Vision-based multi-degree-of-freedom robot vibration detection system

Abstract

The present application relates to the technical field of visual detection, and especially relates to a multi-degree-of-freedom robot vibration detection system based on vision, which monitors the movement trajectory of a machine arm and vibration event characteristic points through an industrial camera, monitors the vibration frequency of the machine arm in different degree-of-freedom directions and the number of vibration event characteristic points through a monitor, identifies the vibration influence period through a priori analyzer, analyzes the vibration influence tendency characteristic value of the machine arm in the corresponding degree-of-freedom direction within different vibration influence periods, and improves the accuracy of vibration detection. The controller distinguishes between a severe risk influence period and a gentle risk influence period, the adaptive regulator predicts whether the vibration risk of the machine arm meets the predetermined standard, thereby reducing the occurrence of vibration risk misjudgment, and the difference between the actual vibration risk influence characteristic parameter and the predetermined vibration risk influence characteristic parameter is used to determine whether to enable the alarm device, further improving the efficiency of the vibration detection of the machine arm of the robot.

Description

Technical Field

[0001] This invention relates to the field of visual inspection, and more particularly to a vision-based multi-degree-of-freedom robot vibration detection system. Background Technology

[0002] With the rapid development of industrial automation and intelligent manufacturing, multi-degree-of-freedom robots are widely used in precision assembly, high-end manufacturing, medical surgery and other fields due to their high flexibility and ability to perform complex tasks. However, when robots move at high speeds or experience load changes, their joints and end effectors are prone to undesirable vibrations, leading to decreased operational accuracy, shortened equipment lifespan, and even safety hazards.

[0003] Traditional vibration detection relies on contact devices such as accelerometers and strain gauges, which require damaging the robot's structure for installation and make it difficult to simultaneously monitor vibrations in multiple locations. Existing visual inspection methods are mostly designed for single-degree-of-freedom or two-dimensional vibration scenarios, and are insufficiently adaptable to the complex three-dimensional vibrations of multi-degree-of-freedom robots, suffering from low dynamic tracking accuracy and weak resistance to environmental interference. Therefore, a visual inspection system capable of non-contact, high-precision, and multi-dimensional vibration monitoring has become a key requirement for improving the operational stability of robots.

[0004] Chinese Patent Publication No. CN117232638A discloses a robot vibration detection method and system. The vibration detection method includes: acquiring robot vibration event stream data using an industrial camera; identifying the vibration event stream data using a trained spiking neural network; determining whether the robot's vibration state is normal based on the identification results, and then outputting a vibration detection report. Acquiring robot vibration data using an industrial camera offers advantages such as high dynamic range, low latency, overcoming motion blur in imaging, and extremely low power consumption. It can completely record the motion state, acquire complete robot vibration event stream data and corresponding timestamps, improving measurement accuracy while reducing noise interference and enhancing algorithm reliability. The powerful noise resistance and high-precision feature extraction capabilities of the spiking neural network enable fast and accurate processing of robot vibration event stream data, ensuring measurement accuracy.

[0005] Chinese Patent Publication No. CN110706198A discloses a vibration detection system for large construction robots based on unmanned aerial vehicles (UAVs). This system includes a UAV equipped with a camera, an image processing module, and a communication module that enables interaction between the video images acquired by the UAV and the image processing module. During vibration detection, the UAV captures video of the robot under test. The image processing module performs the following operations: acquiring the video captured by the UAV; obtaining the degree of freedom (DOF) direction centered on the target point for each frame of the video; performing complex linear filtering on all DEF directions to obtain the phase spectrum of each DEF direction; and using the phase spectrum of each DEF direction as a reference, obtaining the phase difference between each frame starting from the second frame and the first frame, where the phase difference represents the vibration displacement. The areas containing the target point in each frame are segmented to form the DEF directions.

