High stress trajectory extraction method for articulated mechanical devices

Abstract

The application provides a high stress trajectory extraction method of an articulated mechanical device. The method comprises the following steps: acquiring an n-dimensional stress vector; performing dimension reduction processing on the n-dimensional stress vector to obtain a k-dimensional feature vector; generating a stress level index according to the n-dimensional stress vector, the stress level index being used to measure the overall stress of the articulated mechanical device; acquiring a target stress section of the articulated mechanical device in the running process according to the stress level index; constructing a stress distribution model, the stress distribution model comprising a stress amplitude distribution model and a stress mode distribution model; and determining and generating a high stress trajectory sequence for an articulated mechanical device acceleration test according to the stress distribution model.

Description

TECHNICAL FIELD

[0001] The present application mainly relates to the field of testing of articulated mechanical devices, and in particular to a high-stress trajectory extraction method for articulated mechanical devices. BACKGROUND

[0002] Accelerated life test is an important means of product reliability verification. Traditional accelerated life test mainly accelerates the exposure of potential product failures by applying environmental stress (such as high and low temperature, humidity, vibration, etc.). However, for complex mechatronic systems such as surgical robots, simply relying on environmental factors cannot fully reflect the complex changes of mechanical stress experienced in actual operation. When performing surgical operations, the joint motors, connecting rods and surgical instruments of surgical robots will bear multidimensional dynamic loads, and the mechanical stress will change dramatically with the intensity of surgical action. Currently, the accelerated life test of surgical robots usually commands the surgical robot to perform a certain regular motion, such as a straight-line back-and-forth repetitive motion along a fixed length. However, on the one hand, this regular motion is far from the actual running trajectory of the robot; on the other hand, this regular motion scheme is difficult to cover various extreme working conditions that may occur during the operation of the surgical robot. Therefore, there is an urgent need for a test method that can be based on the actual running trajectory data of the robot and focus on the mechanical stress characteristics, in order to improve the coverage of the accelerated life test to the real working conditions and avoid missing potential extreme failure modes. SUMMARY

[0003] The present application is aimed at the above technical problems, and provides a high-stress trajectory extraction method, which can automatically extract a high-stress trajectory sequence for accelerated test of articulated mechanical devices.

[0004] To solve the above technical problems, the application provides a high-stress trajectory extraction method for a joint mechanical device, the joint mechanical device comprising at least one movable joint, comprising: acquiring an n-dimensional stress vector, the n-dimensional stress vector corresponding to n-dimensional stress data of the joint mechanical device during operation, the n-dimensional stress data being related to stress received by the joint mechanical device during operation; performing dimension reduction processing on the n-dimensional stress vector to obtain a k-dimensional feature vector, wherein n and k are positive integers, and n > k; generating a stress level index according to the n-dimensional stress vector, the stress level index being used to measure the overall stress of the joint mechanical device; acquiring a target stress section of the joint mechanical device during the operation according to the stress level index, wherein the stress level index of the joint mechanical device in the target stress section is greater than a stress threshold; constructing a stress distribution model, the stress distribution model comprising a stress amplitude distribution model and a stress mode distribution model, wherein the stress amplitude distribution model is constructed according to a probability distribution of the stress level index during operation of the joint mechanical device, and the stress mode distribution model is constructed according to a distribution of the k-dimensional feature vector in a feature space, wherein the distribution of the k-dimensional feature vector in the feature space is used to reflect load conditions of multiple stress change modes; and determining a strategy for joint mechanical device accelerated testing according to the stress distribution model, comprising: determining a time compression ratio of the joint mechanical device accelerated testing according to the stress amplitude distribution model; selecting a representative stress segment from the target stress section according to the stress mode distribution model; adjusting a target proportion of the representative stress segment in a joint mechanical device accelerated testing sequence according to the time compression ratio; and splicing the representative stress segment according to the target proportion to generate a high-stress trajectory sequence for joint mechanical device accelerated testing.

[0005] In an embodiment of the application, the dimension reduction processing on the n-dimensional stress vector comprises principal component analysis on the n-dimensional stress vector, and the k-dimensional feature vector is a k-dimensional principal component feature vector, and the feature space is a principal component feature space.

[0006] In an embodiment of the application, generating a stress level index according to the n-dimensional stress vector comprises taking a Euclidean norm of the n-dimensional stress vector as the stress level index, or taking a Euclidean norm of the k-dimensional feature vector as the stress level index.

[0007] In an embodiment of the present application, the target stress section of the articulated mechanical device during the operation is obtained according to the stress level indicator, including: determining a current time as a starting time of the target stress section in response to a duration that the stress level indicator is greater than a stress threshold being greater than or equal to a first duration on a time axis; and determining the current time as an ending time of the target stress section in response to a duration that the stress level indicator is less than or equal to the stress threshold being greater than or equal to a second duration.

[0008] In an embodiment of the present application, after the target stress section of the articulated mechanical device during the operation is obtained according to the stress level indicator, the method further includes: merging two adjacent target stress sections with a time interval less than an interval threshold into one target stress section.

[0009] In an embodiment of the present application, the time compression ratio of the articulated mechanical device acceleration test is determined according to the stress amplitude distribution model, including: determining a time proportion of a target stress according to the stress amplitude distribution model, wherein an amplitude of the target stress is greater than an amplitude threshold; obtaining a target time proportion of the target stress expected in the articulated mechanical device acceleration test; and taking a ratio of the target time proportion and the time proportion as the time compression ratio.

