CN117325211B - Deburring robot pose monitoring system and method based on Internet of things - Google Patents
Deburring robot pose monitoring system and method based on Internet of things Download PDFInfo
- Publication number
- CN117325211B CN117325211B CN202311632391.4A CN202311632391A CN117325211B CN 117325211 B CN117325211 B CN 117325211B CN 202311632391 A CN202311632391 A CN 202311632391A CN 117325211 B CN117325211 B CN 117325211B
- Authority
- CN
- China
- Prior art keywords
- data
- pose
- deburring
- historical
- robot
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000000694 effects Effects 0.000 claims abstract description 53
- 230000005540 biological transmission Effects 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 230000033001 locomotion Effects 0.000 claims description 18
- 230000005856 abnormality Effects 0.000 claims description 14
- 238000001514 detection method Methods 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 238000013075 data extraction Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 239000013598 vector Substances 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 description 6
- 230000009471 action Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/0095—Means or methods for testing manipulators
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Manipulator (AREA)
Abstract
The invention discloses a deburring robot pose monitoring system and method based on the Internet of things, and belongs to the technical field of robot pose monitoring. The system comprises a data acquisition and transmission module, a historical pose classification and collection module, a deburring effect judgment module, a pose adjustment module and a feedback and control module; the data acquisition and transmission module is used for acquiring pose data from the industrial camera and the laser sensor equipment and transmitting the data through the Internet of things; the historical pose classification and collection module is used for classifying and collecting historical pose data according to the difference of the distribution densities of burrs of the processed workpiece; the deburring effect judging module is used for evaluating the treatment effect of the deburring robot on burrs of the workpiece; the pose adjustment module is used for carrying out pose adjustment according to analysis and comparison of historical pose data; the feedback and control module is used for monitoring the pose state of the deburring robot in real time and providing feedback information.
Description
Technical Field
The invention relates to the technical field of robot pose monitoring, in particular to a deburring robot pose monitoring system and method based on the Internet of things.
Background
The deburring robot is an automatic device for removing burrs, edges and uneven parts on the surface of a workpiece, and the pose monitoring system plays a key role in the deburring robot and is used for monitoring and measuring the position and pose information of the robot.
While the prior art can meet the current needs to some extent, the following problems still remain: the position and posture information of the robot cannot be monitored and fed back in real time generally, and an operator can only know the motion state of the robot by means of post data analysis and cannot find and correct problems in time; for the situation of abnormality in the robot movement that the ability is relatively weak, once the abnormal situation appears, the system often can not automatically adjust the pose of the robot or provide timely alarm information, and the problem can be solved by manual intervention; to improve the stability and reliability of the monitoring system, after the robot runs for a long time, the sensor equipment may drift, noise interference or faults and other conditions, so that the pose monitoring result is inaccurate.
Disclosure of Invention
The invention aims to provide a deburring robot pose monitoring system and method based on the Internet of things, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
a deburring robot pose monitoring method based on the internet of things, the method comprising the following steps:
s100, acquiring burr data of a workpiece to be processed and historical pose data of a deburring robot by using an industrial camera and laser sensor equipment; the historical pose data comprises the position and the angle of the deburring robot; classifying and collecting the historical pose data based on different burr distribution densities of workpieces processed by a deburring robot in the burr data;
s200, respectively acquiring deburring effect image data corresponding to each historical pose data set in the historical pose data sets corresponding to each burr distribution density, and judging the rationality of the historical pose data of the deburring robot; the burr effect includes complete removal and incomplete removal;
s300, acquiring historical pose data of the deburring robot which is completely removed and incompletely removed, adjusting the motion track and the angle of the deburring robot which is incompletely removed according to the difference of the historical pose data of the deburring robot which is completely removed and incompletely removed, and taking the historical data of the deburring robot which is completely removed and the pose data of the deburring robot which is incompletely removed after adjustment as target pose data;
s400, adaptively adjusting the motion trail and angle of the deburring robot according to the pose data of the actual deburring robot; and comparing the difference between the target pose data and the actual pose data, and performing fine adjustment or calibration on the deburring robot according to the feedback result.
Further, step S100 includes:
s101, acquiring a two-dimensional image of the surface of a workpiece to be processed and historical pose data of a deburring robot by using an industrial camera and laser sensor equipment, identifying burrs on the surface of the workpiece to be processed, and marking the identified burrs on the two-dimensional image;
s102, extracting images of all marking burrs to obtain a burr image set corresponding to the surface images of all workpieces to be processed; respectively acquiring the area of each burr in each burr image set;
s103, according to the formulaCalculating the distribution density of burrs, wherein N is a positive integer and represents the total number of burrs on the surface of each workpiece to be processed, and i represents the values of 1 to N +.>The area of the ith burr on the surface of each workpiece to be processed is represented, and A represents the area of the surface of each workpiece to be processed;
s104, classifying and collecting historical pose data according to the calculated burr distribution density, wherein each burr distribution density value is of a type.
