CN114769021B - Robot spraying system and method based on full-angle template recognition - Google Patents
Robot spraying system and method based on full-angle template recognition Download PDFInfo
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- CN114769021B CN114769021B CN202210434961.8A CN202210434961A CN114769021B CN 114769021 B CN114769021 B CN 114769021B CN 202210434961 A CN202210434961 A CN 202210434961A CN 114769021 B CN114769021 B CN 114769021B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
- B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
- B05B12/00—Arrangements for controlling delivery; Arrangements for controlling the spray area
- B05B12/08—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means
- B05B12/12—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means responsive to conditions of ambient medium or target, e.g. humidity, temperature position or movement of the target relative to the spray apparatus
- B05B12/122—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means responsive to conditions of ambient medium or target, e.g. humidity, temperature position or movement of the target relative to the spray apparatus responsive to presence or shape of target
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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
Abstract
The application relates to the technical field of production and manufacturing, in particular to a robot spraying system and a method based on full-angle template recognition, which comprises the following steps: the robot comprises a robot body, a first mechanical arm and a second mechanical arm; a template making module, an information acquisition module, a first identification module and a first execution module are configured on the first mechanical arm; a second recognition module and a second execution module are configured on the second mechanical arm; the template manufacturing module is used for manufacturing a full-angle template; the information acquisition module is used for acquiring image information of an object to be sprayed; the first identification module is used for carrying out matching identification; the first execution module is used for generating a spraying instruction and spraying the target object according to the spraying instruction; the second identification module is used for detecting whether the first spraying is correct or not and acquiring a detection result; the second execution module is used for executing various spraying operations. The invention can realize the detection of the spraying effect after one-time spraying and has the effect of improving the spraying quality of the product.
Description
Technical Field
The invention relates to the technical field of production and manufacturing, in particular to a robot spraying system and method based on full-angle template recognition.
Background
In the actual production process, an object needs to be sprayed, in the spraying process, a target object to be sprayed needs to be identified first, and spraying can be started only when the corresponding object and angle are identified. The existing identification technology usually adopts an identification template to match an object, and usually only has one or two templates, so that the problems that the identification precision is poor due to incomplete identification angles, the object cannot be identified, the smooth operation of spraying is influenced, and the situation that the spraying is not in place can also occur in the spraying process can often occur.
Disclosure of Invention
In view of the above defects, the present invention aims to provide a robot spraying system and method based on full-angle template recognition, which can accurately recognize a target object, and can detect a spraying effect after spraying once, and has an effect of improving the spraying quality of a product.
In order to achieve the purpose, the invention adopts the following technical scheme:
a robot spraying system based on full-angle template recognition comprises a robot body, wherein at least one group of first mechanical arm and second mechanical arm is arranged on the robot body;
the first mechanical arm is provided with a template manufacturing module, an information acquisition module, a first identification module and a first execution module;
a second recognition module and a second execution module are configured on the second mechanical arm;
the template making module is used for making a full-angle template by taking a reference object as an object and storing the full-angle template into the first identification module, wherein the full-angle template comprises 360 templates, and each template corresponds to an angle;
the information acquisition module is used for acquiring image information of an object to be sprayed on the conveying line;
the first identification module is used for matching and identifying the image information acquired by the information acquisition module and the characteristic information of the full-angle template, judging whether the characteristic information of the template at a certain angle is matched with the image information, if so, the current object is a target object to be sprayed, and the template corresponding to the angle is a matching template;
the first execution module is used for receiving the identification result of the first identification module, acquiring the spraying information of at least one spraying area corresponding to the target object to be sprayed in the matching template, generating at least one spraying instruction of each spraying area according to the spraying information, and spraying the target object according to the spraying instruction;
the second identification module is used for detecting whether the first spraying is correct or not, acquiring a detection result, wherein the detection result comprises that the actual spraying area is consistent with or inconsistent with the preset area, and sending the detection result to the second execution module;
the second execution module is used for analyzing the detection result, and executing a second spraying operation when the fact that the actual spraying area is consistent with the preset area is analyzed, or executing an erasing operation or triggering the first execution module to execute a spray supplementing operation when the fact that the actual spraying area is inconsistent with the preset area is analyzed.
Preferably, the first execution module comprises an instruction generation submodule, an instruction sending submodule and a first spraying submodule, and the instruction generation submodule, the instruction sending submodule and the first spraying submodule are in signal connection;
the command generation submodule is used for acquiring the spraying information corresponding to the matching template and generating a corresponding spraying scheme according to the spraying information of the matching template; the instruction sending submodule is used for sending the spraying scheme to the first spraying submodule, and the first spraying submodule carries out first spraying on the target object;
the second identification module comprises an image detection sub-module and an image calculation sub-module;
the image detection submodule is used for detecting an actually sprayed area, and the image calculation submodule is used for calculating and comparing whether the actually sprayed area is consistent with a planned spraying area of the spraying scheme; the consistent standard is that the area of the actual spraying area is consistent with the area of the preset area, and the inconsistent standard is that the area of the actual spraying area is inconsistent with the area of the preset area;
the second execution module comprises a second spraying submodule and an erasing submodule, and the second spraying submodule is used for executing second spraying operation when the result is that the areas are consistent; the first spraying submodule is used for executing the spray supplementing operation when the result is that the actual area sprayed for the first time is smaller than the preset spraying area, and the erasing submodule is used for executing the erasing operation when the result is that the actual area sprayed for the first time exceeds the preset spraying area.
Preferably, the template making module is further configured to extract feature information of each template:
performing first-layer pyramid gradient quantization and second-layer pyramid gradient quantization on each template to obtain an angle image matrix corresponding to each template, and converting the angle image matrix into a gradient amplitude image matrix;
setting a gradient amplitude threshold, traversing the gradient amplitude image matrix, finding out a pixel point with the maximum gradient amplitude in the gradient amplitude image matrix, judging whether the gradient amplitude of the pixel point with the maximum gradient amplitude is greater than the gradient amplitude threshold, and if so, marking the pixel point as an identification feature;
and setting a quantity threshold, acquiring the quantity of all the identification features, judging whether the quantity of all the identification features is greater than the quantity threshold, if so, adding all the identification features into the feature point set and storing the feature point set in a memory.