[0010] In an embodiment of the present application, the stress pattern distribution model is constructed by the following method: applying a clustering algorithm to divide the k-dimensional principal component feature vectors into several clusters in a principal component feature space, wherein each cluster corresponds to a stress change pattern; and constructing the stress pattern distribution model by using a cluster center and a cluster frequency of each cluster, wherein the cluster center represents an average load of the corresponding stress change pattern, and the cluster frequency represents an occurrence frequency of the corresponding stress change pattern in the operation process of the articulated mechanical device.

[0011] In an embodiment of the present application, the representative stress segment is selected from the target stress section according to the stress pattern distribution model, including: selecting the target stress section in which the cluster center and the cluster center appear as the representative stress segment.

[0012] In an embodiment of the present application, the target proportion of the representative stress segment in the articulated mechanical device acceleration test sequence is adjusted according to the time compression ratio, including: obtaining a cluster probability of the representative stress segment; and taking a product of the cluster probability and the time compression ratio as the target proportion of the representative stress segment in the articulated mechanical device acceleration test sequence, wherein the target proportions of the adjusted stress change patterns satisfy: wherein, represents the target proportion of the jth stress change pattern.

[0013] In an embodiment of the present application, the splicing the representative stress segments according to the target proportion comprises: splicing the plurality of representative stress segments in a cyclic or random order, so that the proportion of each representative stress segment in the high stress trajectory sequence is equal to the target proportion.

[0014] The high stress trajectory extraction method of the articulated mechanical device of the present application can construct a stress distribution model based on actual operation data of the articulated mechanical device, and then determine an acceleration test strategy of the articulated mechanical device according to the stress distribution model, so as to extract a high stress trajectory sequence for the acceleration test of the articulated mechanical device. The high stress trajectory sequence can realistically reflect the mechanical load characteristics of the articulated mechanical device in the actual operation process, and the experimental stress spectrum is consistent with the true stress spectrum, thereby greatly improving the coverage and acceleration efficiency of the acceleration life test of the articulated mechanical device, and providing a powerful tool for the reliability verification of the articulated mechanical device. BRIEF DESCRIPTION OF DRAWINGS

[0015] The accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this application, illustrate embodiments of the present application, and together with the description serve to explain the principles of the present application. In the drawings:

[0016] Figure 1 is an exemplary flowchart of the high stress trajectory extraction method of the articulated mechanical device according to an embodiment of the present application;

[0017] Figure 2 is a histogram diagram of the stress amplitude distribution of the stress level index obtained by the high stress trajectory extraction method according to an embodiment of the present application;

[0018] Figure 3 is a schematic diagram of a target stress section obtained by the high stress trajectory extraction method according to an embodiment of the present application;

[0019] Figure 4 is an exemplary flowchart of constructing a stress mode distribution model by the high stress trajectory extraction method according to an embodiment of the present application;

[0020] Figure 5 is a schematic diagram of the stress mode distribution result of the stress mode distribution model constructed by the high stress trajectory extraction method according to an embodiment of the present application;

[0021] Figure 6 is an exemplary flowchart of determining a time compression ratio of the acceleration test of the articulated mechanical device by the high stress trajectory extraction method according to an embodiment of the present application;

[0022] Figure 7is an exemplary flowchart of adjusting target proportion of representative stress segments in a kinematic mechanical device acceleration test sequence according to time compression ratio according to an embodiment of the present application;

[0023] Figure 8 is a comparison diagram of overall stress spectrum of high stress trajectory sequence generated by the high stress trajectory extraction method according to the present application and the real spectrum;

[0024] Figure 9 is a system block diagram of the high stress trajectory extraction device of the kinematic mechanical device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can also be applied to other similar scenarios without creative labor. Unless it is clear from the language context or otherwise indicated, the same reference numbers in the drawings represent the same structure or operation.

[0026] As shown in the present application, unless the context clearly indicates otherwise, "one", "a", "an", and / or "the" do not specify a single number but can include a plurality. Generally, the terms "comprising" and "including" only indicate including the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0027] Unless otherwise specifically indicated, the relative arrangement of the components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that the sizes of the various parts shown in the drawings are not drawn in accordance with the actual proportional relationship. The technology, methods and devices known to those skilled in the relevant art can not be discussed in detail, but under appropriate circumstances, the technology, methods and devices should be considered as part of the specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, and not as a limitation. Therefore, other examples of exemplary embodiments can have different values. It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0028] In addition, it should be noted that the use of the terms "first", "second" and the like does not imply any special meaning, and is merely used to distinguish the corresponding components, unless otherwise stated. In addition, although the terms used in the present application are selected from commonly known terms, some terms mentioned in the present application may be selected by the applicant according to his or her judgment, and the detailed meanings thereof are described in the relevant part of the description. In addition, the present application is required to be understood not only by the actual terms used, but also by the meaning implied by each term.

[0029] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously. Meanwhile, other operations can be added to or removed from these processes.

[0030] The present inventors have found that in real surgical robot logs, the force on the robot end effector can fluctuate greatly in a very short time. For example, the combined force on the tool tip at a certain moment can suddenly rise to about 68.6 N and then quickly drop to about 15.3 N. Such dramatic mechanical load changes cannot be accurately reproduced by constant high temperature, random vibration and other environmental stress tests. Therefore, the present application proposes a high stress trajectory extraction method and device for articulated mechanical devices, which can scientifically extract high stress segments based on real data of articulated mechanical devices including robots during actual operation, to form a high stress trajectory sequence that can be used for accelerated testing of articulated mechanical devices. According to the high stress trajectory sequence, life testing can effectively reproduce the wear and failure modes of articulated mechanical devices in actual use.

[0031] It should be noted that the articulated mechanical device described in the present application refers to a mechanical device comprising at least one movable joint, such as a mechanical arm, a robot, etc. The present application is described by taking a surgical robot as an example, but is not limited thereto.

