Industrial robot automatic testing method and system

Abstract

The invention relates to the technical field of industrial robots, in particular to an industrial robot automatic testing method and system, and the method comprises the following steps: based on a robot communication interface, collecting a communication signal of a robot, measuring the delay of signal transmission, verifying the integrity of transmission data, and evaluating the quality of a communication link; and generating a signal quality analysis result. According to the method, accurate evaluation of the communication quality of the robot and construction of a path planning task and a dynamic test environment are realized by collecting communication signals, deep evaluation of the path planning capability of the robot is provided, and measurement of the execution precision of the robot is realized by combining deviation identification of an actual execution path and a target path; a sensor is used for collecting vibration data under various loads, abnormal vibration of the robot under load changes is monitored in real time by analyzing vibration amplitude and frequency changes, the stability and performance of the robot under various load conditions are diagnosed, and the load bearing capacity of the robot is judged.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robots, and in particular to an automated testing method and system for industrial robots. Background Art

[0002] The field of industrial robot technology includes the design, manufacture, control, application, and maintenance of industrial robots. It involves robot motion control, sensor application, intelligent systems, and robot collaboration. It is used in a variety of production scenarios, including automated assembly, material handling, welding, and spraying. It focuses on improving production efficiency and product precision, and combines the performance testing and evaluation of robots to ensure stability and reliability in actual applications. By improving the level of automation and intelligence, it reduces manual intervention and improves test accuracy.

[0003] Among them, the automated testing method for industrial robots conducts systematic testing of industrial robots through automated means, involving multiple aspects such as functional verification of the robot control system, testing of the robot's motion accuracy, and evaluation of the robot's load capacity. By setting up multiple standardized testing processes, multiple performance indicators are tested, covering motion control, load detection, and fault diagnosis. Through automated testing systems and sensor equipment, combined with data collection and analysis, multiple performance indicators of the robot are monitored in real time, and test reports are automatically output to ensure the stable operation of the robot in a variety of industrial applications.

[0004] Traditional automated testing methods for industrial robots lack comprehensive monitoring of the dynamic changes of robots under a variety of actual loads. They rely on static test conditions or standardized test processes and fail to respond to changes under different loads in real time, resulting in test results that lack sufficient pertinence and reliability. The evaluation of communication link quality usually relies on a fixed test environment and is unable to capture fluctuations and interference in signal transmission in real time in the actual working environment. It ignores sudden signal loss or delay issues. The evaluation of path planning capabilities only considers simple path errors and lacks evaluation in a dynamic environment. When performing robot vibration detection, it ignores the impact of load changes on vibration signals, and is unable to promptly identify and record abnormal vibration signals caused by load changes. As a result, potential failures of the robot under high loads are not discovered in a timely manner, affecting the accuracy of the test results and the reliability of actual applications. Summary of the invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides an industrial robot automatic testing method and system. The technical solution is as follows: On the one hand, a method for automated testing of an industrial robot is provided, the method comprising: S1: Based on the robot communication interface, collect the robot's communication signal, measure the delay of signal transmission, verify the integrity of the transmitted data, evaluate the quality of the communication link, and generate signal quality analysis results; S2: Based on the signal quality analysis result, a test scenario including multiple obstacle configurations and task objectives is constructed, a path planning task is configured, and the path planning capability of the robot is evaluated by analyzing the actual situation of the robot execution, thereby generating a path planning test result; S3: Based on the path planning test results, using an image acquisition device, real-time image data of the robot execution path is collected, the spatial deviation between the target path and the actual execution path is identified, and the deviation type is analyzed to generate path execution error data; S4: Based on the path execution error data, using sensors, real-time collection of vibration data of the robot under various load conditions, recording acceleration and frequency changes, detecting and recording abnormal vibration signals by analyzing vibration amplitude and frequency, and generating vibration abnormality detection results; S5: Based on the vibration anomaly detection results, by analyzing the working status data of the robot under various loads, including task execution time and task execution accuracy, the performance of the robot under various loads is predicted, the load bearing capacity of the robot is evaluated, and the robot test results are generated.

[0006] As a further solution of the present invention, the signal quality analysis results include signal transmission delay, data integrity verification results, and communication link quality scores; the path planning test results include path planning error information, predicted task completion time, and path planning capability evaluation information; the path execution error data include image data acquisition records, position deviation detection results, and angle error data; the vibration anomaly detection results include vibration amplitude information, vibration frequency change information, and abnormal vibration signal records; the robot test results include multi-load performance information, robot motion execution accuracy change curve, and load bearing capacity evaluation information.

