Industrial robot control method based on visual positioning

Abstract

The invention relates to the technical field of industrial robots, and discloses an industrial robot control method based on visual localization, and the method comprises the steps: monitoring the operation condition of a visual system, the operation system of a robot, the operation region environment of the industrial robot, and the external interference and noise of the industrial robot. Calculating visual system operation performance, machine system operation performance, a regional environment influence value, an interference noise influence value and a comprehensive influence index by combining a visual system operation data set, a machine system operation data set, a regional environment data set and an interference noise data set; whether precision adjustment compensation needs to be carried out or not is judged according to the comprehensive influence index, a corresponding compensation value is calculated for corresponding precision adjustment compensation, the robot positioning state in the current working environment is known more accurately, and it is ensured that the robot always keeps the high-precision positioning capacity; the comprehensive consideration of various factors helps to identify and solve potential system instability.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robots, and specifically to an industrial robot control method based on visual positioning. Background Technique

[0002] Industrial robots are an indispensable and important part of modern industrial manufacturing, and are widely used in various industries, from automobile manufacturing to electronic assembly, and then to chemical industry and logistics. An industrial robot is a multi-joint manipulator or a multi-degree-of-freedom machine device, with a certain degree of automation, and can rely on its own power source and control ability to realize various industrial processing and manufacturing functions. According to the definition of the International Organization for Standardization (ISO), an industrial robot is an "automatically controlled, reprogrammable multi-functional mechanical actuator with three or more joint axes". These robots can complete various tasks through programmed procedures, including handling materials, parts, tools or special equipment. As an important part of modern industrial manufacturing, industrial robots have broad development and application prospects. With the continuous progress of technology and the in-depth expansion of applications, industrial robots will play an important role in more industries, promoting the transformation and upgrading and efficient development of the manufacturing industry.

[0003] Industrial robots play an increasingly important role in modern industrial production, and their positioning accuracy directly affects production efficiency and product quality. However, in actual application scenarios, the positioning accuracy of robots is easily affected by various factors, resulting in problems such as incorrect workpiece processing and misalignment during assembly, affecting the efficiency of the entire production line and product quality. At the same time, the accumulation of positioning errors may cause the robot to deviate from the predetermined trajectory during the execution of tasks, and even dangerous situations such as collisions may occur. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides an industrial robot control method based on visual positioning, which has the advantages of being able to more accurately understand the positioning state of the robot in the current working environment, and when it is found that the positioning accuracy is interfered by external factors, the system can automatically adjust and compensate to ensure that the robot always maintains a high-precision positioning ability. Considering various factors comprehensively helps to identify and solve potential system instability factors, reduce positioning errors, and improve positioning accuracy.

[0006] (II) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solution: An industrial robot control method based on visual positioning, including the following steps:

[0008] Step 1: Monitor the operation of the vision system and obtain monitoring data to form a vision system operation data set;

[0009] Step 2: Monitor the operating system of the robot and obtain the monitoring data to form a machine system operation dataset;

[0010] Step 3: Monitor the operating area environment of the industrial robot and obtain the remaining environmental data to form a regional environment dataset;

[0011] Step 4: Monitor the external interference and noise of the industrial robot and obtain the monitoring data to form an interference noise dataset;

[0012] Step 5: Combine the vision system operation dataset, machine system operation dataset, regional environment dataset, and interference noise dataset to calculate the vision system operation performance, machine system operation performance, regional environment impact value, and interference noise impact value;

[0013] Step 6: Combine the vision system operation performance, machine system operation performance, regional environment impact value, and interference noise impact value to calculate the comprehensive impact index;

[0014] Step 7: Determine whether precision adjustment compensation is required based on the comprehensive impact index, and calculate the corresponding compensation value for corresponding precision adjustment compensation.

[0015] Preferably, the expression method of the vision system operation dataset is: (XJfb, JTzl, TCsd, BDjd, BGcd), where XJfb is the resolution of the camera, JTzl is the quality of the lens, TCsd is the image processing speed, BDjd is the calibration accuracy, and BGcd is the exposure;

[0016] The expression method of the machine system operation dataset is: (JYsj, CDwc, YKpc, CGjd, SDsc, FZbc), where JYsj represents the machine operation time, CDwc represents the repeat positioning error, YKpc represents the motion control deviation, CGjd represents the sensor accuracy, SDsc represents the response time difference when the speed changes, and FZbc represents the system stability standard deviation when the load changes..