[0032] Figure 1 is an exemplary flowchart of the high stress trajectory extraction method of the articulated mechanical device according to an embodiment of the present application. Referring to Figure 1 The high stress trajectory extraction method 100 of the embodiment includes the following steps:

[0033] Step S110: Obtain an n-dimensional stress vector, the n-dimensional stress vector corresponding to n-dimensional stress data of the articulated mechanical device during operation, the n-dimensional stress data being related to the stress received by the articulated mechanical device during operation;

[0034] Step S120: Dimensionality reduction is performed on the n-dimensional stress vector to obtain a k-dimensional feature vector, where n and k are positive integers, and n > k;

[0035] Step S130: A stress level indicator is generated according to the n-dimensional stress vector, and the stress level indicator is used to measure the overall stress of the articulated mechanical device;

[0036] Step S140: A target stress section of the articulated mechanical device during operation is obtained according to the stress level indicator, where the stress level indicator of the articulated mechanical device in the target stress section is greater than a stress threshold;

[0037] Step S150: A stress distribution model is constructed, including a stress amplitude distribution model and a stress mode distribution model, where the stress amplitude distribution model is constructed according to the probability distribution of the stress level indicator during the operation of the articulated mechanical device, and the stress mode distribution model is constructed according to the distribution of the k-dimensional feature vector in the feature space, where the distribution of the k-dimensional feature vector in the feature space is used to reflect the load conditions of multiple stress change modes;

[0038] Step S160: A strategy for the accelerated test of the articulated mechanical device is determined according to the stress distribution model, including: determining a time compression ratio of the accelerated test of the articulated mechanical device according to the stress amplitude distribution model; selecting a representative stress segment from the target stress section according to the stress mode distribution model; adjusting a target proportion of the representative stress segment in the sequence of the accelerated test of the articulated mechanical device according to the time compression ratio; and splicing the representative stress segment according to the target proportion to generate a high stress trajectory sequence for the accelerated test of the articulated mechanical device.

[0039] According to the above high stress trajectory extraction method 100, the stress distribution model can be constructed based on the actual operation data of the articulated mechanical device, and the strategy for the accelerated test of the articulated mechanical device is determined according to the stress distribution model, and the high stress trajectory sequence for the accelerated test of the articulated mechanical device is extracted, which can realistically reflect the mechanical load characteristics of the articulated mechanical device during actual operation, and the experimental stress spectrum is consistent with the true stress spectrum, thereby greatly improving the coverage and acceleration efficiency of the accelerated life test of the articulated mechanical device, and providing a powerful tool for the reliability verification of the articulated mechanical device.

[0040] The above steps S110-S160 will be described below with reference to the accompanying drawings.

[0041] In step S110, n is a positive integer greater than or equal to 1. It should be noted that the n-dimensional stress data is actual data related to the stress received by the articulated mechanical device during operation. The required n-dimensional stress data can be extracted from the operation trajectory log of the surgical robot in a real surgery or a simulation operation. The log usually includes the motion and force information of each joint of the robot, such as time-series joint angle, joint driving force / current, end effector position and attitude, and force, etc. In step S110, the original stress-related data can be first extracted from the log as the n-dimensional stress data. For example, the n-dimensional stress data at least includes the joint motor torque of the articulated mechanical device. Taking a six-axis surgical robot as an example, it includes 6 joints, and each joint has a corresponding joint motor torque, so the dimension of the n-dimensional stress data is equal to 6, including 6 joint motor torques.

[0042] In some embodiments, the n-dimensional stress data includes joint motor torque and end force. The end force can be the force condition of the end effector. The end force can be specifically the three-dimensional force sensor reading of the end tool of the robot recorded in the log. For the robot system without direct force sensor, the joint motor torque and end force can also be obtained by calculation through the dynamics model. The n-dimensional stress data collected at each time is arranged in vector form to obtain an n-dimensional stress vector. For example, at a certain time, the joint motor torque of the six-axis robot is measured as: (N·m), the end force components are F x , F y , and F z (N). Exemplarily, a 9-dimensional stress vector may be constructed as:

[0043] ,

[0044] wherein, are the joint motor torques of the 6 joints. are the force components of the end force in the X-axis, Y-axis and Z-axis in space, respectively. t represents time. It can be understood that the n-dimensional stress vector contains time information and is a function of time.

[0045] In practical applications, the composition of the stress vector can be selected according to the structure and sensor configuration of the specific articulated mechanical device.

[0046] In some embodiments, the n-dimensional stress data further comprises any of joint motor current, driving power and joint speed. For example, for the case of robot force sensor, the joint motor current, driving power, joint speed and other stress-related alternative indicators can be selected as the n-dimensional stress data. These alternative indicators, although not direct stress data, can also reflect the stress situation of the robot. For example, the joint motor current has a positive correlation with the stress, and then by establishing the relationship between the joint motor current and the stress size, the joint motor current can be used as the stress data to reflect the stress size of the robot.

[0047] In some embodiments, after obtaining the n-dimensional stress vector in step S110, further comprising: preprocessing the stress component of each dimension, such as filtering and denoising, removing the gravity static load component, normalizing the dimension, etc., to obtain stable and reliable stress indication signals. After this step, the stress vector in the form of time series is obtained , which lays the foundation for subsequent analysis. Wherein i=1~T, T is the total number of time sampling points.

[0048] In step S120, the specific dimension reduction processing method is not limited in the present application. Any dimension reduction processing method in the prior art can be used. In some embodiments, the n-dimensional stress vector is processed by dimension reduction, including: performing principal component analysis (PCA) on the n-dimensional stress vector, the k-dimensional feature vector is a k-dimensional principal component feature vector, and the feature space is a principal component feature space.