Preferably, the first recognition module is further configured to perform feature extraction on the acquired image information of the target object: gradient extraction and quantification are carried out on image information of a target object, two layers of pyramids are created, gradient diffusion is carried out on each layer of pyramid respectively, and a diffusion gradient matrix diagram corresponding to the image of the target object is obtained; calculating a direction response matrix diagram, and acquiring a linear memory data container of each layer of pyramid;
setting a threshold value, performing feature matching on the features of the target object and the features of the templates according to the feature information of the templates and the feature information of the target object, performing score calculation, and completing matching when the score reaches the threshold value.
Preferably, the instruction generation submodule is further configured to generate a corresponding spraying scheme according to the spraying information of the identification result of the first identification module:
acquiring spraying information of the matched template, wherein the spraying information comprises patterns, colors and area coordinates;
acquiring three-dimensional projection views of a target, wherein the three-dimensional projection views comprise a front view, a top view and a left view, and determining a plurality of surfaces to be sprayed of the target according to the three-dimensional projection views;
taking the spraying information of the template as a spraying parameter corresponding to a target object, determining a spraying node of each surface of the multiple surfaces to be sprayed according to the spraying parameter and the three-dimensional projection view, and obtaining two-dimensional point coordinates of the spraying nodes;
calculating a three-dimensional coordinate corresponding to the spraying node of each surface to be sprayed according to the corresponding relation of the two-dimensional point coordinates of each view of the three-dimensional projection view;
calculating a normal vector of each spraying node according to the three-dimensional coordinate of each spraying node and the three-dimensional coordinate of the adjacent node, wherein the normal vector represents the space state of the spray gun at the corresponding spraying node;
generating a spraying track of a surface to be sprayed according to the three-dimensional coordinates of the spraying nodes;
carrying out spatial fitting on the spraying track to obtain a fitting spraying track;
and calculating the spraying track and the normal vector of the spray gun according to the fitting spraying track and the normal vector of the spraying node to obtain a spraying scheme of a first spraying area of the target object.
A robot spraying method based on all-angle template recognition is applied to the robot spraying system based on all-angle template recognition, the spraying system comprises a robot body, at least one group of first mechanical arm and second mechanical arm is arranged on the robot body, and the spraying method comprises the following steps:
step A0: making a plurality of templates of the full angle of a reference object, and extracting the characteristic information of each template;
step A1: collecting image information of a target object to be sprayed;
step A2: matching and identifying the acquired image information and the characteristic information of the full-angle template, judging whether the characteristic information of the template at a certain angle is matched with the image information, if so, determining that the current object is a target object to be sprayed and the template corresponding to the angle is a matching template;
step A3: acquiring spraying information of at least one spraying area corresponding to the target object to be sprayed in the matching template, generating at least one spraying instruction of each spraying area according to the spraying information, and controlling the first mechanical arm or the second mechanical arm to perform spraying operation on the target object according to the spraying instruction;
step A4: after the first mechanical arm is controlled to execute the first spraying operation, whether an actual spraying area after the first spraying is consistent with a preset area or not is detected, if so, the second mechanical arm is controlled to execute the second spraying operation, and if not, the first mechanical arm is controlled to execute a spray supplementing operation or the second mechanical arm is controlled to execute an erasing operation.
Preferably, in the step A0, the feature information of each template is extracted, and the specific steps are as follows:
step A01: performing first-layer pyramid gradient quantization and second-layer pyramid gradient quantization on each template to obtain an angle image matrix corresponding to each template, and converting the angle image matrix into a gradient amplitude image matrix;
step A02: setting a gradient amplitude threshold, traversing the gradient amplitude image matrix, finding out a pixel point with the maximum gradient amplitude in the gradient amplitude image matrix, judging whether the gradient amplitude of the pixel point with the maximum gradient amplitude is greater than the gradient amplitude threshold, and if so, marking the pixel point as an identification feature;
step A03: and setting a quantity threshold, acquiring the quantity of all the identification features, judging whether the quantity of all the identification features is greater than the quantity threshold, if so, adding all the identification features into the feature point set and storing the feature point set in a memory.
Preferably, the step A2 includes:
step A21: carrying out feature extraction on the acquired image information of the target object: gradient extraction and quantification are carried out on image information of a target object, two layers of pyramids are created, gradient diffusion is carried out on each layer of pyramids respectively, and a diffusion gradient matrix diagram corresponding to the image of the target object is obtained; calculating a directional response matrix diagram, and acquiring a linear memory data container of each pyramid layer;
step A22: setting a threshold, performing feature matching on the features of the target object and the features of the templates according to the feature information of the templates and the feature information of the target object, performing score calculation, and completing matching when the score reaches the threshold.
Preferably, in the step A3, the generating at least one spraying instruction for each spraying area according to the spraying information includes the following steps:
step A31: acquiring spraying information of the matched template, wherein the spraying information comprises patterns, colors and area coordinates;
step A32: acquiring three-dimensional projection views of a target, wherein the three-dimensional projection views comprise a front view, a top view and a left view, and determining a plurality of surfaces to be sprayed of the target according to the three-dimensional projection views;
step A33: taking the spraying information of the template as a spraying parameter corresponding to a target object, determining a spraying node of each surface of the multiple surfaces to be sprayed according to the spraying parameter and the three-dimensional projection view, and obtaining two-dimensional point coordinates of the spraying nodes;
step A34: calculating a three-dimensional coordinate corresponding to the spraying node of each surface to be sprayed according to the corresponding relation of the two-dimensional point coordinates of each view of the three-dimensional projection view;
step A35: calculating a normal vector of each spraying node according to the three-dimensional coordinate of each spraying node and the three-dimensional coordinate of the adjacent node, wherein the normal vector represents the space state of the spray gun at the corresponding spraying node;
step A36: generating a spraying track of a surface to be sprayed according to the three-dimensional coordinates of the spraying nodes;
step A37: carrying out spatial fitting on the spraying track to obtain a fitting spraying track;
step A38: and calculating the spraying track and the normal vector of the spray gun according to the fitting spraying track and the normal vector of the spraying node to obtain a spraying scheme of a first spraying area of the target object.
Preferably, in the step A4, the method further includes:
step A41: acquiring spraying information corresponding to the matching template to generate a spraying scheme, and executing a first spraying operation based on the spraying scheme;
step A42: after the first spraying operation is executed, calculating and comparing whether the area size of an actually sprayed area is consistent with that of a spraying area to be sprayed of the spraying scheme, if so, executing a second spraying operation based on the spraying scheme, and if not, executing a step A43;
step A43: when the actual area of the first spraying is smaller than the preset spraying area, executing a supplementary spraying operation; when the actual area of the first painting exceeds the predetermined painting area, the erasing operation is performed.