[0006] However, the following problems still exist in the existing technology:

[0007] In the existing technology, the issue of the difference in vibration characteristics of the robot arm in different degrees of freedom in different periods due to the influence of assembly load weight factors is not considered, which in turn affects the assembly effect. Summary of the Invention

[0008] To address the aforementioned issues, this invention provides a vision-based multi-degree-of-freedom robot vibration detection system. This system overcomes the problem in existing technologies that fail to consider the differences in vibration characteristics of the robot arm in different degrees of freedom across different historical periods due to the influence of assembly load weight, which in turn affects the assembly effect and results in low vibration detection efficiency.

[0009] To achieve the above objectives, the present invention provides a vision-based multi-degree-of-freedom robot vibration detection system, comprising:

[0010] The machine assembly assembly includes a robotic arm for building construction assembly and an industrial camera connected to the robotic arm for monitoring the movement trajectory of the robotic arm and vibration event feature points.

[0011] The monitor includes a plurality of piezoelectric accelerometers for monitoring the vibration frequencies of the robotic arm in different degrees of freedom directions and an event monitoring unit for acquiring the number of vibration event feature points in different degrees of freedom directions of an industrial camera.

[0012] The prior analyzer is used to store historical data monitored by the monitor, and to identify the vibration influence cycle based on the vibration frequency difference of the robot arm in each degree of freedom direction in different historical cycles. Based on the number of vibration event feature points in a single degree of freedom direction of the robot arm and the response time of the industrial camera monitoring the robot arm's motion trajectory, the vibration influence tendency characterization value of the robot arm in different vibration influence cycles in the corresponding degree of freedom direction is analyzed.

[0013] The controller is connected to the machine assembly components, the monitor and the prior analyzer respectively, and is used to distinguish between severe risk impact cycles and mild risk impact cycles based on the vibration impact tendency characterization value of the robot arm in the degree of freedom direction corresponding to different vibration impact cycles.

[0014] An adaptive regulator, connected to both the controller and the prior analyzer, is used to determine vibration risk impact characterization parameters based on the response time of the robot arm's motion trajectory monitored by the industrial camera within a predetermined period and the robot arm's load weight. It predicts whether the robot arm's vibration risk meets a predetermined standard and determines whether to activate the alarm device based on the difference between the actual vibration risk impact characterization parameters and the predetermined vibration risk impact characterization parameters.

[0015] Furthermore, the prior analyzer is used to identify the vibration influence period based on the vibration frequency differences of the robotic arm in each degree of freedom direction within different historical periods, wherein,

[0016] Calculate the variance of the vibration frequency of the robotic arm in each degree of freedom direction within each historical period;

[0017] If the variance corresponding to a single historical period is greater than a predetermined variance threshold, then the historical period is determined to be a vibration influence period.

[0018] Furthermore, the vibration influence tendency characterization value is the sum of the first historical data feature and the second historical data feature, wherein,

[0019] The first historical data feature is the ratio of the number of vibration event feature points in a single degree of freedom direction of the robotic arm to a predetermined threshold for the number of vibration event feature points.

[0020] The second historical data feature is the ratio of the response time of the motion trajectory in a single degree of freedom direction of the robotic arm to the predetermined response time threshold of the motion trajectory.

[0021] Furthermore, the controller is used to distinguish between periods of severe risk impact and periods of mild risk impact, including,

[0022] Used to obtain the vibration influence tendency characterization value of the robot arm in the direction of freedom corresponding to different vibration influence cycles;

[0023] If the vibration impact tendency characterization value is greater than or equal to the preset vibration impact tendency characterization value, it is determined to be a period of severe risk impact.

[0024] If the vibration impact tendency characterization value is less than the preset vibration impact tendency characterization value, it is determined to be a period of mild risk impact.

[0025] Furthermore, the adaptive regulator is used to extract the average response time and average load weight of the robot arm's motion trajectory within a predetermined period when the risk impact period is smoothed out.