[0049] In these embodiments, the n-dimensional stress vector sequence obtained in step S110 can be input into a principal component analysis module to extract the main stress change mode feature. The principal component analysis module here can be a software module for implementing principal component analysis. For example, the following steps can be used for the principal component analysis:

[0050] (1) The n-dimensional stress vector is centered by dimension, that is, the mean value of each dimension component is subtracted, to eliminate the influence of different dimensions.

[0051] (2) Construct a data matrix , where T is the total number of time sampling points, and n is the dimension of the stress vector. For example, n=9 dimensions. The length T of the stress vector sequence can reach millions (depending on the log length and sampling frequency). Since the direct feature decomposition of the high-dimensional matrix has a huge calculation amount, in some embodiments, the singular value decomposition (SVD) technique is used to efficiently implement PCA. Including: using existing linear algebra library to perform SVD on the matrix X:

[0052] ,

[0053] Where , , The column vectors of matrix V are the eigenvectors of matrix V , representing the principal component directions of the original stress data. The singular values on the diagonal matrix are proportional to the variance of the corresponding principal component directions. By selecting the first k principal components (such that reaches a predetermined variance contribution rate threshold, such as 95%), the original stress data can be approximately reconstructed by a linear combination of the k principal component directions.

[0054] (3) Project the original stress vector onto the selected principal component subspace:

[0055] ,

[0056] to obtain the k-dimensional principal component eigenvector . These principal component features are used to reveal the main change patterns in the robot stress data. The stress change patterns of the robot can include: changes in "overall force amplitude", "patterns of specific two joints in opposite directions", "independent patterns of the end in a certain direction", etc. The stress change patterns of the robot are related to the n-dimensional stress vector. However, due to the large amount of data of the n-dimensional stress vector, in step S120, the k-dimensional eigenvector is obtained after dimensionality reduction processing, which also has a high correlation with the stress change pattern. That is, each stress change pattern is related to the plurality of principal component features, so that in the subsequent step, different stress change patterns can be reflected by the k-dimensional principal component eigenvector.

[0057] Step S120 reduces the data dimension and noise interference of subsequent analysis through dimensionality reduction processing, making it easier for us to identify abnormal stress changes.

[0058] In step S130, the stress level indicator can be defined in different ways. In some embodiments, generating the stress level indicator according to the n-dimensional stress vector includes taking the Euclidean norm of the n-dimensional stress vector as the stress level indicator . Following the previous example, it can be specifically expressed as:

[0059] ,

[0060] i.e. the Euclidean norm of each stress component, as a measure of the overall force and the stress degree at each moment.

[0061] In other embodiments, generating the stress level indicator according to the n-dimensional stress vector includes taking the k-dimensional principal component eigenvector the Euclidean norm of the stress vector as the stress level indicator. According to these embodiments, only the information in the principal component space is considered, and the stress vector norm reconstructed using the first k principal components is used As an approximation, the following is used:

[0062] ,

[0063] The time instants with large stress will also have large projections in the principal component space, and thus can also be used as a measure of the overall stress and the stress level at each time instant.

[0064] In other embodiments, the stress level indicator can also include, for specific failure modes, the percentage of the joint motor torque approaching the upper limit of the rating, or the force in a specific direction at the end exceeding a certain threshold, etc.

[0065] In step S140, the target stress section, i.e., the high stress period of the joint mechanical device during operation. To determine the high stress period, a high stress instant is determined according to a preset stress threshold. In some embodiments, the high stress instant can be located by traversing the stress level indicator time series: when the stress threshold is exceeded , the instant is marked as a "high stress instant".

[0066] In some embodiments, the stress threshold is determined according to the statistical distribution of the n-dimensional stress data.

[0067] Figure 2 is a histogram diagram of the stress amplitude distribution of the stress level indicator obtained by the high stress trajectory extraction method according to an embodiment of the present application. The horizontal axis is the stress magnitude of the stress level indicator, and the scale is Newton (N) or normalized vector (norm). The vertical axis is the frequency or frequency (Frequency), which is unitless. As Figure 2 shown, the stress amplitudes with higher occurrence frequencies are in the middle position. The occurrence frequency of the relatively high stress amplitude is low. For example, the value corresponding to the tail 5% of the stress data distribution is selected as the stress threshold (High Stress Threshold) , which is represented by a vertical dashed line. The stress amplitudes higher than the stress threshold correspond to high stress. In other embodiments, an absolute value can be set as the stress threshold according to the mechanical tolerance of the material / component .

[0068] Considering that the failure of a joint or component in practical applications is often not caused by a transient peak value, but by the cumulative effect of sustained high stress, after obtaining the high stress instant, the "high stress section" also needs to be further identified.

[0069] In some embodiments, the duration of the target stress section is greater than or equal to a preset duration. According to these embodiments, some unexpected high stress moments, such as noise data, can be avoided from being divided into target stress sections. Therefore, a preset duration is set, and only when the duration of stress data exceeding the stress threshold value reaches the preset duration, the stress data is regarded as a target stress section, and if the duration is less than the preset duration, the stress data is not regarded as a target stress section.