The technical scheme comprises the following beneficial effects:
in the embodiment, before spraying, a camera is used for acquiring images of a reference object at all angles as templates, wherein the images of the reference object at all angles are 360 azimuth angles, one template is acquired corresponding to each angle, the total number of the templates is 360, the reference object can be understood as a spraying template corresponding to a target object, then image acquisition is performed on the target object to be sprayed in actual production, corresponding templates can be matched for each angle of the target object, and if matching is possible, the identification is successful, the target object can be sprayed, so that the problem that the accuracy and the efficiency of spraying are influenced due to the fact that the target object is unsuccessfully identified in the spraying process is avoided.
To same target object, can use two arms to carry out the spraying, also can set up and be more than two arms, only need satisfy per two arms and be a set of, realize once can detecting the spraying effect spraying after the spraying, can improve the quality of product, the while spraying is examined, need not to unify the inspection again after the spraying, has the advantage that improves spraying efficiency.
Drawings
FIG. 1 is a flow chart of one embodiment of a method of the present invention;
fig. 2 is a schematic structural diagram of an embodiment of the system of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention. Furthermore, features defined as "first" and "second" may explicitly or implicitly include one or more of the features for distinguishing between descriptive features, non-sequential, non-trivial and non-trivial.
In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
A full-angle template recognition-based robot painting system and method according to an embodiment of the present invention is described below with reference to fig. 1 to 2:
a robot spraying system based on full-angle template recognition comprises a robot body, wherein at least one group of first mechanical arm and second mechanical arm is arranged on the robot body;
the first mechanical arm is provided with a template making module, an information acquisition module, a first identification module and a first execution module;
a second recognition module and a second execution module are configured on the second mechanical arm;
the template making module is used for making a full-angle template by taking a reference object as an object and storing the full-angle template into the first identification module, wherein the full-angle template comprises 360 templates, and each template corresponds to an angle;
the information acquisition module is used for acquiring image information of an object to be sprayed on the conveying line;
the first identification module is used for matching and identifying the image information acquired by the information acquisition module and the characteristic information of the full-angle template, judging whether the characteristic information of the template at a certain angle is matched with the image information, if so, the current object is a target object to be sprayed, and the template corresponding to the angle is a matching template;
the first execution module is used for receiving the identification result of the first identification module, acquiring the spraying information of at least one spraying area corresponding to the target object to be sprayed in the matching template, generating at least one spraying instruction of each spraying area according to the spraying information, and spraying the target object according to the spraying instruction;
the second identification module is used for detecting whether the first spraying is correct or not, acquiring a detection result, wherein the detection result comprises that the actual spraying area is consistent with or inconsistent with the preset area, and sending the detection result to the second execution module;
the second execution module is used for analyzing the detection result, and executing a second spraying operation when the fact that the actual spraying area is consistent with the preset area is analyzed, or executing an erasing operation or triggering the first execution module to execute a spray supplementing operation when the fact that the actual spraying area is inconsistent with the preset area is analyzed.
Specifically, in the actual production process, spraying processing needs to be performed on the object, in the spraying processing process, the object on the conveying line needs to be recognized first, and spraying can be started only after the object is recognized successfully. The existing identification technology usually adopts an identification template to match an object, and usually only has one or two templates, so that the problem that the identification precision is poor due to incomplete identification angles, the object cannot be identified, the smooth proceeding of spraying is influenced, and the condition that the spraying is not in place, such as the out-of-bounds spraying or incomplete spraying, can also occur in the spraying process can often occur.
Specifically, in this embodiment, the template making module collects a full-angle template of the reference object in advance and performs feature extraction on the template, then the information collection module obtains image information of an object to be sprayed on the conveying line in the actual spraying process, and the object is identified by performing feature matching on the object and the template. The information acquisition module acquires image information of an object, and then transmits the image information to the first identification module, the first identification module performs processing such as feature extraction on the image information, performs processing such as feature extraction on a pre-acquired template, performs feature matching according to the image information of the object and the image information of the template, and if matching is performed, the object is a target object to be sprayed, and the corresponding template is a matching template.
The information acquisition module and the first identification module jointly judge whether the current object is the target object, if so, a spraying scheme can be obtained, and the spraying scheme comprises a first spraying scheme and a second spraying scheme.
The first execution module is used for executing a first spraying scheme, and is provided with a first mechanical arm which executes first spraying operation to form a first spraying area; the second execution module is provided with a second mechanical arm, after the first spraying is finished, the second mechanical arm carries out second spraying on the target object, the second identification module firstly detects the first spraying area, judges whether the first spraying operation meets the requirement or not, and then calls the second mechanical arm to carry out spraying operation on the target object. The second identification module executes detection on the first spraying area to obtain two results, namely the actual spraying area is consistent with the preset area or the area is inconsistent; different spraying schemes are implemented corresponding to the two results respectively.
To same target object, can use two arms to carry out the spraying, also can set up and be more than two arms, only need satisfy per two arms and be a set of, realize once can detecting the spraying effect spraying after the spraying, can improve the quality of product, the while spraying is examined, need not to unify the inspection again after the spraying, has the advantage that improves spraying efficiency.
Specifically, the information acquisition module may be a camera for acquiring images; the first recognition module may be an image processor, and may perform image feature extraction and matching.
Preferably, the first execution module comprises an instruction generation submodule, an instruction sending submodule and a first spraying submodule, and the instruction generation submodule, the instruction sending submodule and the first spraying submodule are in signal connection;
the instruction generation submodule is used for acquiring the spraying information corresponding to the matching template and generating a corresponding spraying scheme according to the spraying information of the matching template; the instruction sending submodule is used for sending the spraying scheme to the first spraying submodule, and the first spraying submodule carries out first spraying on the target object;
the second identification module comprises an image detection sub-module and an image calculation sub-module;
the image detection submodule is used for detecting an actually sprayed area, and the image calculation submodule is used for calculating and comparing whether the actually sprayed area is consistent with a planned spraying area of the spraying scheme; the consistent standard is that the area of the actual spraying area is consistent with the area of the preset area, and the inconsistent standard is that the area of the actual spraying area is inconsistent with the area of the preset area;
the second execution module comprises a second spraying submodule and an erasing submodule, and the second spraying submodule is used for executing second spraying operation when the result is that the areas are consistent; the first spraying submodule is used for executing the spray supplementing operation when the result is that the actual area sprayed for the first time is smaller than the preset spraying area, and the erasing submodule is used for executing the erasing operation when the result is that the actual area sprayed for the first time exceeds the preset spraying area.