Further, step S200 includes:
s201, acquiring deburring effect image data corresponding to each historical pose data set for each historical pose data set corresponding to each burr distribution density, and preprocessing the deburring effect image data; the preprocessing comprises image denoising and morphological processing;
s202, counting the number N 'of burrs on the surface of each workpiece after deburring according to the preprocessed image data of the deburring effect, and obtaining the total area A' of the burrs on the surface of each workpiece after deburring;
s203, after the deburring operation, if the threshold value condition P is met 1 Wherein the threshold condition P 1 If N' =0, it is reasonable to say that the historical pose data of the deburring robot will satisfy the threshold condition P 1 Is defined as M 1 A grade; if the threshold condition P is satisfied 2 Wherein the threshold condition P 2 Is N'>0 and Q.ltoreq.a, whereA is a positive real number; it is reasonable to say that the historical pose data of the deburring robot will meet the threshold condition P 2 Is defined as M 2 A grade; if the threshold condition P is satisfied 3 Wherein the threshold condition P 3 Is N'>0, and Q>a, the historical pose data of the deburring robot is unreasonable, and the threshold value condition P is met 3 Is defined as M 3 Grade.
Further, step S300 includes:
s301, obtaining M 1 Grade, M 2 Grade and M 3 A hierarchical deburring robot historical pose data set;
s302, M 1 The historical pose data set of the deburring robot of the grade is used as a reference and is respectively matched with M 2 Grade and M 3 Comparing the historical pose data sets of the deburring robot one by one;
the difference between the two is calculated according to the following formula:
wherein,representing the difference in position of two data points; />、/>And->Represents M 1 The position coordinates of the ith data point in the historical pose data of the level deburring robot; />、And->Represents M 2 Grade or M 3 Position coordinates of a jth data point in the historical pose data of the level deburring robot; i and j represent the position numbers of the data points and take positive integers from 1 to n;
wherein,representing the pose difference of two data points; />Represents M 1 The pose of the ith data point in the historical pose data of the deburring robot in the grade; the gesture may be an angle, and if the gesture is represented by an angle, the gesture is typically described by using a euler angle or a rotation matrix; euler angles generally contain three angular components, such as roll, pitch, and yaw, which correspond to rotational angles about X, Y and Z axes, respectively; the rotation matrix is a 3*3 matrix describing the rotation relationship from the reference coordinate system to the target coordinate system; />Represents M 2 Grade or M 3 The pose of the jth data point in the historical pose data of the deburring robot in the level; measuring the size of the gesture difference by calculating the dot product between the two gesture vectors and taking the inverse cosine value of the dot product; a larger posture difference value indicates that the larger the difference in posture between the two pieces of posture data;
wherein diff_pos (i, j) represents a position difference, diff_ori (i, j) represents a posture difference, diff_total (i, j) represents a total difference;
s303, M 1 Grade and M 2 The minimum value and the maximum value in the historical pose data difference value set of the level deburring robot are used as the lower bound and the upper bound of the threshold S, and according to the calculation M 1 Grade and M 3 Level deburring robot historical pose data difference value, and M is adjusted 3 The motion trail and angle of the grade deburring robot are adjusted until the threshold S is met; will M 1 Grade, M 2 Rank and adjusted M 3 The historical pose data set of the deburring robot is used as target pose data.
Further, step S400 includes:
acquiring pose data of an actual deburring robot, and comparing differences between target pose data and the actual pose data; if the comparison result meets the threshold S and the deburring meets the threshold condition P 1 Or P 2 Then the position and posture data of the deburring robot at the moment are consistent with the target position and posture data; if the comparison result meets the threshold S and the deburring meets the threshold condition P 3 Judging the abnormal condition, and prompting a manager to overhaul the deburring robot; and if the comparison result does not meet the threshold S, performing fine adjustment or calibration on the deburring robot, wherein the fine adjustment or calibration is to adjust the motion trail and angle of the deburring robot until the threshold S is met, and finishing the adjustment.
