CN112025677B - Automatic guiding glue supplementing system and method based on visual detection - Google Patents
Automatic guiding glue supplementing system and method based on visual detection Download PDFInfo
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- CN112025677B CN112025677B CN202010739543.0A CN202010739543A CN112025677B CN 112025677 B CN112025677 B CN 112025677B CN 202010739543 A CN202010739543 A CN 202010739543A CN 112025677 B CN112025677 B CN 112025677B
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- 239000003292 glue Substances 0.000 title claims abstract description 201
- 238000001514 detection method Methods 0.000 title claims abstract description 80
- 230000001502 supplementing effect Effects 0.000 title claims abstract description 43
- 230000000007 visual effect Effects 0.000 title claims abstract description 12
- 238000000034 method Methods 0.000 title claims description 30
- 238000000576 coating method Methods 0.000 claims abstract description 22
- 239000011248 coating agent Substances 0.000 claims abstract description 21
- 238000004026 adhesive bonding Methods 0.000 claims description 35
- 239000002390 adhesive tape Substances 0.000 claims description 30
- 238000004891 communication Methods 0.000 claims description 12
- 238000010586 diagram Methods 0.000 claims description 11
- 238000011179 visual inspection Methods 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 9
- 238000003892 spreading Methods 0.000 claims description 6
- 230000007480 spreading Effects 0.000 claims description 6
- 239000003086 colorant Substances 0.000 claims description 3
- 239000012634 fragment Substances 0.000 claims description 3
- 230000017525 heat dissipation Effects 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 239000013589 supplement Substances 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 229910000831 Steel Inorganic materials 0.000 description 3
- 238000005520 cutting process Methods 0.000 description 3
- 239000010959 steel Substances 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000003708 edge detection Methods 0.000 description 2
- 229910000838 Al alloy Inorganic materials 0.000 description 1
- 229910000861 Mg alloy Inorganic materials 0.000 description 1
- 239000000853 adhesive Substances 0.000 description 1
- 230000001070 adhesive effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 229920006351 engineering plastic Polymers 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000007711 solidification Methods 0.000 description 1
- 230000008023 solidification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
- B25J11/0075—Manipulators for painting or coating
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
- B05C—APPARATUS FOR APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
- B05C5/00—Apparatus in which liquid or other fluent material is projected, poured or allowed to flow on to the surface of the work
- B05C5/02—Apparatus in which liquid or other fluent material is projected, poured or allowed to flow on to the surface of the work the liquid or other fluent material being discharged through an outlet orifice by pressure, e.g. from an outlet device in contact or almost in contact, with the work
-
- 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
- B25J19/02—Sensing devices
- B25J19/04—Viewing devices
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/0081—Programme-controlled manipulators with leader teach-in means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
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- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Manipulator (AREA)
Abstract
The invention relates to the technical field of glue coating, in particular to an automatic guiding glue supplementing system based on visual detection, which comprises an industrial robot, a glue gun, a three-eye all-round camera and an industrial control cabinet, wherein the glue gun is fixed on the industrial robot, and the three-eye all-round camera is fixed on the glue gun.
Description
Technical Field
The invention relates to the technical field of gluing, in particular to an automatic guiding glue supplementing system and method based on visual detection.
Background
The automobile material is developed towards diversification of high-strength steel, aluminum alloy, magnesium alloy, engineering plastic and composite materials on the basis of traditional steel, and is different from the traditional steel automobile body, new materials are applied to the large-area white automobile body, the different physical properties of different materials lead to the difficulty in realizing effective connection by using the conventional welding technology, the bonding technology is a connecting technology for connecting materials together through the effects of chemical reaction or physical solidification and the like between an adhesive and a connected piece, the difficult problem of connection of dissimilar materials is solved, the automobile body strength is improved to a great extent, the application of the automobile is wider and wider in the automobile light weight process, the bonding technology is realized in a gluing mode of an industrial robot, and plays a special role in improving the comfort and safety of the automobile and prolonging the service life of the automobile. The existing enterprises generally adopt machine vision to detect the gluing quality, and once the glue breaking defect occurs, the production line is stopped and the alarm is given, and the glue breaking quality is repaired in a manual intervention mode, so that the whole automobile manufacturing process slows down the production beat and consumes manpower.
