Mechanical arm posture measurement offset calibration method and device, and electronic equipment

Abstract

The application discloses a mechanical arm posture measurement offset calibration method and device, electronic equipment and computer storage medium, and the method comprises the following steps: acquiring a digital twin model corresponding to a mechanical device; adjusting the posture of a target mechanical arm on the mechanical device, and acquiring first posture information obtained by a target measurement device measuring the posture of the target mechanical arm after the posture adjustment; adjusting the posture of a corresponding target virtual mechanical arm on the digital twin model to be consistent with the posture of the target mechanical arm after the adjustment, and acquiring second posture information of the target virtual mechanical arm in the digital twin model when the posture of the target virtual mechanical arm is consistent with the posture of the target mechanical arm; and determining the measurement offset of the target measurement device to the posture of the target mechanical arm according to the first posture information and the second posture information. The application can automatically calibrate the measurement offset of the posture of the mechanical arm without manual intervention, and the calibrated measurement offset is more accurate.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a method, apparatus, electronic device, and computer-readable storage medium for measuring and calibrating the posture offset of a robotic arm. Background Technology

[0002] Currently, accurate measurement of the loader's robotic arm attitude is a prerequisite for autonomous operation of unmanned loaders. Generally, the attitude of the loader's robotic arm is sensed by IMU (Inertial Measurement Unit) sensors deployed on the loader. However, due to various factors affecting IMU sensors in actual use, there is a certain offset in the measurement of the robotic arm's attitude. Therefore, before using IMU sensors to calibrate the robotic arm's attitude, the measurement offset of the IMU sensors is usually calibrated to improve the accuracy of the IMU measurement data, thereby more accurately calibrating the loader's robotic arm's attitude.

[0003] In related technologies, it is necessary to manually level the loader's robotic arm, i.e., visually determine that the arm's angle is 0 degrees. The IMU sensor readings at this point are recorded, and the difference between the IMU sensor's angle measurement and 0 degrees is calculated as the IMU sensor's measurement offset of the robotic arm's attitude. However, this method relies on the calibration personnel's judgment of "arm leveling," is significantly influenced by subjective factors, and requires professional personnel to perform the measurement offset calibration. This approach cannot meet the efficiency and cost requirements for large-scale delivery of unmanned loaders.

[0004] Therefore, there is an urgent need for an automatic robotic arm posture calibration method that can automatically calibrate the IMU sensor offset measurement without human intervention, thereby more accurately calibrating the posture of the loader robotic arm. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and computer-readable storage medium for calibrating the measurement offset of a robotic arm posture, so as to provide a method that can automatically complete the measurement offset calibration of the robotic arm posture without human intervention, thereby calibrating the posture of the loader robotic arm more accurately.

[0006] In a first aspect, embodiments of this application provide a method for calibrating the offset of a robotic arm's posture, the method comprising:

[0007] Obtain a digital twin model corresponding to the mechanical equipment. Each virtual robotic arm on the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm on the digital twin model is consistent with the initial posture of each robotic arm on the mechanical equipment.

[0008] The posture of the target robotic arm on the mechanical equipment is adjusted, and the first posture information is obtained by the target measuring device measuring the posture of the target robotic arm after the posture adjustment.

[0009] The posture of the target virtual robotic arm corresponding to the digital twin model is adjusted to be consistent with the posture of the adjusted target robotic arm, and when the posture of the target virtual robotic arm is consistent with that of the target robotic arm, the second posture information of the target virtual robotic arm in the digital twin model is obtained.

[0010] Based on the first attitude information and the second attitude information, the measurement offset of the target measuring device on the attitude of the target robotic arm is determined.

[0011] Secondly, embodiments of this application provide a device for measuring and calibrating the posture offset of a robotic arm, the device comprising:

[0012] The acquisition module is used to acquire the digital twin model corresponding to the mechanical equipment. Each virtual robotic arm on the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm on the digital twin model is consistent with the initial posture of each robotic arm on the mechanical equipment.

[0013] The adjustment module is used to adjust the posture of the target robotic arm on the mechanical equipment and to obtain the first posture information obtained by the target measuring device after posture adjustment of the target robotic arm.

[0014] The processing module is used to adjust the posture of the target virtual robotic arm corresponding to the digital twin model to be consistent with the adjusted posture of the target robotic arm, and when the posture of the target virtual robotic arm is consistent with that of the target robotic arm, to obtain the second posture information of the target virtual robotic arm in the digital twin model.

[0015] The determination module is used to determine the measurement offset of the target measuring device on the target robotic arm's posture based on the first posture information and the second posture information.

[0016] Thirdly, embodiments of this application provide an electronic device, the electronic device comprising:

[0017] The memory and the processor are coupled;

[0018] The memory is used to store one or more computer instructions;

[0019] The processor is used to execute one or more computer instructions to implement the method for measuring offset calibration of robotic arm posture as described in any of the first aspects above.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing one or more computer instructions, characterized in that the instructions are executed by a processor to implement the method for measuring offset calibration of robotic arm posture as described in any of the first aspects above.

[0021] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method for measuring and calibrating the posture of a robotic arm as described in any of the first aspects above.

[0022] Compared with the prior art, this application has the following advantages:

[0023] The method for calibrating the measurement offset of a robotic arm's posture provided in this application first obtains a digital twin model corresponding to the mechanical equipment. Each virtual robotic arm in the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm in the digital twin model is consistent with the initial posture of the corresponding robotic arm on the mechanical equipment. The posture of the target robotic arm on the mechanical equipment is adjusted, and the first posture information obtained by the target measuring device is acquired. The posture of the corresponding target virtual robotic arm in the digital twin model is adjusted to be consistent with the posture of the adjusted target robotic arm, and when the postures of the target virtual robotic arm and the target robotic arm are consistent, the second posture information of the target virtual robotic arm in the digital twin model is acquired. In this way, reading the posture of the target virtual robotic arm in the digital twin model yields the true posture of the target robotic arm in the mechanical equipment. By comparing the first posture information and the second posture information obtained by the target measuring device, the measurement offset of the target measuring device on the target robotic arm's posture can be determined.