The system comprises a data acquisition and transmission module, a historical pose classification and collection module, a deburring effect judgment module, a pose adjustment module and a feedback and control module;
the data acquisition and transmission module is used for acquiring pose data from the industrial camera and the laser sensor equipment and transmitting the data to the historical pose classification and collection module through the connection of the Internet of things; the historical pose classification and collection module is used for classifying and collecting historical pose data according to the difference of workpiece burr distribution densities processed by the deburring robot in the historical pose data; the deburring effect judging module is used for evaluating the processing effect of the historical pose data of the deburring robot on the workpiece burrs; the pose adjustment module is used for carrying out pose adjustment aiming at the situation of incomplete deburring according to analysis and comparison of historical pose data; the feedback and control module is used for monitoring the pose state of the deburring robot, providing feedback information and controlling and adjusting according to the comparison result;
the output end of the data acquisition module is connected with the input end of the historical pose classification collecting module; the output end of the historical pose classification collecting module is connected with the input end of the deburring effect judging module; the output end of the deburring effect judging module is connected with the input end of the pose adjusting module; the output end of the pose adjusting module is connected with the input end of the feedback and control module.
Further, the data acquisition and transmission module comprises a data acquisition unit and a data transmission unit;
the data acquisition unit is used for acquiring burr data of a workpiece to be processed and pose data of the deburring robot from the industrial camera and the laser sensor equipment; the data transmission unit is used for transmitting the acquired data to the historical pose classification and collection module through the connection of the Internet of things;
the output end of the data acquisition unit is connected with the input end of the data transmission unit.
Further, the deburring effect judging module comprises an effect image data extracting unit and a rationality judging unit;
the effect image data extraction unit is used for extracting corresponding deburring effect image data from each classified historical pose data set; the rationality judging unit is used for judging rationality of historical pose data of the deburring robot;
the output end of the effect image data extraction unit is connected with the input end of the rationality judgment unit.
Further, the pose adjusting module comprises a difference analyzing unit and a target pose generating unit;
the difference analysis unit is used for comparing the difference of the historical pose data of the deburring robot which is completely removed and the deburring robot which is not completely removed, and adjusting the motion trail and the angle of the robot which is not completely removed; the target pose generation unit is used for taking the completely removed historical pose data and the adjusted incompletely removed pose data as target pose data;
the output end of the difference analysis unit is connected with the input end of the target pose generation unit.
Further, the feedback and control module comprises a pose difference analysis unit, an abnormality detection unit and a fine adjustment or calibration unit;
the pose difference analysis unit is used for comparing the difference between the target pose data and the actual pose data and providing feedback information; the abnormality detection unit is used for judging whether an abnormal condition exists according to the comparison result and the deburring effect index, and prompting a manager to overhaul; the fine tuning or calibrating unit is used for carrying out fine tuning or calibration on the deburring robot according to the comparison result;
the output end of the pose difference analysis unit is connected with the input end of the abnormality detection unit; the output end of the abnormality detection unit is connected with the input end of the fine tuning or calibrating unit.
Compared with the prior art, the invention has the following beneficial effects: by acquiring burr data of a workpiece to be processed and historical pose data of a deburring robot using an industrial camera and laser sensor equipment, more accurate position and pose information can be provided; the position and posture information of the robot can be monitored and fed back in real time, so that an operator can know the motion state of the robot in time and carry out necessary adjustment, the operation response speed is improved, and accumulation and delay of problems are avoided; by comparing historical pose data of the deburring robot which is completely removed and the deburring robot which is not completely removed, the motion track and the angle of the robot which is not completely removed are adjusted, so that the self-adaptive pose adjustment can be realized; by comparing the difference between the target pose data and the actual pose data and carrying out fine adjustment or calibration on the robot according to the feedback result, abnormal conditions can be found in time and corresponding processing can be carried out. This helps to improve system stability and reliability, reducing failures and errors.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic structural diagram of a deburring robot pose monitoring system based on the internet of things of the present invention;
fig. 2 is a schematic step diagram of a deburring robot pose monitoring method based on the internet of things.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
a deburring robot pose monitoring method based on the internet of things, the method comprising the following steps:
s100, acquiring burr data of a workpiece to be processed and historical pose data of a deburring robot by using an industrial camera and laser sensor equipment; the historical pose data comprises the position and the angle of the deburring robot; classifying and collecting the historical pose data based on different burr distribution densities of workpieces processed by a deburring robot in the burr data;
s200, respectively acquiring deburring effect image data corresponding to each historical pose data set in the historical pose data sets corresponding to each burr distribution density, and judging the rationality of the historical pose data of the deburring robot; the burr effect includes complete removal and incomplete removal;
s300, acquiring historical pose data of the deburring robot which is completely removed and incompletely removed, adjusting the motion track and the angle of the deburring robot which is incompletely removed according to the difference of the historical pose data of the deburring robot which is completely removed and incompletely removed, and taking the historical data of the deburring robot which is completely removed and the pose data of the deburring robot which is incompletely removed after adjustment as target pose data;
s400, adaptively adjusting the motion trail and angle of the deburring robot according to the pose data of the actual deburring robot; and comparing the difference between the target pose data and the actual pose data, and performing fine adjustment or calibration on the deburring robot according to the feedback result.