[0007] As a further solution of the present invention, based on the robot communication interface, the communication signal of the robot is collected, the delay of signal transmission is measured, the integrity of the transmitted data is verified, the quality of the communication link is evaluated, and the steps of generating the signal quality analysis result are specifically as follows: S101: Based on the robot communication interface, monitor the communication signal of the robot, record the transmission status data of the signal, and generate original signal data; S102: Based on the original signal data, by analyzing the difference between the sending and receiving time points of the signal, measuring the transmission delay of the communication signal, and generating signal delay data; S103: Based on the signal delay data, verify the integrity of the transmitted data, identify packet loss and data corruption, evaluate the quality of the communication link, and generate a signal quality analysis result.

[0008] As a further solution of the present invention, based on the signal quality analysis result, by constructing a test scenario including multiple obstacle configurations and task objectives, configuring a path planning task, and evaluating the path planning capability of the robot by analyzing the actual situation of the robot's execution, the steps of generating the path planning test result are specifically as follows: S201: Based on the signal quality analysis result, a test scenario including multiple obstacle configurations and task objectives is constructed, a path planning task is configured, and test scenario data is generated; S202: Based on the test scenario data, record the deviation between the planned path of the robot and the preset path, calculate the path error, and record the task completion time to generate path planning execution data; S203: Based on the path planning execution data, the path planning capability of the robot is evaluated by analyzing the path error and the task completion time, and a path planning test result is generated.

[0009] As a further solution of the present invention, based on the path planning test results, an image acquisition device is used to collect image data of the robot execution path in real time, identify the spatial deviation between the target path and the actual execution path, and analyze the deviation type. The specific steps of generating path execution error data are as follows: S301: Based on the path planning test result, using an image acquisition device, capturing images of the robot's execution path in real time to generate image data acquisition records; S302: Based on the image data acquisition record, by analyzing the image, identifying the actual execution path of the robot and comparing it with the ideal path, identifying and recording the spatial deviation, and generating spatial deviation data; S303: Based on the spatial deviation data, identify the deviation type of path execution, including position deviation and angle error, and generate path execution error data.

[0010] As a further solution of the present invention, the specific formula for identifying and recording the spatial deviation is: ; Where D represents the spatial deviation between the actual path of the robot and the ideal path. Indicates the x-coordinate of the target point on the actual path of the robot, represents the x-coordinate of the corresponding point on the ideal path, Indicates the y coordinate of the target point on the actual path of the robot, represents the y coordinate of the corresponding point on the ideal path, Indicates the z coordinate of the target point on the actual path of the robot, Represents the z-coordinate of the corresponding point on the ideal path.

[0011] As a further solution of the present invention, based on the path execution error data, using sensors, real-time collection of vibration data of the robot under various load conditions, recording acceleration and frequency changes, and detecting and recording abnormal vibration signals by analyzing vibration amplitude and frequency, the steps of generating vibration abnormality detection results are specifically as follows: S401: Based on the path execution error data, collect vibration data of the machine under various load conditions in real time through sensors, record vibration amplitude and frequency changes, and generate a vibration data set; S402: Analyze the vibration amplitude and frequency based on the vibration data set, detect abnormal change vibration data, and generate vibration fluctuation analysis results; S403: Based on the vibration fluctuation analysis result, the time of abnormal fluctuation of the vibration signal, the real-time load, and the abnormality degree information are recorded to generate a vibration abnormality detection result.

[0012] As a further solution of the present invention, based on the vibration anomaly detection result, by analyzing the working status data of the robot under various loads, including task execution time and task execution accuracy, predicting the performance of the robot under various loads, and evaluating the load carrying capacity of the robot, the steps of generating the robot test results are specifically as follows: S501: Based on the vibration abnormality detection result, collect the working status data of the robot under various loads, including task execution time and action execution accuracy, and generate working status data; S502: Based on the working status data, by analyzing the error range of the robot under various load conditions, evaluating the impact of load changes on the execution accuracy of the robot, and generating a correlation analysis result; S503: Based on the correlation analysis results, predict the robot's task performance under various load conditions, evaluate and identify the robot's load bearing threshold, and generate robot test results.

[0013] As a further solution of the present invention, the specific formula for evaluating the impact of load changes on the robot's execution accuracy is: ; Where r represents the correlation coefficient between load and execution accuracy, represents the load value under the i-th load condition, represents the robot error value under the i-th load condition, N represents the total number of load conditions considered during the analysis, represents the average value of the load data, represents the average value of the error data, and i refers to the index value under specific load conditions.