[0024] Compared to existing technologies, this application, when the postures of each virtual robotic arm on the digital twin model are consistent with the postures of the corresponding robotic arms on the mechanical equipment, only requires adjusting the posture of the target robotic arm to any posture. Then, by adjusting the posture of the corresponding target virtual robotic arm on the digital twin model to match the adjusted posture of the target robotic arm, the true posture of the target robotic arm after posture adjustment can be quickly determined with the help of the digital twin model. There is no need for manual intervention to calibrate the robotic arm to a horizontal position by human eyes. It can achieve unmanned, automatic, and more accurate calibration of the measurement offset of the target robotic arm posture by the target measuring device, lowering the calibration threshold and enabling non-professionals to calibrate results that meet the accuracy requirements through this method, thereby improving the delivery efficiency of large-scale mechanical equipment and reducing deployment costs. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0026] Figure 1 A flowchart illustrating a method for calibrating the posture offset of a robotic arm according to one embodiment of this application;

[0027] Figure 2 A schematic diagram of a digital twin model of a loader provided in one embodiment of this application;

[0028] Figure 3 This is a schematic diagram illustrating whether the pose of the target virtual robotic arm is consistent with the pose of the target robotic arm, based on the difference between the second point cloud information and the first point cloud information, according to one embodiment of this application.

[0029] Figure 4 A schematic diagram of the structure of a robotic arm posture measurement offset calibration device provided in one embodiment of this application;

[0030] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application.

[0031] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0032] To make the objectives, advantages, and features of this application clearer, the application will be described clearly and completely below in conjunction with the accompanying drawings and specific embodiments. In the following description, many specific details are set forth to provide a full understanding of this application. However, the described embodiments are only some, not all, of the embodiments of this application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0033] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance, or a specific order or sequence. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, the term "multiple" refers to two or more. The term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0034] To address the problems existing in the aforementioned related technologies, this application provides a method for calibrating the measurement offset of a robotic arm's posture, a corresponding device for calibrating the measurement offset of a robotic arm's posture, an electronic device for implementing the method for calibrating the measurement offset of a robotic arm's posture, and a computer-readable storage medium. The following embodiments provide a detailed description of the above-mentioned method, device, electronic device, and computer-readable storage medium.

[0035] To make the objectives and technical solutions of this application clearer and more intuitive, the methods provided by the embodiments of this application will be described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for explaining this application and are not intended to limit this application. It is understood that the following embodiments may exist independently, and the embodiments and features described below may be combined with each other where there is no conflict between the various embodiments provided in this application. For the same or similar content, it will not be repeated in different embodiments. Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation. In some cases, the steps shown or described may be performed in a different order.

[0036] This application provides a method, apparatus, electronic device, and computer-readable storage medium for calibrating the measurement offset of a robotic arm's posture. Specifically, the method for calibrating the measurement offset of a robotic arm's posture according to one embodiment of this application can be executed by a computer device, which can be a terminal or a server. The terminal can be a smartphone, tablet, laptop, touch screen, or other terminal device. The terminal can also include a client, which can be an application client, a browser client carrying an application, or an instant messaging client. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, and big data and artificial intelligence platforms.

[0037] Below, in conjunction with Figure 1 This application describes a method for calibrating the measurement offset of a robotic arm's posture according to one embodiment. Figure 1 This is a flowchart illustrating a method for calibrating the offset of a robotic arm posture according to one embodiment of this application.

[0038] like Figure 1 As shown, the method for calibrating the measurement offset of the robotic arm's posture includes S10-S40:

[0039] S10. Obtain the digital twin model corresponding to the mechanical equipment. Each virtual robotic arm on the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm on the digital twin model is consistent with the initial posture of each robotic arm on the corresponding mechanical equipment.

[0040] S20. Adjust the posture of the target robotic arm on the mechanical equipment and obtain the first posture information obtained by the target measuring device measuring the posture of the target robotic arm after posture adjustment.

[0041] S30. Adjust the posture of the target virtual robotic arm on the digital twin model to match the posture of the adjusted target robotic arm, and when the posture of the target virtual robotic arm matches that of the target robotic arm, obtain the second posture information of the target virtual robotic arm in the digital twin model.

[0042] S40. Based on the first posture information and the second posture information, determine the measurement offset of the target measuring device on the posture of the target robotic arm.

[0043] The following is a detailed explanation of steps S10-S40.

[0044] The aforementioned mechanical equipment refers to mechanical devices that include robotic arms, such as loaders, excavators, industrial robots, etc.

[0045] As mentioned above, digital twins are a technology that uses digital means to construct virtual models of physical entities, used for real-time monitoring, analysis, and optimization of the operational status of physical entity systems. The digital twin model corresponding to mechanical equipment refers to a digital virtual model constructed for the mechanical equipment using the aforementioned digital twin techniques. This model completely corresponds to the actual mechanical equipment in terms of structure, function, and operational status, and can reflect the dynamic behavior and performance of the mechanical equipment in real time.

[0046] For example, combined Figure 2 Taking a loader as an example, this paper illustrates the digital twin model corresponding to a loader. Figure 2 This is a schematic diagram of a digital twin model of a loader provided in one embodiment of this application. Figure 2 As shown, Figure 2 The left image shows the physical loader, and the right image shows its corresponding digital twin model. The digital twin model of the loader completely corresponds to the physical loader in terms of structure, size, function, and operating status.

[0047] In this embodiment, a digital twin model corresponding to the mechanical equipment is obtained. Each virtual robotic arm on the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm on the digital twin model is consistent with the initial posture of the corresponding robotic arm on the mechanical equipment. For example, when the mechanical equipment includes robotic arms such as a large arm and a forearm, when the posture of the digital twin model corresponding to the mechanical equipment is consistent with that of the mechanical equipment, the posture of the large arm on the mechanical equipment is consistent with the posture of the virtual large arm in the digital twin model, and the posture of the forearm on the mechanical equipment is consistent with the posture of the virtual forearm in the digital twin model.