The step S100 includes:
s101, acquiring a two-dimensional image of the surface of a workpiece to be processed and historical pose data of a deburring robot by using an industrial camera and laser sensor equipment, identifying burrs on the surface of the workpiece to be processed, and marking the identified burrs on the two-dimensional image;
s102, extracting images of all marking burrs to obtain a burr image set corresponding to the surface images of all workpieces to be processed; respectively acquiring the area of each burr in each burr image set;
s103, according to the formulaCalculating the distribution density of burrs, wherein N is a positive integer and represents the total number of burrs on the surface of each workpiece to be processed, and i represents the values of 1 to N +.>The area of the ith burr on the surface of each workpiece to be processed is represented, and A represents the area of the surface of each workpiece to be processed;
s104, classifying and collecting historical pose data according to the calculated burr distribution density, wherein each burr distribution density value is of a type.
Step S200 includes:
s201, acquiring deburring effect image data corresponding to each historical pose data set for each historical pose data set corresponding to each burr distribution density, and preprocessing the deburring effect image data; the preprocessing comprises image denoising and morphological processing;
s202, counting the number N 'of burrs on the surface of each workpiece after deburring according to the preprocessed image data of the deburring effect, and obtaining the total area A' of the burrs on the surface of each workpiece after deburring;
s203, after the deburring operation, if the threshold value condition P is met 1 Wherein the threshold condition P 1 If N' =0, it is reasonable to say that the historical pose data of the deburring robot will satisfy the threshold condition P 1 Is defined as M 1 A grade; if the threshold condition P is satisfied 2 Wherein the threshold condition P 2 Is N'>0 and Q.ltoreq.a, whereA is a positive real number; it is reasonable to say that the historical pose data of the deburring robot will meet the threshold condition P 2 Is defined as M 2 A grade; if the threshold condition P is satisfied 3 Wherein the threshold condition P 3 Is N'>0, and Q>a, the historical pose data of the deburring robot is unreasonable, and the threshold value condition P is met 3 Is defined as M 3 Grade.
Step S300 includes:
s301, obtaining M 1 Grade, M 2 Grade and M 3 A hierarchical deburring robot historical pose data set;
s302, M 1 The historical pose data set of the deburring robot of the grade is used as a reference and is respectively matched with M 2 Grade and M 3 Comparing the historical pose data sets of the deburring robot one by one;
the difference between the two is calculated according to the following formula:
wherein,representing the difference in position of two data points; />、/>And->Represents M 1 The position coordinates of the ith data point in the historical pose data of the level deburring robot; />、And->Represents M 2 Grade or M 3 Position coordinates of a jth data point in the historical pose data of the level deburring robot; i and j represent the position numbers of the data points and take positive integers from 1 to n;
wherein,representing the pose difference of two data points; />Represents M 1 The pose of the ith data point in the historical pose data of the deburring robot in the grade; the gesture may be an angle, and if the gesture is represented by an angle, the gesture is typically described by using a euler angle or a rotation matrix; euler angles generally contain three angular components, such as roll, pitch, and yaw, which correspond to rotational angles about X, Y and Z axes, respectively; the rotation matrix is a 3*3 matrix describing the sitting from the referenceThe rotation relation from the target coordinate system to the standard system; />Represents M 2 Grade or M 3 The pose of the jth data point in the historical pose data of the deburring robot in the level; measuring the size of the gesture difference by calculating the dot product between the two gesture vectors and taking the inverse cosine value of the dot product; a larger posture difference value indicates that the larger the difference in posture between the two pieces of posture data;
wherein diff_pos (i, j) represents a position difference, diff_ori (i, j) represents a posture difference, diff_total (i, j) represents a total difference;
s303, M 1 Grade and M 2 The minimum value and the maximum value in the historical pose data difference value set of the level deburring robot are used as the lower bound and the upper bound of the threshold S, and according to the calculation M 1 Grade and M 3 Level deburring robot historical pose data difference value, and M is adjusted 3 The motion trail and angle of the grade deburring robot are adjusted until the threshold S is met; will M 1 Grade, M 2 Rank and adjusted M 3 The historical pose data set of the deburring robot is used as target pose data.
Step S400 includes:
acquiring pose data of an actual deburring robot, and comparing differences between target pose data and the actual pose data; if the comparison result meets the threshold S and the deburring meets the threshold condition P 1 Or P 2 Then the position and posture data of the deburring robot at the moment are consistent with the target position and posture data; if the comparison result meets the threshold S and the deburring meets the threshold condition P 3 Judging the abnormal condition, and prompting a manager to overhaul the deburring robot; if the comparison does not meet the threshold S, fine tuning or calibration of the deburring robot is performed to adjust the movement track and angle of the deburring robot until the threshold is metThe value S is adjusted.