Specifically, the second identification module detects whether the first spraying is correct or not, and comprises the following steps: inputting a spraying scheme of a first spraying area; the image detection submodule detects an actually sprayed area; and the image calculation submodule calculates the size of the actually sprayed area and the spraying area to be sprayed of the spraying scheme, compares the areas, judges that the spraying is out of bounds if the actually sprayed area is larger than the spraying area, judges that the spraying is not in place if the actually sprayed area is smaller than the spraying area, and outputs a comparison result.
The erasing submodule is used for erasing the spraying position exceeding the preset area during the first spraying, the erasing submodule comprises an erasing head and a cleaning sprayer, the cleaning sprayer can spray cleaning agent, the cleaning agent is firstly sprayed to the exceeding area, and then the erasing head is controlled to erase the area sprayed with the cleaning agent. The second mechanical arm is provided with a cleaning spray head and a wiping head, and the wiping sub-modules are all in the prior art.
When in spraying operation, the area to be sprayed of the target object is firstly divided into two parts, or the area to be sprayed of the target object can be divided into even number parts according to the number of the mechanical arms. Calling an erasing submodule when the spraying is out of bounds; and when the first spraying is not in place, the first mechanical arm firstly performs coating supplement on the defect block, and the second mechanical arm performs spraying of the second spraying area.
The first spraying submodule and the second spraying submodule respectively comprise the following parts: the spraying gun is arranged at the tail end of the mechanical arm body and used for aligning a target object and spraying; compressed gas cylinder, paint delivery pipe and paint pump are located on the spraying robot, and the paint pump is used for storing the pigment that the spraying was used, and paint delivery pipe intercommunication paint pump and spray gun, compressed gas cylinder are used for drawing the pigment in the paint pump to the spray gun blowout through the paint delivery pipe.
Specifically, after the first spraying submodule or the second spraying submodule receives a spraying instruction, the compressed air cylinder is started, the pigment in the paint pump is extracted to the spray gun through the paint conveying pipe, and the spray gun sprays the pigment to a corresponding position of a target object.
Preferably, the template making module is further configured to extract feature information of each template:
performing first-layer pyramid gradient quantization and second-layer pyramid gradient quantization on each template to obtain an angle image matrix corresponding to each template, and converting the angle image matrix into a gradient amplitude image matrix;
setting a gradient amplitude threshold value, traversing the gradient amplitude image matrix, finding out a pixel point with the maximum gradient amplitude in the gradient amplitude image matrix, judging whether the gradient amplitude of the pixel point with the maximum gradient amplitude is greater than the gradient amplitude threshold value, and if so, marking the pixel point as an identification feature;
and setting a quantity threshold, acquiring the quantity of all the identification features, judging whether the quantity of all the identification features is greater than the quantity threshold, if so, adding all the identification features into the feature point set and storing the feature point set in a memory.
Preferably, the first recognition module is further configured to perform feature extraction on the acquired image information of the target object: gradient extraction and quantification are carried out on image information of a target object, two layers of pyramids are created, gradient diffusion is carried out on each layer of pyramid respectively, and a diffusion gradient matrix diagram corresponding to the image of the target object is obtained; calculating a directional response matrix diagram, and acquiring a linear memory data container of each pyramid layer;
setting a threshold, performing feature matching on the features of the target object and the features of the templates according to the feature information of the templates and the feature information of the target object, performing score calculation, and completing matching when the score reaches the threshold.
Preferably, the instruction generation submodule is further configured to generate a corresponding spraying scheme according to the spraying information of the identification result of the first identification module:
acquiring spraying information of the matched template, wherein the spraying information comprises patterns, colors and area coordinates;
acquiring three-dimensional projection views of a target, wherein the three-dimensional projection views comprise a front view, a top view and a left view, and determining a plurality of surfaces to be sprayed of the target according to the three-dimensional projection views;
taking the spraying information of the template as a spraying parameter corresponding to a target object, determining a spraying node of each surface of the multiple surfaces to be sprayed according to the spraying parameter and the three-dimensional projection view, and obtaining two-dimensional point coordinates of the spraying nodes;
calculating a three-dimensional coordinate corresponding to the spraying node of each surface to be sprayed according to the corresponding relation of the two-dimensional point coordinates of each view of the three-dimensional projection view;
calculating a normal vector of each spraying node according to the three-dimensional coordinate of each spraying node and the three-dimensional coordinate of the adjacent node, wherein the normal vector represents the space state of the spray gun at the corresponding spraying node;
generating a spraying track of a surface to be sprayed according to the three-dimensional coordinates of the spraying nodes;
carrying out spatial fitting on the spraying track to obtain a fitting spraying track;
and calculating the spraying track and the normal vector of the spray gun according to the fitting spraying track and the normal vector of the spraying node to obtain a spraying scheme of a first spraying area of the target object.
A robot spraying method based on all-angle template recognition is applied to any one of the robot spraying systems based on all-angle template recognition, the spraying system comprises a robot body, at least one group of first mechanical arm and second mechanical arm are arranged on the robot body, and the spraying method comprises the following steps:
step A0: making a plurality of templates of the full angle of a reference object, and extracting the characteristic information of each template;
step A1: collecting image information of a target object to be sprayed;
step A2: matching and identifying the acquired image information and the characteristic information of the full-angle template, judging whether the characteristic information of the template at a certain angle is matched with the image information, if so, determining that the current object is a target object to be sprayed and the template corresponding to the angle is a matching template;
step A3: acquiring spraying information of at least one spraying area corresponding to the target object to be sprayed in the matching template, generating at least one spraying instruction of each spraying area according to the spraying information, and controlling the first mechanical arm or the second mechanical arm to perform spraying operation on the target object according to the spraying instruction;
step A4: after the first mechanical arm is controlled to execute the first spraying operation, whether an actual spraying area after the first spraying is consistent with a preset area or not is detected, if so, the second mechanical arm is controlled to execute the second spraying operation, and if not, the first mechanical arm is controlled to execute a spray supplementing operation or the second mechanical arm is controlled to execute an erasing operation.
Specifically, in the actual spraying production process, the target object to be sprayed needs to be identified first, and spraying can be started only when the corresponding object and angle are identified. The existing identification technology usually adopts an identification template to match an object, and usually only has one or two templates, when the position of a target object to be identified deviates, only one or two templates can cause the situation that the object cannot be identified, or because the problem of incomplete identification angle causes poor identification precision, the object cannot be accurately sprayed according to the identified position, and the spraying effect and efficiency are poor.