[0048] It should be noted that due to the interconnectedness of the robotic arms on the mechanical equipment, such as changing the posture of the upper arm simultaneously affecting the position of the lower arm, the only way to ensure that the posture of each virtual robotic arm in the digital twin model matches the posture of its corresponding robotic arm on the mechanical equipment is to simultaneously adjust the posture of the target robotic arm on the mechanical equipment. This ensures that for both the mechanical equipment and the digital twin model, the only variable is either the target robotic arm or its corresponding virtual robotic arm. For example, by first obtaining a digital twin model with postures completely identical to the mechanical equipment, adjusting the posture of the upper arm (the target robotic arm) on the mechanical equipment, and simultaneously adjusting the posture of the virtual upper arm in the digital twin model, the difference in posture between the adjusted mechanical equipment and the digital twin model is only reflected in the upper arm or the virtual upper arm.

[0049] In this embodiment, the mechanical equipment includes different robotic arms that are interconnected; that is, a change in the posture of one robotic arm may simultaneously cause a corresponding change in the posture of another robotic arm. For example, a change in the posture of the upper arm on the mechanical equipment will cause a change in the posture of the lower arm. The robotic arm posture measurement offset calibration method provided in this embodiment is used to measure the difference between the posture measurement value of the robotic arm on the mechanical equipment by a target measuring device such as an IMU sensor (Inertial Measurement Unit) and the actual posture of the robotic arm, i.e., the measurement offset (or error) of the robotic arm posture by the target measuring device. To avoid measurement errors caused by linkage, only one robotic arm in the mechanical equipment is measured in a single measurement to obtain the measurement offset of the robotic arm posture by the target measuring device.

[0050] The target robotic arm mentioned above refers to any robotic arm in the mechanical equipment, such as the upper arm or the lower arm.

[0051] In this embodiment, the operator can first adjust the posture of the target robotic arm in the mechanical equipment, so that the posture of the target robotic arm before and after the adjustment is inconsistent. After the posture adjustment of the target robotic arm is completed, a target measurement device, such as an IMU sensor, is used to measure the current posture of the target robotic arm on the mechanical equipment. Then, the first posture information is obtained by the target measurement device measuring the posture of the target robotic arm after the posture adjustment.

[0052] Since the digital twin model corresponding to the mechanical equipment is a digital virtual model, the digital twin model can promptly provide feedback on the posture state of the target virtual robotic arm after posture adjustment when controlling the posture adjustment of the target virtual robotic arm in the digital twin model. Based on this, in this embodiment of the application, when the posture of the target virtual robotic arm on the digital twin model is adjusted to be consistent with the posture of the target robotic arm after posture adjustment, the true posture of the target robotic arm after posture adjustment—that is, the second posture information—can be obtained based on the digital twin model.

[0053] In this embodiment, the measurement offset of the target measuring device to the target robotic arm's posture is determined based on the first posture information and the second posture information. Specifically, the measurement offset of the target measuring device to the target robotic arm's posture is determined by the posture difference information between the first posture information and the second posture information.

[0054] In one optional implementation, the first posture information includes the joint angles of the target robotic arm measured by the target measuring device, and the second posture information includes the joint angles of the target virtual robotic arm in the digital twin model.

[0055] The above-mentioned step S40, "determining the measurement offset of the target measuring device to the target robotic arm's posture based on the first posture information and the second posture information," can be implemented in one possible way, including step S401:

[0056] S401. Based on the joint angles of the target robotic arm and the joint angles of the target virtual robotic arm measured by the target measuring device, determine the measurement offset of the target measuring device for the posture of the target robotic arm.

[0057] In one optional implementation, the difference between the joint angle of the target robotic arm measured by the target measuring device and the joint angle of the target virtual robotic arm is determined as the measurement offset of the target measuring device for the posture of the target robotic arm.

[0058] For example, taking the target robotic arm as the upper arm, assume the target measuring device measures the joint angle of the target robotic arm as 50°, and the joint angle of the target virtual robotic arm as 55°. Then, the difference between the joint angle of the target robotic arm measured by the target measuring device and the joint angle of the target virtual robotic arm is 50° - 55° = -5°. Therefore, the measurement offset of the target robotic arm's posture by the target measuring device is -5°. In other words, the measured value of the joint angle of the target robotic arm by the target measuring device is 5° smaller than the actual joint angle of the target robotic arm. Alternatively, assume the target measuring device measures the joint angle of the target robotic arm as 50°, and the joint angle of the target virtual robotic arm as 45°. Then, the difference between the joint angle of the target robotic arm measured by the target measuring device and the joint angle of the target virtual robotic arm is 50° - 45° = 5°. Therefore, the measurement offset of the target robotic arm's posture by the target measuring device is 5°. In other words, the measured value of the joint angle of the target robotic arm by the target measuring device is 5° larger than the actual joint angle of the target robotic arm.

[0059] In another optional implementation, the difference between the joint angle of the target virtual robotic arm and the joint angle of the target robotic arm measured by the target measuring device is determined as the measurement offset of the target measuring device for the posture of the target robotic arm.

[0060] For example, taking the target robotic arm as the upper arm, assume the target measuring device measures the joint angle of the target robotic arm as 50°, and the joint angle of the target virtual robotic arm as 55°. Then, the difference between the joint angle of the target virtual robotic arm and the joint angle of the target robotic arm measured by the target measuring device is 55° - 50° = 5°. Therefore, the measurement offset of the target robotic arm's posture by the target measuring device is 5°. In other words, the measured value of the joint angle of the target robotic arm by the target measuring device is 5° smaller than the actual joint angle of the target robotic arm. Alternatively, assume the target measuring device measures the joint angle of the target robotic arm as 50°, and the joint angle of the target virtual robotic arm as 45°. Then, the difference between the joint angle of the target virtual robotic arm and the joint angle of the target robotic arm measured by the target measuring device is 45° - 50° = -5°. Therefore, the measurement offset of the target robotic arm's posture by the target measuring device is -5°. In other words, the measured value of the joint angle of the target robotic arm by the target measuring device is 5° larger than the actual joint angle of the target robotic arm.