[0070] In some embodiments, the target stress section of the articulated mechanical device during the operation process is obtained according to the stress level index in step S140, including: in the time axis, in response to the duration that the stress level index is greater than the stress threshold value is greater than or equal to the first duration, determining the current time as the starting time of the target stress section; in response to the duration that the stress level index is less than or equal to the stress threshold value is greater than or equal to the second duration, determining the current time as the ending time of the target stress section. According to these embodiments, in order to avoid the interference of noise, a delay or a required duration is added to the stress threshold value described in the foregoing. For example, when the duration that the stress level index continuously exceeds the first duration is greater than or equal to the second duration, it is determined that the high stress section is entered, and the time point is recorded as the starting point of the high stress section. When the duration that the stress level index continuously exceeds the first duration is less than the second duration, the high stress section is exited, and the time point is recorded as the ending point of the high stress section. In this way, a series of time periods are obtained, …, and the mechanical stress of the robot in each section is maintained at a high level. These sections correspond to the relatively violent or laborious action fragments in the operation process, and are most likely to be the working conditions that have a greater impact on the service life of the robot. The identified high stress trajectory fragment set is output in this step, together with the starting and ending time stamps of each fragment and the corresponding stress characteristic description.

[0071] In some embodiments, the first duration and the second duration can be the same or different.

[0072] In some embodiments, after the target stress section is obtained in step S140, it further includes: merging two adjacent target stress sections with a time interval less than an interval threshold into one target stress section. In some cases, there can be some short pauses in the continuous operation of the robot, and these operations actually belong to the same type of action or the same stress change mode. According to these embodiments, merging adjacent target stress sections can further reduce the number of target stress sections.

[0073] ​​​​​​​​​​​Figure 3 This is a schematic diagram of a target stress section obtained according to a high-stress trajectory extraction method according to an embodiment of this application. Figure 3 As shown, the horizontal axis represents time (in seconds), and the vertical axis represents stress magnitude. The horizontal dashed line represents the stress threshold. ,and Figure 3 The stress threshold shown Same. Gray areas 310 and 320 represent several target stress segments (smaller areas) located within [60, 80].

[0074] In step S150, the stress amplitude distribution model can be the probability distribution of s(t) during the operation of the articulated mechanical device. For example, it can be specifically represented as... Figure 2 The histogram of the stress level index s(t) is shown. Alternatively, the probability function p(s) of the stress level index s(t) can be used. From this, the proportion of time covered by different stress levels (low, medium, high) can be determined. For example, a robot may experience combined stress below 50% of its rated load for 80% of the time, between 50% and 90% for 19% of the time, and less than 1% of the time above 90% of its limit load. This distribution helps to set the stress spectrum for accelerated testing, making its overall distribution consistent with real-world usage, while also allowing for the artificial amplification of the proportion of high-stress components to shorten the testing time.

[0075] Figure 4 This is an exemplary flowchart illustrating the construction of a stress mode distribution model using a high-stress trajectory extraction method according to an embodiment of this application. (Reference) Figure 4 As shown, in some embodiments, the stress mode distribution model in step S150 can be constructed using the following method:

[0076] Step S410: Apply a clustering algorithm to divide the n-dimensional stress vector into several clusters in the principal component space, where each cluster corresponds to a stress change mode;

[0077] Step S420: Construct a stress mode distribution model using the cluster center and frequency of each cluster, where the cluster center represents the average load of the corresponding stress change mode, and the cluster frequency represents the frequency of occurrence of the corresponding stress change mode during the operation of the articulated mechanical device.

[0078] According to steps S410 and S420, a clustering algorithm (such as k-means or Gaussian mixture model) is applied to divide all stress vectors into several clusters in the principal component space, with each cluster corresponding to a common stress variation pattern.

[0079] Figure 5This is a schematic diagram of the stress mode distribution result of a stress mode distribution model constructed according to a high-stress trajectory extraction method of an embodiment of this application. (Reference) Figure 5 As shown, three clusters obtained based on the first and second principal components are illustrated: Cluster 1, Cluster 2, and Cluster 3. The cluster center is indicated by an "×". The horizontal axis represents the direction of the first principal component, indicating the pattern with the largest variance in the stress data. The vertical axis represents the direction of the second principal component, which is orthogonal to the first principal component. Both axes are mathematically projected feature quantities and have no physical units. Cluster 1 might correspond to a "low-load idle / small-motion pattern," Cluster 2 to a "high-load pattern with a combination of specific joints," and Cluster 3 to a "high-resistance pattern encountered in a specific end effector direction," etc. After clustering, the cluster centers can be obtained. and the frequency of clusters (The proportion of the total data points). The cluster center represents the average load condition of this stress mode, and the frequency reflects its probability of occurrence in actual operation. In some embodiments, the dispersion (variance or covariance matrix) of the data within each cluster can also be calculated to understand the range of stress variation under this mode.

[0080] The stress variation modes described above are merely examples, and specific modes can be defined based on the motion characteristics of the articulated mechanical device itself. In some embodiments, the stress variation modes include any of the following: an idle / small motion mode where most joints are under low load; a mode where two joints are subjected to opposite forces; a mode where a combination of several specific joints bears a high load; and a mode where high resistance is encountered in a specific direction at the end.

[0081] also, Figure 5 The illustration is merely an example. In other embodiments, multiple clusters can be obtained simultaneously based on three or more principal components, each cluster corresponding to a stress variation pattern. According to these embodiments, it is equivalent to each stress variation pattern being associated with the eigenvectors of the three or more principal components.

[0082] In some embodiments, a stress mode distribution model can also be constructed by fitting a multivariate probability distribution.

[0083] Step S150 allows for the simultaneous acquisition of two stress distribution models: a stress amplitude distribution model and a stress mode distribution model. Combining these two models provides a complete description of the robot's stress distribution characteristics over time: the cumulative probability of amplitude and the proportion of stress modes. This provides a data foundation for developing accelerated testing load spectra.

[0084] The step S160 is to use the stress distribution model established in step S150 to formulate a motion trajectory sampling and combination strategy for the accelerated life test, and output several typical high stress trajectory segment sequences for the test. According to step S160, the frequency of occurrence of high stress rare events in the test can be improved while maintaining the overall stress spectrum similar to the real spectrum.