The system comprises a data acquisition and transmission module, a historical pose classification and collection module, a deburring effect judgment module, a pose adjustment module and a feedback and control module;
the data acquisition and transmission module is used for acquiring pose data from the industrial camera and the laser sensor equipment and transmitting the data to the historical pose classification and collection module through the connection of the Internet of things; the historical pose classification and collection module is used for classifying and collecting historical pose data according to the difference of workpiece burr distribution densities processed by the deburring robot in the historical pose data; the deburring effect judging module is used for evaluating the processing effect of the historical pose data of the deburring robot on the workpiece burrs; the pose adjustment module is used for carrying out pose adjustment aiming at the situation of incomplete deburring according to analysis and comparison of historical pose data; the feedback and control module is used for monitoring the pose state of the deburring robot, providing feedback information and controlling and adjusting according to the comparison result;
the output end of the data acquisition module is connected with the input end of the historical pose classification collecting module; the output end of the historical pose classification collecting module is connected with the input end of the deburring effect judging module; the output end of the deburring effect judging module is connected with the input end of the pose adjusting module; the output end of the pose adjusting module is connected with the input end of the feedback and control module.
The data acquisition and transmission module comprises a data acquisition unit and a data transmission unit;
the data acquisition unit is used for acquiring burr data of a workpiece to be processed and pose data of the deburring robot from the industrial camera and the laser sensor equipment; the data transmission unit is used for transmitting the acquired data to the historical pose classification and collection module through the connection of the Internet of things;
the output end of the data acquisition unit is connected with the input end of the data transmission unit.
The deburring effect judging module comprises an effect image data extracting unit and a rationality judging unit;
the effect image data extraction unit is used for extracting corresponding deburring effect image data from each classified historical pose data set; the rationality judging unit is used for judging rationality of historical pose data of the deburring robot;
the output end of the effect image data extraction unit is connected with the input end of the rationality judgment unit.
The pose adjusting module comprises a difference analyzing unit and a target pose generating unit;
the difference analysis unit is used for comparing the difference of the historical pose data of the deburring robot which is completely removed and the deburring robot which is not completely removed, and adjusting the motion trail and the angle of the robot which is not completely removed; the target pose generation unit is used for taking the completely removed historical pose data and the adjusted incompletely removed pose data as target pose data;
the output end of the difference analysis unit is connected with the input end of the target pose generation unit.
The feedback and control module comprises a pose difference analysis unit, an abnormality detection unit and a fine adjustment or calibration unit;
the pose difference analysis unit is used for comparing the difference between the target pose data and the actual pose data and providing feedback information; the abnormality detection unit is used for judging whether an abnormal condition exists according to the comparison result and the deburring effect index, and prompting a manager to overhaul; the fine tuning or calibrating unit is used for carrying out fine tuning or calibration on the deburring robot according to the comparison result;
the output end of the pose difference analysis unit is connected with the input end of the abnormality detection unit; the output end of the abnormality detection unit is connected with the input end of the fine tuning or calibrating unit.
In this embodiment:
assume that there are 5 burrs on the surface of the workpiece to be treated, which have areas of 1 square centimeter, 2 square centimeters, 3 square centimeters, 1.5 square centimeters and 2.5 square centimeters, respectively. Whereas the total area of the entire surface of the workpiece to be treated is 20 square cm.
Then according to the formulaDistribution density of burrs->The calculation is as follows:
= (1 + 2 + 3 + 1.5 + 2.5) / 20 = 10 / 20 = 0.5；
therefore, the burr distribution density was 0.5.
Assume a historical pose data set with a burr distribution density of 0.5, which contains 10 historical pose data. From these data, Q is calculated and classified according to a threshold condition.
Firstly, deburring effect image data corresponding to each historical pose data are obtained and preprocessed.
And then, according to the preprocessed deburring effect image data, counting the number N 'of burrs on the surface of each workpiece after deburring, and obtaining the total area A' of burrs on the surface of each workpiece after deburring.
Assuming that the total area A 'of burrs on the surface of the workpiece after deburring is x and the number N' of burrs is 1 after the deburring.
Then, Q is calculated according to the formula:
and Q obtained by calculation is b.