[0061] Furthermore, steps S20-S40 can be repeated multiple times, and the average value of the measured offsets obtained from each repetition can be determined as the measured offset of the target measuring device for the target robotic arm's posture. For example, if steps S20-S40 are repeated 5 times, the measured offsets of the target measuring device for the target robotic arm's posture obtained from each of the 5 repetitions are k1, k2, k3, k4, and k5, respectively. Then, the final measured offset of the target measuring device for the target robotic arm's posture is (k1+k2+k3+k4+k5) / 5.

[0062] In the method for calibrating the measurement offset of a robotic arm's posture provided in this application embodiment, a digital twin model corresponding to the mechanical equipment is first obtained. Each virtual robotic arm on the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm on the digital twin model is consistent with the initial posture of the corresponding robotic arm on the mechanical equipment. The posture of the target robotic arm on the mechanical equipment is adjusted, and the first posture information obtained by the target measuring device measuring the posture of the adjusted target robotic arm is obtained. The posture of the corresponding target virtual robotic arm on the digital twin model is adjusted to be consistent with the posture of the adjusted target robotic arm, and when the posture of the target virtual robotic arm is consistent with that of the target robotic arm, the second posture information of the target virtual robotic arm in the digital twin model is obtained. In this way, reading the posture of the target virtual robotic arm in the digital twin model yields the true posture of the target robotic arm in the mechanical equipment. By comparing the first posture information and the second posture information obtained by the target measuring device measuring the target robotic arm, the measurement offset of the target measuring device on the target robotic arm's posture can be determined.

[0063] Compared to existing technologies, this application, when the postures of each virtual robotic arm on the digital twin model are consistent with the postures of the corresponding robotic arms on the mechanical equipment, only requires adjusting the posture of the target robotic arm to any posture. Then, by adjusting the posture of the corresponding target virtual robotic arm on the digital twin model to match the adjusted posture of the target robotic arm, the true posture of the target robotic arm after posture adjustment can be quickly determined with the help of the digital twin model. There is no need for manual intervention to calibrate the robotic arm to a horizontal position by human eyes. It can achieve unmanned, automatic, and more accurate calibration of the measurement offset of the target robotic arm posture by the target measuring device, lowering the calibration threshold and enabling non-professionals to calibrate results that meet the accuracy requirements through this method, thereby improving the delivery efficiency of large-scale mechanical equipment and reducing deployment costs.

[0064] Based on the above embodiments, the method for measuring and calibrating the posture of a robotic arm provided in this application will be further described below.

[0065] In one optional implementation, a specific way to implement step S30, "adjusting the posture of the corresponding target virtual robotic arm on the digital twin model to be consistent with the posture of the adjusted target robotic arm," includes steps S301-S303:

[0066] S301. Obtain the first point cloud information of the target robotic arm after attitude adjustment on the mechanical equipment.

[0067] S302. Perform preliminary posture adjustments on the target virtual robotic arm in the digital twin model.

[0068] S303. Repeat the first step until the point cloud difference between the second point cloud information of the target virtual robotic arm after attitude adjustment and the first point cloud information of the target robotic arm is less than the preset difference threshold.

[0069] The first step includes steps S3031-S3033:

[0070] S3031. Obtain the second point cloud information of the target virtual robotic arm after attitude adjustment simulated by the digital twin model.

[0071] S3032. Calculate the point cloud difference between the second point cloud information and the first point cloud information. The magnitude of the point cloud difference is used to characterize the magnitude of the posture difference between the target virtual robotic arm and the target robotic arm.

[0072] S3033. Adjust the posture of the target virtual robotic arm on the digital twin model to reduce the posture difference between the target virtual robotic arm and the target robotic arm.

[0073] The following is a detailed explanation of steps S301-S303.

[0074] The point cloud information of the target robotic arm mentioned above refers to the spatial geometric data of the surface of the target robotic arm obtained through a certain measurement method (such as lidar scanning, depth camera and other three-dimensional sensing devices). This data is represented in the form of a point cloud, which is a collection of a large number of points in three-dimensional space.

[0075] It should be noted that, taking LiDAR as the current point cloud measurement method as an example, in order to ensure that the pose of the target robotic arm on the mechanical equipment has the same point cloud information as the pose of the target virtual robotic arm on the digital twin model, it is necessary to ensure that the deployment method of LiDAR on the mechanical equipment (such as deployment location, quantity, etc.) is completely consistent with the deployment method of virtual LiDAR on the corresponding digital twin model of the mechanical equipment.

[0076] The second point cloud information of the target virtual robotic arm after attitude adjustment, as described above, refers to the point cloud information obtained by the digital twin model through point cloud simulation of the target virtual robotic arm after attitude adjustment. For example, the second point cloud information of the target virtual robotic arm after attitude adjustment can be obtained by using a virtual LiDAR deployed on the digital twin model to simulate the point cloud of the target virtual robotic arm.

[0077] In this embodiment, after the posture adjustment of the target robotic arm on the mechanical device is completed in step S20, the first point cloud information of the target robotic arm after posture adjustment is obtained. To adjust the target virtual robotic arm on the digital twin model to match the posture of the target robotic arm, the posture of the target virtual robotic arm in the digital twin model is attempted to be adjusted. During the first posture adjustment, the target virtual robotic arm in the digital twin model can be arbitrarily adjusted. For example, the target virtual robotic arm includes a virtual joint 1. Adjusting the posture of the target virtual robotic arm means adjusting the angle of the virtual joint 1 on the robotic arm. There are only two adjustment directions for the angle: either increasing the joint angle or decreasing the joint angle. Therefore, during the first posture adjustment, the posture adjustment of the virtual joint 1 on the target virtual robotic arm can be either increasing or decreasing the joint angle; either one is acceptable, and this embodiment does not impose any limitation on this.