[0014] On the other hand, an industrial robot automated testing system is provided, which is applied to an industrial robot automated testing method, and the system comprises: The communication signal analysis module is based on the robot communication interface, collects the robot's communication signals, including current waveforms and voltage changes, measures the delay of signal transmission and performs integrity check on the transmitted data, evaluates the quality of the communication link by calculating the signal attenuation and delay, and generates signal quality analysis results; The automation capability analysis module configures the robot path planning task based on the signal quality analysis result, constructs a test scenario including obstacle configuration and task objectives, evaluates the robot's path planning capability, and generates a path planning test result; The error detection module uses an image acquisition device to capture the image data of the robot execution path in real time based on the path planning test results, calculates the spatial deviation between the target path and the actual path, analyzes the deviation type, and generates path execution error data; The vibration monitoring module collects the vibration data of the robot under various loads in real time based on the path execution error data and uses sensors to detect abnormal vibration signals and generate vibration abnormality detection results; The load capacity evaluation module analyzes the working status data of the robot under various load conditions based on the vibration anomaly detection results, predicts the performance of the robot under different loads, evaluates and identifies the load bearing threshold of the robot, and generates robot test results.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: By collecting communication signals, the communication quality of the robot can be accurately evaluated. The construction of path planning tasks and dynamic test environments provides an in-depth evaluation of the robot's path planning capabilities. Combined with the deviation identification between the actual execution path and the target path, the robot's execution accuracy can be measured. Sensors are used to collect vibration data under various loads, and by analyzing the vibration amplitude and frequency changes, abnormal vibrations of the robot under load changes can be monitored in real time. The stability and performance of the robot under various load conditions can be diagnosed, and the robot's load-bearing capacity can be judged. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flow chart of S1 of the present invention; Figure 3 This is a detailed flow chart of S2 of the present invention; Figure 4 This is a detailed flow chart of S3 of the present invention; Figure 5 This is a detailed flow chart of S4 of the present invention; Figure 6 This is a detailed flow chart of S5 of the present invention; Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0023] The embodiment of the present invention provides an industrial robot automated testing method, such as Figure 1 The process flow of the method for automated testing of industrial robots shown in the figure may include the following steps: S1: Based on the robot communication interface, collect the robot's communication signal, measure the delay of signal transmission, verify the integrity of the transmitted data, evaluate the quality of the communication link, and generate signal quality analysis results; S2: Based on the signal quality analysis results, a test scenario including multiple obstacle configurations and task objectives is constructed, a path planning task is configured, and the robot's path planning capability is evaluated by analyzing the actual situation of the robot's execution, generating a path planning test result; S3: Based on the path planning test results, use the image acquisition device to collect the image data of the robot execution path in real time, identify the spatial deviation between the target path and the actual execution path, analyze the deviation type, and generate path execution error data; S4: Based on the path execution error data, sensors are used to collect the vibration data of the robot under various load conditions in real time, record the acceleration and frequency changes, detect and record abnormal vibration signals by analyzing the vibration amplitude and frequency, and generate vibration anomaly detection results; S5: Based on the vibration anomaly detection results, by analyzing the robot's working status data under various loads, including task execution time and task execution accuracy, the robot's performance under various loads is predicted, the robot's load carrying capacity is evaluated, and the robot test results are generated.

[0024] The signal quality analysis results include signal transmission delay, data integrity verification results, and communication link quality score. The path planning test results include path planning error information, predicted task completion time, and path planning capability evaluation information. The path execution error data includes image data acquisition records, position deviation detection results, and angle error data. The vibration anomaly detection results include vibration amplitude information, vibration frequency change information, and abnormal vibration signal records. The robot test results include multi-load performance information, robot motion execution accuracy change curve, and load bearing capacity evaluation information.

[0025] See also Figure 2 ,Based on the robot communication interface, the robot's communication signal is collected, the delay of signal transmission is measured, and the integrity of the transmitted data is verified to evaluate the quality of the communication link. The specific steps for generating the signal quality analysis results are as follows: S101: Based on the robot communication interface, monitor the communication signal of the robot, record the transmission status data of the signal, and generate original signal data; In sub-step S101, based on the robot communication interface, the original data of the robot communication signal is obtained. The communication signal is connected to the sensor device or communication module to monitor the sending and receiving status of the signal in real time, and the voltage, current and other information of the communication signal are collected and stored as the original signal data. In order to ensure the accuracy of the signal, the original data is processed by filtering technology to remove non-signal parts such as electromagnetic interference and noise to ensure the quality of the signal. The original data is converted into a time domain signal using a data acquisition device, and relevant parameters, including the amplitude and frequency of the signal, are extracted as a basis for preliminary analysis of the communication signal status. The generated original signal data will be used for signal delay measurement and communication link quality analysis in subsequent steps. The accuracy of data acquisition ensures the reliability and accuracy of the data in the subsequent analysis process. The generated original signal data will include the basic characteristics of the signal, providing an effective basis for subsequent signal delay and integrity detection.