[0085] Figure 6 is an exemplary flowchart of determining the time compression ratio of the accelerated test of the articulated mechanical device according to the high stress trajectory extraction method of an embodiment of the present application. In some embodiments, determining the time compression ratio of the accelerated test of the articulated mechanical device according to the stress amplitude distribution model in step S160 comprises:

[0086] Step S610: determining the time proportion of the target stress according to the stress amplitude distribution model, wherein the amplitude of the target stress is greater than the amplitude threshold;

[0087] Step S620: obtaining the target time proportion of the target stress expected in the accelerated test of the articulated mechanical device;

[0088] Step S630: taking the ratio of the target time proportion and the time proportion as the time compression ratio.

[0089] The following illustrates steps S610-S630.

[0090] Suppose according to the actual data, the time proportion of the stress with amplitude or intensity > 90% in the total time T is 0.5% (as shown in Figure 2 the stress part on the right side of the stress threshold in the figure), and according to the design of the accelerated test, it is expected that such high stress accounts for 5% in the test, which is equivalent to 10 times of time acceleration, i.e. the time compression ratio R = 10. The expected target time proportion can be determined in combination with the project time requirement and equipment tolerance.

[0091] In some embodiments, selecting the representative stress segment from the target stress section according to the stress pattern distribution model in step S160 comprises: selecting the target stress section that has appeared at the cluster center and near the cluster center as the representative stress segment. According to these embodiments, in each main stress pattern cluster, the representative trajectory segment is selected as the test load unit, which should cover the typical situation of stress change in the cluster. Wherein, near the cluster center can be defined as the area within the circumference or any shape with the cluster center as the center and the distance from the cluster center within the preset radius. In some embodiments, for particularly important or extreme stress patterns (for example, the stress level of the cluster is close to the upper limit of the robot capability), multiple different segments can be selected to prevent accidental deviation.

[0092] Figure 7is an exemplary flowchart of adjusting target proportion of a representative stress segment in a sequence of accelerated test trajectories of a jointed mechanical device according to a time compression ratio according to an embodiment of the present application. In some embodiments, adjusting target proportion of a representative stress segment in a sequence of accelerated test trajectories of a jointed mechanical device according to a time compression ratio in step S160 comprises:

[0093] Step S710: obtaining cluster probability of a representative stress segment;

[0094] Step S720: taking the product of the cluster probability and the time compression ratio as the target proportion of the representative stress segment in the sequence of accelerated test trajectories of the jointed mechanical device, wherein the adjusted target proportions of the stress change patterns satisfy: wherein, represents the target proportion of the jth stress change pattern.

[0095] In the accelerated test, in order to achieve the purpose of acceleration, it is necessary to appropriately increase the proportion of high-stress low-probability clusters. Therefore, according to steps S710 and S720, the target proportion of the representative stress segment can be adjusted according to the cluster probability.

[0096] For example, assume that the cluster probability of a representative stress segment is = 0.5%. The time compression ratio R = 10. Then the target proportion of the representative stress segment is = 5%. The sum of the adjusted target proportions of the stress change patterns is equal to 1, and corresponding to the high-stress cluster.

[0097] In some embodiments, splicing the representative stress segments according to the target proportions in step S160 comprises: splicing the plurality of representative stress segments in a cyclic or random order, so that the proportion of each representative stress segment in the sequence of test trajectories is equal to the target proportion. After splicing, a long-time test trajectory sequence can be generated. The target proportion also corresponds to the number of repetitions of each type of stress segment in the experimental load sequence. The higher the target proportion, the more the number of repetitions. The present application does not limit the specific splicing order of each representative stress segment.

[0098] In some embodiments, after splicing the representative stress segments according to the target proportions, it further comprises: inserting a transition segment between adjacent representative stress segments, the transition segment corresponding to a smooth movement of low stress. According to these embodiments, it can avoid the robot jumping suddenly from one extreme condition to another extreme condition, and can ensure the rationality and safety of the test load change.

[0099] The overall length of the high-stress trajectory sequence generated by the high-stress trajectory extraction method according to the present application is shortened by about R times relative to the real use time, and various high-stress events that may occur several times in the real situation are covered in this compressed time. At the same time, the overall stress spectrum of the high-stress trajectory sequence is similar to the real spectrum.

[0100] Figure 8 is a comparison diagram of the overall stress spectrum of the high-stress trajectory sequence generated by the high-stress trajectory extraction method according to the present application and the real spectrum. Among them, the test stress spectrum 820 (Test Stress Spectrum) represents the overall stress spectrum of the high-stress trajectory sequence, and the real stress spectrum 810 (Real Stress Spectrum) represents the overall stress spectrum of the real data. As shown in Figure 8 , the horizontal axis is time, in seconds (s), and the vertical axis is stress magnitude (Stress Magnitude), in Newton (N) or normalized quantity (norm). Obviously, the change trend of the real stress spectrum 810 and the test stress spectrum 820 is similar. As can be seen, the high-stress trajectory sequence generated by the method of the present application can represent the stress distribution of the real data.

[0101] In carrying out the accelerated life test, the above-mentioned high-stress trajectory sequence can be loaded into the surgical robot control system for repeated execution, together with the traditional environmental stress (such as normal room temperature or slightly increased environmental vibration). In the test process, the response of the key components of the robot under these high-stress repeated loads (such as temperature rise, wear, gap change, etc.) is monitored, and the abnormal or failure time point is recorded. Compared with the traditional method, since the high-stress events are greatly condensed, the present test can induce potential failure modes in a shorter time.