[0078] After the first attitude adjustment, S3031, acquire the second point cloud information of the target virtual robotic arm after attitude adjustment. S3032, calculate the point cloud difference between the second point cloud information and the first point cloud information. The magnitude of the point cloud difference is used to characterize the attitude difference between the target virtual robotic arm and the target robotic arm. S3033, adjust the attitude of the target virtual robotic arm on the digital twin model to reduce the attitude difference between the target virtual robotic arm and the target robotic arm. Repeat S3031-S3033 until the point cloud difference between the second point cloud information of the target virtual robotic arm after attitude adjustment and the first point cloud information of the target robotic arm is less than a preset difference threshold. At this point, it is considered that the attitude of the target virtual robotic arm on the digital twin model has been adjusted to be consistent with the attitude of the target robotic arm after attitude adjustment.

[0079] In this embodiment, the consistency between the second point cloud information and the first point cloud information of the target virtual robotic arm is determined by comparing whether the posture of the target virtual robotic arm matches that of the target robotic arm. This allows for accurate assessment of whether the posture of the target virtual robotic arm matches that of the target robotic arm, avoiding misjudgments due to simple angle or position errors. Furthermore, the setting of a preset difference threshold clarifies the allowable deviation range between the second and first point cloud information. By setting this threshold, the subjective "consistency" is transformed into an objective quantitative indicator, facilitating automated judgment and systematic operation.

[0080] Below, in conjunction with Figure 3 This paper provides an example of determining whether the pose of the target virtual robotic arm is consistent with the pose of the target robotic arm by comparing whether the second point cloud information of the target virtual robotic arm is consistent with the first point cloud information of the target robotic arm. Figure 3 This is a schematic diagram illustrating whether the pose of the target virtual robotic arm is consistent with the pose of the target robotic arm, based on the difference between the second point cloud information and the first point cloud information, according to one embodiment of this application. Figure 3 The following example illustrates the concept of using the target robotic arm as the main arm and the target virtual robotic arm as the virtual main arm.

[0081] like Figure 3 As shown in Figures (a) and (b), the second point cloud information of the virtual boom corresponding to the loader is shown in the green point set in Figure (a). When the placement position of the real loader is aligned with the digital twin model, the first point cloud information of the boom on the real loader is shown in the white point set in Figure (a). The point cloud information of the second point cloud information and the first point cloud information shown in Figure (a) has a large difference (i.e., the point cloud difference is greater than or equal to the preset difference threshold) (i.e., it can be seen that the points of the second point cloud information and the points of the first point cloud information are almost completely non-overlapping). Therefore, it is determined that the posture of the virtual boom corresponding to the digital twin model is inconsistent with the posture of the boom corresponding to the real loader. Subsequently, the posture of the virtual boom corresponding to the digital twin model is further adjusted to reduce the posture difference between the posture of the virtual boom corresponding to the digital twin model and the posture of the boom corresponding to the real loader. As shown in Figure (b), after adjusting the posture of the virtual boom corresponding to the digital twin model, the difference between the second point cloud information of the virtual boom and the first point cloud information of the real loader is small (i.e., the point cloud difference is less than the preset difference threshold) (i.e., it can be seen that the points of the second point cloud information and the points of the first point cloud information almost completely overlap). At this time, it can be determined that the posture of the virtual boom corresponding to the digital twin model is consistent with the posture of the boom corresponding to the real loader.

[0082] In one optional implementation, a specific way to implement step S3033, "adjusting the posture of the target virtual robotic arm on the digital twin model to reduce the posture difference between the target virtual robotic arm and the target robotic arm," includes S30331:

[0083] S30331. Adjust the posture of the target virtual robotic arm based on the point cloud difference between the second point cloud information and the first point cloud information.

[0084] In one optional implementation, the attitude adjustment includes a first attitude adjustment direction and a second attitude adjustment direction opposite to the first attitude adjustment direction. Specifically, the implementation of step S30331, "adjusting the attitude of the target virtual robotic arm based on the point cloud difference between the second point cloud information and the first point cloud information," includes steps A1-A3:

[0085] A1. Compared with the point cloud difference before the current posture adjustment, determine whether the point cloud difference between the second point cloud information and the first point cloud information of the target virtual robotic arm after the posture adjustment has been reduced.

[0086] A2. If so, adjust the orientation of the target virtual robotic arm according to the target orientation adjustment direction. The target orientation adjustment direction is the orientation adjustment direction of the previous orientation adjustment of the target virtual robotic arm.

[0087] A3. If not, adjust the orientation of the target virtual robotic arm in the opposite direction of the target orientation adjustment direction.

[0088] The target posture adjustment direction is the posture adjustment direction of the last time the target virtual robotic arm was adjusted. The target posture adjustment direction is either the first posture adjustment direction or the second posture adjustment direction opposite to the first posture adjustment direction.

[0089] In this embodiment, taking the target posture adjustment direction as the first posture adjustment direction as an example, compared to the point cloud difference before the current posture adjustment, if it is determined that the point cloud difference between the second point cloud information and the first point cloud information of the target virtual robotic arm has decreased after the posture adjustment, the posture of the target virtual robotic arm continues to be adjusted according to the first posture adjustment direction to further reduce the point cloud difference between the second point cloud information and the first point cloud information. Alternatively, compared to the point cloud difference before the current posture adjustment, if it is determined that the point cloud difference between the second point cloud information and the first point cloud information of the target virtual robotic arm has increased after the posture adjustment, the posture of the target virtual robotic arm is adjusted according to the second posture adjustment direction, which is opposite to the first posture adjustment direction, to reduce the point cloud difference between the second point cloud information and the first point cloud information, so as to avoid the point cloud difference between the second point cloud information and the first point cloud information from further increasing.