[0026] S102: Based on the original signal data, by analyzing the difference between the sending and receiving time points of the signal, measuring the transmission delay of the communication signal, and generating signal delay data; In sub-step S102, the original signal data is divided into multiple time periods, and the sending and receiving time points of the signal are marked in each time period. The signal delay in each time period is calculated by measuring the transmission time of the signal from the sending end to the receiving end. The timing analysis technology is used to further calculate the time difference of the signal by comparing the timestamps of each sending and receiving signal. According to the acquired transmission delay time data, the stability of the communication link at each moment is analyzed to monitor whether any abnormal delay occurs. Combined with the signal change trend, the average delay and maximum delay are extracted as key indicators to obtain signal delay data, evaluate the transmission performance of the communication signal under different environments and loads, and provide a basis for the quality analysis of the communication link.

[0027] S103: Based on the signal delay data, verify the integrity of the transmitted data, identify packet loss and data corruption, evaluate the quality of the communication link, and generate a signal quality analysis result; In sub-step S103, for each data packet, the difference between its expected transmission time and actual reception time is calculated. If the difference exceeds the set threshold, it is determined that the data is lost or damaged. By comparing the transmission status and reception status of the data packet, a verification algorithm is used to verify the data integrity to ensure that each data packet is not damaged during the transmission process. If the verification fails, the system will mark the data packet as damaged and record the specific location and time of the packet loss. Combined with the actual data packet transmission rate, time delay and packet loss, the communication link quality is evaluated. Based on the analysis results, the signal quality analysis results are output, including key data such as packet loss rate and delay distribution, and a detailed report on the health of the communication link is provided to help with subsequent optimization of the communication link or troubleshooting.

[0028] See also Figure 3 Based on the signal quality analysis results, a test scenario including multiple obstacle configurations and task objectives is constructed, and a path planning task is configured. The path planning capability of the robot is evaluated by analyzing the actual situation of the robot's execution. The specific steps for generating the path planning test results are as follows: S201: Based on the signal quality analysis result, a test scenario including multiple obstacle configurations and task objectives is constructed, a path planning task is configured, and test scenario data is generated; In sub-step S201, an environmental simulation method is used to create a data model of obstacle position, task objectives and the current state of the robot. The size, position and type of each obstacle will affect the path planning task. The task objectives include the start and end points of the task. By loading scene information in the robot control system and using the path planning algorithm to generate the optimal motion path of the robot in the target test scene, the path planning task will take into account different obstacle configurations and calculate the movement trajectory of the robot according to the preset target path. During the process, the path planning task will adjust the obstacle position configuration in real time and re-plan the path to ensure that the task objectives can be successfully completed in different scenarios. The generated test scene data includes the path point coordinates output by the path planning algorithm, the environmental information of each point and the relative position of the task objectives. The data provides necessary input for subsequent steps.

[0029] S202: Based on the test scenario data, record the deviation between the planned path of the robot and the preset path, calculate the path error, and record the task completion time to generate path planning execution data; In sub-step S202, the robot executes the path according to the path planning task. The sensor will record the coordinate difference between the robot's current position and the preset path point in real time, and obtain the path error by calculating the deviation of each path point. The error calculation formula adopts the Euclidean distance calculation method, compares the distance between the actual path point and the preset path point, and sums the squares of the path errors to generate a total error value. The system records the time for task completion, including the total time from the start of the task to the robot completing the path execution. Based on the time data for task completion and the path error data, the robot's task execution efficiency is calculated. The data will be used to analyze the robot's path planning and task execution performance in a given test scenario. The generated path planning execution data includes the path error value, task completion time and the deviation record of each path point, which provides execution details and performance indicators for subsequent evaluation steps.

[0030] S203: Based on the path planning execution data, the path planning capability of the robot is evaluated by analyzing the path error and the task completion time, and a path planning test result is generated; In sub-step S203, a regression analysis method is used to establish a relationship model between error and time to evaluate the accuracy and efficiency of the robot when performing different path planning tasks. The calculation of the path error reflects the accuracy of the robot's execution path, and the task completion time reflects the robot's work efficiency in a given test scenario. Statistical methods are used to analyze the target data, and based on the relationship between the error size, time consumption and the preset target, the evaluation results of the path planning capability are generated. The results include comprehensive evaluation values ​​of the two dimensions of path planning accuracy and efficiency, and the robot's performance under different scenario configurations. The generated path planning test results provide specific data support for the robot's path planning capability in different environments, which helps to subsequently optimize the path planning algorithm or improve the accuracy and efficiency of task execution.

