicating the accuracy requirement includes: Based on the intraoperative image data and the type of surgery, a motion path of an end tool of the robot under test is generated; Based on the motion path, calculating a distance parameter between the end tool and a boundary of an anatomical structure represented in the intraoperative imaging data; generating accuracy requirement data based on the safety factor and the distance parameter; Based on the accuracy requirement data, the optimal operating condition information is determined from the accuracy information or from the accuracy analysis result information.

9. A system for generating accuracy information, characterized in that: include: A first marking device, which is used to be connected to the end tool of the robot under test and is provided with a first calibration point; wherein the first calibration point is used to calibrate the position and posture of the end tool; A second marking device, which is used to provide a target position to which the end tool will move when measuring the accuracy of the robot being measured, and is provided with a second calibration point; wherein the second calibration point is used to calibrate the position and posture of the target position; A measuring device, which is used to detect the position and posture information of the first calibration point and the second calibration point; A processing device, which is used to receive operating condition information representing a current operating condition, and generate accuracy information based on the operating condition information, the posture information of the first calibration point, and the posture information of the second calibration point acquired in advance; wherein the processing device is used to execute a method for generating accuracy information as described in any one of claims 1-3.

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