[0090] In one optional implementation, the posture of the target virtual robotic arm includes the joint angles on the target virtual robotic arm, with a first posture adjustment direction of increasing the joint angles and a second posture adjustment direction of decreasing the joint angles.

[0091] In this embodiment, taking increasing joint angles as an example of adjusting the target posture, if the point cloud difference between the second and first point cloud information of the target virtual robotic arm is determined to be smaller after the posture adjustment, the joint angles of the target virtual robotic arm are further increased in the manner of increasing joint angles. Alternatively, if the point cloud difference between the second and first point cloud information of the target virtual robotic arm is determined to be larger after the posture adjustment, the joint angles of the target virtual robotic arm are decreased.

[0092] For example, since the direction of joint movement is fixed, it is a one-dimensional search. The initial search randomly selects a direction for optimization. For instance, if the joint angle of the current target robotic arm is 0° and the joint angle of the target virtual robotic arm in the simulation is 20°, the first calculation error is 20°. Since there is no historical data, a direction is randomly selected for optimization, and the joint angle of the target virtual robotic arm is adjusted to 40°, resulting in an error of 40°. The next time, the posture will be adjusted to 15°, resulting in an error of 15°. The next time, the joint angle of the target virtual robotic arm is adjusted to -5°, resulting in an error of 5°. The next time, the joint angle of the target virtual robotic arm may be adjusted to -10°, resulting in an error of 10°. Finally, the joint angle of the target virtual robotic arm may be adjusted to -0.2°, resulting in an error of 0.2°. If the error is less than a preset threshold, it is considered that the posture of the target virtual robotic arm is consistent with the posture of the target robotic arm.

[0093] In one optional implementation, the specific implementation of step S3032, "calculating the point cloud difference between the second point cloud information and the first point cloud information," includes steps B1-B3:

[0094] B1. For each target point in the second point cloud information, determine the point closest to the target point from the points contained in the first point cloud information, and determine the closest point as the nearest neighbor point of the target point in the first point cloud information.

[0095] B2. For each target point, calculate the distance between the target point and its corresponding nearest neighbor.

[0096] B3. The average distance between all target points and their corresponding nearest neighbors is determined as the point cloud difference between the second point cloud information and the first point cloud information.

[0097] The following is a detailed explanation of steps B1-B3.

[0098] In one optional implementation, the specific method for "determining a point closest to the target point from the points contained in the first point cloud information" in step B1 includes steps B11-B12:

[0099] B11. Construct a KD tree based on the information of the first point cloud.

[0100] B12. Use the nearest neighbor search algorithm to determine the point closest to the target point from the KD tree.

[0101] In this embodiment, constructing a KD-tree based on the first point cloud information involves recursively dividing multiple three-dimensional points included in the first point cloud data into sub-regions to form a binary tree. Each division selects the median of one dimension as the split point. Then, a nearest neighbor search algorithm is used to determine the point closest to the target point from the KD-tree. This allows for a quick query of the point closest to the target point from the points contained in the first point cloud information.

[0102] In one alternative implementation, the distance between the target point and its corresponding nearest neighbor can be calculated by calculating the Chef distance.

[0103] In an optional implementation, the method for measuring and calibrating the posture offset of a robotic arm provided in this application further includes step S50:

[0104] S50. Based on the posture and measurement offset of the target robotic arm measured by the target measurement device, determine the true posture of the target robotic arm.

[0105] In one optional implementation, when the measurement offset of the target measuring device for the target robotic arm's posture is the difference between the joint angle of the target robotic arm measured by the target measuring device and the joint angle of the target virtual robotic arm, the difference between the posture of the target robotic arm measured by the target measuring device and the measurement offset is determined as the true posture of the target robotic arm.

[0106] For example, taking a loader as the mechanical equipment, the target robotic arm as the boom, and the target measuring device as an IMU sensor, the true posture of the target robotic arm on the loader is calculated as: the measured value of the IMU sensor minus the measurement offset. Assuming the boom joint angle on the loader is at 0°, the IMU sensor reading is 11.3°, and the measurement offset k of the IMU sensor for the boom joint angle is 11.3°. Subsequently, if the boom joint angle on the loader is rotated to a certain position and the IMU sensor reading is 21.3°, according to the formula: True posture (joint angle) of the boom on the loader = IMU sensor measured value for the boom joint angle - IMU sensor measurement offset k, the true posture (joint angle) of the boom on the loader at this point can be calculated as 21.3° - 11.3° = 10°.

[0107] In another optional implementation, if the difference between the joint angle of the target virtual robotic arm and the joint angle of the target robotic arm measured by the target measuring device is determined as the measurement offset of the target robotic arm posture by the target measuring device, then the sum of the posture of the target robotic arm measured by the target measuring device and the measurement offset is determined as the true posture of the target robotic arm.

[0108] For example, taking a loader as the mechanical equipment, the target robotic arm as the boom, and the target measuring device as an IMU sensor, the true posture of the target robotic arm on the loader = the measured value of the IMU sensor + the measurement offset. Assuming the boom joint angle on the loader is at 0°, the IMU sensor reading is 11.3°, then the measurement offset k of the IMU sensor for the boom joint angle is -11.3°. Subsequently, if the boom joint angle on the loader is rotated to a certain position, and the IMU sensor reading is 21.3°, according to the formula: True posture (joint angle) of the boom on the loader = IMU sensor measured value for the boom joint angle + IMU sensor measurement offset k, the true posture (joint angle) of the boom on the loader at this time can be calculated as 21.3° + (-11.3°) = 10°.

[0109] The following describes the calibration device for measuring the offset of the robotic arm posture provided in this application. The calibration device for measuring the offset of the robotic arm posture described below can be referred to in correspondence with the calibration method for measuring the offset of the robotic arm posture described above.

[0110] Figure 4 This is a schematic diagram of a device for measuring and calibrating the posture of a robotic arm, provided in one embodiment of this application. Figure 4 As shown, the robotic arm posture measurement offset calibration device 400 includes: an acquisition module 401, an adjustment module 402, a processing module 403, and a determination module 404.