[0031] See also Figure 4 Based on the path planning test results, the image data of the robot execution path is collected in real time using the image acquisition device, the spatial deviation between the target path and the actual execution path is identified, and the deviation type is analyzed. The specific steps for generating path execution error data are as follows: S301: Based on the path planning test results, using an image acquisition device, capturing images of the robot's execution path in real time to generate image data acquisition records; In sub-step S301, the image of the robot's execution path is captured in real time. The image acquisition device continuously records the real-time image data of the robot during its movement through a high-resolution camera installed on the robot or in the test environment. The image data is collected by the sensor module and transmitted to the processing system in real time. The image record will cover the entire motion trajectory of the robot, and an image data acquisition record is generated based on the real-time collected image information. The timestamp of each image frame is associated with the coordinate information of the robot's current position to ensure accurate correspondence between each collected image frame and the robot's position. To ensure the quality of the image data, the image acquisition device uses adaptive exposure technology to adjust the shooting parameters according to the lighting conditions to avoid image blur caused by insufficient or excessive light. The image data acquisition record provides basic image data for subsequent path error analysis and helps calculate the difference between the actual execution path and the preset path.

[0032] S302: Based on the image data acquisition record, by analyzing the image, identifying the actual execution path of the robot and comparing it with the ideal path, identifying and recording the spatial deviation, and generating spatial deviation data; The specific formula for identifying and recording spatial deviation is: ; Where D represents the spatial deviation between the actual path of the robot and the ideal path. Indicates the x-coordinate of the target point on the actual path of the robot, represents the x-coordinate of the corresponding point on the ideal path, Indicates the y coordinate of the target point on the actual path of the robot, represents the y coordinate of the corresponding point on the ideal path, Indicates the z coordinate of the target point on the actual path of the robot, Represents the z-coordinate of the corresponding point on the ideal path.

[0033] formula: ; Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the spatial deviation between the actual execution path of the robot and the ideal path, and the result is used to evaluate the execution accuracy of the robot; Parameter meaning and setting value: is the actual x-coordinate value of the target point on the robot's execution path, assumed to be 10.5 mm; is the preset x-coordinate of the point on the ideal path, assumed to be 10.0 mm; is the actual y coordinate value of the target point on the robot's execution path, assumed to be 5.2 mm; is the preset y coordinate of the point on the ideal path, assumed to be 5.0 mm; The actual z coordinate of the robot's target point on the execution path, assuming it is 3.0 mm is the preset z coordinate of the point on the ideal path, assumed to be 2.9 mm; Substitute the parameters into the formula for calculation: Substitute actual data values: ; ; ; result It shows that the spatial deviation of the robot at this test path point is approximately 0.5477 mm. The value reflects the overall deviation between the actual execution path of the robot and the ideal path. The calculation process is used to evaluate the path planning accuracy and execution accuracy of the robot.

[0034] S303: Based on the spatial deviation data, identifying the deviation type of the path execution, including position deviation and angle error, and generating path execution error data; In sub-step S303, cluster analysis technology is used to classify deviation types and identify position deviation and angle error. Position deviation is evaluated by calculating the straight-line distance difference of path points, and angle error is identified by comparing the angle change between adjacent path points. Based on the spatial deviation data of each point, the execution accuracy of the robot is calculated to generate path execution error data. During the process, the spatial deviation data is input into the data processing system, and the larger error area is identified by the data clustering algorithm and marked as angle error, and the smaller error area is classified as position deviation. The generated path execution error data includes position deviation value, angle error value and coordinate information of the corresponding path point, providing specific data of various errors in the robot path execution, helping to comprehensively evaluate the robot's execution accuracy, and providing a basis for subsequent path optimization or adjustment.

[0035] See also Figure 5 Based on the path execution error data, sensors are used to collect the vibration data of the robot under various load conditions in real time, record the acceleration and frequency changes, and detect and record abnormal vibration signals by analyzing the vibration amplitude and frequency. The specific steps for generating vibration anomaly detection results are as follows: S401: Based on the path execution error data, the vibration data of the machine under various load conditions are collected in real time through sensors, the vibration amplitude and frequency changes are recorded, and a vibration data set is generated; In sub-step S401, an accelerometer and a vibration sensor are used to monitor the robot's motion state in real time, including the vibration response when the load changes. Each collected vibration data includes vibration amplitude and vibration frequency data. The data is transmitted to the data acquisition system in real time through the sensor for storage. During the acquisition process, the sensor records data at a fixed sampling frequency to capture the vibration characteristics of the robot when the load changes. By monitoring the changes in vibration amplitude in real time, the dynamic response of the robot under different loads is captured, and the frequency changes at each moment are recorded. Under different load conditions, the data acquisition system will continuously record and store vibration data to generate a vibration data set. The data set includes the vibration amplitude change, the real-time load of the robot and the vibration frequency for subsequent analysis and processing. The data set provides a basis for subsequent vibration analysis and reveals the performance of the robot under different working conditions.