[0102] The beneficial effects of the present application are as follows:

[0103] (1) Realistic reproduction of real working conditions. Based on the actual running data of the surgical robot, the stress distribution model is constructed, and the extracted test trajectory can realistically reflect the mechanical load characteristics of the robot in the surgical process, rather than just a simple constant environmental stress. This ensures that the test is more close to the real working condition, the stress spectrum is more reasonable and comprehensive, and the relevance of the life test results to the actual use is improved.

[0104] (2) Automatically extract high-stress segments and cover extreme scenarios. The present application automatically scans the massive running logs through the algorithm, accurately identifies the key segments with the highest mechanical stress, and avoids the omission that may occur in manual experience screening. The typical trajectory library generated thereby covers various high-stress scenarios, including extreme working conditions that are rare in normal operation but may cause serious wear or failure, greatly improving the coverage rate of the accelerated life test on potential failure modes and avoiding the risk of missing extreme failure modes.

[0105] (3) High test efficiency and significant acceleration effect. By increasing the proportion of high stress trajectories in the test sequence, the acceleration of robot life consumption is realized. That is, in a shorter period of time, repeatedly apply high stress loads far exceeding the usual frequency, so that key components can accumulate fatigue and wear faster. Compared with traditional random vibration or uniform task sequence, the test sequence generated by the application can more effectively "accelerate" the equivalent use time, shorten the required period of life test, and significantly improve the test efficiency.

[0106] (4) Quantitative guidance for test design. Stress principal component analysis and statistical modeling are used to provide quantitative basis for test load spectrum design. Test planners can intuitively understand the importance and frequency of various stress modes according to the stress distribution model, and then adjust the acceleration factor and sampling strategy accordingly. For example, according to the one-in-ten thousand probability of an extreme torque event in actual use, the occurrence frequency can be appropriately increased to one in one thousand in the accelerated life test, so as to ensure the rigor of the test while considering the reasonable test time. This method makes the design of accelerated life test more scientific and transparent.

[0107] (5) The method of the application can adaptively adjust the selection of stress indicators and threshold settings according to different types of robots and different types of surgical operations, and has universality.

[0108] The actual application shows that the life test by the test trajectory generated by the application can more effectively reproduce the wear and failure modes of the robot in clinical use. On the one hand, the test data verify that the high stress trajectories extracted by the method do indeed play a dominant role in causing failure; on the other hand, it also proves that the accelerated test does not introduce unrealistic invalid stress, thereby ensuring the effectiveness and scientificity of the test. Therefore, the application provides an efficient and intelligent loading trajectory generation technology for accelerated life test of articulated mechanical devices, which can significantly improve the coverage depth and acceleration ratio of reliability test, and provide protection for the safe and stable operation of the product.

[0109] The application also includes a high stress trajectory extraction device for an articulated mechanical device, comprising a memory and a processor. The memory is used to store instructions executable by the processor; the processor is used to execute the instructions to realize the high stress trajectory extraction method for the articulated mechanical device described above.

[0110] Figure 9 is a system block diagram of the high stress trajectory extraction device for the articulated mechanical device according to an embodiment of the application. Referring to Figure 9As shown, the high stress trajectory extraction device 900 can include an internal communication bus 901, a processor 902, a read only memory (ROM) 903, a random access memory (RAM) 904, and a communication port 905. When applied on a personal computer, the high stress trajectory extraction device 900 can also include a hard disk 906. The internal communication bus 901 can enable data communication among the components of the high stress trajectory extraction device 900. The processor 902 can make decisions and issue prompts. In some embodiments, the processor 902 can be composed of one or more processors. The communication port 905 can enable data communication between the high stress trajectory extraction device 900 and the outside. In some embodiments, the high stress trajectory extraction device 900 can send and receive information and data from a network through the communication port 905. The high stress trajectory extraction device 900 can also include different forms of program storage units and data storage units, such as the hard disk 906, the read only memory (ROM) 903, and the random access memory (RAM) 904, which can store various data files used by the computer processing and / or communication, and possible program instructions executed by the processor 902. The processor executes these instructions to implement the main part of the method. The results of the processor processing are transmitted to the user equipment through the communication port, and displayed on the user interface.

[0111] The high stress trajectory extraction method described above can be implemented as a computer program, stored in the hard disk 906, and loaded into the processor 902 for execution, to implement the high stress trajectory extraction method of the present application.

[0112] The present application also includes a computer readable medium storing computer program code, which, when executed by a processor, implements the high stress trajectory extraction method described above.

[0113] When the high stress trajectory extraction method is implemented as a computer program, it can also be stored in a computer readable storage medium as an article of manufacture. For example, the computer readable storage medium can include, but is not limited to, a magnetic storage device (e.g., a hard disk, a floppy disk, a magnetic strip), an optical disk (e.g., a compact disk (CD), a digital versatile disk (DVD)), a smart card, and a flash memory device (e.g., an electrically erasable programmable read only memory (EPROM), a card, a stick, a key drive). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media (and / or storage media) that can store, contain, and / or carry code and / or instructions and / or data.

[0114] It should be understood that the embodiments described above are merely illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or combinations thereof.

[0115] Some aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The aforementioned hardware or software may be referred to as a "data block," "module," "engine," "unit," "component," or "system." The processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, aspects of this application may manifest as computer products residing in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical discs (e.g., compressed CDs, digital multifunction DVDs, etc.), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0116] A computer-readable medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or similar media, or any combination of the above media.

[0117] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0118] Also, the use of "a" or "an" or "the" are intended to include "one or more" and any singular form "a" or "an" or "the" is intended to include the plural forms as well, unless the context clearly indicates otherwise. Also, the term "comprising" is intended to include the terms "including", "including but not limited to", "including one or more of", and "constituting of", but not excluding other non-specified elements or steps.