[0017] Preferably, the expression method of the regional environment dataset is: Among them, is the regional environmental data collected for the first time in the regional environment dataset, is the regional environmental data collected for the nth time in the regional environment dataset, s is the corresponding humidity data in the regional environmental data, w is the corresponding temperature data in the regional environmental data, g is the corresponding light data in the regional environmental data, and c is the corresponding dust and pollutant data in the regional environmental data;

[0018] The expression of the interference noise dataset is as follows: Among them, is the first interference noise data in the interference noise dataset, is the last interference noise data in the interference noise dataset, g is the electromagnetic interference data in the corresponding interference noise data, s is the noise data in the corresponding interference noise data, and z is the vibration data in the corresponding interference noise data.

[0019] Preferably, the calculation formula for the operating performance of the vision system is:

[0020] SYxn = f(XJfb * JTzl * TCsd * BDjd * BGcd)

[0021] In the above formula, SYxn represents the operating performance of the vision system, and f is used to comprehensively consider the influence of each parameter on the performance of the vision system.

[0022] Preferably, the calculation formula for the operating performance of the machine system is:

[0023]

[0024] In the above formula, JYxn represents the operating performance of the machine system, JYs j d is the rated operating time of the machine system, JYs j i is the actual operating time of the machine system, -α is a positive exponent used to adjust the influence degree of time pressure on the performance index;

[0025] σ R is the standard deviation of the repeat positioning error, used to control the weight of the error;

[0026] YKpc max is the maximum deviation value of the motion control deviation, and β is a positive exponent used to adjust the influence degree of motion control accuracy on the performance index;

[0027] CGjd i is the actual sensor accuracy, CGjd max is the maximum or ideal accuracy of the sensor, and γ is a positive exponent used to adjust the influence degree of sensor accuracy on the performance index;

[0028] SDsc i is the response time difference when the actual speed changes, SDsc d is the response time difference when the ideal speed changes, -δ is a positive exponent indicating the negative impact of the response time on the performance index;

[0029] FZbc iis the actual standard deviation of system stability when the load changes, FZbc max is the maximum standard deviation of system stability when the load changes, and ε is a positive exponent used to adjust the influence degree of load change adaptability on the performance index.

[0030] Preferably, the calculation formula for the regional environmental impact value is:

[0031]

[0032] In the above formula, QYyx is the regional environmental impact value, the subscript max is the maximum data in the corresponding data in the regional environmental dataset, min is the minimum data in the corresponding data in the regional environmental dataset, b is the standard deviation data in the corresponding data in the regional environmental dataset, and p is used to comprehensively consider the influence of each parameter on the regional environmental impact value.

[0033] Preferably, the calculation formula for the interference noise impact value is:

[0034]

[0035] In the above formula, GZyx represents the interference noise impact value, represents the current electromagnetic interference data in the interference noise dataset, represents the current noise data in the interference noise dataset, represents the current vibration data in the interference noise dataset, and ω1, ω2, ω3 are the corresponding weight factors.

[0036] Preferably, the calculation formula for the comprehensive influence index is;

[0037]

[0038] In the above formula, ZHyx is the comprehensive influence index, and BZ is the comprehensive influence value under standard conditions.

[0039] Preferably, when the comprehensive influence index ZHyx is greater than the comprehensive influence index threshold, it represents that precision adjustment compensation is required.

[0040] Preferably, the calculation method for the compensation value is:

[0041] BCz = ZHyx - ZHyx yz

[0042] In the above formula, BCz is the compensation value, and ZHyx yz is the comprehensive influence index threshold.

[0043] Compared with the prior art, the present invention provides an industrial robot control method based on visual positioning, having the following beneficial effects:

[0044] 1. By analyzing the operation of the vision system, the present invention can detect minor defects in the vision system, ensure product quality, improve the processing speed and positioning accuracy of the vision system. By monitoring the operation of the machine system, higher control accuracy can be achieved, errors can be reduced, product quality can be improved, and the wear and failure rate of the machine can be comprehensively reduced, thereby reducing maintenance costs and downtime. By analyzing the results of the environmental data set, the time point when the robot may fail can be predicted, so as to perform preventive maintenance in advance, reduce unexpected downtime, and improve production efficiency. Electromagnetic interference, noise, and vibration may all affect the image acquisition and processing of the robot vision system, and thus affect the positioning accuracy. By evaluating these interference factors, corresponding measures can be taken to reduce or eliminate their influence, thereby improving the positioning accuracy of the robot and ensuring that the robot can still operate efficiently and stably in a complex environment, thus enhancing the overall production efficiency.

[0045] 2. The present invention calculates the comprehensive influence index by combining the operation performance of the vision system, the operation performance of the machine system, the numerical value of the regional environment impact, and the numerical value of the interference noise impact. By judging whether precision adjustment and compensation are required based on the comprehensive influence index, the positioning state of the robot in the current working environment can be more accurately understood. When it is found that the positioning accuracy is interfered by external factors, the system can automatically adjust and compensate to ensure that the robot always maintains high-precision positioning ability. Considering multiple factors comprehensively helps to identify and solve potential system instability factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the method steps of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0048] Please refer to Figure 1 , an industrial robot control method based on vision positioning, including the following steps:

[0049] Step 1: Monitor the operation of the vision system and obtain monitoring data to form a vision system operation data set;

[0050] The expression method of the visual system operation dataset is: (XJfb, JTzl, TCsd, BDjd, BGcd), where XJfb is the resolution of the camera, JTzl is the quality of the lens, TCsd is the image processing speed, BDjd is the calibration accuracy, and BGcd is the exposure. By analyzing the operation of the visual system, small defects of the visual system can be detected, ensuring product quality, improving the processing speed and positioning accuracy of the visual system, facilitating real-time adjustment according to the error between target objects, comprehensively improving the output and quality of products, while reducing production costs. At the same time, referring to these parameters can reduce false positive and false negative results and improve the reliability of overall detection;

[0051] Step 2: Monitor the operation system of the robot and obtain the monitoring data to form the machine system operation dataset;

[0052] The expression method of the machine system operation dataset is: (JYsj, CDwc, YKpc, CGjd, SDsc, FZbc), where JYsj represents the machine operation time, CDwc represents the repeat positioning error, YKpc represents the motion control deviation, CGjd represents the sensor accuracy, SDsc represents the response time difference when the speed changes, and FZbc represents the system stability standard deviation when the load changes. By monitoring the operation of the machine system, evaluate the effective working time and maintenance cycle of the machine system, determine the production efficiency, evaluate the adaptability of the control system to speed changes, optimize the working rhythm, adapt to dynamic production requirements, improve production efficiency, optimize the sensor accuracy and motion control deviation, and the visual positioning system can achieve higher control accuracy, reduce errors, improve product quality, comprehensively reduce the wear and failure rate of the machine, thereby reducing maintenance costs and downtime;

[0053] Step 3: Monitor the operation area environment of the industrial robot and obtain the remaining environmental data to form the regional environment dataset;

[0054] The expression method of the regional environment dataset is: Among them, is the regional environment data collected for the first time in the regional environment dataset, The regional environmental data collected for the nth time in the regional environmental dataset, s is the corresponding humidity data in the regional environmental data, w is the corresponding temperature data in the regional environmental data, g is the corresponding light data in the regional environmental data, c is the corresponding dust and pollutant data in the regional environmental data. By monitoring the light intensity, the robot vision system can adjust parameters such as the exposure time and aperture size of the camera in real time to ensure clear and accurate image information can be obtained under different light conditions, thereby improving the accuracy of visual positioning. Dust and pollutants in the environment may adhere to the camera lens or the target object, affecting the clarity of the image. The robot control system can take cleaning measures or perform image correction in a timely manner to reduce the positioning error. By monitoring the temperature and humidity of the regional environment, the control system can adjust the working state of the robot or initiate protection measures in a timely manner to ensure that the robot operates under suitable environmental conditions and extends its service life. According to the analysis results of the environmental dataset, the time point when the robot may fail can be predicted, so as to carry out preventive maintenance in advance, reduce unexpected downtime, and improve production efficiency;

[0055] Step Four: Monitor the external interference and noise of the industrial robot and obtain the monitoring data to form an interference noise dataset;

[0056] The expression form of the interference noise dataset is: Wherein, is the first interference noise data in the interference noise dataset, is the last interference noise data in the interference noise dataset, g is the electromagnetic interference data in the corresponding interference noise data, s is the noise data in the corresponding interference noise data, z is the vibration data in the corresponding interference noise data. Electromagnetic interference, noise, and vibration may all affect the image acquisition and processing of the robot vision system, and thus affect the positioning accuracy. By evaluating these interference factors, corresponding measures can be taken to reduce or eliminate their influence, thereby improving the positioning accuracy of the robot and ensuring that the robot can still operate efficiently and stably in a complex environment, thus enhancing the overall production efficiency;

[0057] Step Five: Combine the vision system operation dataset, machine system operation dataset, regional environmental dataset, and interference noise dataset to calculate the vision system operation performance, machine system operation performance, regional environmental impact value, and interference noise impact value;

[0058] The calculation formula for the vision system operation performance is:

[0059] SYxn = f(XJfb * JTzl * TCsd * BDjd * BGcd)

[0060] In the above formula, SYxn represents the operating performance of the vision system, and f is used to comprehensively consider the influence of various parameters on the performance of the vision system;

[0061] The calculation formula for the operating performance of the machine system is:

[0062]

[0063] In the above formula, JYxn represents the operating performance of the machine system, which reflects the relationship between the actual operating time of the machine and the rated operating time or design life. If the actual operating time is close to or exceeds the rated time, this ratio will decrease, indicating an increase in the operating pressure of the machine. JYsJ d is the rated operating time of the machine system, and JYsJ i is the actual operating time of the machine system. -α is a positive exponent used to adjust the influence degree of time pressure on the performance index. The larger α is, the greater the influence of time pressure on the performance index;

[0064] used to represent the distribution of the repeat positioning error, σ R is the standard deviation of the repeat positioning error, used to control the weight of the error;

[0065] reflects the relationship between the motion control deviation and the maximum possible deviation, YKpc max is the maximum deviation value of the motion control deviation, and β is a positive exponent used to adjust the influence degree of motion control accuracy on the performance index;

[0066] reflects the relationship between the actual accuracy of the sensor and the maximum or ideal accuracy. The reciprocal form is used to facilitate consistency in subsequent calculations, that is, the higher the accuracy, the larger the performance index, CGjd i is the actual sensor accuracy, and CGjd max is the maximum or ideal accuracy of the sensor, and γ is a positive exponent used to adjust the influence degree of sensor accuracy on the performance index;

[0067] reflects the relationship between the actual response time and the ideal response time of the machine when the speed changes, SDsc i is the response time difference when the actual speed changes, and SDsc d is the ideal response time difference when the speed changes. -δ is a positive exponent but is used as a negative exponent here to represent the negative impact of the response time on the performance index, that is, the longer the response time, the smaller the performance index;

[0068] Reflects the relationship between the stability of the machine under load changes and the maximum possible stability, FZbc i Is the standard deviation of the actual stability of the system under load changes, FZbc max Is the standard deviation of the maximum stability of the system under load changes, ε is a positive exponent used to adjust the influence degree of the load change adaptability on the performance index;

[0069] The calculation formula for the numerical value of the regional environmental impact is:

[0070]

[0071] In the above formula, QYyx is the numerical value of the regional environmental impact, the subscript max is the maximum data in the corresponding data in the regional environmental dataset, min is the minimum data in the corresponding data in the regional environmental dataset, b is the standard deviation data in the corresponding data in the regional environmental dataset, and p is used to comprehensively consider the influence of each parameter on the numerical value of the regional environmental impact;

[0072] The calculation formula for the numerical value of the interference noise impact is:

[0073]

[0074] In the above formula, GZyx represents the numerical value of the interference noise impact, Represents the current electromagnetic interference data in the interference noise dataset, Represents the current noise data in the interference noise dataset, Represents the current vibration data in the interference noise dataset, ω1, ω2, ω3 are the corresponding weight factors;

[0075] Step Six: Combine the operating performance of the vision system, the operating performance of the machine system, the numerical value of the regional environmental impact, and the numerical value of the interference noise impact to calculate the comprehensive impact index;

[0076] The calculation formula for the comprehensive impact index is;

[0077]

[0078] In the above formula, ZHyx is the comprehensive impact index, and BZ is the comprehensive impact value under standard conditions;

[0079] Step Seven: Determine whether precision adjustment compensation is required based on the comprehensive impact index, and calculate the corresponding compensation value for the corresponding precision adjustment compensation.

[0080] When the comprehensive impact index ZHyx is greater than the comprehensive impact index threshold, it represents that precision adjustment compensation is required. The calculation method of the compensation value is:

[0081] BCz = ZHyx - ZHyx yz

[0082] In the above formula, BCz is the compensation value, and ZHyx yz is the threshold value of the comprehensive influence index;

[0083] Combined with the operating performance of the vision system, the operating performance of the machine system, the numerical value of the regional environmental impact, and the numerical value of the interference noise impact, calculate the comprehensive influence index. Then, judge whether precision adjustment compensation is required based on the comprehensive influence index, which can more accurately understand the robot positioning state in the current working environment. When it is found that the positioning accuracy is interfered by external factors, the system can automatically adjust and compensate to ensure that the robot always maintains high-precision positioning ability. Considering multiple factors comprehensively helps to identify and solve potential system instability factors.

[0084] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An industrial robot control method based on visual positioning, characterized in that, It includes the following steps: Step 1: Monitor the operation of the vision system and obtain monitoring data to form a vision system operation dataset; Step 2: Monitor the operation system of the robot and obtain monitoring data to form a machine system operation dataset; Step 3: Monitor the operating area environment of the industrial robot and obtain other environmental data to form a regional environment dataset; Step 4: Monitor the external interference and noise of the industrial robot and obtain monitoring data to form an interference noise dataset; Step 5: Combine the vision system operation dataset, machine system operation dataset, regional environment dataset, and interference noise dataset to calculate the vision system operation performance, machine system operation performance, regional environment impact value, and interference noise impact value; Step 6: Combine the vision system operation performance, machine system operation performance, regional environment impact value, and interference noise impact value to calculate the comprehensive impact index; Step 7: Judge whether precision adjustment compensation is required according to the comprehensive impact index, and calculate the corresponding compensation value for corresponding precision adjustment compensation.

2. The industrial robot control method based on visual positioning according to claim 1, wherein: The expression method of the vision system operation dataset is: (XJfb, JTzl, TCsd, BDjd, BGcd), where XJfb is the resolution of the camera, JTzl is the quality of the lens, TCsd is the image processing speed, BDjd is the calibration accuracy, and BGcd is the exposure; The expression method of the machine system operation dataset is: (JYsj, CDwc, YKpc, CGjd, SDsc, FZbc), where JYsj represents the machine operation time, CDwc represents the repeat positioning error, YKpc represents the motion control deviation, and CGjd represents the sensor accuracy; In the above formula, JYxn represents the operating performance of the machine system, and JYsj d is the rated operating time of the machine system, and JYsj i is the actual operating time of the machine system, and -α is a positive exponent used to adjust the influence degree of time pressure on the performance index; σ R is the standard deviation of the repeat positioning error and is used to control the weight of the error; YKpc max is the maximum deviation value of the motion control deviation, and β is a positive exponent used to adjust the influence degree of the motion control precision on the performance index; CGjd i is the actual sensor accuracy, CGjd max is the maximum or ideal accuracy of the sensor, and γ is a positive exponent used to adjust the degree of influence of sensor accuracy on the performance index; SDsc i is the response time difference when the actual speed changes, SDsc d is the response time difference when the ideal speed changes, and -δ is a positive exponent representing the negative impact of the response time on the performance index; FZbc i is the actual standard deviation of system stability when the load changes, FZbc max is the maximum standard deviation of system stability when the load changes, and ε is a positive exponent used to adjust the influence degree of the load change adaptability on the performance index.

3. The industrial robot control method based on visual positioning according to claim 3, wherein: The calculation formula for the regional environment impact value is: In the above formula, QYyx is the regional environment impact value, subscript max is the maximum data in the corresponding data in the regional environment dataset, min is the minimum data in the corresponding data in the regional environment dataset, b is the standard deviation data in the corresponding data in the regional environment dataset, and p is used to comprehensively consider the influence of each parameter on the regional environment impact value.

4. The industrial robot control method based on visual positioning according to claim 3, characterized in that: The calculation formula for the interference noise impact value is: In the above formula, GZyx represents the interference noise impact value, represents the current electromagnetic interference data in the interference noise dataset, represents the current noise data in the interference noise dataset, represents the current vibration data in the interference noise dataset, and ω1, ω2, ω3 are the corresponding weighting factors.

5. The industrial robot control method based on visual positioning according to claim 7, wherein: The calculation formula for the comprehensive impact index is; In the above formula, ZHyx is the comprehensive impact index, and BZ is the comprehensive impact value under standard conditions.

6. The industrial robot control method based on visual positioning according to claim 8, characterized in that: When the comprehensive impact index ZHyx is greater than the comprehensive impact index threshold, it means that precision adjustment compensation is required.

7. The industrial robot control method based on visual positioning according to claim 9, characterized in that: The calculation method of the compensation value is: BCz = ZHyx - ZHyx yz In the above formula, BCz is the compensation value, and ZHyx yz is the threshold of the comprehensive influence index.

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