[0111] The acquisition module is used to acquire the digital twin model corresponding to the mechanical equipment. Each virtual robotic arm on the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm on the digital twin model is consistent with the initial posture of each robotic arm on the mechanical equipment.

[0112] The adjustment module is used to adjust the posture of the target robotic arm on the mechanical equipment and to obtain the first posture information obtained by the target measuring device after posture adjustment of the target robotic arm.

[0113] The processing module is used to adjust the posture of the target virtual robotic arm corresponding to the digital twin model to be consistent with the adjusted posture of the target robotic arm, and when the posture of the target virtual robotic arm is consistent with that of the target robotic arm, to obtain the second posture information of the target virtual robotic arm in the digital twin model.

[0114] The determination module is used to determine the measurement offset of the target measuring device on the target robotic arm's posture based on the first posture information and the second posture information.

[0115] In one optional implementation, the processing module is specifically used for:

[0116] Acquire the first point cloud information of the target robotic arm on the mechanical equipment after attitude adjustment;

[0117] The target virtual robotic arm on the digital twin model is initially adjusted in posture;

[0118] Repeat the first step until the point cloud difference between the second point cloud information of the target virtual robotic arm after attitude adjustment and the first point cloud information of the target robotic arm is less than a preset difference threshold.

[0119] The first step includes:

[0120] Obtain the second point cloud information of the target virtual robotic arm after attitude adjustment simulated by the digital twin model;

[0121] Calculate the point cloud difference between the second point cloud information and the first point cloud information, and the magnitude of the point cloud difference is used to characterize the magnitude of the attitude difference between the target virtual robotic arm and the target robotic arm;

[0122] The attitude of the target virtual robotic arm on the digital twin model is adjusted to reduce the attitude difference between the target virtual robotic arm and the target robotic arm.

[0123] In one optional implementation, the processing module is specifically used for:

[0124] The target virtual robotic arm is adjusted in posture based on the point cloud differences between the second point cloud information and the first point cloud information.

[0125] In one optional implementation, the attitude adjustment includes a first attitude adjustment direction and a second attitude adjustment direction opposite to the first attitude adjustment direction; the processing module is specifically used for:

[0126] Compared to the point cloud differences before the current posture adjustment, determine whether the point cloud differences between the second point cloud information and the first point cloud information of the target virtual robotic arm after the posture adjustment have been reduced;

[0127] If so, adjust the posture of the target virtual robotic arm according to the target posture adjustment direction, wherein the target posture adjustment direction is the posture adjustment direction of the previous posture adjustment of the target virtual robotic arm;

[0128] If not, adjust the posture of the target virtual robotic arm in the opposite direction to the target posture adjustment direction;

[0129] The target attitude adjustment direction is either a first attitude adjustment direction or a second attitude adjustment direction opposite to the first attitude adjustment direction.

[0130] In one optional implementation, the posture of the target virtual robotic arm includes the joint angles on the target virtual robotic arm, wherein the first posture adjustment direction is to increase the joint angles, and the second posture adjustment direction is to decrease the joint angles.

[0131] In one optional implementation, the processing module is specifically used for:

[0132] For each target point in the second point cloud information, a point that is closest to the target point is determined from the points contained in the first point cloud information, and the closest point is determined as the nearest neighbor point of the target point in the first point cloud information;

[0133] For each target point, calculate the distance between the target point and its corresponding nearest neighbor point;

[0134] The average distance between all the target points and their corresponding nearest neighbors is determined as the point cloud difference between the second point cloud information and the first point cloud information.

[0135] In one optional implementation, the processing module is specifically used for:

[0136] Construct a KD tree based on the first point cloud information;

[0137] The nearest neighbor search algorithm is used to determine the point that is closest to the target point from the KD tree.

[0138] In one optional implementation, the first posture information includes the joint angles of the target robotic arm measured by the target measuring device, and the second posture information includes the joint angles of the target virtual robotic arm in the digital twin model; the determining module is specifically used to include:

[0139] Based on the joint angles of the target robotic arm and the joint angles of the target virtual robotic arm measured by the target measuring device, the measurement offset of the target robotic arm posture by the target measuring device is determined.

[0140] In one optional implementation, calculating the distance between the target point and its corresponding nearest neighbor points includes:

[0141] Calculate the Chef distance between the target point and its corresponding nearest neighbor.

[0142] In one optional implementation, the method further includes:

[0143] The true posture of the target robotic arm is determined based on the posture of the target robotic arm measured by the target measuring device and the measurement offset.

[0144] The robotic arm posture measurement offset calibration device provided in this embodiment can be used to execute the technical solution of the above-described robotic arm posture measurement offset calibration method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0145] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application, such as... Figure 5 As shown, the electronic device 500 of this embodiment includes: a processor 501 and a memory 502; wherein

[0146] Memory 502 is used to store instructions executed by the computer;

[0147] The processor 501 is used to execute computer execution instructions stored in the memory to implement the various steps of the robotic arm posture measurement offset calibration method in the above embodiments. For details, please refer to the relevant descriptions in the foregoing method embodiments.

[0148] Alternatively, the memory 502 can be either standalone or integrated with the processor 501.

[0149] When the memory 502 is set up independently, the electronic device also includes a bus 503 for connecting the memory 502 and the processor 501.

[0150] One embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the technical solution corresponding to the measurement offset calibration method for the robotic arm posture in any of the above embodiments executed by the above electronic device.

[0151] One embodiment of this application also provides a computer program product, which includes: a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium. The at least one processor executes the computer program to cause the electronic device to execute the technical solution corresponding to the measurement offset calibration method of the robotic arm posture in any of the above embodiments.

[0152] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0154] The integrated modules implemented as software functional modules described above can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) or a processor to execute some steps of the methods described in the various embodiments of this application.