[0119] In some embodiments, numerical descriptions of components, quantities of attributes are used. It should be understood that such numerical descriptions used in the description of the embodiments are, in some examples, modified by the words "about", "approximately", or "generally". Unless otherwise stated, "about", "approximately", or "generally" indicates that the stated numerical value allows for a ±20% variation. Accordingly, in some embodiments, the numerical parameters in the description are approximations which can vary depending on the desired properties sought to be obtained in light of the particular implementation of the embodiments. In some embodiments, numerical parameters should be considered in the context of the number of significant digits and errors inherent to measurement. Although the numerical ranges and parameters setting forth the broad scope of the embodiments recited in this application are approximations, unless otherwise indicated in a specific example, the numerical values set forth in specific examples are reported as precisely as possible.

Claims

1. A method for high stress trajectory extraction of an articulated mechanical device, said articulated mechanical device comprising at least one movable joint, characterized in that, The method comprises the following steps: acquiring an n-dimensional stress vector corresponding to n-dimensional stress data of the articulated mechanical device during operation, the n-dimensional stress data being related to stress suffered by the articulated mechanical device during operation; dimensionality reduction processing is performed on the n-dimensional stress vector to obtain a k-dimensional feature vector, wherein n and k are positive integers, and n>k; generating a stress level index according to the n-dimensional stress vector, the stress level index being used to measure the overall stress of the articulated mechanical device; acquiring a target stress section of the articulated mechanical device during the operation according to the stress level index, wherein the stress level index of the articulated mechanical device in the target stress section is greater than a stress threshold value; constructing a stress distribution model, the stress distribution model comprising a stress amplitude distribution model and a stress mode distribution model, wherein the stress amplitude distribution model is constructed according to a probability distribution of the stress level index during operation of the articulated mechanical device, and the stress mode distribution model is constructed according to a distribution of the k-dimensional feature vector in a feature space, wherein the distribution of the k-dimensional feature vector in the feature space is used to reflect load conditions of multiple stress change modes; and determining a strategy for an articulated mechanical device acceleration test according to the stress distribution model, comprising: determining a time compression ratio of the articulated mechanical device acceleration test according to the stress amplitude distribution model; selecting a representative stress segment from the target stress section according to the stress mode distribution model; adjusting a target proportion of the representative stress segment in the articulated mechanical device acceleration test sequence according to the time compression ratio; and splicing the representative stress segment according to the target proportion to generate a high stress trajectory sequence for the articulated mechanical device acceleration test.

2. The high-stress trajectory extraction method of claim 1, wherein, The dimensionality reduction processing on the n-dimensional stress vector comprises: performing principal component analysis on the n-dimensional stress vector, and the k-dimensional feature vector is a k-dimensional principal component feature vector, and the feature space is a principal component feature space.

3. The high-stress trajectory extraction method of claim 1, wherein, The generation of the stress level index according to the n-dimensional stress vector comprises: taking a Euclidean norm of the n-dimensional stress vector as the stress level index, or taking a Euclidean norm of the k-dimensional feature vector as the stress level index.

4. The high-stress trajectory extraction method of claim 1, wherein, The acquisition of the target stress section of the articulated mechanical device during the operation according to the stress level index comprises: in response to a duration during which the stress level index is greater than a stress threshold value being greater than or equal to a first duration, determining a current time as a starting time of the target stress section on a time axis; and in response to a duration during which the stress level index is less than or equal to a stress threshold value being greater than or equal to a second duration, determining the current time as an ending time of the target stress section.

5. The high-stress trajectory extraction method of claim 1, wherein, After the acquisition of the target stress section of the articulated mechanical device during the operation according to the stress level index, the method further comprises: merging two adjacent target stress sections with a time interval less than an interval threshold value into one target stress section.

6. The high-stress trajectory extraction method of claim 1, wherein, The determination of the time compression ratio of the articulated mechanical device acceleration test according to the stress amplitude distribution model comprises: determining a time proportion of a target stress according to the stress amplitude distribution model, wherein the target stress has an amplitude greater than an amplitude threshold; obtaining a target time proportion of the target stress expected in the acceleration test of the articulated mechanical device; and using a ratio of the target time proportion and the time proportion as the time compression ratio.

7. The high-stress trajectory extraction method of claim 2, wherein, The stress pattern distribution model is constructed by the following method: applying a clustering algorithm to divide the k-dimensional principal component feature vectors into several clusters in the principal component feature space, wherein each cluster corresponds to a stress change pattern; and constructing the stress pattern distribution model using a cluster center and a cluster frequency of each cluster, wherein the cluster center represents an average load of the corresponding stress change pattern, and the cluster frequency represents an occurrence frequency of the corresponding stress change pattern in the operation process of the articulated mechanical device.

8. The high-stress trajectory extraction method of claim 7, wherein, The selecting of the representative stress segment from the target stress section according to the stress pattern distribution model comprises: selecting the target stress section in which the cluster center and the cluster center appear as the representative stress segment.

9. The high-stress trajectory extraction method of claim 7, wherein, The adjusting of the target proportion of the representative stress segment in the acceleration test sequence of the articulated mechanical device according to the time compression ratio comprises: obtaining a cluster probability of the representative stress segment; and multiplication of the cluster probability and the time compression ratio as a target proportion of the representative stress segment in the articulated mechanical device acceleration test sequence, wherein the target proportions of the respective stress change patterns after adjustment satisfy: wherein, represents the target proportion of the jth stress change pattern.

10. The high-stress trajectory extraction method of claim 1, wherein, The splicing of the representative stress segment according to the target proportion comprises: splicing a plurality of the representative stress segments in a cyclic or random order, so that the proportion of each representative stress segment in the high stress trajectory sequence is equal to the target proportion.

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