CN116755402A - Workpiece detection track planning method, device, equipment and medium - Google Patents
Workpiece detection track planning method, device, equipment and medium Download PDFInfo
- Publication number
- CN116755402A CN116755402A CN202310675734.9A CN202310675734A CN116755402A CN 116755402 A CN116755402 A CN 116755402A CN 202310675734 A CN202310675734 A CN 202310675734A CN 116755402 A CN116755402 A CN 116755402A
- Authority
- CN
- China
- Prior art keywords
- workpiece
- geometric shape
- shape data
- algorithm
- preset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 92
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 67
- 238000004458 analytical method Methods 0.000 claims abstract description 21
- 238000007781 pre-processing Methods 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 14
- 238000004364 calculation method Methods 0.000 claims description 20
- 238000001914 filtration Methods 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 17
- 238000003860 storage Methods 0.000 claims description 15
- 238000007689 inspection Methods 0.000 claims description 10
- 239000011159 matrix material Substances 0.000 claims description 5
- 230000002068 genetic effect Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 5
- 238000013135 deep learning Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31282—Data acquisition, BDE MDE
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The embodiment of the application discloses a method, a device, equipment and a medium for planning a detection track of a workpiece, wherein the method comprises the following steps: acquiring geometric shape data and position information of a workpiece to be measured; preprocessing the geometric shape data to obtain preprocessed geometric shape data; analyzing and processing the geometric shape data according to a preset geometric shape analysis algorithm to determine characteristic parameters of the workpiece to be detected; and generating a detection track plan of the workpiece to be detected through a preset track planning algorithm according to the characteristic parameters and the position information. By implementing the method provided by the embodiment of the application, the accuracy and the efficiency of complex workpiece track planning can be improved, and the intelligent production and the production efficiency and the product quality can be realized conveniently.
Description
Technical Field
The present application relates to the field of product detection, and in particular, to a method, apparatus, device, and medium for planning a workpiece detection track.
Background
With the continuous development of industrial automation, the application of automatic production equipment is also increasing. In industrial production, detection of complex workpieces is particularly important for improving production efficiency and product quality. At present, complex tools are usually detected by using automatic equipment, and detection track planning is usually carried out by relying on a deep learning algorithm in the complex workpiece detection process so as to meet the detection requirements of complex workpieces. However, when a deep learning algorithm is used for planning the track of a complex workpiece, the generated data volume is too large, so that the calculation time is too long and the interpretation is difficult.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a medium for planning a workpiece detection track, which aim to solve the problems of inaccurate detection and low efficiency of complex workpieces.
In a first aspect, an embodiment of the present application provides a method for planning a detection track of a workpiece, including: acquiring geometric shape data and position information of a workpiece to be measured; preprocessing the geometric shape data to obtain preprocessed geometric shape data; analyzing and processing the geometric shape data according to a preset geometric shape analysis algorithm to determine characteristic parameters of the workpiece to be detected; and generating a detection track plan of the workpiece to be detected through a preset track planning algorithm according to the characteristic parameters and the position information.
In a second aspect, an embodiment of the present application further provides a workpiece detection track planning apparatus, including: the acquisition unit is used for acquiring geometric shape data and position information of the workpiece to be detected; the preprocessing unit is used for preprocessing the geometric shape data to obtain preprocessed geometric shape data; the analysis unit is used for analyzing and processing the geometric shape data according to a preset geometric shape analysis algorithm to determine characteristic parameters of the workpiece to be detected; and the generating unit is used for generating a detection track plan of the workpiece to be detected through a preset track planning algorithm according to the characteristic parameters and the position information.
In a third aspect, an embodiment of the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the method when executing the computer program.
In a fourth aspect, embodiments of the present application also provide a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, implement the above-described method.