[0155] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0156] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0157] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0158] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0159] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for measuring and calibrating the offset of a robotic arm's posture, characterized in that, The method includes: Obtain a digital twin model corresponding to the mechanical equipment. Each virtual robotic arm on the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm on the digital twin model is consistent with the initial posture of each robotic arm on the mechanical equipment. The posture of the target robotic arm on the mechanical equipment is adjusted, and the first posture information is obtained by the target measuring device measuring the posture of the target robotic arm after the posture adjustment. The posture of the target virtual robotic arm corresponding to the digital twin model is adjusted to be consistent with the posture of the adjusted target robotic arm, and when the posture of the target virtual robotic arm is consistent with that of the target robotic arm, the second posture information of the target virtual robotic arm in the digital twin model is obtained. Based on the first attitude information and the second attitude information, the measurement offset of the target measuring device on the attitude of the target robotic arm is determined.

2. The method according to claim 1, characterized in that, Adjusting the posture of the target virtual robotic arm corresponding to the digital twin model to match the adjusted posture of the target robotic arm includes: Acquire the first point cloud information of the target robotic arm on the mechanical equipment after attitude adjustment; The target virtual robotic arm on the digital twin model is initially adjusted in posture; Repeat the first step until the point cloud difference between the second point cloud information of the target virtual robotic arm after attitude adjustment and the first point cloud information of the target robotic arm is less than a preset difference threshold. The first step includes: Obtain the second point cloud information of the target virtual robotic arm after attitude adjustment simulated by the digital twin model; Calculate the point cloud difference between the second point cloud information and the first point cloud information, and the magnitude of the point cloud difference is used to characterize the magnitude of the attitude difference between the target virtual robotic arm and the target robotic arm; The attitude of the target virtual robotic arm on the digital twin model is adjusted to reduce the attitude difference between the target virtual robotic arm and the target robotic arm.

3. The method according to claim 2, characterized in that, The posture adjustment of the target virtual robotic arm on the digital twin model includes: The target virtual robotic arm is adjusted in posture based on the point cloud differences between the second point cloud information and the first point cloud information.

4. The method according to claim 3, characterized in that, The step of adjusting the posture of the target virtual robotic arm based on the point cloud difference between the second point cloud information and the first point cloud information includes: Compared to the point cloud differences before the current posture adjustment, determine whether the point cloud differences between the second point cloud information and the first point cloud information of the target virtual robotic arm after the posture adjustment have been reduced; If so, adjust the posture of the target virtual robotic arm according to the target posture adjustment direction, wherein the target posture adjustment direction is the posture adjustment direction of the previous posture adjustment of the target virtual robotic arm; If not, adjust the posture of the target virtual robotic arm in the opposite direction to the target posture adjustment direction; The target attitude adjustment direction is either a first attitude adjustment direction or a second attitude adjustment direction opposite to the first attitude adjustment direction.

5. The method according to claim 4, characterized in that, The posture of the target virtual robotic arm includes the joint angles on the target virtual robotic arm. The first posture adjustment direction is to increase the joint angles, and the second posture adjustment direction is to decrease the joint angles.

6. The method according to claim 2, characterized in that, The calculation of the point cloud difference between the second point cloud information and the first point cloud information includes: For each target point in the second point cloud information, a point that is closest to the target point is determined from the points contained in the first point cloud information, and the closest point is determined as the nearest neighbor point of the target point in the first point cloud information; For each target point, calculate the distance between the target point and its corresponding nearest neighbor point; The average distance between all the target points and their corresponding nearest neighbors is determined as the point cloud difference between the second point cloud information and the first point cloud information.

7. The method according to claim 6, characterized in that, Determining the point closest to the target point from the points contained in the first point cloud information includes: Construct a KD tree based on the first point cloud information; The nearest neighbor search algorithm is used to determine the point that is closest to the target point from the KD tree.

8. The method according to claim 1, characterized in that, The first attitude information includes the joint angles of the target robotic arm measured by the target measuring device, and the second attitude information includes the joint angles of the target virtual robotic arm in the digital twin model; determining the measurement offset of the target robotic arm attitude by the target measuring device based on the first attitude information and the second attitude information includes: Based on the joint angles of the target robotic arm and the joint angles of the target virtual robotic arm measured by the target measuring device, the measurement offset of the target robotic arm posture by the target measuring device is determined.

9. The method according to claim 7, characterized in that, The calculation of the distance between the target point and its corresponding nearest neighbor points includes: Calculate the Chef distance between the target point and its corresponding nearest neighbor.

10. The method according to claim 1, characterized in that, The method further includes: The true posture of the target robotic arm is determined based on the posture of the target robotic arm measured by the target measuring device and the measurement offset.

11. A device for measuring and calibrating the posture offset of a robotic arm, characterized in that, The device includes: The acquisition module is used to acquire the digital twin model corresponding to the mechanical equipment. Each virtual robotic arm on the digital twin model corresponds one-to-one with each robotic arm on the mechanical equipment, and the posture of each virtual robotic arm on the digital twin model is consistent with the initial posture of each robotic arm on the mechanical equipment. The adjustment module is used to adjust the posture of the target robotic arm on the mechanical equipment and to obtain the first posture information obtained by the target measuring device after posture adjustment of the target robotic arm. The processing module is used to adjust the posture of the target virtual robotic arm corresponding to the digital twin model to be consistent with the adjusted posture of the target robotic arm, and when the posture of the target virtual robotic arm is consistent with that of the target robotic arm, to obtain the second posture information of the target virtual robotic arm in the digital twin model. The determination module is used to determine the measurement offset of the target measuring device on the target robotic arm's posture based on the first posture information and the second posture information.

12. An electronic device, characterized in that, The electronic device includes: Processor; and A memory for storing a data processing program, which, when powered on and run by the processor, executes the method for measuring the offset of the robotic arm posture as described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The system contains a data processing program that is executed by a processor to perform a method for calibrating the measurement offset of the robotic arm posture as described in any one of claims 1-10.

Images