In summary, the present invention solves the existing problems by designing an automatic guiding glue supplementing system and method based on visual detection.
Disclosure of Invention
The invention aims to provide an automatic guiding glue supplementing system and method based on visual detection, which are used for solving the problem that the production line is required to be stopped and manual glue supplementing is required to be carried out after the glue breaking phenomenon is found in the existing automobile manufacturing glue coating technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the utility model provides an automatic guiding glue supplementing system based on visual inspection, includes industrial robot, glues rifle, three mesh all-round camera and industrial control cabinet, glue the rifle and fix at industrial robot, three mesh all-round camera is fixed on gluing the rifle.
Further, the industrial control cabinet comprises an industrial control computer, a power supply module and a heat dissipation module, the industrial robot is connected with the industrial control computer in the industrial control cabinet through a DeviceNet network cable and is connected with the three-eye omnidirectional camera through an IO (input/output) line, the communication mode of the industrial robot and the industrial control computer comprises a ProfiNet, profiBus, ethernet protocol cable, and the industrial robot is a six-axis industrial robot.
Further, the three-eye omnidirectional camera has the functions of hard triggering and soft triggering, the hard triggering wiring terminal of the three-eye omnidirectional camera is connected with the industrial robot through an IO wire, and the three-eye omnidirectional camera is an integrated light source.
Further, the teaching method comprises a teaching stage and an operation stage.
Further, the teaching stage specifically comprises the following steps:
s1: teaching the gluing track of the industrial robot, fixing a workpiece at a tool position, keeping the workpiece still, manually teaching the movement track, the movement speed and the gluing amount of the gluing of the industrial robot, storing the setting in an industrial robot controller, and then performing gluing operation according to the setting by the industrial robot;
s2: the method comprises the steps that an industrial robot glues and controls a three-eye omnidirectional camera to collect sample pictures, the industrial robot glues according to a teaching track, when the gluing is started, the industrial robot triggers the three-eye omnidirectional camera to shoot through an I/O line, and triggers the three-eye omnidirectional camera to stop shooting when the gluing is finished, an image collected by the three-eye omnidirectional camera is transmitted to an industrial computer through network communication (TCP), in the whole gluing process, the industrial robot performs periodic data exchange with the industrial computer through an industrial bus (DeviceNet), communication data comprise gluing state data, industrial robot tail end speed, industrial computer state data and the like, and the industrial computer stores the received relationship of the industrial robot tail end speed changing along with time and is used for a subsequent glue supplementing algorithm;
s3: teaching pictures and parameters, the industrial personal computer receives pictures photographed by the camera at different positions, and in order to detect the quality of the adhesive tapes at the different positions, parameters are set frame by frame:
selecting the optimal picture: at each shooting position, three pictures can be acquired by the three-eye omnidirectional camera, and a picture which is most easily identified by an adhesive tape is required to be selected as a picture to be detected.
Selecting an adhesive tape detection section: the adhesive tape in the picture to be detected needs to be selected as a detection section. The method comprises the steps that a point selecting tool is utilized, a starting point, a plurality of intermediate points and a terminal point are sequentially selected near the central line of the adhesive tape to serve as detection anchor points, detection segments are formed by adjacent detection anchor points, detection segments are formed by a plurality of detection segments, and the detection segments detect edges of two sides of the adhesive tape by default, but because the workpiece background where the adhesive tape is located is possibly complex, the detection segments can be set to be single-side edge detection or non-detection;
adjusting parameters of a detection algorithm: the picture brightness, the picture contrast, the quality tolerance of the adhesive tape and the like of the adhesive tape pictures shot at different positions are different, so that parameters of a detection algorithm need to be adjusted, and the detection parameters comprise filtering method selection, filtering coefficients, edge searching range values, position error values, adhesive tape width error values, background colors and the like;
besides the parameters set frame by frame, the pixel position of the glue gun head in the picture is also required to be selected, and the physical size corresponding to a single pixel of the image is measured;
s4: and storing data, namely storing the teaching pictures into a local folder, and storing teaching parameters and the tail end speed of continuous motion of the industrial robot into a database.
Further, the specific steps of the operation stage are as follows:
s5: the industrial robot is coated with the glue and controlled to take a picture, and similar to the teaching stage, the industrial robot is coated with the glue according to the teaching track and controlled to take a picture, and the upper computer system keeps data exchange with the industrial robot and receives the image transmitted by the three-eye camera;
s6: detecting the glue situation of the picture fragments, and carrying out glue breaking detection on the collected pictures one by one according to the teaching parameters;
s7: repeatedly detecting all pictures, and finishing gluing of the current workpiece if all the pictures are free from glue breaking;
s8: if the picture has broken glue, the glue supplementing mode is entered.
Further, the specific step of detecting the S6 interrupt glue is as follows:
s61: interpolation is carried out on taught detection anchor points to obtain more detection anchor points, and detection segments are formed between adjacent anchor points;
s62: for one detection segment, a rectangular detection area is intercepted, and in the rectangle, fuzzy filtering and gradient statistics are carried out along the width direction; if the gradient value of a certain place exceeds the threshold value, the position is considered to be the edge position, otherwise, the segmentation is considered to be incapable of positioning the edge;
s63: repeating the steps to locate the edges of all the detection segments. If any detection segment can not locate the edge, the picture is considered to have the glue breaking condition at the segment, and a pair of anchor points of the segment are recorded. If the adjacent segments have glue breaking, combining the two segments into one glue breaking segment.
Further, the specific mode of the glue supplementing mode in S8 is as follows:
s81: determining the starting time and the finishing time of the glue breaking section according to a glue supplementing algorithm;
s82: the industrial robot moves to supplement glue, the industrial robot moves again according to the glue coating track and controls the camera to pick up the picture, the glue coating head is closed by default at the time, when the glue is started to be fed, the upper computer sends a glue head opening command, and the industrial robot starts to glue; when the glue supplementing terminal time t2 is reached, the upper computer sends a glue head closing command, the industrial robot stops gluing, and the command can be sent in advance by a certain amount of time due to the time delay of communication;
s83, rechecking the picture, detecting the picture of the glue supplementing again, and finishing gluing the current workpiece if the glue is not broken; if the glue is still broken, other accidents are indicated, and an alarm is given.
Further, the specific manner of determining the starting time and the ending time of the glue breaking section in S81 is as follows:
s811: calculating the pixel distance between the glue breaking section and the glue gun head, wherein the glue breaking position of the glue breaking picture is the glue breaking section, the glue breaking section is described by a pair of detection anchor points, namely a start anchor point and a stop anchor point, and the pixel distances between the start anchor point and the stop anchor point and the glue gun head are calculated along a glue coating path respectively;
s812: the pixel distance is converted into the physical distance, the physical size corresponding to a single pixel is measured, and the physical distance s1 between the initial anchor point and the glue gun head and the physical distance s2 between the termination anchor point and the glue gun head can be calculated by multiplying the parameter by the pixel distance;
s813: and calculating the glue breaking time through a V-T diagram of the industrial robot. The relation of the tail end speed of the industrial robot, which is collected in the teaching stage, and changes along with time forms a V-T diagram, the current glue breaking picture shooting time is known to be T0, the total glue spreading distance of the industrial robot between T1 and T0 is assumed to be s1, and the glue spreading speed of the industrial robot between T1 and T0 can be found out in the V-T diagram, so that the starting moment T1 of glue breaking can be solved, and the ending moment T2 can be solved in the same way.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the glue coating continuity is judged by taking a glue coating picture by a three-eye omnibearing camera, the glue coating quality is automatically supplemented to the glue cutting position according to the time period corresponding to the glue cutting position, the problem that the existing enterprises generally adopt a machine vision detection method to detect the glue coating quality, and once the glue cutting defect occurs, the production line is stopped and an alarm is adopted, and the glue is restored by a manual intervention mode, so that the whole automobile manufacturing process slows down the production beat and consumes manpower is caused.
Drawings
FIG. 1 is a schematic diagram of an automatic glue supplementing system based on visual inspection;
FIG. 2 is a communication schematic diagram of an automatic glue supplementing system based on visual inspection according to the present invention;
FIG. 3 is a flow chart of a teaching phase of an automatic glue supplementing method based on visual detection;
FIG. 4 is a flow chart showing the operation phases of an automatic glue supplementing method based on visual inspection;
fig. 5 is a flow chart of a glue supplementing algorithm of the automatic glue supplementing method based on visual detection.
In the figure: 1-industrial robot, 2-glue gun, 3-three-eye omnibearing camera and 4-industrial control cabinet.
Detailed Description
The technical solutions of 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, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
Referring to fig. 1-5, the present invention provides a technical solution:
the utility model provides an automatic guiding glue supplementing system based on visual inspection, includes industrial robot 1, glues rifle 2, three mesh all-round camera 3 and industrial control cabinet 4, glues rifle 2 and fixes at industrial robot 1, and three mesh all-round camera 3 is fixed on gluey rifle 2.
The working flow is as follows: when the industrial robot 1 starts to carry the glue gun 2 to glue on a workpiece in a fixed track, the three-eye omnidirectional camera 3 is triggered by an IO line to start shooting, after receiving a shooting instruction of the industrial robot 1, the three-eye omnidirectional camera 3 starts to shoot pictures of glue coating conditions at a fixed frame rate and transmits the pictures to an industrial control computer in the industrial control cabinet 4, a vision detection system in the industrial control computer detects continuity of a glue strip in real time, if the vision detection system detects that the glue strip breaks, the system calculates a time period corresponding to a glue breaking section, after the workpiece is glued, the system sends the time period corresponding to the glue breaking section to the industrial robot 1, and the industrial robot 1 moves to the time period corresponding to the glue breaking section again according to a preset track to start glue supplementing.
Specific examples:
as shown in fig. 1, an automatic glue supplementing system based on visual detection includes: the device comprises an industrial robot 1, a three-eye omnidirectional camera 3, a glue gun 2 and an industrial control cabinet 4, wherein the industrial control cabinet 4 comprises an industrial control computer, a power module and a heat dissipation module;
the three-eye omnidirectional camera 3 is fixed on the glue gun 2, and the glue gun 2 is fixed on the industrial robot 1, wherein the three-eye omnidirectional camera 3 integrates a light source, can image the periphery of the glue gun 2 at all angles, and avoids the situation that the glue gun 2 shields the adhesive tape in the visual field of the single-phase camera;
as shown in fig. 2, an industrial robot 1 is connected with an industrial personal computer in an industrial control cabinet 4 through a DeviceNet network cable and is connected with a hard trigger wiring terminal of a three-eye omnidirectional camera 3 through an IO line;
an automatic guiding glue supplementing method based on visual detection comprises two stages of teaching and operation;
as shown in fig. 3, the teaching phase includes the following steps:
step 1: teaching the gluing track of the industrial robot 1, fixing a workpiece at a tooling position, keeping the workpiece still, manually teaching the movement track, the movement speed and the gluing amount of the gluing of the industrial robot 1, storing the settings in a controller of the industrial robot 1, and then performing the gluing operation of the industrial robot 1 according to the settings;
step 2: the industrial robot 1 is coated with glue and controls the three-eye omnidirectional camera 3 to collect sample pictures, the industrial robot 1 carries out glue coating according to teaching tracks, when the glue coating is started, the industrial robot 1 triggers the three-eye omnidirectional camera 3 to shoot through an I/O line, and triggers the three-eye omnidirectional camera 3 to stop shooting when the glue coating is finished, images collected by the three-eye omnidirectional camera 3 are transmitted to an industrial personal computer through network communication (TCP), in the whole glue coating process, the industrial robot 1 carries out periodic data exchange with the industrial personal computer through an industrial bus (DeviceNet), communication data comprise glue coating state data, tail end speed of the industrial robot 1, state data of the industrial personal computer and the like, and the industrial personal computer stores the received relationship of the tail end speed of the industrial robot 1 changing along with time and is used for a follow-up glue supplementing algorithm.
Step 3: teaching pictures and parameters, the industrial personal computer receives pictures photographed by the camera at different positions, and in order to detect the quality of the adhesive tapes at the different positions, parameters are set frame by frame:
selecting the optimal picture: in each shooting position, three pictures are collected by the three-eye omnidirectional camera 3, and a picture which is most easily identified by an adhesive tape is needed to be selected as a picture to be detected;
selecting an adhesive tape detection section: the method comprises the steps that a section of adhesive tape in a picture to be detected needs to be selected as a detection section, a point selecting tool is utilized, a starting point, a plurality of intermediate points and a terminal point are sequentially selected near the central line of the adhesive tape to serve as detection anchor points, detection sections are formed by adjacent detection anchor points, detection sections are formed by a plurality of detection sections, and the detection sections detect edges of two sides of the adhesive tape by default, but because the workpiece background of the adhesive tape is possibly complex, the detection sections can be set to be single-side edge detection or non-detection;
adjusting parameters of a detection algorithm: the picture brightness, the picture contrast, the quality tolerance of the adhesive tape and the like of the adhesive tape pictures shot at different positions are different, so that parameters of a detection algorithm need to be adjusted, and the detection parameters comprise filtering method selection, filtering coefficients, edge searching range values, position error values, adhesive tape width error values, background colors and the like;
besides the parameters set frame by frame, the pixel position of the head of the glue gun 2 in the picture is also required to be selected, and the physical size corresponding to a single pixel of the image is measured;
step 4: saving data, saving teaching pictures into a local folder, and saving teaching parameters and the tail end speed of continuous motion of the industrial robot 1 into a database;
as shown in fig. 4, the run phase includes the steps of:
step 5: the industrial robot 1 is coated with glue and controls the camera to shoot, and similar to the teaching stage, the industrial robot 1 is coated with glue according to the teaching track and controls the camera to pick up the picture, and the upper computer system keeps data exchange with the industrial robot 1 and receives the image transmitted by the three-eye camera;
step 6: detecting the glue situation of the picture fragments, and carrying out glue breaking detection on the collected pictures one by one according to the teaching parameters;
step 7: repeatedly detecting all pictures, and finishing gluing of the current workpiece if all the pictures are free from glue breaking;
step 8: if the picture has broken glue, entering a glue supplementing mode;
further, the glue breaking detection specifically comprises the following steps:
step 61: interpolation is carried out on taught detection anchor points to obtain more detection anchor points, and detection segments are formed between adjacent anchor points;
step 62: for one detection segment, a rectangular detection area is truncated. In the rectangle, fuzzy filtering and gradient statistics are carried out along the width direction, if a gradient value at a certain position exceeds a threshold value, the position is considered to be an edge position, otherwise, the segmentation is considered to be incapable of positioning the edge;
step 63: repeating the steps to locate the edges of all the detection segments, if any one detection segment can not locate the edges, considering that the picture has a broken glue condition at the segment, simultaneously recording a pair of anchor points of the segment, and if the adjacent segments have broken glue, merging the two segments into one broken glue segment;
as shown in fig. 5, the glue filling mode specifically includes the following steps:
step 81: determining the starting time and the finishing time of the glue breaking section according to a glue supplementing algorithm;
step 82: the industrial robot 1 moves to supplement glue, the industrial robot 1 moves again according to a glue coating track and controls a camera to pick up a picture, the glue coating head is closed by default at the time, when the glue is operated to a glue supplementing starting time t1, the upper computer sends a glue head opening command, the industrial robot 1 starts to glue, when the glue is operated to a glue supplementing finishing time t2, the upper computer sends a glue head closing command, the industrial robot 1 stops gluing, and because of time delay of communication, a certain time quantity can be advanced to send the command;
and 83, rechecking the picture, detecting the picture of the glue supplementing again, and finishing the gluing of the current workpiece if the glue is not broken. If the glue is still broken, other accidents are indicated, and an alarm prompt is given;
further, the starting time and the end time of the glue interruption section are determined, and the specific modes are as follows:
step 811: calculating the pixel distance between the glue breaking section and the glue gun 2 head, wherein the glue breaking position of the glue breaking picture is the glue breaking section, the glue breaking section is described by a pair of detection anchor points, namely a start anchor point and a stop anchor point, and the pixel distance between the start anchor point and the stop anchor point and the glue gun 2 head is calculated along a glue coating path;
step 812: the pixel distance is converted into a physical distance, the physical size corresponding to a single pixel is measured, and the physical distance s1 between the initial anchor point and the head of the glue gun 2 and the physical distance s2 between the termination anchor point and the head of the glue gun 2 can be calculated by multiplying the parameter by the pixel distance;
step 813: the glue breaking time is calculated through an industrial robot 1V-T diagram, the relation of the change of the tail end speed of the industrial robot 1 along with time acquired in the teaching stage forms the V-T diagram, the current glue breaking picture shooting time is known to be T0, the total glue spreading distance of the industrial robot 1 between T1 and T0 is assumed to be s1, the glue spreading speed of the industrial robot 1 between T1 and T0 can be found out in the V-T diagram, therefore, the starting moment T1 of glue breaking can be solved, and the finishing moment T2 can be solved in the same way.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. An automatic guiding glue supplementing method based on visual detection is characterized by comprising a teaching stage and an operation stage;
the teaching stage comprises the following specific steps:
s1: teaching the gluing track of the industrial robot (1), fixing a workpiece at a tooling position, keeping the workpiece still, manually teaching the movement track, the movement speed and the gluing amount of the gluing of the industrial robot (1), storing the settings in a controller of the industrial robot (1), and then performing the gluing operation according to the settings by the industrial robot (1);
s2: the method comprises the steps that an industrial robot (1) is used for gluing and controlling a three-eye omnidirectional camera (3) to collect sample pictures, the industrial robot (1) is used for gluing according to a teaching track, when the gluing is started, the industrial robot (1) triggers the three-eye omnidirectional camera (3) to shoot through an I/O line, and triggers the three-eye omnidirectional camera (3) to stop shooting when the gluing is finished, images collected by the three-eye omnidirectional camera (3) are transmitted to an industrial personal computer through network communication, in the whole gluing process, the industrial robot (1) and the industrial personal computer are subjected to periodic data exchange through an industrial bus, communication data comprise gluing state data, the tail end speed of the industrial robot (1) and industrial personal computer state data, and the industrial personal computer is used for storing the received relationship of the tail end speed of the industrial robot (1) changing along with time for a subsequent glue supplementing algorithm;
s3: teaching pictures and parameters, the industrial personal computer receives pictures photographed by the camera at different positions, and in order to detect the quality of the adhesive tapes at the different positions, parameters are set frame by frame:
selecting the optimal picture: at each shooting position, three pictures are collected by a three-eye omnidirectional camera (3), and a picture which is most easily identified by an adhesive tape is needed to be selected as a picture to be detected;
selecting an adhesive tape detection section: a section of the adhesive tape in the picture to be detected needs to be selected as a detection section, a point selecting tool is utilized to sequentially select a starting point, a plurality of intermediate points and a terminal point near the central line of the adhesive tape as detection anchor points, the adjacent detection anchor points form detection sections, the detection sections form detection sections, and the detection sections detect the edge of the two sides or the edge of the single side of the adhesive tape or do not detect the edge;
adjusting parameters of a detection algorithm: the picture brightness, the picture contrast, the quality tolerance of the adhesive tape and the like of the adhesive tape pictures shot at different positions are different, so that parameters of a detection algorithm need to be adjusted, and the detection parameters comprise filtering method selection, filtering coefficients, edge searching range values, position error values, adhesive tape width error values and background colors;
besides the parameters set frame by frame, the pixel position of the head of the glue gun (2) in the picture is also required to be selected, and the physical size corresponding to a single pixel of the image is measured;
s4: saving data, saving teaching pictures into a local folder, and saving teaching parameters and the tail end speed of continuous motion of the industrial robot (1) into a database;
the specific steps of the operation stage are as follows:
s5: the industrial robot (1) is coated with glue and controls the camera to shoot, and similar to the teaching stage, the industrial robot (1) is coated with glue according to the teaching track and controls the camera to pick up the image, and the upper computer system keeps data exchange with the industrial robot (1) and receives the image transmitted by the three-eye camera;
s6: detecting the glue situation of the picture fragments, and carrying out glue breaking detection on the collected pictures one by one according to the teaching parameters;
s7: repeatedly detecting all pictures, and finishing gluing of the current workpiece if all the pictures are free from glue breaking;
s8: if the picture has broken glue, the glue supplementing mode is entered.
2. The automatic guiding glue supplementing method based on visual inspection according to claim 1, wherein the specific steps of the step of S6 glue interruption detection are as follows:
s61: interpolation is carried out on taught detection anchor points to obtain more detection anchor points, and detection segments are formed between adjacent anchor points;
s62: for one detection segment, a rectangular detection area is intercepted, and in the rectangle, fuzzy filtering and gradient statistics are carried out along the width direction; if the gradient value of a certain place exceeds the threshold value, the position is considered to be the edge position, otherwise, the segmentation is considered to be incapable of positioning the edge;
s63: repeating the steps to locate the edges of all the detection segments, if any one detection segment can not locate the edges, considering that the picture has a glue breaking condition at the segment, and simultaneously recording a pair of anchor points of the segment; if the adjacent segments have glue breaking, combining the two segments into one glue breaking segment.
3. The automatic guiding glue supplementing method based on visual inspection according to claim 1, wherein the specific mode of glue supplementing in S8 is as follows:
s81: determining the starting time and the finishing time of the glue breaking section according to a glue supplementing algorithm;
s82: the industrial robot (1) moves to supplement glue, the industrial robot (1) moves again according to a glue coating track and controls a camera to pick up a picture, the glue coating head is closed by default at the time, when the glue coating head is operated to a glue supplementing starting time t1, the upper computer sends a glue head opening command, and the industrial robot (1) starts to glue; when the glue supplementing terminal time t2 is reached, the upper computer sends a glue head closing command, the industrial robot (1) stops gluing, and a certain amount of time is advanced to send the command due to the time delay of communication;
s83, rechecking the picture, detecting the picture of the glue supplementing again, and finishing gluing the current workpiece if the glue is not broken; if the glue is still broken, other accidents are indicated, and an alarm is given.
4. The automatic guiding glue-supplementing method based on visual inspection according to claim 3, wherein the specific manner of determining the starting time and the ending time of the glue-breaking section in S81 is as follows:
s811: calculating the pixel distance between the glue breaking section and the head of the glue gun (2), wherein the glue breaking position of the glue breaking picture is the glue breaking section, the glue breaking section is described by a pair of detection anchor points, namely a start anchor point and a stop anchor point, and the pixel distance between the start anchor point and the stop anchor point and the head of the glue gun (2) is calculated along a glue coating path;
s812: the pixel distance is converted into a physical distance, the physical size corresponding to a single pixel is measured, and the physical distance s1 between the initial anchor point and the head of the glue gun (2) and the physical distance s2 between the final anchor point and the head of the glue gun (2) can be calculated by multiplying the parameter by the pixel distance;
s813: the method comprises the steps of calculating the glue breaking time through a V-T diagram of an industrial robot (1), forming the V-T diagram according to the relation of the change of the tail end speed of the industrial robot (1) acquired in the teaching stage along with time, knowing that the current glue breaking picture shooting time is T0, assuming that the starting time of glue breaking is T1, the total glue spreading distance of the industrial robot (1) between T1 and T0 is s1, and the glue spreading speed of the industrial robot (1) between T1 and T0 can be found out in the V-T diagram, so that the starting moment T1 of glue breaking can be solved, and similarly, the finishing moment T2 can be solved.
5. The automatic guiding glue supplementing method based on visual inspection according to claim 1, further comprising an automatic guiding glue supplementing system, wherein the automatic guiding glue supplementing system comprises an industrial robot (1), a glue gun (2), a three-eye omnidirectional camera (3) and an industrial control cabinet (4), wherein the glue gun (2) is fixed on the industrial robot (1), and the three-eye omnidirectional camera (3) is fixed on the glue gun (2).
6. The automatic guiding glue-supplementing method based on visual inspection according to claim 5, wherein the method comprises the following steps: the industrial control cabinet (4) comprises an industrial control computer, a power supply module and a heat dissipation module, wherein the industrial robot (1) is connected with the industrial control computer in the industrial control cabinet (4) through a DeviceNet network cable and is connected with the three-eye omnidirectional camera (3) through an IO line, the communication mode of the industrial robot (1) and the industrial control computer comprises a ProfiNet, profiBus, ethernet protocol cable, and the industrial robot (1) is a six-axis industrial robot.
7. The automatic guiding glue-supplementing method based on visual inspection according to claim 5, wherein the method comprises the following steps: the three-eye omnidirectional camera (3) has the functions of hard triggering and soft triggering, a hard triggering wiring terminal of the three-eye omnidirectional camera (3) is connected with the industrial robot (1) through an IO line, and the three-eye omnidirectional camera (3) is an integrated light source.
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| CN118305051B (en) * | 2024-06-07 | 2024-08-06 | 深圳市鸣志科技有限责任公司 | Control method of gluing system and gluing system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11106U (en) * | 1998-11-13 | 1999-08-17 | 極東産機株式会社 | Automatic gluing machine |
| CN1921954A (en) * | 2003-12-23 | 2007-02-28 | 奎斯有限公司 | Method and apparatus for identifying a structure to be applied to a substrate with multiple cameras |
| CN106881242A (en) * | 2017-04-12 | 2017-06-23 | 广州瑞松北斗汽车装备有限公司 | Vision follows detection coating system |
| CN207133539U (en) * | 2017-07-31 | 2018-03-23 | 昆山龙腾光电有限公司 | A kind of frame gum coating equipment |
| CN108031614A (en) * | 2017-12-14 | 2018-05-15 | 四川福德机器人股份有限公司 | A kind of gluing automatic compensating method of gas meter, flow meter and the device using this method |
| CN109972827A (en) * | 2019-05-09 | 2019-07-05 | 广东博智林机器人有限公司 | Spray robot and control method and control device, computer readable storage medium |
| CN110355759A (en) * | 2019-07-05 | 2019-10-22 | 保定科海自动化科技有限公司 | A kind of industrial robot gluing control system of view-based access control model |
| WO2020037641A1 (en) * | 2018-08-24 | 2020-02-27 | 深圳配天智能技术研究院有限公司 | Glue applying robot and glue applying method |
| CN210279674U (en) * | 2019-02-22 | 2020-04-10 | 吉林省吉邦自动化科技有限公司 | Prevent marginal leakage but continuity rubber coating rotation type rubber coating equipment |
| CN111192237A (en) * | 2019-12-16 | 2020-05-22 | 重庆大学 | A deep learning-based glue detection system and method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE10048749A1 (en) * | 2000-09-29 | 2002-04-11 | Josef Schucker | Arrangement for applying adhesive to a workpiece |
-
2020
- 2020-07-28 CN CN202010739543.0A patent/CN112025677B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11106U (en) * | 1998-11-13 | 1999-08-17 | 極東産機株式会社 | Automatic gluing machine |
| CN1921954A (en) * | 2003-12-23 | 2007-02-28 | 奎斯有限公司 | Method and apparatus for identifying a structure to be applied to a substrate with multiple cameras |
| CN106881242A (en) * | 2017-04-12 | 2017-06-23 | 广州瑞松北斗汽车装备有限公司 | Vision follows detection coating system |
| CN207133539U (en) * | 2017-07-31 | 2018-03-23 | 昆山龙腾光电有限公司 | A kind of frame gum coating equipment |
| CN108031614A (en) * | 2017-12-14 | 2018-05-15 | 四川福德机器人股份有限公司 | A kind of gluing automatic compensating method of gas meter, flow meter and the device using this method |
| WO2020037641A1 (en) * | 2018-08-24 | 2020-02-27 | 深圳配天智能技术研究院有限公司 | Glue applying robot and glue applying method |
| CN210279674U (en) * | 2019-02-22 | 2020-04-10 | 吉林省吉邦自动化科技有限公司 | Prevent marginal leakage but continuity rubber coating rotation type rubber coating equipment |
| CN109972827A (en) * | 2019-05-09 | 2019-07-05 | 广东博智林机器人有限公司 | Spray robot and control method and control device, computer readable storage medium |
| CN110355759A (en) * | 2019-07-05 | 2019-10-22 | 保定科海自动化科技有限公司 | A kind of industrial robot gluing control system of view-based access control model |
| CN111192237A (en) * | 2019-12-16 | 2020-05-22 | 重庆大学 | A deep learning-based glue detection system and method |
Non-Patent Citations (1)
| Title |
|---|
| 煤矿胶带裂纹检测方案研究;张海波;;煤(02);第9-10页 * |
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