[0036] S402: Analyze the vibration amplitude and frequency based on the vibration data set, detect abnormal change vibration data, and generate vibration fluctuation analysis results; In sub-step S402, preprocessing is performed through filtering technology to remove noise and smooth the signal to ensure data accuracy. Fourier transform is used to perform spectral analysis on the vibration signal, extract frequency components, identify the trend of frequency changes, and analyze abnormal fluctuations in vibration modes. The sliding window technology is used to segment the vibration data for each time period, and the data in each window is used to calculate the vibration amplitude and frequency within that period of time. During the analysis process, the system compares the difference between the current data and the historical benchmark value to identify abnormal changes in frequency and amplitude. The abnormal change data is calibrated using a set threshold, and vibration fluctuations that exceed the threshold are identified as abnormal. The change in vibration energy is used to assist in determining whether there is an abnormal vibration phenomenon. The generated vibration fluctuation analysis results will record the frequency characteristics and amplitude changes of the abnormal vibration mode, and mark the time point of the abnormal fluctuation for subsequent further fault analysis and diagnosis.

[0037] S403: Based on the vibration fluctuation analysis result, the time of abnormal fluctuation of the vibration signal, the real-time load, and the abnormal degree information are recorded to generate a vibration abnormality detection result; In sub-step S403, the time of occurrence of abnormal vibration fluctuations is marked, and the corresponding real-time load conditions are recorded. The real-time load data is obtained through the load sensor to ensure that the load information is synchronized with the vibration data time. For each fluctuation event, the degree of abnormality is recorded by calculating the change in vibration amplitude and the abnormal frequency deviation. The severity of the abnormality is evaluated using a relative difference index. The intensity and duration of the abnormality are calculated based on the amplitude change and frequency change of the vibration signal. The vibration abnormality detection results are generated, including the time of occurrence of abnormal vibration fluctuations, amplitude change and frequency deviation of the vibration signal, load information and the severity of the abnormality. Detailed records of the vibration behavior and abnormal status of the robot under different loads are provided to provide data support for subsequent performance evaluation and fault analysis.

[0038] See also Figure 6 ,Based on the vibration anomaly detection results, by analyzing the robot's working status data under various loads, including task execution time and task execution accuracy, the robot's performance under various loads is predicted and the robot's load carrying capacity is evaluated. The specific steps for generating robot test results are as follows: S501: Based on the vibration anomaly detection result, collect the working status data of the robot under various loads, including task execution time and action execution accuracy, and generate working status data; In sub-step S501, various data of the robot when performing tasks are recorded in real time through embedded sensors. The sensors include accelerometers, angle sensors, and force sensors. The amplitude, position, speed and other information of the robot's movements are recorded in real time. Under different load conditions, with the assistance of load sensors, the time the robot takes to perform tasks under various load conditions and the accuracy of each action are recorded. The task execution time is calculated based on the start time and end time of the task, and the execution accuracy is calculated by comparing with the difference with the preset path or target position. The working status data will be recorded for analysis in subsequent steps to ensure that the working status of the robot under different loads can be fully evaluated.

[0039] S502: Based on the working status data, by analyzing the error range of the robot under various load conditions, evaluating the impact of load changes on the execution accuracy of the robot, and generating correlation analysis results; The specific formula for evaluating the impact of load changes on the robot's execution accuracy is: ; Where r represents the correlation coefficient between load and execution accuracy, represents the load value under the i-th load condition, represents the robot error value under the i-th load condition, N represents the total number of load conditions considered during the analysis, represents the average value of the load data, represents the average value of the error data, and i refers to the index value under specific load conditions.

[0040] formula: ; Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the correlation coefficient between load and execution accuracy, which is used to evaluate the impact of load changes on the robot's execution accuracy; Parameter meaning and setting value: r is the correlation coefficient.

[0041] represents the load value under the i-th load condition, assuming =5, =7, =10; represents the robot error value under the i-th load condition, assuming =0.1, =0.3, =0.5mm; N represents the total number of load conditions, assumed to be 3; represents the average value of the load data, ; represents the average value of the error data, ; Substitute the parameters into the formula for calculation: ; Calculate the numerator: ; ; ; ; Calculate the denominator: ; ; ; Calculate the correlation coefficient: ; The results show a positive correlation between load variation and robot execution accuracy, indicating that as the load increases, the error in robot execution also increases.