[0026] Furthermore, the vibration risk impact characterization parameter is the sum of the first vibration risk parameter characteristic and the second vibration risk parameter characteristic, wherein,

[0027] The first vibration risk parameter is the ratio of the average response time of the robot arm's motion trajectory to a predetermined response time baseline threshold.

[0028] The second vibration risk parameter is the ratio of the mean load weight of the robotic arm to a predetermined load weight threshold.

[0029] Furthermore, the adaptive regulator is used to predict whether the vibration risk of the robotic arm meets predetermined criteria, including:

[0030] If the vibration risk impact characterization parameter is less than or equal to the preset vibration risk impact characterization parameter, then the predicted vibration risk of the robot arm meets the predetermined standard.

[0031] If the vibration risk impact characterization parameter is greater than the preset vibration risk impact characterization parameter, then the predicted vibration risk of the robot arm does not meet the predetermined standard.

[0032] Furthermore, the adaptive regulator is used to analyze the difference between the actual vibration risk impact characterization parameter and the predetermined vibration risk impact characterization parameter when the predicted vibration risk of the robotic arm does not meet the predetermined standard.

[0033] Furthermore, the adaptive regulator is used to determine whether to activate the alarm device based on the difference between the actual vibration risk impact characterization parameter and the predetermined vibration risk impact characterization parameter, including:

[0034] Calculate the difference between the actual vibration risk impact characterization parameters and the predetermined vibration risk impact characterization parameters;

[0035] If the difference is less than or equal to a predetermined difference threshold, then the alarm device is determined not to be activated.

[0036] If the difference is greater than a predetermined difference threshold, then the alarm device is activated.

[0037] Furthermore, the adaptive regulator is used to activate the alarm device during periods of severe risk impact.

[0038] Compared with existing technologies, this invention provides a vision-based multi-degree-of-freedom robot vibration detection system, including a machine assembly component, a monitor, a prior analyzer, a controller, and an adaptive regulator. It monitors the robot arm's motion trajectory and vibration event feature points using an industrial camera. The monitor tracks the vibration frequency of the robot arm in different degrees of freedom directions and acquires the number of vibration event feature points in different degrees of freedom directions from the industrial camera. The prior analyzer identifies vibration impact cycles to analyze the vibration tendency of the robot arm in different degrees of freedom directions within each vibration impact cycle, improving the accuracy of vibration detection. The controller distinguishes between severe and mild risk impact cycles. The adaptive regulator predicts whether the robot arm's vibration risk meets predetermined standards, reducing the occurrence of false alarms. The system determines whether to activate an alarm device based on the difference between the actual vibration risk impact parameters and predetermined vibration risk impact parameters, further improving the efficiency of robot arm vibration detection.

[0039] In particular, this invention can accurately obtain the pattern of the influence of vibration frequency on the robot arm in different cycles by identifying the vibration influence cycle. By calculating the variance of the vibration frequency in each degree of freedom direction of the robot arm in each historical cycle, the vibration influence cycle of the historical cycle can be accurately determined. Moreover, by using the number of vibration event feature points in a single degree of freedom direction of the robot arm and the response time of the industrial camera monitoring the robot arm's motion trajectory, the vibration influence tendency characterization value of the robot arm in different vibration influence cycles can be accurately analyzed, thereby improving the efficiency of robot arm assembly.

[0040] In particular, the present invention can determine the vibration risk impact characterization parameters by monitoring the response time of the robot arm's motion trajectory and the robot arm's load weight through an industrial camera, thereby improving the accuracy of the robot arm's vibration risk. By using the vibration risk impact characterization parameters, the vibration risk of the robot arm can be predicted, and it is possible to predict in advance whether the robot arm's performance will decline. This reduces the occurrence of the robot arm's assembly efficiency being reduced due to vibration during the vibration impact cycle, and improves the stability of the robot arm.