Assuming threshold condition P 1 N' =0, threshold condition P 2 Is N'>0 and Q is less than or equal to a, threshold condition P 3 Is N'>0 and Q>a。
According to threshold condition P 1 、P 2 And P 3 The historical pose data of the deburring robot can be classified; since the number of burrs N' is 1 and Q is b, let b be>a, then satisfy the threshold condition P 3 It is explained that the historical pose data in this embodiment can be classified into M 3 Grade.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A deburring robot pose monitoring method based on the Internet of things is characterized by comprising the following steps of: the method comprises the following steps:
s100, acquiring burr data of a workpiece to be processed and historical pose data of a deburring robot by using an industrial camera and laser sensor equipment; the historical pose data comprises the position and the angle of the deburring robot; classifying and collecting the historical pose data based on different burr distribution densities of workpieces processed by a deburring robot in the burr data;
s200, respectively acquiring deburring effect image data corresponding to each historical pose data set in the historical pose data sets corresponding to each burr distribution density, and judging the rationality of the historical pose data of the deburring robot; the deburring effect comprises complete removal and incomplete removal;
s300, acquiring historical pose data of the deburring robot which is completely removed and incompletely removed, adjusting the motion track and the angle of the deburring robot which is incompletely removed according to the difference of the historical pose data of the deburring robot which is completely removed and incompletely removed, and taking the historical data of the deburring robot which is completely removed and the pose data of the deburring robot which is incompletely removed after adjustment as target pose data;
s400, adaptively adjusting the motion trail and angle of the deburring robot according to the pose data of the actual deburring robot; comparing the difference between the target pose data and the actual pose data, and performing fine adjustment or calibration on the deburring robot according to the feedback result;
the step S100 specifically includes:
s101, acquiring a two-dimensional image of the surface of a workpiece to be processed and historical pose data of a deburring robot by using an industrial camera and laser sensor equipment, identifying burrs on the surface of the workpiece to be processed, and marking the identified burrs on the two-dimensional image;
s102, extracting images of all marking burrs to obtain a burr image set corresponding to the surface images of all workpieces to be processed; respectively acquiring the area of each burr in each burr image set;
s103, according to the formulaCalculating the distribution density of burrs, wherein N is a positive integer and represents the total number of burrs on the surface of each workpiece to be processed, and i represents the values of 1 to N +.>The area of the ith burr on the surface of each workpiece to be processed is represented, and A represents the area of the surface of each workpiece to be processed;
s104, classifying and collecting historical pose data according to the calculated burr distribution density, wherein each burr distribution density value is of a type;
the step S200 specifically includes:
s201, acquiring deburring effect image data corresponding to each historical pose data set for each historical pose data set corresponding to each burr distribution density, and preprocessing the deburring effect image data; the preprocessing comprises image denoising and morphological processing;
s202, counting the number N 'of burrs on the surface of each workpiece after deburring according to the preprocessed image data of the deburring effect, and obtaining the total area A' of the burrs on the surface of each workpiece after deburring;
s203, after the deburring operation, if the threshold value condition P is met 1 Wherein the threshold condition P 1 If N' =0, it is reasonable to say that the historical pose data of the deburring robot will satisfy the threshold condition P 1 Is defined as M 1 A grade; if the threshold condition P is satisfied 2 Wherein the threshold condition P 2 Is N'>0 and Q.ltoreq.a, whereA is a positive real number; it is reasonable to say that the historical pose data of the deburring robot will meet the threshold condition P 2 Is defined as M 2 A grade; if the threshold condition P is satisfied 3 Wherein the threshold condition P 3 Is N'>0, and Q>a, the historical pose data of the deburring robot is unreasonable, and the threshold value condition P is met 3 Is defined as M 3 A grade;
the step S300 specifically includes:
s301, obtaining M 1 Grade, M 2 Grade and M 3 A hierarchical deburring robot historical pose data set;
s302, M 1 The historical pose data set of the deburring robot of the grade is used as a reference and is respectively matched with M 2 Grade and M 3 Comparing the historical pose data sets of the deburring robot one by one;
the difference between the two is calculated according to the following formula:
；
wherein,representing the difference in position of two data points; />、/>And->Represents M 1 The position coordinates of the ith data point in the historical pose data of the level deburring robot; />、/>Andrepresents M 2 Grade or M 3 Position coordinates of a jth data point in the historical pose data of the level deburring robot; i and j represent the position numbers of the data points and take positive integers from 1 to n;
；
wherein,representing the pose difference of two data points; />Represents M 1 Ith in historical pose data of level deburring robotThe pose of the data points; />Represents M 2 Grade or M 3 The pose of the jth data point in the historical pose data of the deburring robot in the level; measuring the size of the gesture difference by calculating the dot product between the two gesture vectors and taking the inverse cosine value of the dot product;
；
wherein diff_pos (i, j) represents a position difference, diff_ori (i, j) represents a posture difference, diff_total (i, j) represents a total difference;
s303, M 1 Grade and M 2 The minimum value and the maximum value in the historical pose data difference value set of the level deburring robot are used as the lower bound and the upper bound of the threshold S, and according to the calculation M 1 Grade and M 3 Level deburring robot historical pose data difference value, and M is adjusted 3 The motion trail and angle of the grade deburring robot are adjusted until the threshold S is met; will M 1 Grade, M 2 Rank and adjusted M 3 The historical pose data set of the deburring robot is used as target pose data.