[0042] S503: Based on the correlation analysis results, predict the task performance of the robot under various load conditions, evaluate and identify the load bearing threshold of the robot, and generate a robot test result; In sub-step S503, a relationship model between load conditions and task performance is established through regression analysis method, combined with data on robot execution accuracy, task completion time and load conditions. By analyzing the changes in the robot's execution accuracy and fluctuations in task completion time under different loads, the robot's performance under different loads is evaluated, and its performance in future tasks is predicted. The model will input the robot's working status data, output the predicted task completion time and the expected execution accuracy, and through further analysis of the load-bearing capacity, identify the impact of load changes on the robot's execution performance, as well as the possible performance degradation of the robot when it exceeds a certain load threshold, identify the robot's load-bearing threshold, and generate robot test results including load-bearing capacity evaluation, predicted task execution performance accuracy and time, etc., to provide data support for optimizing robot control and path planning.

[0043] See also Figure 7 , an industrial robot automated testing system, the industrial robot automated testing system is used to execute the above industrial robot automated testing method, the system comprises: The communication signal analysis module is based on the robot communication interface, collects the robot's communication signals, including current waveforms and voltage changes, measures the delay of signal transmission and performs integrity check on the transmitted data, evaluates the quality of the communication link by calculating the signal attenuation and delay, and generates signal quality analysis results; Based on the signal quality analysis results, the automation capability analysis module constructs a test scenario including obstacle configuration and task objectives, configures the robot path planning task, evaluates the robot's path planning capability, and generates path planning test results. Based on the path planning test results, the error detection module uses image acquisition equipment to capture the image data of the robot's execution path in real time, calculates the spatial deviation between the target path and the actual path, analyzes the deviation type, and generates path execution error data; The vibration monitoring module uses sensors to collect the vibration data of the robot under various loads in real time based on the path execution error data, detect abnormal vibration signals, and generate vibration anomaly detection results; The load capacity assessment module analyzes the robot's working status data under various load conditions based on the vibration anomaly detection results, predicts the robot's performance under different loads, evaluates and identifies the robot's load bearing threshold, and generates robot test results.

[0044] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0045] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0046] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0047] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0048] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0049] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0050] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0051] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0052] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0053] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0054] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An industrial robot automated testing method, characterized in that: The method comprises: S1: Based on the robot communication interface, collect the robot's communication signal, measure the delay of signal transmission, verify the integrity of the transmitted data, evaluate the quality of the communication link, and generate signal quality analysis results; S2: Based on the signal quality analysis result, a test scenario including multiple obstacle configurations and task objectives is constructed, a path planning task is configured, and the path planning capability of the robot is evaluated by analyzing the actual situation of the robot execution, thereby generating a path planning test result; S3: Based on the path planning test results, using an image acquisition device, real-time image data of the robot execution path is collected, the spatial deviation between the target path and the actual execution path is identified, and the deviation type is analyzed to generate path execution error data; S4: Based on the path execution error data, using sensors, real-time collection of vibration data of the robot under various load conditions, recording acceleration and frequency changes, detecting and recording abnormal vibration signals by analyzing vibration amplitude and frequency, and generating vibration abnormality detection results; S5: Based on the vibration anomaly detection results, by analyzing the working status data of the robot under various loads, including task execution time and task execution accuracy, the performance of the robot under various loads is predicted, the load bearing capacity of the robot is evaluated, and the robot test results are generated.

2. The industrial robot automated testing method according to claim 1, characterized in that: The signal quality analysis results include signal transmission delay, data integrity verification results, and communication link quality scores; the path planning test results include path planning error information, predicted task completion time, and path planning capability evaluation information; the path execution error data include image data acquisition records, position deviation detection results, and angle error data; the vibration anomaly detection results include vibration amplitude information, vibration frequency change information, and abnormal vibration signal records; the robot test results include multi-load performance information, robot motion execution accuracy change curve, and load bearing capacity evaluation information.

3. The industrial robot automated testing method according to claim 1, characterized in that: Based on the robot communication interface, the robot's communication signal is collected, the delay of signal transmission is measured, the integrity of the transmitted data is verified, the quality of the communication link is evaluated, and the steps for generating the signal quality analysis results are as follows: S101: Based on the robot communication interface, monitor the communication signal of the robot, record the transmission status data of the signal, and generate original signal data; S102: Based on the original signal data, by analyzing the difference between the sending and receiving time points of the signal, measuring the transmission delay of the communication signal, and generating signal delay data; S103: Based on the signal delay data, verify the integrity of the transmitted data, identify packet loss and data corruption, evaluate the quality of the communication link, and generate a signal quality analysis result.

4. The industrial robot automated testing method according to claim 1, characterized in that: Based on the signal quality analysis results, a test scenario including multiple obstacle configurations and task objectives is constructed, a path planning task is configured, and the path planning capability of the robot is evaluated by analyzing the actual situation of the robot's execution. The specific steps for generating the path planning test results are as follows: S201: Based on the signal quality analysis result, a test scenario including multiple obstacle configurations and task objectives is constructed, a path planning task is configured, and test scenario data is generated; S202: Based on the test scenario data, record the deviation between the planned path of the robot and the preset path, calculate the path error, and record the task completion time to generate path planning execution data; S203: Based on the path planning execution data, the path planning capability of the robot is evaluated by analyzing the path error and the task completion time, and a path planning test result is generated.