[0041] In particular, the present invention can distinguish between severe risk impact cycles and mild risk impact cycles by using vibration impact tendency characterization values ​​corresponding to different vibration impact cycles, thereby improving the accuracy of vibration detection. Moreover, by determining whether the robot arm has activated the vibration damping compensation device, the efficiency of vibration detection is further improved. Attached Figure Description

[0042] Figure 1 This is a structural block diagram of a vision-based multi-degree-of-freedom robot vibration detection system according to an embodiment of the present invention;

[0043] Figure 2This is a logic diagram for identifying the vibration influence period in an embodiment of the present invention;

[0044] Figure 3 This is a logic decision diagram for analyzing the vibration influence tendency of the robotic arm in an embodiment of the present invention.

[0045] Figure 4 This is a logic diagram illustrating how the impact periods of severe and mild risks are distinguished in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0047] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0048] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0049] Please see Figure 1 The diagram shown is a structural block diagram of a vision-based multi-degree-of-freedom robot vibration detection system according to an embodiment of the present invention. The present invention provides a vision-based multi-degree-of-freedom robot vibration detection system, comprising:

[0050] The machine assembly assembly includes a robotic arm for building construction assembly and an industrial camera connected to the robotic arm for monitoring the movement trajectory of the robotic arm and vibration event feature points.

[0051] The monitor includes a plurality of piezoelectric accelerometers for monitoring the vibration frequencies of the robotic arm in different degrees of freedom directions and an event monitoring unit for acquiring the number of vibration event feature points in different degrees of freedom directions of an industrial camera.

[0052] The prior analyzer is used to store historical data monitored by the monitor, and to identify the vibration influence cycle based on the vibration frequency difference of the robot arm in each degree of freedom direction in different historical cycles. Based on the number of vibration event feature points in a single degree of freedom direction of the robot arm and the response time of the industrial camera monitoring the robot arm's motion trajectory, the vibration influence tendency characterization value of the robot arm in different vibration influence cycles in the corresponding degree of freedom direction is analyzed.

[0053] The controller is connected to the machine assembly components, the monitor and the prior analyzer respectively, and is used to distinguish between severe risk impact cycles and mild risk impact cycles based on the vibration impact tendency characterization value of the robot arm in the degree of freedom direction corresponding to different vibration impact cycles.

[0054] An adaptive regulator, connected to both the controller and the prior analyzer, is used to determine vibration risk impact characterization parameters based on the response time of the robot arm's motion trajectory monitored by the industrial camera within a predetermined period and the robot arm's load weight. It predicts whether the robot arm's vibration risk meets a predetermined standard and determines whether to activate the alarm device based on the difference between the actual vibration risk impact characterization parameters and the predetermined vibration risk impact characterization parameters.

[0055] Specifically, this system includes machine assembly components, a monitor, a priori analyzer, a controller, and an adaptive regulator. It monitors the robot arm's motion trajectory and vibration event feature points using an industrial camera. The monitor tracks the vibration frequency of the robot arm in different degrees of freedom and acquires the number of vibration event feature points in each direction from the industrial camera. The priori analyzer identifies vibration impact cycles, analyzing the vibration tendency of the robot arm in different degrees of freedom within each cycle, thus improving the accuracy of vibration detection. The controller distinguishes between severe and mild risk impact cycles. The adaptive regulator predicts whether the robot arm's vibration risk meets predetermined standards, reducing misjudgments. The system determines whether to activate an alarm device based on the difference between the actual and predetermined vibration risk impact parameters, further improving the efficiency of robot arm vibration detection.

[0056] Specifically, this invention is applied to the assembly of multi-axis robotic arms in construction robots. Commonly, the assembly components include robotic arms and industrial cameras, and the robotic arms have six or more degrees of freedom.

[0057] Specifically, the structure of the monitor, prior analyzer, controller, adaptive regulator and its units are not specifically limited, and can be composed of logic components, including field-programmable processors, computers or microprocessors in computers.

[0058] Specifically, there are no restrictions on the form of the alarm device; it can be an audible alarm or other forms, which will not be elaborated here.

[0059] Specifically, the event detection unit can be a logical component that acquires information, for example, it can access the management system to obtain relevant parameters, which will not be elaborated further.