2. The deburring robot pose monitoring method based on the internet of things of claim 1, wherein the method comprises the following steps: the step S400 specifically includes:
acquiring pose data of an actual deburring robot, and comparing differences between target pose data and the actual pose data; if the comparison result meets the threshold S and the deburring meets the threshold condition P 1 Or P 2 Then the position and posture data of the deburring robot at the moment are consistent with the target position and posture data; if the comparison result meets the threshold S and the deburring meets the threshold condition P 3 Judging the abnormal condition, and prompting a manager to overhaul the deburring robot; and if the comparison result does not meet the threshold S, performing fine adjustment or calibration on the deburring robot.
3. The deburring robot pose monitoring system based on the internet of things, which is applied to the deburring robot pose monitoring method based on the internet of things, and is characterized in that: the system comprises a data acquisition and transmission module, a historical pose classification and collection module, a deburring effect judgment module, a pose adjustment module and a feedback and control module;
the data acquisition and transmission module is used for acquiring pose data from the industrial camera and the laser sensor equipment and transmitting the data to the historical pose classification and collection module through the connection of the Internet of things; the historical pose classification and collection module is used for classifying and collecting historical pose data according to the differences of the distribution densities of burrs of the workpiece processed by the deburring robot in the historical pose data; the deburring effect judging module is used for evaluating the processing effect of historical pose data of the deburring robot on workpiece burrs; the pose adjustment module is used for carrying out pose adjustment aiming at the situation of incomplete deburring according to analysis and comparison of historical pose data; the feedback and control module is used for monitoring the pose state of the deburring robot, providing feedback information and controlling and adjusting according to the comparison result;
the output end of the data acquisition and transmission module is connected with the input end of the historical pose classification collecting module; the output end of the historical pose classification collecting module is connected with the input end of the deburring effect judging module; the output end of the deburring effect judging module is connected with the input end of the pose adjusting module; the output end of the pose adjusting module is connected with the input end of the feedback and control module.
4. A deburring robot pose monitoring system based on the internet of things as set forth in claim 3, wherein: the data acquisition and transmission module comprises a data acquisition unit and a data transmission unit;
the data acquisition unit is used for acquiring burr data of a workpiece to be processed and pose data of the deburring robot from the industrial camera and the laser sensor equipment; the data transmission unit is used for transmitting the acquired data to the historical pose classification and collection module through the connection of the Internet of things;
the output end of the data acquisition unit is connected with the input end of the data transmission unit.
5. A deburring robot pose monitoring system based on the internet of things as set forth in claim 3, wherein: the deburring effect judging module comprises an effect image data extracting unit and a rationality judging unit;
the effect image data extraction unit is used for extracting corresponding deburring effect image data from each classified historical pose data set; the rationality judging unit is used for judging rationality of historical pose data of the deburring robot;
the output end of the effect image data extraction unit is connected with the input end of the rationality judgment unit.
6. A deburring robot pose monitoring system based on the internet of things as set forth in claim 3, wherein: the pose adjusting module comprises a difference analyzing unit and a target pose generating unit;
the difference analysis unit is used for comparing the difference of the historical pose data of the deburring robot which is completely removed and the deburring robot which is not completely removed, and adjusting the motion trail and the angle of the robot which is not completely removed; the target pose generation unit is used for taking the completely removed historical pose data and the adjusted incompletely removed pose data as target pose data;
the output end of the difference analysis unit is connected with the input end of the target pose generation unit.