5. The industrial robot automated testing method according to claim 1, characterized in that: Based on the path planning test results, the image data of the robot execution path is collected in real time using an image acquisition device, the spatial deviation between the target path and the actual execution path is identified, and the deviation type is analyzed. The specific steps of generating path execution error data are as follows: S301: Based on the path planning test result, using an image acquisition device, capturing images of the robot's execution path in real time to generate image data acquisition records; S302: Based on the image data acquisition record, by analyzing the image, identifying the actual execution path of the robot and comparing it with the ideal path, identifying and recording the spatial deviation, and generating spatial deviation data; S303: Based on the spatial deviation data, identify the deviation type of path execution, including position deviation and angle error, and generate path execution error data.

6. The industrial robot automated testing method according to claim 5, characterized in that: The specific formula for identifying and recording the spatial deviation is: ； Where D represents the spatial deviation between the actual path of the robot and the ideal path. Indicates the x-coordinate of the target point on the actual path of the robot, represents the x-coordinate of the corresponding point on the ideal path, Indicates the y coordinate of the target point on the actual path of the robot, represents the y coordinate of the corresponding point on the ideal path, Indicates the z coordinate of the target point on the actual path of the robot, Represents the z-coordinate of the corresponding point on the ideal path.

7. The industrial robot automated testing method according to claim 1, characterized in that: Based on the path execution error data, using sensors, real-time collection of vibration data of the robot under various load conditions, recording acceleration and frequency changes, and detecting and recording abnormal vibration signals by analyzing vibration amplitude and frequency. The specific steps of generating vibration abnormality detection results are as follows: S401: Based on the path execution error data, collect vibration data of the machine under various load conditions in real time through sensors, record vibration amplitude and frequency changes, and generate a vibration data set; S402: Analyze the vibration amplitude and frequency based on the vibration data set, detect abnormal change vibration data, and generate vibration fluctuation analysis results; S403: Based on the vibration fluctuation analysis result, the time of abnormal fluctuation of the vibration signal, the real-time load, and the abnormality degree information are recorded to generate a vibration abnormality detection result.

8. The industrial robot automated testing method according to claim 1, characterized in that: Based on the vibration anomaly detection results, by analyzing the working status data of the robot under various loads, including task execution time and task execution accuracy, predicting the performance of the robot under various loads, and evaluating the load carrying capacity of the robot, the steps of generating the robot test results are specifically as follows: S501: Based on the vibration abnormality detection result, collect the working status data of the robot under various loads, including task execution time and action execution accuracy, and generate working status data; S502: Based on the working status data, by analyzing the error range of the robot under various load conditions, evaluating the impact of load changes on the execution accuracy of the robot, and generating a correlation analysis result; S503: Based on the correlation analysis results, predict the robot's task performance under various load conditions, evaluate and identify the robot's load bearing threshold, and generate robot test results.

9. The industrial robot automated testing method according to claim 8, characterized in that: The specific formula for evaluating the impact of load changes on the robot's execution accuracy is: ； Where r represents the correlation coefficient between load and execution accuracy, represents the load value under the i-th load condition, represents the robot error value under the i-th load condition, N represents the total number of load conditions considered during the analysis, represents the average value of the load data, represents the average value of the error data, and i refers to the index value under specific load conditions.

10. An industrial robot automated testing system, characterized in that: Using the industrial robot automated testing method according to any one of claims 1 to 9, the system comprises: The communication signal analysis module is based on the robot communication interface, collects the robot's communication signals, including current waveforms and voltage changes, measures the delay of signal transmission and performs integrity check on the transmitted data, evaluates the quality of the communication link by calculating the signal attenuation and delay, and generates signal quality analysis results; The automation capability analysis module configures the robot path planning task based on the signal quality analysis result, constructs a test scenario including obstacle configuration and task objectives, evaluates the robot's path planning capability, and generates a path planning test result; The error detection module uses an image acquisition device to capture the image data of the robot execution path in real time based on the path planning test results, calculates the spatial deviation between the target path and the actual path, analyzes the deviation type, and generates path execution error data; The vibration monitoring module collects the vibration data of the robot under various loads in real time based on the path execution error data and uses sensors to detect abnormal vibration signals and generate vibration abnormality detection results; The load capacity evaluation module analyzes the working status data of the robot under various load conditions based on the vibration anomaly detection results, predicts the performance of the robot under different loads, evaluates and identifies the load bearing threshold of the robot, and generates robot test results.

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