[0060] Please see Figure 2 The above describes a logical decision diagram for identifying the vibration influence period in an embodiment of the present invention. The prior analyzer in this embodiment is used to identify the vibration influence period based on the difference in vibration frequency in each degree of freedom direction of the robotic arm within different historical periods.

[0061] Calculate the variance of the vibration frequency of the robotic arm in each degree of freedom direction within each historical period;

[0062] If the variance corresponding to a single historical period is greater than a predetermined variance threshold, then the historical period is determined to be a vibration influence period.

[0063] In this embodiment, the variance threshold is determined based on the average variance of the vibration frequencies obtained over several historical periods, and is set to 1.15 times the average variance.

[0064] In this embodiment, to demonstrate the monitorability of vibration frequency changes, the historical period is the interval [2h, 3h].

[0065] Specifically, by identifying the vibration impact cycle, the pattern of the robot arm being affected by vibration frequency in different cycles can be accurately obtained. By calculating the variance of the vibration frequency in each degree of freedom direction of the robot arm in each historical cycle, the vibration impact cycle of the historical cycle can be accurately determined. Moreover, by using the number of vibration event feature points in a single degree of freedom direction of the robot arm and the response time of the industrial camera monitoring the robot arm's motion trajectory, the vibration impact tendency characterization value of the robot arm in different vibration impact cycles can be accurately analyzed, thereby improving the efficiency of robot arm assembly.

[0066] Please see Figure 3 As shown, this is a logical decision diagram for analyzing the vibration influence tendency characterization value of a robotic arm according to an embodiment of the present invention. The vibration influence tendency characterization value is the sum of the first historical data feature and the second historical data feature, wherein...

[0067] The first historical data feature is the ratio of the number of vibration event feature points in a single degree of freedom direction of the robotic arm to a predetermined threshold for the number of vibration event feature points.

[0068] The second historical data feature is the ratio of the response time of the motion trajectory in a single degree of freedom direction of the robotic arm to the predetermined response time threshold of the motion trajectory.

[0069] This invention embodiment is used to analyze the vibration influence tendency characterization value of a robotic arm in a single degree of freedom direction within different vibration influence periods, including:

[0070] The ratio of the number of vibration event feature points in a single degree of freedom direction of the computer robot arm to a predetermined threshold for the number of vibration event feature points is determined as the first historical data feature.

[0071] The ratio of the response time of the motion trajectory in a single degree of freedom direction of the computer robot arm to a predetermined response time threshold of the motion trajectory is determined as the second historical data feature;

[0072] The sum of the first historical data feature and the second historical data feature is determined as the vibration influence tendency characterization value.

[0073] Specifically, the predetermined threshold for the number of vibration event feature points is obtained in advance and determined based on the average number of vibration event feature points in a single degree of freedom direction of the robot arm in the historical cycle. It is preset to be 0.85 times the average number of vibration event feature points.

[0074] Specifically, the predetermined response time threshold for the motion trajectory is obtained in advance and determined based on the average response time of the motion trajectory in a single degree of freedom direction of the robotic arm in the historical cycle. It is preset to be 0.75 times the average response time of the motion trajectory.

[0075] Please see Figure 4 As shown, this is a logic diagram for distinguishing between severe risk impact periods and mild risk impact periods according to an embodiment of the present invention. The controller described in this embodiment of the present invention is used to distinguish between severe risk impact periods and mild risk impact periods, including,

[0076] Used to obtain the vibration influence tendency characterization value of the robot arm in the direction of freedom corresponding to different vibration influence cycles;

[0077] If the vibration impact tendency characterization value is greater than or equal to the preset vibration impact tendency characterization value, it is determined to be a period of severe risk impact.

[0078] If the vibration impact tendency characterization value is less than the preset vibration impact tendency characterization value, it is determined to be a period of mild risk impact.

[0079] Specifically, the preset vibration impact tendency characterization value serves as a key judgment criterion, playing a role in dividing the severe risk impact cycle and the mild risk impact cycle. In the embodiment, the preset vibration impact tendency characterization value is in the range [2.25, 2.50].

[0080] Specifically, when the vibration influence tendency characterization value is greater than or equal to the preset vibration influence tendency characterization value, it is determined to be a period of severe risk impact. This indicates that within this period, the vibration frequency has a significant impact on the robotic arm, which may lead to a substantial change in the robotic arm's assembly performance. Preferably, the vibration frequencies differ in each degree of freedom direction, affecting the accuracy of the robotic arm assembly.

[0081] Specifically, if the vibration impact tendency value is less than the preset vibration impact tendency value, it is determined to be a period of mild risk impact. Within this period, the vibration frequency has a relatively small impact on the robotic arm, and the performance changes of the robotic arm assembly are relatively gradual. At this time, the robotic arm assembly can work in a relatively stable state, and the robotic arm assembly is relatively stable.

[0082] Specifically, by using the vibration influence tendency characterization values ​​corresponding to different vibration influence cycles, it is possible to distinguish between severe risk influence cycles and mild risk influence cycles, thereby improving the accuracy of vibration detection. Furthermore, by determining whether the robot arm has activated the vibration damping compensation device, the efficiency of the system's vibration detection is further improved.

[0083] Specifically, the adaptive regulator is used to extract the average response time and average load weight of the robot arm's motion trajectory within a predetermined period when the risk impact period is smoothed out.

[0084] Specifically, the vibration risk impact characterization parameter is the sum of the first vibration risk parameter characteristic and the second vibration risk parameter characteristic, wherein,

[0085] The first vibration risk parameter is the ratio of the average response time of the robot arm's motion trajectory to a predetermined response time baseline threshold.

[0086] The second vibration risk parameter is the ratio of the mean load weight of the robotic arm to a predetermined load weight threshold.

[0087] The adaptive regulator is used to determine vibration risk impact characterization parameters based on the response time of the robot arm's motion trajectory monitored by the industrial camera within a predetermined period and the robot arm's load weight, including:

[0088] The ratio of the average response time of the computer arm's motion trajectory to a predetermined response time benchmark threshold is determined as the first vibration risk parameter characteristic;

[0089] The ratio of the average load weight of the computer arm to a predetermined load weight threshold is determined as the second vibration risk parameter characteristic.

[0090] The sum of the first vibration risk parameter feature and the second vibration risk parameter feature is determined as the vibration risk impact characterization parameter.

[0091] In this embodiment, the predetermined response time baseline threshold is set based on the average response time of the robot arm's motion trajectory within a historical period, and is preset to be 0.85 times the average response time of the robot arm's motion trajectory.

[0092] The average load weight of the robotic arm is set based on the average load weight threshold in the historical period, and is preset to be 0.75 times the average load weight of the robotic arm in the historical period.

[0093] Specifically, the vibration risk impact characterization parameter is obtained by adding the first vibration risk parameter characteristic and the second vibration risk parameter characteristic. This parameter comprehensively considers the impact of the robot arm's motion trajectory response time and the robot arm's load weight on the overall system vibration risk.

[0094] Specifically, by monitoring the response time of the robot arm's motion trajectory and the robot arm's load weight through industrial cameras, vibration risk impact parameters can be determined, improving the accuracy of the robot arm's vibration risk. These vibration risk impact parameters can be used to predict the robot arm's vibration risk and anticipate whether the robot arm's performance will decline. This reduces the occurrence of vibration-induced assembly efficiency reductions due to vibration during the vibration impact cycle, thus improving the robot arm's stability.

[0095] Specifically, the control unit is used to predict whether the vibration risk of the robotic arm meets predetermined standards based on vibration risk impact characterization parameters, including:

[0096] If the vibration risk impact characterization parameter is less than or equal to the preset vibration risk impact characterization parameter, then the predicted vibration risk of the robot arm meets the predetermined standard.

[0097] If the vibration risk impact characterization parameter is greater than the preset vibration risk impact characterization parameter, then the predicted vibration risk of the robot arm does not meet the predetermined standard.

[0098] Specifically, the preset vibration risk impact characterization parameter, serving as a key reference value for judging whether the vibration risk of the robotic arm meets the predetermined standard, is determined comprehensively based on factors such as the design performance of the robotic arm, historical operating data, and the system's requirements for vibration risk. Preferably, a value capable of distinguishing whether the vibration risk meets the standard is determined through extensive analysis of the energy conversion of similar robotic arms under different working conditions.

[0099] Specifically, the control unit determines whether the vibration risk of the robotic arm meets the standards based on vibration risk impact characteristics parameters, achieving a precise and quantitative assessment of vibration risk. When the parameters do not exceed preset values, the vibration risk is confirmed to be compliant, ensuring the robotic arm operates safely. If the parameters exceed limits, a timely warning is issued to avoid problems such as decreased accuracy and equipment damage caused by vibration. This clear judgment logic simplifies the vibration risk assessment process and improves the objectivity and timeliness of the judgment. It provides a reliable basis for real-time control and maintenance decisions of the robotic arm, effectively reducing the potential hazards caused by vibration and ensuring operational stability and safety.

[0100] Specifically, the adaptive regulator is used to analyze the difference between the actual vibration risk impact characterization parameter and the predetermined vibration risk impact characterization parameter when the predicted vibration risk of the robotic arm does not meet the predetermined standard.

[0101] Specifically, the adaptive regulator is used to determine whether to activate the alarm device based on the difference between the actual vibration risk impact characterization parameter and the predetermined vibration risk impact characterization parameter, including:

[0102] Calculate the difference between the actual vibration risk impact characterization parameters and the predetermined vibration risk impact characterization parameters;

[0103] If the difference is less than or equal to a predetermined difference threshold, then the alarm device is determined not to be activated.

[0104] If the difference is greater than a predetermined difference threshold, then the alarm device is activated.

[0105] Specifically, the adaptive regulator is used to activate the alarm device during periods of severe risk impact.

[0106] Specifically, there are no restrictions on the automatic activation method of the alarm device, as long as it can be activated automatically, which will not be elaborated further.

[0107] Specifically, the adaptive regulator analyzes the difference between actual and predetermined vibration risk parameters to achieve refined judgment and response to vibration risks. When the vibration risk exceeds the limit, it not only quantifies the degree of deviation but also decides whether to activate an alarm based on this, avoiding the blindness of simple threshold triggering. Difference analysis makes alarms more targeted; for small deviations, vibration can be suppressed by adjustment first, while for large deviations, an alarm is triggered in a timely manner to prompt intervention, reducing unnecessary downtime and ensuring rapid response to high-risk situations. This mechanism improves the flexibility and accuracy of vibration risk management, ensures the stable operation of the robotic arm under complex working conditions, and reduces accuracy errors and equipment wear caused by vibration.

[0108] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A vision-based multi-degree-of-freedom robot vibration detection system, characterized in that, include: The machine assembly assembly includes a robotic arm for building construction assembly and an industrial camera connected to the robotic arm for monitoring the movement trajectory of the robotic arm and vibration event feature points. The monitor includes a plurality of piezoelectric accelerometers for monitoring the vibration frequencies of the robotic arm in different degrees of freedom directions and an event monitoring unit for acquiring the number of vibration event feature points in different degrees of freedom directions of an industrial camera. The prior analyzer is used to store historical data monitored by the monitor, and to identify the vibration influence cycle based on the vibration frequency difference of the robot arm in each degree of freedom direction in different historical cycles. Based on the number of vibration event feature points in a single degree of freedom direction of the robot arm and the response time of the industrial camera monitoring the robot arm's motion trajectory, the vibration influence tendency characterization value of the robot arm in different vibration influence cycles in the corresponding degree of freedom direction is analyzed. The controller is connected to the machine assembly components, the monitor and the prior analyzer respectively, and is used to distinguish between severe risk impact cycles and mild risk impact cycles based on the vibration impact tendency characterization value of the robot arm in the degree of freedom direction corresponding to different vibration impact cycles. The vibration influence tendency characterization value is the sum of the first historical data feature and the second historical data feature, wherein, The first historical data feature is the ratio of the number of vibration event feature points in a single degree of freedom direction of the robotic arm to a predetermined threshold for the number of vibration event feature points. The second historical data feature is the ratio of the response time of the motion trajectory in a single degree of freedom direction of the robotic arm to the predetermined response time threshold of the motion trajectory. Obtain the vibration influence tendency characterization values ​​of the robot arm in the degree of freedom direction corresponding to different vibration influence periods; If the vibration impact tendency characterization value is greater than or equal to the preset vibration impact tendency characterization value, it is determined to be a period of severe risk impact. If the vibration impact tendency characterization value is less than the preset vibration impact tendency characterization value, it is determined to be a period of mild risk impact. An adaptive regulator, connected to both the controller and the prior analyzer, is used to extract the average response time and average load weight of the robot arm's motion trajectory within a predetermined period during a period of smooth risk impact. Based on the average response time and average load weight of the robot arm's motion trajectory monitored by the industrial camera within the predetermined period, it determines vibration risk impact characterization parameters, predicts whether the robot arm's vibration risk meets predetermined standards, and determines whether to activate the alarm device based on the difference between the actual vibration risk impact characterization parameters and the predetermined vibration risk impact characterization parameters.

2. The vision-based multi-degree-of-freedom robot vibration detection system according to claim 1, characterized in that, The prior analyzer is used to identify the vibration influence period based on the vibration frequency differences of the robotic arm in each degree of freedom direction over different historical periods, wherein, Calculate the variance of the vibration frequency of the robotic arm in each degree of freedom direction within each historical period; If the variance corresponding to a single historical period is greater than a predetermined variance threshold, then the historical period is determined to be a vibration influence period.

3. The vision-based multi-degree-of-freedom robot vibration detection system according to claim 1, characterized in that, The vibration risk impact characterization parameter is the sum of the first vibration risk parameter characteristic and the second vibration risk parameter characteristic, wherein, The first vibration risk parameter is the ratio of the average response time of the robot arm's motion trajectory to a predetermined response time baseline threshold. The second vibration risk parameter is the ratio of the mean load weight of the robotic arm to a predetermined load weight threshold.

4. The vision-based multi-degree-of-freedom robot vibration detection system according to claim 3, characterized in that, The adaptive regulator is used to predict whether the vibration risk of the robotic arm meets predetermined criteria, including: If the vibration risk impact characterization parameter is less than or equal to the preset vibration risk impact characterization parameter, then the predicted vibration risk of the robot arm meets the predetermined standard. If the vibration risk impact characterization parameter is greater than the preset vibration risk impact characterization parameter, then the predicted vibration risk of the robot arm does not meet the predetermined standard.

5. The vision-based multi-degree-of-freedom robot vibration detection system according to claim 4, characterized in that, The adaptive regulator is used to analyze the difference between the actual vibration risk impact characterization parameter and the predetermined vibration risk impact characterization parameter when the predicted vibration risk of the robotic arm does not meet the predetermined standard.

6. The vision-based multi-degree-of-freedom robot vibration detection system according to claim 5, characterized in that, The adaptive regulator is used to calculate the difference between the actual vibration risk impact characterization parameters and the predetermined vibration risk impact characterization parameters; wherein, If the difference is less than or equal to a predetermined difference threshold, then the alarm device is determined not to be activated. If the difference is greater than a predetermined difference threshold, then the alarm device is activated.

7. The vision-based multi-degree-of-freedom robot vibration detection system according to claim 6, characterized in that, The adaptive regulator is used to activate the alarm device during periods of severe risk impact.

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