In the embodiment, before spraying, a camera is used for acquiring images of a reference object at all angles as templates, wherein the images of the reference object at all angles are 360 azimuth angles, one template is acquired corresponding to each angle, the total number of the templates is 360, the reference object can be understood as a spraying template corresponding to a target object, then image acquisition is performed on the target object to be sprayed in actual production, corresponding templates can be matched for each angle of the target object, and if matching is possible, the identification is successful, the target object can be sprayed, so that the problem that the accuracy and the efficiency of spraying are influenced due to the fact that the target object is unsuccessfully identified in the spraying process is avoided.
Specifically, in this embodiment, after the information acquisition module acquires image information of the target object, the image information is transmitted to the first recognition module, feature matching is performed, a matched template is selected, a signal is sent to the first execution module, the instruction generation submodule generates a spraying scheme, the spraying scheme is sent to the first spraying submodule through the instruction sending submodule, and the first spraying submodule performs first spraying on the target object to form a first spraying area. After the first spraying is finished, the second mechanical arm carries out second spraying on the target object, the second recognition module firstly detects the first spraying area, judges whether the first spraying operation meets the requirement or not, and then calls the second execution module to carry out spraying operation on the target object.
Preferably, in the step A0, the feature information of each template is extracted, and the specific steps are as follows:
step A01: performing first-layer pyramid gradient quantization and second-layer pyramid gradient quantization on each template to obtain an angle image matrix corresponding to each template, and converting the angle image matrix into a gradient amplitude image matrix;
step A02: setting a gradient amplitude threshold, traversing the gradient amplitude image matrix, finding out a pixel point with the maximum gradient amplitude in the gradient amplitude image matrix, judging whether the gradient amplitude of the pixel point with the maximum gradient amplitude is greater than the gradient amplitude threshold, and if so, marking the pixel point as an identification feature;
step A03: and setting a quantity threshold, acquiring the quantity of all the identification features, judging whether the quantity of all the identification features is greater than the quantity threshold, if so, adding all the identification features into the feature point set and storing the feature point set in a memory.
Specifically, the process of performing the first-layer pyramid gradient quantization and the second-layer pyramid gradient quantization on each template is as follows:
calculating the gradient of the gradient image through sobel, and if the template image is a three-channel image, extracting a single-channel gradient amplitude maximum image matrix through a gradient square-sum non-maximum suppression algorithm in the X and Y directions;
obtaining an angle image matrix from the gradient image matrices in the X and Y directions;
quantizing the range of the angle image matrix from 0 to 360 degrees into an integer of 1 to 15, then continuously quantizing 7 remainder in 8 directions, taking pixels which are larger than a threshold value in the amplitude image matrix, then taking a quantized image matrix corresponding to the pixel field 3*3 to form a histogram, taking more than 5 same directions of the field, assigning values to the directions, and carrying out shift coding of 00000001 to 10000000 on the index;
wherein the gradient amplitude maximum image matrix calculation formula is as follows:
x represents the position of the object to be imaged,
is the value of the gradient at the x position,
r channel, G channel and B channel.
After the gradient quantization is finished, traversing the image matrix with the maximum gradient amplitude value, finding out pixel points with the maximum gradient amplitude value in each field in the image matrix with the maximum gradient amplitude value, and if the pixel points with the maximum gradient amplitude value are found out in the field, setting the gradient amplitude values of the pixel points except the pixel points with the maximum gradient amplitude value in the field to be zero;
judging whether the gradient amplitude of the pixel point with the maximum gradient amplitude in all the fields is larger than a gradient amplitude threshold value or not, and if so, marking the pixel point as an identification feature;
acquiring the quantity of all identification features, judging whether the quantity of all identification features is larger than a quantity threshold value, if so, adding all identification features into a feature point set and storing the feature point set in the memory; if not, judging whether the identification features have at least one other identification feature in the range of the distance quantity threshold value, if so, rejecting the identification features and the identification features in the distance quantity threshold value, and if not, storing the identification features in the memory.
Preferably, the step A2 includes:
step A21: carrying out feature extraction on the acquired image information of the target object: gradient extraction and quantification are carried out on image information of a target object, two layers of pyramids are created, gradient diffusion is carried out on each layer of pyramid respectively, and a diffusion gradient matrix diagram corresponding to the image of the target object is obtained; calculating a directional response matrix diagram, and acquiring a linear memory data container of each pyramid layer;
step A22: setting a threshold, performing feature matching on the features of the target object and the features of the templates according to the feature information of the templates and the feature information of the target object, performing score calculation, and completing matching when the score reaches the threshold.
Specifically, in step a21, gradient extraction and quantization are performed on the image of the target object, and the process is substantially consistent with the above-described gradient quantization training process for the template: acquiring the magnitude of a gradient diffusion translation value of a pyramid, and acquiring a first layer of pyramid linear memory data container; performing bit translation on the image quantization gradient of the target object within the range of 4*4 to obtain a diffusion gradient matrix image of the target object image with gradient diffusion; creating 8 response gradient matrix diagrams corresponding to 8 directions to form a list data container; in order to meet MIPP parallel computation, 8 directions are divided into the first four directions and the last four directions, and gradient direction matrixes are respectively created; converting the gradient matrix image of the target object image into gradient matrix images in the first four directions and the last four directions through AND operation; through the look-up table of various combinations of 8 directions calculated in advance, the total number of table elements is 8 (16 + 16), the maximum similarity between each part of angle and the current angle is obtained, then the maximum value in the front part and the rear part is taken, each pixel is traversed, a similarity response matrix diagram in a certain direction is obtained, namely 8 similarity response matrix diagrams exist in 8 directions.
For each layer of pyramid, 8 similarity matrix data containers are created, 8 similarity matrix images are converted into a 16-order or 64-order mode to be stored in a continuous memory in a linear mode, and the access speed of subsequent matching is facilitated; namely, converting into 8 linear similarity response graphs of 16 orders or 64 orders; dividing the collected target object image into two layers of pyramids, calculating a direction response matrix diagram through gradient diffusion, and storing the direction response matrix diagram into a linear memory to obtain two linear memories in 8 directions.