7. A deburring robot pose monitoring system based on the internet of things as set forth in claim 3, wherein: the feedback and control module comprises a pose difference analysis unit, an abnormality detection unit and a fine adjustment or calibration unit;
the pose difference analysis unit is used for comparing the difference between the target pose data and the actual pose data and providing feedback information; the abnormality detection unit is used for judging whether an abnormality exists according to the comparison result and the deburring effect index, and prompting a manager to overhaul; the fine tuning or calibrating unit is used for carrying out fine tuning or calibration on the deburring robot according to the comparison result;
the output end of the pose difference analysis unit is connected with the input end of the abnormality detection unit; the output end of the abnormality detection unit is connected with the input end of the fine adjustment or calibration unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311632391.4A CN117325211B (en) | 2023-12-01 | 2023-12-01 | Deburring robot pose monitoring system and method based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311632391.4A CN117325211B (en) | 2023-12-01 | 2023-12-01 | Deburring robot pose monitoring system and method based on Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117325211A CN117325211A (en) | 2024-01-02 |
| CN117325211B true CN117325211B (en) | 2024-02-09 |
Family
ID=89293902
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311632391.4A Active CN117325211B (en) | 2023-12-01 | 2023-12-01 | Deburring robot pose monitoring system and method based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117325211B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118466410A (en) * | 2024-04-30 | 2024-08-09 | 江苏中科云控智能工业装备有限公司 | Multi-source data control system and method for deburring equipment based on Internet of things |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1994029776A1 (en) * | 1993-06-08 | 1994-12-22 | Masayuki Hamura | Method and apparatus for controlling deburring robot |
| CN104249195A (en) * | 2013-06-28 | 2014-12-31 | 发那科株式会社 | Deburring device including visual sensor and force sensor |
| CN110802615A (en) * | 2019-11-19 | 2020-02-18 | 北京鸿恒基幕墙装饰工程有限公司 | Cloud automatic deburring robot based on big data and using method thereof |
| CN115239661A (en) * | 2022-07-19 | 2022-10-25 | 河南牧业经济学院 | Mechanical part burr detection method and system based on image processing |
| CN115729188A (en) * | 2022-11-18 | 2023-03-03 | 江苏中科云控智能工业装备有限公司 | Deburring production line control signal transmission system based on digital twinning |
-
2023
- 2023-12-01 CN CN202311632391.4A patent/CN117325211B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1994029776A1 (en) * | 1993-06-08 | 1994-12-22 | Masayuki Hamura | Method and apparatus for controlling deburring robot |
| CN104249195A (en) * | 2013-06-28 | 2014-12-31 | 发那科株式会社 | Deburring device including visual sensor and force sensor |
| CN110802615A (en) * | 2019-11-19 | 2020-02-18 | 北京鸿恒基幕墙装饰工程有限公司 | Cloud automatic deburring robot based on big data and using method thereof |
| CN115239661A (en) * | 2022-07-19 | 2022-10-25 | 河南牧业经济学院 | Mechanical part burr detection method and system based on image processing |
| CN115729188A (en) * | 2022-11-18 | 2023-03-03 | 江苏中科云控智能工业装备有限公司 | Deburring production line control signal transmission system based on digital twinning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117325211A (en) | 2024-01-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117325211B (en) | Deburring robot pose monitoring system and method based on Internet of things | |
| CN108982546B (en) | Intelligent robot gluing quality detection system and method | |
| CN110065068B (en) | Robot assembly operation demonstration programming method and device based on reverse engineering | |
| WO2015120734A1 (en) | Special testing device and method for correcting welding track based on machine vision | |
| WO2016055031A1 (en) | Straight line detection and image processing method and relevant device | |
| CN110910350B (en) | Nut loosening detection method for wind power tower cylinder | |
| CN111897349A (en) | Underwater robot autonomous obstacle avoidance method based on binocular vision | |
| DE102019104310A1 (en) | System and method for simultaneously viewing edges and normal image features through a vision system | |
| CN111553310B (en) | Security inspection image acquisition method and system based on millimeter wave radar and security inspection equipment | |
| CN109389105B (en) | Multitask-based iris detection and visual angle classification method | |
| CN113284179A (en) | Robot multi-object sorting method based on deep learning | |
| CN113752086A (en) | Method and device for detecting state of numerical control machine tool cutter | |
| CN115685876B (en) | Planar laser cutting control method and system based on track compensation | |
| Stachniss et al. | Analyzing gaussian proposal distributions for mapping with rao-blackwellized particle filters | |
| CN113313135A (en) | Marking control method and device and computer readable storage medium | |
| CN113012228B (en) | Workpiece positioning system and workpiece positioning method based on deep learning | |
| CN113936291A (en) | Aluminum template quality inspection and recovery method based on machine vision | |
| CN117092964A (en) | Numerical control machine tool fault early warning system and method for building material processing | |
| CN115933534B (en) | Numerical control intelligent detection system and method based on Internet of things | |
| CN116736814A (en) | Control method and system of production equipment for manufacturing cable protection tube | |
| CN111696131B (en) | Handle tracking method based on online pattern segmentation | |
| CN114663402A (en) | Cable prolapse detection method based on Hough linear detection and curve fitting | |
| CN114926531A (en) | Binocular vision based method and system for autonomously positioning welding line of workpiece under large visual field | |
| CN112614172A (en) | Plane and/or curved surface dividing method and system based on three-dimensional vision | |
| CN115582840B (en) | Method and system for calculating sorting and grabbing pose of borderless steel plate workpiece and sorting method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |