Industrial robot with visual system and control method thereof

Abstract

The invention discloses an industrial robot with a visual system and a control method of the industrial robot. Comprising an industrial robot working area numbering module, an industrial robot image data acquisition module, a self-adaptive illumination adjustment processing module, an environment adaptability compensation module, an industrial robot motion control module, an industrial robot image recognition monitoring performance module, an image recognition monitoring performance evaluation module and a machine performance fault early warning module. According to the invention, image identification data of each specified working area of the industrial robot is collected, a machine vision light source regulation index and a machine motion control index are calculated, and a machine vision environment compensation index is calculated for an abnormal working area of the industrial robot, so that a machine performance monitoring coefficient and a machine image identification fault early warning coefficient are further obtained; and control optimization and early warning are carried out on the industrial robot, so that the problem that the image recognition capability is reduced due to uneven illumination is effectively solved, and the working stability and accuracy of the industrial robot in a complex illumination environment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robot control, and more specifically, to an industrial robot with a visual system and a control method thereof. Background Art

[0002] With the rise of intelligent manufacturing, by introducing advanced technologies and equipment and using computer technology to process and analyze images, robots can simulate the functions of the human visual system and realize functions such as object recognition, tracking, and positioning. In industrial automation, this technology can significantly improve production efficiency and product quality, reduce production costs, and improve the production environment.

[0003] Industrial robot vision systems usually include image acquisition, image processing and motion control parts, which automatically integrate comprehensive and accurate environmental information, help to achieve more complex operation tasks, thereby realizing automated monitoring and management of the production process and improving the intelligence level of the manufacturing process.

[0004] However, there are still some shortcomings in actual use. For example, the performance of existing machine vision systems is affected by various environmental factors such as lighting, shadows, and reflections, resulting in uneven lighting, which leads to a decrease in the image recognition ability of industrial robots. These factors affect the accuracy and stability of quality inspection.

[0005] In actual production, industrial robots are in complex production environments, and their visual systems may be affected by factors such as electromagnetic interference and vibration, resulting in unstable operation, which in turn leads to performance degradation or failure. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an industrial robot with a visual system and a control method thereof, which are used to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: an industrial robot with a visual system, comprising:

[0008] Industrial robot work area numbering module: used to number the designated work areas of the industrial robot in sequence as 1, 2, ...i, ...n.

[0009] Industrial robot image data acquisition module: used to collect image recognition data of each designated working area of ​​the industrial robot through the robot camera. The industrial robot image data acquisition module includes a lighting adjustment data acquisition unit and a motion control data acquisition unit. The image recognition data includes lighting adjustment data and motion control data.

[0010] Adaptive lighting adjustment processing module: The machine vision light source control index of each designated working area of ​​the industrial robot is calculated based on the lighting adjustment data of the lighting adjustment data acquisition unit.

[0011] Environmental adaptability compensation module: used to compare the obtained machine vision light source control index of each designated working area of ​​the industrial robot with the preset machine vision light source control index, screen out abnormal industrial robot working areas, calculate the machine vision environment compensation index of the abnormal industrial robot working area, and take light source adjustment measures for the industrial robot working area.

[0012] Industrial robot motion control module: The machine motion control index of each designated working area of ​​the industrial robot is calculated based on the motion control data of the motion control data acquisition unit.

[0013] Industrial robot image recognition monitoring performance module: used to analyze the machine performance monitoring coefficient of each designated working area of ​​the industrial robot through the machine vision light source control index and machine motion control index of each designated working area of ​​the industrial robot.

[0014] Image recognition monitoring performance evaluation module: used to compare and process the obtained machine performance monitoring coefficients of each designated working area of ​​the industrial robot with the preset machine performance monitoring coefficients.

[0015] Machine performance fault warning module: Based on the machine vibration amplitude and image shape matching degree of each designated working area of ​​the industrial robot, the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot is calculated.

[0016] Preferably, the industrial robot image data acquisition module is specifically:

[0017] Lighting adjustment data acquisition unit: The robot camera collects the number of pixels, shadow area, and camera angle of each designated working area of ​​the industrial robot, which are marked as jw i 、jm i ,θ i , where i = 1, 2, ... n, i represents the number of the i-th designated working area;

[0018] Motion control data acquisition unit: The robot camera collects the robot coordinate system image pixel coordinates, robot coordinates, and robot arm angles of each designated working area of ​​the industrial robot, which are marked as (X i , Y i ),σ i , where m = 1, 2, ... q, and m represents the number of the mth image pixel.

[0019] Preferably, the calculation formula of the machine vision light source control index is:

[0020]

[0021] where α i It is expressed as the machine vision light source control index of the i-th specified working area, jm i Represented as the shaded area of ​​the i-th specified working area, θ i Represented as the camera angle of the i-th specified working area, jw i It is represented as the number of pixels of the i-th specified working area, n is represented as the total number of working areas of the industrial robot, and e is represented as a natural constant.

[0022] Preferably, the environmental adaptability compensation module is specifically:

[0023] Obtain the machine vision light source control index of each designated working area of ​​the industrial robot, and compare it with the preset machine vision light source control index. If the machine vision light source control index of a designated working area of ​​the industrial robot is greater than the preset machine vision light source control index, it indicates that there is an abnormality in the light source of the designated working area of ​​the industrial robot, and the light source of the working area of ​​the industrial robot should be adjusted. Otherwise, it indicates that the light source conditions of the designated working area of ​​the industrial robot meet expectations.

[0024] Preferably, the machine vision environment compensation index is specifically:

[0025] Step S01: Screening out the numbers of designated working areas of industrial robots with abnormal light sources;

[0026] Step S02: The robot camera collects the brightness, light source irradiation area, and light source irradiation height of each designated working area of ​​the industrial robot, which are marked as yl i , yc i ,yh i ;

[0027] Step S03: Calculate the machine visual environment compensation index, the specific calculation formula is:

[0028]

[0029] where ω i It is expressed as the machine vision environment compensation index of the i-th working area, yh i It is expressed as the light source irradiation height of the i-th working area, yl i Represented as the brightness of the i-th working area, yc i It is represented as the illumination area of ​​the light source in the i-th working area, e is represented as a natural constant, jm i Represented as the shaded area of ​​the i-th specified working area.

[0030] Preferably, the calculation formula of the machine motion control index is:

[0031]

[0032] where β i Expressed as the machine motion control index of the i-th specified working area, σ i It is represented as the robot arm angle of the i-th specified working area, It is expressed as the robot lateral distortion of the mth image pixel in the ith specified working area, It is expressed as the radial distortion of the robot at the mth image pixel in the ith specified working area, q is expressed as the number of image pixels, and e is expressed as a natural constant.

[0033] Preferably, the calculation formula of the machine performance monitoring coefficient is:

[0034] θ i =β i +α i

[0035] where θ i Expressed as the machine performance monitoring coefficient of the i-th specified working area, α i It is expressed as the machine vision light source control index of the i-th specified working area, β i It is expressed as the machine motion control index of the i-th specified industrial robot working area.

[0036] Preferably, the image recognition monitoring performance evaluation module is specifically:

[0037] The machine performance monitoring coefficient of each designated working area of ​​the industrial robot is obtained, and compared with the preset machine performance monitoring coefficient. If the machine performance monitoring coefficient of a designated working area of ​​the industrial robot is greater than the preset machine performance monitoring coefficient, it indicates that the image vision recognition performance of the industrial robot in the designated working area is abnormal; otherwise, it indicates that there is no abnormality in the image vision recognition performance of the industrial robot in the working area.

[0038] Preferably, the machine performance fault warning module is specifically:

[0039] Step S01: Compare the image recognition shapes of each designated working area of ​​the industrial robot with the standard image shapes to obtain the matching degree of each image shape in the image recognition shapes of each designated working area of ​​the industrial robot and the standard image shapes, marked as gp i ;

[0040] Step S02: collecting the machine vibration amplitude of each designated working area of ​​the industrial robot through a vibration sensor;

[0041] Step S03: The calculation formula of the machine image recognition fault warning coefficient is:

[0042]

[0043] Among them GY i It is expressed as the machine image recognition fault warning coefficient of the i-th working area, θ i is represented by the machine performance monitoring coefficient of the i-th working area, Δθ is represented by the mean value of the machine performance monitoring coefficient, gp i It is expressed as the image shape matching degree of the i-th working area, GP 预 Represented as the preset image shape matching degree, gd i Expressed as the machine vibration amplitude in the ith working area, GD 预 It is expressed as the preset machine vibration amplitude, and e is expressed as a natural constant;

[0044] Step S04: Obtain the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot, and compare it with the preset machine image recognition fault warning coefficient. If the machine image recognition fault warning coefficient of a designated working area of ​​the industrial robot is greater than the preset machine image recognition fault warning coefficient, it indicates that the image recognition capability of the designated working area of ​​the industrial robot is at risk of failure, and the number of the industrial robot working area at risk of failure should be immediately sent to the management personnel. Otherwise, it indicates that the image recognition capability of the designated working area of ​​the industrial robot is normal.

[0045] Preferably, a method for controlling an industrial robot comprises the following steps:

[0046] Step S01: Industrial robot work area numbering: the designated work areas of the industrial robot are numbered 1, 2, ...i, ...n in sequence;

[0047] Step S02: Industrial robot image data acquisition: collecting image recognition data of each designated working area of ​​the industrial robot, wherein the industrial robot image data acquisition includes a light adjustment data acquisition unit and a motion control data acquisition unit, and the image recognition data includes light adjustment data and motion control data;

[0048] Step S03: Adaptive illumination adjustment processing: calculating the machine vision light source control index of each designated working area of ​​the industrial robot according to the illumination adjustment data of the illumination adjustment data acquisition unit;

[0049] Step S04: Environmental adaptability compensation: obtaining the machine vision light source control index of each designated working area of ​​the industrial robot, comparing it with the preset machine vision light source control index, screening out abnormal industrial robot working areas, and calculating the machine vision environment compensation index of the abnormal industrial robot working areas;

[0050] Step S05: Industrial robot motion control: calculating the machine motion control index of each designated working area of ​​the industrial robot according to the motion control data of the motion control data acquisition unit;

[0051] Step S06: industrial robot image recognition monitoring performance: obtaining the machine vision light source control index and the machine motion control index of each designated working area of ​​the industrial robot, and analyzing to obtain the machine performance monitoring coefficient of each designated working area of ​​the industrial robot;

[0052] Step S07: Image recognition monitoring performance evaluation: obtaining the machine performance monitoring coefficient of each designated working area of ​​the industrial robot, comparing it with the preset machine performance monitoring coefficient, and processing it;

[0053] Step S08: Machine performance fault warning: Based on the machine vibration amplitude and image shape matching degree of each designated working area of ​​the industrial robot, the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot is calculated.

[0054] Technical effects and advantages of the present invention:

[0055] 1. The present invention provides an industrial robot with a visual system and a control method thereof. The image recognition data of each designated working area of ​​the industrial robot is collected, and the machine vision light source control index of each designated working area of ​​the industrial robot is calculated according to the illumination adjustment data of the illumination adjustment data collection unit. The machine motion control index of each designated working area of ​​the industrial robot is calculated according to the motion control data of the motion control data collection unit. The machine performance monitoring coefficient of each designated working area of ​​the industrial robot is obtained by analysis and compared with the preset machine performance monitoring coefficient. If the machine performance monitoring coefficient of a certain working area of ​​the industrial robot is greater than the preset machine performance monitoring coefficient, it indicates that the image visual recognition performance of the working area of ​​the industrial robot is abnormal. Otherwise, it indicates that there is no abnormal phenomenon in the image visual recognition performance of the working area of ​​the industrial robot. The working state of the industrial robot is calculated and analyzed, and an alarm is quickly identified and issued, so that the maintenance personnel can take measures immediately to avoid the expansion of the problem, thereby improving the working stability and accuracy of the industrial robot in a complex lighting environment and improving the level of production automation.

[0056] 2. The present invention provides an industrial robot with a visual system and a control method thereof. The environmental adaptability compensation module is used to obtain the machine vision light source control index of each designated working area of ​​the industrial robot, and compare it with the preset machine vision light source control index to screen out abnormal industrial robot working areas, thereby calculating the machine vision environment compensation index of the abnormal industrial robot working area, regulating and compensating the machine vision light source in the industrial robot working area, ensuring the stability and accuracy of the visual system of the industrial robot in different working environments. The machine performance fault warning module is used to calculate the machine vibration amplitude and image shape matching degree of each designated working area of ​​the industrial robot. The machine image recognition fault warning coefficient of the working area is compared with the preset machine image recognition fault warning coefficient. If the machine image recognition fault warning coefficient of a certain industrial robot working area is greater than the preset machine image recognition fault warning coefficient, it indicates that there is a risk of failure in the image recognition ability of the industrial robot working area. The number of the industrial robot working area with failure risk should be sent to the management personnel immediately. Otherwise, it indicates that the image recognition ability of the industrial robot working area is normal. The machine vibration amplitude and image shape matching parameter settings should be increased to improve the accuracy of fault warning signal processing, effectively prevent the occurrence of faults, and quickly locate the problem when a fault occurs, shorten the maintenance time, and reduce production losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the module process connection of the present invention.

[0058] Figure 2 It is a structural schematic diagram of the industrial robot image data acquisition module of the present invention.

[0059] Figure 3 It is a schematic diagram of the connection of the method steps of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] See also Figure 1As shown, the present invention provides an industrial robot with a visual system, including an industrial robot working area numbering module, an industrial robot image data acquisition module, an adaptive lighting adjustment processing module, an environmental adaptability compensation module, an industrial robot motion control module, an industrial robot image recognition monitoring performance module, an image recognition monitoring performance evaluation module, and a machine performance fault warning module.

[0062] The industrial robot working area numbering module is connected to the industrial robot image data acquisition module, the industrial robot image data acquisition module is connected to the adaptive lighting adjustment processing module and the industrial robot motion control module, the adaptive lighting adjustment processing module and the industrial robot motion control module are connected to the industrial robot image recognition monitoring performance module, the industrial robot image recognition monitoring performance module is connected to the image recognition monitoring performance evaluation module, the image recognition monitoring performance evaluation module is connected to the machine performance fault early warning module, and the adaptive lighting adjustment processing module is connected to the environmental adaptability compensation module.

[0063] The industrial robot work area numbering module is used to number the designated work areas of the industrial robot in sequence as 1, 2, ...i, ...n.

[0064] See also Figure 2 As shown, the industrial robot image data acquisition module is used to collect image recognition data of each designated working area of ​​the industrial robot. The industrial robot image data acquisition module includes a lighting adjustment data acquisition unit and a motion control data acquisition unit. The image recognition data includes lighting adjustment data and motion control data.

[0065] In a possible design, the industrial robot image data acquisition module is specifically:

[0066] Lighting adjustment data acquisition unit: used to collect the number of pixels, shadow area, and camera angle of each designated working area of ​​the industrial robot, marked as jw i 、jm i ,θ i , where i = 1, 2, ... n, i represents the number of the i-th industrial robot working area;

[0067] Motion control data acquisition unit: used to collect the robot coordinate system image pixel coordinates, robot coordinates, and robot arm angles of each specified working area of ​​the industrial robot, marked as (X i , Y i ),σ i , where m = 1, 2, ... q, and m represents the number of the mth image pixel.

[0068] The adaptive illumination adjustment processing module is used to receive the image recognition data transmitted by the industrial robot image data acquisition module, and calculate the machine vision light source control index of each designated working area of ​​the industrial robot according to the illumination adjustment data of the illumination adjustment data acquisition unit.

[0069] In a possible design, the calculation formula of the machine vision light source control index is:

[0070]

[0071] where α i It is expressed as the machine vision light source control index of the i-th industrial robot working area, jm i Represented as the shaded area of ​​the i-th industrial robot working area, θ i It is represented as the camera angle of the i-th industrial robot working area, jw i It is represented as the number of pixels in the working area of ​​the ith industrial robot, n is represented as the total number of working areas of industrial robots, and e is represented as a natural constant.

[0072] The environmental adaptability compensation module is used to obtain the machine vision light source control index of each designated working area of ​​the industrial robot, compare it with the preset machine vision light source control index, screen out abnormal industrial robot working areas, calculate the machine vision environment compensation index of the abnormal industrial robot working area, and take light source adjustment measures for the industrial robot working area.

[0073] In a possible design, the environmental adaptability compensation module is specifically:

[0074] Step S01: obtaining a machine vision light source control index of each designated working area of ​​the industrial robot, and comparing it with a preset machine vision light source control index. If the machine vision light source control index of a designated working area of ​​the industrial robot is greater than the preset machine vision light source control index, it indicates that the light source of the designated working area of ​​the industrial robot is abnormal, and the light source of the working area of ​​the industrial robot should be adjusted. Otherwise, it indicates that the light source condition of the designated working area of ​​the industrial robot meets expectations.

[0075] Step S02: Screening out the numbers of designated working areas of industrial robots with abnormal light sources;

[0076] Step S03: Collect the brightness, light source irradiation area, and light source irradiation height of each designated working area of ​​the industrial robot, and mark them as yl i , yc i ,yh i ;

[0077] Step S04: Calculate the machine visual environment compensation index, the specific calculation formula is:

[0078]

[0079] where ω i It is expressed as the machine vision environment compensation index of the i-th industrial robot working area, yh i It is expressed as the light source irradiation height of the i-th industrial robot working area, yl i Represented as the brightness of the i-th industrial robot working area, yc i It is represented as the light source irradiation area of ​​the i-th industrial robot working area, e is represented as a natural constant, jm i Represented as the shaded area of ​​the i-th specified working area.

[0080] In this embodiment, it should be specifically explained that the environmental adaptability compensation module further includes:

[0081] According to the machine vision light source controller of the industrial robot, the light source parameters of each designated working area where the light source is abnormal are precisely adjusted. For example, when the machine vision environment compensation index is higher than the preset machine vision environment compensation index, the brightness is increased by lowering the height to improve the brightness and maintain stable recognition quality.

[0082] The industrial robot motion control module is used to receive the image recognition data transmitted by the industrial robot image data acquisition module, and calculate the machine motion control index of each designated working area of ​​the industrial robot according to the motion control data of the motion control data acquisition unit.

[0083] In a possible design, the calculation formula of the machine motion control index is:

[0084]

[0085] where β i Expressed as the machine motion control index of the i-th industrial robot working area, σ i It is represented as the robot arm angle of the i-th industrial robot working area, It is represented as the robot lateral distortion of the mth image pixel in the working area of ​​the i-th industrial robot, It is expressed as the robot radial distortion of the mth image pixel in the working area of ​​the ith industrial robot, m is the number of image pixels, and e is a natural constant.

[0086] In this embodiment, it should be specifically noted that the industrial robot motion control module further includes:

[0087] Step S01: Calculate the lateral distortion of the robot. The specific calculation formula is:

[0088]

[0089] in It is represented as the robot lateral distortion of the mth image pixel in the working area of ​​the i-th industrial robot, It is represented as the x-axis coordinate of the image pixel point in the robot coordinate system of the m-th image pixel point in the working area of ​​the ith industrial robot, X i Expressed as the robot x-axis coordinate of the i-th industrial robot working area, It is represented as the y-axis coordinate of the image pixel point in the robot coordinate system of the m-th image pixel point in the working area of ​​the ith industrial robot, Y i It is represented as the y-axis coordinate of the robot in the working area of ​​the i-th industrial robot, ε1 and ε2 are the distortion weight factors of the x-axis and y-axis coordinates of the image pixel points in the robot coordinate system, and m is represented as the number of image pixels;

[0090] Step S02: Calculate the radial distortion of the robot. The specific calculation formula is:

[0091]

[0092] in It is expressed as the robot radial distortion of the mth image pixel in the working area of ​​the ith industrial robot.

[0093] The industrial robot image recognition monitoring performance module is used to obtain the machine vision light source regulation index and the machine motion control index of each designated working area of ​​the industrial robot, and analyze and obtain the machine performance monitoring coefficient of each designated working area of ​​the industrial robot.

[0094] In a possible design, the calculation formula of the machine performance monitoring coefficient is:

[0095] θ i =β i +α i

[0096] where θ i Expressed as the machine performance monitoring coefficient of the i-th industrial robot working area, α i It is expressed as the machine vision light source control index of the i-th industrial robot working area, β i It is expressed as the machine motion control index of the i-th industrial robot working area.

[0097] The image recognition monitoring performance evaluation module is used to obtain the machine performance monitoring coefficient of each designated working area of ​​the industrial robot, compare it with the preset machine performance monitoring coefficient, and process it.

[0098] In a possible design, the image recognition monitoring performance evaluation module is specifically:

[0099] Obtain the machine performance monitoring coefficient of each designated working area of ​​the industrial robot and compare it with the preset machine performance monitoring coefficient. If the machine performance monitoring coefficient of a certain industrial robot's working area is greater than the preset machine performance monitoring coefficient, it indicates that the image vision recognition performance of the industrial robot's working area is abnormal; otherwise, it indicates that there is no abnormality in the image vision recognition performance of the industrial robot's working area.

[0100] The machine performance fault warning module calculates the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot based on the machine vibration amplitude and image shape matching degree of each designated working area of ​​the industrial robot.

[0101] In a possible design, the machine performance fault warning module is specifically:

[0102] Step S01: Compare the image recognition shapes of each designated working area of ​​the industrial robot with the standard image shapes to obtain the matching degree of each image shape in the image recognition shapes of each designated working area of ​​the industrial robot and the standard image shapes, marked as gp i ;

[0103] Step S02: The calculation formula of the machine image recognition fault warning coefficient is:

[0104]

[0105] Among them GY i It is expressed as the machine image recognition fault warning coefficient of the i-th industrial robot working area, θ i is represented by the machine performance monitoring coefficient of the i-th industrial robot working area, Δθ is represented by the mean value of the machine performance monitoring coefficient, gp i It is expressed as the image shape matching degree of the i-th industrial robot working area, GP 预 Represented as the preset image shape matching degree, gd i It is expressed as the machine vibration amplitude of the i-th industrial robot working area, GD 预 It is expressed as the preset machine vibration amplitude, and e is expressed as a natural constant;

[0106] Step S03: Obtain the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot, and compare it with the preset machine image recognition fault warning coefficient. If the machine image recognition fault warning coefficient of a certain industrial robot working area is greater than the preset machine image recognition fault warning coefficient, it indicates that the image recognition capability of the industrial robot working area has a failure risk, and the number of the industrial robot working area with a failure risk should be immediately sent to the management personnel. Otherwise, it indicates that the image recognition capability of the industrial robot working area is normal.

[0107] See also Figure 3 As shown, in this embodiment, it should be specifically explained that the present invention provides an industrial robot control method, comprising the following steps:

[0108] Step S01: Industrial robot work area numbering: the designated work areas of the industrial robot are numbered 1, 2, ...i, ...n in sequence;

[0109] Step S02: Industrial robot image data acquisition: collecting image recognition data of each designated working area of ​​the industrial robot, wherein the industrial robot image data acquisition includes a light adjustment data acquisition unit and a motion control data acquisition unit, and the image recognition data includes light adjustment data and motion control data;

[0110] Step S03: Adaptive illumination adjustment: Calculate the machine vision light source control index of each designated working area of ​​the industrial robot according to the illumination adjustment data of the illumination adjustment data acquisition unit;

[0111] Step S04: Environmental adaptability compensation: obtaining the machine vision light source control index of each designated working area of ​​the industrial robot, comparing it with the preset machine vision light source control index, screening out abnormal industrial robot working areas, and calculating the machine vision environment compensation index of the abnormal industrial robot working areas;

[0112] Step S05: Industrial robot motion control: calculating the machine motion control index of each designated working area of ​​the industrial robot according to the motion control data of the motion control data acquisition unit;

[0113] Step S06: industrial robot image recognition performance monitoring: obtaining the machine vision light source control index and the machine motion control index of each designated working area of ​​the industrial robot, and analyzing to obtain the machine performance monitoring coefficient of each designated working area of ​​the industrial robot;

[0114] Step S07: Industrial robot image recognition evaluation: obtaining the machine performance monitoring coefficient of each designated working area of ​​the industrial robot, comparing it with the preset machine performance monitoring coefficient, and processing it;

[0115] Step S08: Machine performance fault warning: Based on the machine vibration amplitude and image shape matching degree of each designated working area of ​​the industrial robot, the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot is calculated.

[0116] In the present embodiment, it is necessary to specifically explain that the present invention collects image recognition data of each designated working area of ​​the industrial robot, calculates the machine vision light source control index of each designated working area of ​​the industrial robot according to the lighting adjustment data of the lighting adjustment data acquisition unit, calculates the machine motion control index of each designated working area of ​​the industrial robot according to the motion control data of the motion control data acquisition unit, analyzes and obtains the machine performance monitoring coefficient of each designated working area of ​​the industrial robot, and compares it with the preset machine performance monitoring coefficient. If the machine performance monitoring coefficient of a working area of ​​an industrial robot is greater than the preset machine performance monitoring coefficient, it indicates that the image vision recognition performance of the working area of ​​the industrial robot is abnormal, otherwise it indicates that there is no abnormality in the image vision recognition performance of the working area of ​​the industrial robot. By accurately calculating and regulating the machine vision light source, the problem of decreased image recognition ability due to uneven lighting is effectively solved, the working stability and accuracy of the industrial robot in a complex lighting environment are improved, and the level of production automation is improved.

[0117] In this embodiment, it should be specifically explained that the present invention utilizes an environmental adaptability compensation module to obtain the machine vision light source control index of each designated working area of ​​the industrial robot, compare it with the preset machine vision light source control index, screen out abnormal industrial robot working areas, and calculate the machine vision environment compensation index of the abnormal industrial robot working area, thereby realizing dynamic control and compensation of the machine vision light source in the working area of ​​the industrial robot, ensuring the stability and accuracy of the visual system of the industrial robot in different working environments, and utilizing the machine performance fault warning module to calculate the machine vibration amplitude and image shape matching degree of each designated working area of ​​the industrial robot. The machine image recognition fault warning coefficient of the domain is compared with the preset machine image recognition fault warning coefficient. If the machine image recognition fault warning coefficient of a certain industrial robot working area is greater than the preset machine image recognition fault warning coefficient, it indicates that there is a risk of failure in the image recognition ability of the industrial robot working area. The number of the industrial robot working area with failure risk should be sent to the management personnel immediately. Otherwise, it indicates that the image recognition ability of the industrial robot working area is normal. The machine vibration amplitude and image shape matching parameter settings should be increased to improve the accuracy of fault warning signal processing, effectively prevent the occurrence of faults, and quickly locate the problem when a fault occurs, shorten the maintenance time, and reduce production losses.

[0118] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An industrial robot with a visual system, characterized in that: include: Industrial robot work area numbering module: used to number the designated work areas of the industrial robot as 1, 2, ...i, ...n in sequence; Industrial robot image data acquisition module: used to collect image recognition data of each designated working area of ​​the industrial robot through the robot camera, the industrial robot image data acquisition module includes a light adjustment data acquisition unit and a motion control data acquisition unit, and the image recognition data includes light adjustment data and motion control data; Adaptive lighting adjustment processing module: calculates the machine vision light source control index of each designated working area of ​​the industrial robot according to the lighting adjustment data of the lighting adjustment data acquisition unit; Environmental adaptability compensation module: used to compare the obtained machine vision light source control index of each designated working area of ​​the industrial robot with the preset machine vision light source control index, screen out abnormal industrial robot working areas, calculate the machine vision environment compensation index of the abnormal industrial robot working area, and take light source adjustment measures for the industrial robot working area; Industrial robot motion control module: calculates the machine motion control index of each designated working area of ​​the industrial robot according to the motion control data of the motion control data acquisition unit; Industrial robot image recognition monitoring performance module: used to analyze and obtain the machine performance monitoring coefficient of each designated working area of ​​the industrial robot through the machine vision light source control index and machine motion control index of each designated working area of ​​the industrial robot; Image recognition monitoring performance evaluation module: used to compare and process the obtained machine performance monitoring coefficients of each designated working area of ​​the industrial robot with the preset machine performance monitoring coefficients; Machine performance fault warning module: Based on the machine vibration amplitude and image shape matching degree of each designated working area of ​​the industrial robot, the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot is calculated.

2. The industrial robot with a visual system according to claim 1, characterized in that: The industrial robot image data acquisition module is specifically: Lighting adjustment data acquisition unit: The robot camera collects the number of pixels, shadow area, and camera angle of each designated working area of ​​the industrial robot, which are marked as jw i 、jm i ,θ i , where i = 1, 2, ... n, i represents the number of the i-th designated working area; Motion control data acquisition unit: The robot camera collects the robot coordinate system image pixel coordinates, robot coordinates, and robot arm angles of each designated working area of ​​the industrial robot, which are marked as (X i , Y i ),σ i , where m = 1, 2, ... q, and m represents the number of the mth image pixel.

3. The industrial robot with a visual system according to claim 2, characterized in that: The calculation formula of the machine vision light source control index is: where α i It is expressed as the machine vision light source control index of the i-th specified working area, jm i Represented as the shaded area of ​​the i-th specified working area, θ i Represented as the camera angle of the i-th specified working area, jw i It is represented as the number of pixels of the i-th specified working area, n is represented as the total number of working areas of the industrial robot, and e is represented as a natural constant.

4. The industrial robot with a visual system according to claim 1, characterized in that: The environmental adaptability compensation module is specifically: Obtain the machine vision light source control index of each designated working area of ​​the industrial robot, and compare it with the preset machine vision light source control index. If the machine vision light source control index of a designated working area of ​​the industrial robot is greater than the preset machine vision light source control index, it indicates that there is an abnormality in the light source of the designated working area of ​​the industrial robot, and the light source of the working area of ​​the industrial robot should be adjusted. Otherwise, it indicates that the light source conditions of the designated working area of ​​the industrial robot meet expectations.

5. The industrial robot with a visual system according to claim 1, characterized in that: The machine vision environment compensation index is specifically: Step S01: Screening out the numbers of designated working areas of industrial robots with abnormal light sources; Step S02: The robot camera collects the brightness, light source irradiation area, and light source irradiation height of each designated working area of ​​the industrial robot, which are marked as yl i , yc i ,yh i ; Step S03: Calculate the machine visual environment compensation index, the specific calculation formula is: where ω i It is expressed as the machine vision environment compensation index of the i-th working area, yh i It is expressed as the light source irradiation height of the i-th working area, yl i Represented as the brightness of the i-th working area, yc i It is represented as the illumination area of ​​the light source in the i-th working area, e is represented as a natural constant, jm i Represented as the shaded area of ​​the i-th specified working area.

6. The industrial robot with a visual system according to claim 5, characterized in that: The calculation formula of the machine motion control index is: where β i Expressed as the machine motion control index of the i-th specified working area, σ i It is represented as the robot arm angle of the i-th specified working area, It is expressed as the robot lateral distortion of the mth image pixel in the ith specified working area, It is expressed as the radial distortion of the robot at the mth image pixel in the ith specified working area, q is expressed as the number of image pixels, and e is expressed as a natural constant.

7. The industrial robot with a visual system according to claim 1, characterized in that: The calculation formula of the machine performance monitoring coefficient is: i i =b i +a i where θ i Expressed as the machine performance monitoring coefficient of the i-th specified working area, α i It is expressed as the machine vision light source control index of the i-th specified working area, β i It is expressed as the machine motion control index of the i-th specified industrial robot working area.

8. The industrial robot with a visual system according to claim 7, characterized in that: The image recognition monitoring performance evaluation module is specifically: The machine performance monitoring coefficient of each designated working area of ​​the industrial robot is obtained, and compared with the preset machine performance monitoring coefficient. If the machine performance monitoring coefficient of a designated working area of ​​the industrial robot is greater than the preset machine performance monitoring coefficient, it indicates that the image vision recognition performance of the industrial robot in the designated working area is abnormal; otherwise, it indicates that there is no abnormality in the image vision recognition performance of the industrial robot in the working area.

9. The industrial robot with a visual system according to claim 8, characterized in that: The machine performance fault warning module is specifically: Step S01: Compare the image recognition shapes of each designated working area of ​​the industrial robot with the standard image shapes to obtain the matching degree of each image shape in the image recognition shapes of each designated working area of ​​the industrial robot and the standard image shapes, marked as gp i ; Step S02: collecting the machine vibration amplitude of each designated working area of ​​the industrial robot through a vibration sensor; Step S03: The calculation formula of the machine image recognition fault warning coefficient is: Among them GY i It is expressed as the machine image recognition fault warning coefficient of the i-th working area, θ i is represented by the machine performance monitoring coefficient of the i-th working area, Δθ is represented by the mean value of the machine performance monitoring coefficient, gp i It is expressed as the image shape matching degree of the i-th working area, GP 预 Represented as the preset image shape matching degree, gd i Expressed as the machine vibration amplitude in the ith working area, GD 预 It is expressed as the preset machine vibration amplitude, and e is expressed as a natural constant; Step S04: Obtain the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot, and compare it with the preset machine image recognition fault warning coefficient. If the machine image recognition fault warning coefficient of a designated working area of ​​the industrial robot is greater than the preset machine image recognition fault warning coefficient, it indicates that the image recognition capability of the industrial robot in the designated working area has a failure risk, and the number of the industrial robot working area with the failure risk should be immediately sent to the management personnel. Otherwise, it indicates that the image recognition capability of the industrial robot in the designated working area is normal.

10. An industrial robot control method, using an industrial robot with a visual system as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step S01: Industrial robot work area numbering: the designated work areas of the industrial robot are numbered 1, 2, ...i, ...n in sequence; Step S02: Industrial robot image data acquisition: collecting image recognition data of each designated working area of ​​the industrial robot, wherein the industrial robot image data acquisition includes a light adjustment data acquisition unit and a motion control data acquisition unit, and the image recognition data includes light adjustment data and motion control data; Step S03: Adaptive illumination adjustment processing: calculating the machine vision light source control index of each designated working area of ​​the industrial robot according to the illumination adjustment data of the illumination adjustment data acquisition unit; Step S04: Environmental adaptability compensation: obtaining the machine vision light source control index of each designated working area of ​​the industrial robot, comparing it with the preset machine vision light source control index, screening out abnormal industrial robot working areas, and calculating the machine vision environment compensation index of the abnormal industrial robot working areas; Step S05: Industrial robot motion control: calculating the machine motion control index of each designated working area of ​​the industrial robot according to the motion control data of the motion control data acquisition unit; Step S06: industrial robot image recognition monitoring performance: obtaining the machine vision light source control index and the machine motion control index of each designated working area of ​​the industrial robot, and analyzing to obtain the machine performance monitoring coefficient of each designated working area of ​​the industrial robot; Step S07: Image recognition monitoring performance evaluation: obtaining the machine performance monitoring coefficient of each designated working area of ​​the industrial robot, comparing it with the preset machine performance monitoring coefficient, and processing it; Step S08: Machine performance fault warning: Based on the machine vibration amplitude and image shape matching degree of each designated working area of ​​the industrial robot, the machine image recognition fault warning coefficient of each designated working area of ​​the industrial robot is calculated.

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