Further, in step a22, feature related data corresponding to two layers of pyramids of one template is taken, similarity matrix maps of 8 directions of the bottom layer pyramid of the target image are taken, linear memory access entries of corresponding directions are found according to template feature point information, the similarity of corresponding positions is calculated through iterative circulation of the template position range information obtained through calculation, and a matching similarity matrix of a corresponding similarity response matrix map of the template corresponding to a second layer pyramid and the target image feature point direction is obtained through MIPP accumulation;
through all the feature point information of the template, acquiring MIPP accumulated matching similarity matrixes of all the templates, namely pyramid matching similarity matrixes of a second layer of the template;
and iterating the similarity matrix, converting all elements in the similarity matrix into 100 systems, selecting information such as positions and scores of points larger than the similarity score threshold according to the set similarity score threshold, and storing the information into a corresponding data container.
And selecting a linear similarity matrix diagram in a certain direction of 8 directions of the first layer of target object images according to information such as the point position selected by the second layer of pyramids of the template and the characteristic point information of the first layer of template, finding a linear memory access entry in a certain direction of the matrix diagram of the first layer of target object images, selecting 16 x 16 due to the SIMD limitation, and calculating a similarity matrix. And converting the similarity matrix into 100 scores, finding out position information with the highest score, and updating matching information corresponding to the bottom pyramid. And repeating the loop to obtain information such as the optimized matching position, the score and the like. And deleting some optimized matching position and score information structure data with scores lower than a threshold value according to the set matching score threshold value.
According to the flow, 360 templates are subjected to iterative processing, and a series of template matching information is obtained. And finally, sequencing a series of matched template data information according to the scores, and deleting the repeatedly matched template positions and score information to obtain a final series of template positions, scores and other information, thereby basically completing the matching of the template.
The lookup table formula for calculating the 8 similarity gradient directions off line:
where i is the index of the quantization direction and L is the set of directions that occur in a neighborhood of the gradient direction i, expressed as integers, as indices to the look-up table.
Similarity response matrix chart calculation formula:
the similarity calculation formula:
the similarity at the c + r position is calculated,
representing a template;
an image is input.
Preferably, in the step A3, the generating at least one spraying instruction for each spraying area according to the spraying information includes the following steps:
step A31: acquiring spraying information of the matched template, wherein the spraying information comprises patterns, colors and area coordinates;
step A32: acquiring three-dimensional projection views of a target, wherein the three-dimensional projection views comprise a front view, a top view and a left view, and determining a plurality of surfaces to be sprayed of the target according to the three-dimensional projection views;
step A33: taking the spraying information of the template as a spraying parameter corresponding to a target object, determining a spraying node of each surface of the multiple surfaces to be sprayed according to the spraying parameter and the three-dimensional projection view, and obtaining two-dimensional point coordinates of the spraying nodes;
step A34: calculating a three-dimensional coordinate corresponding to the spraying node of each surface to be sprayed according to the corresponding relation of the two-dimensional point coordinates of each view of the three-dimensional projection view;
step A35: calculating a normal vector of each spraying node according to the three-dimensional coordinate of each spraying node and the three-dimensional coordinate of the adjacent node, wherein the normal vector represents the space state of the spray gun at the corresponding spraying node;
step A36: generating a spraying track of a surface to be sprayed according to the three-dimensional coordinates of the spraying nodes;
step A37: carrying out spatial fitting on the spraying track to obtain a fitting spraying track;
step A38: and calculating the spraying track and the normal vector of the spray gun according to the fitting spraying track and the normal vector of the spraying node to obtain a spraying scheme of a first spraying area of the target object.
Preferably, in the step A4, the method further includes:
step A41: acquiring spraying information corresponding to the matching template to generate a spraying scheme, and executing a first spraying operation based on the spraying scheme;
step A42: after the first spraying operation is executed, calculating and comparing whether the area size of an actually sprayed area is consistent with that of a spraying area to be sprayed of the spraying scheme, if so, executing second spraying operation based on the spraying scheme, otherwise, executing step A43;
step A43: when the actual area of the first spraying is smaller than the preset spraying area, executing a supplementary spraying operation; when the actual area sprayed for the first time exceeds the predetermined spraying area, the erasing operation is performed.
Other configurations and operations of a full-angle template recognition based robotic spray coating system and method according to embodiments of the present invention are known to those of ordinary skill in the art and will not be described in detail herein.
The modules in the robot spraying system based on the full-angle template recognition can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the electronic device, and can also be stored in a memory of the electronic device in a software form, so that the processor can call and execute operations corresponding to the modules.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above.
The above description of the embodiments of the present invention is provided for the purpose of illustrating the technical lines and features of the present invention and is provided for the purpose of enabling those skilled in the art to understand the contents of the present invention and to implement the present invention, but the present invention is not limited to the above specific embodiments. It is intended that all such changes and modifications as fall within the scope of the appended claims be embraced therein.
Claims (9)
1. The utility model provides a spraying system of robot based on full angle template discernment which characterized in that: the robot comprises a robot body, wherein at least one group of first mechanical arm and second mechanical arm is arranged on the robot body;
the first mechanical arm is provided with a template making module, an information acquisition module, a first identification module and a first execution module;
a second recognition module and a second execution module are configured on the second mechanical arm;
the template making module is used for making a full-angle template by taking a reference object as an object and storing the full-angle template into the first identification module, wherein the full-angle template comprises 360 templates, and each template corresponds to an angle;
the information acquisition module is used for acquiring image information of an object to be sprayed on the conveying line;
the first identification module is used for matching and identifying the image information acquired by the information acquisition module and the characteristic information of the full-angle template, judging whether the characteristic information of the template at a certain angle is matched with the image information, if so, the current object is a target object to be sprayed, and the template corresponding to the angle is a matching template;
the first execution module is used for receiving the identification result of the first identification module, acquiring the spraying information of at least one spraying area corresponding to the target object to be sprayed in the matching template, generating at least one spraying instruction of each spraying area according to the spraying information, and spraying the target object according to the spraying instruction;
the second identification module is used for detecting whether the first spraying is correct or not, acquiring a detection result, wherein the detection result comprises that the actual spraying area is consistent with or inconsistent with the preset area, and sending the detection result to the second execution module;
the second execution module is used for analyzing the detection result, executing a second spraying operation when the fact that the actual spraying area is consistent with the preset area is analyzed, or executing an erasing operation or triggering the first execution module to execute a spray supplementing operation when the fact that the actual spraying area is inconsistent with the preset area is analyzed;
the template making module is also used for extracting the characteristic information of each template:
performing first-layer pyramid gradient quantization and second-layer pyramid gradient quantization on each template to obtain an angle image matrix corresponding to each template, and converting the angle image matrix into a gradient amplitude image matrix;
setting a gradient amplitude threshold, traversing the gradient amplitude image matrix, finding out a pixel point with the maximum gradient amplitude in the gradient amplitude image matrix, judging whether the gradient amplitude of the pixel point with the maximum gradient amplitude is greater than the gradient amplitude threshold, and if so, marking the pixel point as an identification feature;
setting a quantity threshold, acquiring the quantity of all identification features, judging whether the quantity of all identification features is greater than the quantity threshold, if so, adding all identification features into a feature point set and storing the feature point set into a memory, if not, judging whether the identification features have at least one other identification feature in the range from the quantity threshold, if so, rejecting the identification features and the identification features in the distance threshold, and if not, storing the identification features into the memory.
2. The full-angle template recognition-based robotic painting system of claim 1, wherein: the first execution module comprises a command generation submodule, a command sending submodule and a first spraying submodule which are in signal connection;
the command generation submodule is used for acquiring the spraying information corresponding to the matching template and generating a corresponding spraying scheme according to the spraying information of the matching template; the instruction sending submodule is used for sending the spraying scheme to the first spraying submodule, and the first spraying submodule carries out first spraying on the target object;
the second identification module comprises an image detection sub-module and an image calculation sub-module;
the image detection submodule is used for detecting an actually sprayed area, and the image calculation submodule is used for calculating and comparing whether the actually sprayed area is consistent with a planned spraying area of the spraying scheme; the consistent standard is that the area of the actual spraying area is consistent with the area of the preset area, and the inconsistent standard is that the area of the actual spraying area is inconsistent with the area of the preset area;
the second execution module comprises a second spraying submodule and an erasing submodule, and the second spraying submodule is used for executing second spraying operation when the result is that the areas are consistent; the first spraying submodule is used for executing the spray supplementing operation when the result is that the actual area sprayed for the first time is smaller than the preset spraying area, and the erasing submodule is used for executing the erasing operation when the result is that the actual area sprayed for the first time exceeds the preset spraying area.
3. The robot spraying system based on the full-angle template recognition of claim 1, wherein the first recognition module is further configured to perform feature extraction on the acquired image information of the target object: gradient extraction and quantification are carried out on image information of a target object, two layers of pyramids are created, gradient diffusion is carried out on each layer of pyramids respectively, and a diffusion gradient matrix diagram corresponding to the image of the target object is obtained; calculating a directional response matrix diagram, and acquiring a linear memory data container of each pyramid layer;
setting a threshold, performing feature matching on the features of the target object and the features of the plurality of templates according to the feature information of the templates and the feature information of the target object, performing score calculation, and completing matching when the score reaches the threshold.
4. The robot spraying system based on the full-angle template recognition of claim 2, wherein the instruction generation submodule is further configured to generate a corresponding spraying scheme according to the spraying information of the recognition result of the first recognition module:
acquiring spraying information of the matched template, wherein the spraying information comprises patterns, colors and area coordinates;
acquiring three-dimensional projection views of a target, wherein the three-dimensional projection views comprise a front view, a top view and a left view, and determining a plurality of surfaces to be sprayed of the target according to the three-dimensional projection views;
taking the spraying information of the template as a spraying parameter corresponding to a target object, determining a spraying node of each surface of the multiple surfaces to be sprayed according to the spraying parameter and the three-dimensional projection view, and obtaining two-dimensional point coordinates of the spraying nodes;
calculating a three-dimensional coordinate corresponding to the spraying node of each surface to be sprayed according to the corresponding relation of the two-dimensional point coordinates of each view of the three-dimensional projection view;
calculating a normal vector of each spraying node according to the three-dimensional coordinates of each spraying node and the three-dimensional coordinates of the nodes adjacent to the spraying node, wherein the normal vector represents the space state of the spray gun at the corresponding spraying node;
generating a spraying track of a surface to be sprayed according to the three-dimensional coordinates of the spraying nodes;
carrying out spatial fitting on the spraying track to obtain a fitting spraying track;
and calculating the spraying track and the normal vector of the spray gun according to the fitting spraying track and the normal vector of the spraying node to obtain a spraying scheme of a first spraying area of the target object.
5. A robot spraying method based on full-angle template recognition is characterized by comprising the following steps: the robot spraying system based on the full-angle template recognition is applied to any one of claims 1 to 4, the spraying system comprises a robot body, at least one set of a first mechanical arm and a second mechanical arm is arranged on the robot body, and the spraying method comprises the following steps:
step A0: making a plurality of templates of the full angle of a reference object, and extracting the characteristic information of each template;
step A1: collecting image information of a target object to be sprayed;
step A2: matching and identifying the acquired image information and the characteristic information of the full-angle template, judging whether the characteristic information of the template at a certain angle is matched with the image information, if so, determining that the current object is a target object to be sprayed and the template corresponding to the angle is a matching template;
step A3: acquiring spraying information of at least one spraying area corresponding to the target object to be sprayed in the matching template, generating at least one spraying instruction of each spraying area according to the spraying information, and controlling the first mechanical arm or the second mechanical arm to perform spraying operation on the target object according to the spraying instruction;
step A4: after the first mechanical arm is controlled to execute the first spraying operation, whether an actual spraying area after the first spraying is consistent with a preset area or not is detected, if so, the second mechanical arm is controlled to execute the second spraying operation, and if not, the first mechanical arm is controlled to execute a spray supplementing operation or the second mechanical arm is controlled to execute an erasing operation.
6. The robot spraying method based on the full-angle template recognition is characterized in that: in the step A0, the feature information of each template is extracted, and the specific steps are as follows:
step A01: performing first-layer pyramid gradient quantization and second-layer pyramid gradient quantization on each template to obtain an angle image matrix corresponding to each template, and converting the angle image matrix into a gradient amplitude image matrix;
step A02: setting a gradient amplitude threshold, traversing the gradient amplitude image matrix, finding out a pixel point with the maximum gradient amplitude in the gradient amplitude image matrix, judging whether the gradient amplitude of the pixel point with the maximum gradient amplitude is greater than the gradient amplitude threshold, and if so, marking the pixel point as an identification feature;
step A03: and setting a quantity threshold, acquiring the quantity of all the identification features, judging whether the quantity of all the identification features is greater than the quantity threshold, if so, adding all the identification features into the feature point set and storing the feature point set in a memory.
7. The robot spraying method based on the full-angle template recognition is characterized in that: the step A2 comprises the following steps:
step A21: carrying out feature extraction on the acquired image information of the target object: gradient extraction and quantification are carried out on image information of a target object, two layers of pyramids are created, gradient diffusion is carried out on each layer of pyramids respectively, and a diffusion gradient matrix diagram corresponding to the image of the target object is obtained; calculating a directional response matrix diagram, and acquiring a linear memory data container of each pyramid layer;
step A22: setting a threshold, performing feature matching on the features of the target object and the features of the templates according to the feature information of the templates and the feature information of the target object, performing score calculation, and completing matching when the score reaches the threshold.
8. The robot spraying method based on the full-angle template recognition is characterized in that: in the step A3, at least one spraying instruction of each spraying area is generated according to the spraying information, which includes the following steps:
step A31: acquiring spraying information of the matched template, wherein the spraying information comprises patterns, colors and area coordinates;
step A32: acquiring a three-dimensional projection view of a target, wherein the three-dimensional projection view comprises a main view, a top view and a left view, and determining a plurality of surfaces to be sprayed of the target according to the three-dimensional projection view;
step A33: taking the spraying information of the template as a spraying parameter corresponding to a target object, determining a spraying node of each surface of the multiple surfaces to be sprayed according to the spraying parameter and the three-dimensional projection view, and obtaining two-dimensional point coordinates of the spraying nodes;
step A34: calculating a three-dimensional coordinate corresponding to the spraying node of each surface to be sprayed according to the corresponding relation of the two-dimensional point coordinates of each view of the three-dimensional projection view;
step A35: calculating a normal vector of each spraying node according to the three-dimensional coordinates of each spraying node and the three-dimensional coordinates of the nodes adjacent to the spraying node, wherein the normal vector represents the space state of the spray gun at the corresponding spraying node;
step A36: generating a spraying track of a surface to be sprayed according to the three-dimensional coordinates of the spraying nodes;
step A37: carrying out spatial fitting on the spraying track to obtain a fitting spraying track;
step A38: and calculating the spraying track and the normal vector of the spray gun according to the fitting spraying track and the normal vector of the spraying node so as to obtain a spraying scheme for the first spraying area of the target object.
9. The robot spraying method based on the full-angle template recognition is characterized in that: in the step A4, the method further includes:
step A41: acquiring spraying information corresponding to the matching template to generate a spraying scheme, and executing a first spraying operation based on the spraying scheme;
step A42: after the first spraying operation is executed, calculating and comparing whether the area size of an actually sprayed area is consistent with that of a spraying area to be sprayed of the spraying scheme, if so, executing a second spraying operation based on the spraying scheme, and if not, executing a step A43;
step A43: when the actual area of the first spraying is smaller than the preset spraying area, executing a supplementary spraying operation; when the actual area of the first painting exceeds the predetermined painting area, the erasing operation is performed.
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| CN115205165B (en) * | 2022-09-16 | 2022-12-09 | 山东联博新材料科技有限公司 | Spraying method of anticorrosive material for industrial machine housing |
| CN115741739B (en) * | 2022-11-30 | 2023-12-15 | 深圳进化动力数码科技有限公司 | Dead angle spraying method, device, equipment and medium of spraying robot |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104525420A (en) * | 2015-01-04 | 2015-04-22 | 成都思达特电器有限公司 | Spraying robot control method based on three-dimensional model recognition |
| 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 |
| CN111013883A (en) * | 2019-11-25 | 2020-04-17 | 浙江明泉工业涂装有限公司 | Robot control method for intelligent spraying of multiple vehicle types |
| CN112703492A (en) * | 2018-08-08 | 2021-04-23 | 摩拜力特有限责任公司 | System and method for operating in augmented reality display device |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6135615B2 (en) * | 2014-07-31 | 2017-05-31 | マツダ株式会社 | Painting equipment |
| CN106733525A (en) * | 2016-11-24 | 2017-05-31 | 杭州力视科技有限公司 | A kind of method and device of the automatically dropping glue based on dual camera |
| CN106863355A (en) * | 2016-12-27 | 2017-06-20 | 北京光年无限科技有限公司 | A kind of object identification method and robot for robot |
| CN108398139B (en) * | 2018-03-01 | 2021-07-16 | 北京航空航天大学 | Dynamic environment vision mileometer method fusing fisheye image and depth image |
| CN110152938A (en) * | 2019-04-02 | 2019-08-23 | 华中科技大学 | A kind of component dispensing track extraction method and automatically control machine people system |
| CN110162175B (en) * | 2019-05-16 | 2022-04-19 | 腾讯科技（深圳）有限公司 | Vision-based tactile measurement method, device, equipment and storage medium |
| CN110434860B (en) * | 2019-07-04 | 2022-08-16 | 佛山科学技术学院 | Robot automatic spraying method and device |
| CN110421561B (en) * | 2019-07-18 | 2022-02-22 | 天津大学 | Method for spraying clothes by using cooperative robot |
| CN112614085A (en) * | 2019-09-19 | 2021-04-06 | 漳州立达信光电子科技有限公司 | Object detection method and device and terminal equipment |
| CN111123853B (en) * | 2019-11-25 | 2021-05-14 | 浙江明泉工业涂装有限公司 | Control method of robot for detecting and remedying spraying on inner surface of automobile |
| CN111178394B (en) * | 2019-12-12 | 2023-04-28 | 华南师范大学 | Feature point extraction method based on self-adaptive image information entropy |
| CN111080525B (en) * | 2019-12-19 | 2023-04-28 | 成都海擎科技有限公司 | Distributed image and graphic primitive splicing method based on SIFT features |
| CN114792373B (en) * | 2022-04-24 | 2022-11-25 | 广东天太机器人有限公司 | Visual identification spraying method and system of industrial robot |
-
2022
- 2022-04-24 CN CN202210434961.8A patent/CN114769021B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104525420A (en) * | 2015-01-04 | 2015-04-22 | 成都思达特电器有限公司 | Spraying robot control method based on three-dimensional model recognition |
| CN112703492A (en) * | 2018-08-08 | 2021-04-23 | 摩拜力特有限责任公司 | System and method for operating in augmented reality display device |
| 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 |
| CN111013883A (en) * | 2019-11-25 | 2020-04-17 | 浙江明泉工业涂装有限公司 | Robot control method for intelligent spraying of multiple vehicle types |
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