The embodiment of the application provides a method, a device, equipment and a medium for planning a detection track of a workpiece. Wherein the method comprises the following steps: acquiring geometric shape data and position information of a workpiece to be measured; preprocessing the geometric shape data to obtain preprocessed geometric shape data; analyzing and processing the geometric shape data according to a preset geometric shape analysis algorithm to determine characteristic parameters of the workpiece to be detected; and generating a detection track plan of the workpiece to be detected through a preset track planning algorithm according to the characteristic parameters and the position information. According to the embodiment of the application, after the geometric shape data are acquired, the geometric shape data are analyzed and subjected to track planning by utilizing the preset geometric shape analysis algorithm and the preset track planning algorithm, so that the generated detection track planning can adapt to complex workpiece detection of various shapes and sizes, the calculation amount is greatly reduced without depending on a deep learning algorithm, the accuracy and the efficiency of track planning are improved, and intelligent production is realized, and the production efficiency and the product quality are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for planning a detection track of a workpiece according to an embodiment of the present application;
FIG. 2 is a schematic sub-flowchart of a method for planning a detection track of a workpiece according to an embodiment of the present application;
FIG. 3 is a schematic sub-flowchart of a method for planning a detection track of a workpiece according to an embodiment of the present application;
FIG. 4 is a schematic sub-flowchart of a method for planning a detection track of a workpiece according to an embodiment of the present application;
FIG. 5 is a schematic block diagram of a workpiece inspection trajectory planning device provided by an embodiment of the application;
fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, fig. 1 is a schematic flowchart of a method for planning a detection track of a workpiece according to an embodiment of the application. The workpiece detection track planning method in the embodiment can be applied to detection of complex workpieces, the complex workpieces are subjected to simple algorithm to generate detection track planning, the generated detection track planning can be applied to robots, automatic production lines and other automatic equipment to complete detection tasks of the complex workpieces, so that efficient and accurate detection of the complex workpieces is realized, and production efficiency and product quality are improved.
Fig. 1 is a flow chart of a method for planning a detection track of a workpiece according to an embodiment of the present application. As shown, the method includes the following steps S110-140.
S110, acquiring geometric shape data and position information of the workpiece to be detected.
In this embodiment, the workpiece to be measured is a complex workpiece to be measured, the geometric shape data is a three-dimensional data set formed by a large number of points and describing geometric shape characteristics of the workpiece to be measured, and 3D point cloud data, depth images or RGB images of the workpiece to be measured are obtained through a sensor or a three-dimensional camera and other devices. The 3D point cloud data of the workpiece to be measured contains geometric shape data and position information of the workpiece to be measured, wherein the point cloud data is a three-dimensional data set consisting of a large number of points, each point has coordinate and attribute information of the point, and it can be understood that the geometric shape data is also a three-dimensional data set consisting of a large number of points, and the geometric shape characteristics of the workpiece to be measured can be described by the three-dimensional data set. After receiving an input working instruction, the geometric shape data and the position information of the workpiece to be detected are acquired, so that the workpiece detection track planning method can adapt to complex workpiece detection of various shapes and sizes, the applicability and universality of the workpiece detection track planning method are improved, and data support is provided for subsequent complex workpiece detection track planning.
In one embodiment, as shown in FIG. 2, the step S110 includes steps S111-S112;
s111, acquiring position information to be converted of the workpiece to be detected;
s112, the position information to be converted is converted through a matrix, and the position information of the workpiece to be detected is obtained.
In this embodiment, the position coordinates of the workpiece to be measured in the camera coordinate system may be obtained by the three-dimensional camera, where the position coordinates in the camera coordinate system are position information to be converted, and the position information to be converted is converted into position information in the world coordinate system after being obtained, for example, the position coordinates in the camera coordinate system may be converted into position information in the world coordinate system by using methods such as hand-eye matrix conversion. By converting the position information to be converted into a matrix, accurate position information of the workpiece to be detected under a world coordinate system can be obtained, and a specific position of the workpiece to be detected is provided for detection track planning of the workpiece to be detected.
S120, preprocessing the geometric shape data to obtain preprocessed geometric shape data.
In this embodiment, the preprocessing is a preprocessing operation performed on the geometry data before the processing of the geometry data, and the preprocessing method includes performing a plurality of methods such as point cloud filtering, point cloud registration, point cloud segmentation and point cloud reconstruction on the geometry data. The preprocessed geometric data is more accurate geometric data, the quality and the accuracy of the geometric data can be improved by preprocessing the geometric data, and the calculation speed can be improved by filtering part of useless point cloud data in the geometric data with less but accurate geometric data.
In one embodiment, as shown in FIG. 3, the step S120 includes steps S121-S122;
s121, filtering the geometric shape data according to a preset filtering algorithm to obtain filtered geometric shape data; and/or
S122, denoising the geometric shape data according to a preset denoising algorithm to obtain denoised geometric shape data.
In this embodiment, the filtering process is to reject outlier data in the geometry data; the denoising processing is to clean the cloud data in the geometric data and clean the redundant, chaotic and invalid data. The preset denoising algorithm comprises a filtering algorithm or a noise removal algorithm, wherein the noise removal algorithm mainly comprises a method based on a threshold value, a method based on a connected region and the like. The preset filtering algorithm mainly comprises mean filtering, median filtering, gaussian filtering and the like. For example, the median filtering is to sort the points around each point cloud according to the distance, and take the intermediate value as the value of the point cloud. Thereby removing noise and point cloud data of outliers. Specifically, the outliers and noise in the geometric shape data may be removed using a median filtering method in this embodiment. Or smoothing the geometric data by using an average value filtering method based on an average value to remove outliers and noise in the geometric data. The geometric shape data can be obtained with high precision by processing the geometric shape data according to the preset filtering algorithm and/or denoising algorithm, and the speed of the algorithm is increased on the premise of not influencing the precision of the geometric shape data so as to quickly generate the detection track plan of the workpiece to be detected.
S130, analyzing and processing the geometric shape data according to a preset geometric shape analysis algorithm to determine characteristic parameters of the workpiece to be detected.
In this embodiment, the characteristic parameter is a geometric parameter related to a three-dimensional shape of the workpiece to be measured, for example, the characteristic parameter of the workpiece to be measured may be a curvature, a radius, an area, or the like of the workpiece to be measured, where the curvature may be used to describe a smoothness of the workpiece to be measured, and the geometric data may be analyzed and processed by the preset geometric analysis algorithm, for example, since the geometric data is a three-dimensional data set composed of a large number of points, and each point has its coordinates and attribute information, and the attribute information includes a normal vector, a curvature, a direction, and the like of a curved surface in the geometric data may be calculated according to the normal vector calculation. And analyzing and processing the geometric shape data through a preset geometric shape analysis algorithm, determining the required characteristic parameters such as curvature, radius, area and the like of the workpiece to be detected, determining the parameter value of the workpiece to be detected, and providing data support for the execution of a subsequent method.
The preset geometric shape analysis algorithm comprises one of curvature calculation, normal vector calculation, area calculation and convex hull calculation. In this embodiment, the geometric shape data is analyzed according to the preset geometric shape analysis algorithm to determine a characteristic parameter of the workpiece to be measured, where the preset geometric shape analysis algorithm includes one of curvature calculation, normal vector calculation, area calculation, and convex hull calculation. For example, curvature calculation is a calculation method of multiple reference functions, which can be used to measure curvature and deformation of the surface of the workpiece to be measured; the convex hull calculation is a geometric operation algorithm for operating point cloud in three-dimensional space, and the three-dimensional bounding box and the convex hull can be detected from the geometric shape data through the convex hull calculation. The normal vector is the normal direction of the surface whose value is at a certain point, and therefore the curvature and direction of the surface can be calculated from the normal vector. Therefore, the characteristic parameters such as curvature, radius, area and the like of the workpiece to be measured in the geometric shape data can be obtained through a preset geometric shape analysis algorithm.
And S140, generating a detection track plan of the workpiece to be detected through a preset track planning algorithm according to the characteristic parameters and the position information.
In this embodiment, the track planning includes planning a detection path and generating a smooth track by adjusting parameters such as a speed, an acceleration, an angular speed, an angular acceleration, and the like on the basis of the planned path, so that the workpiece to be measured can smoothly move according to the planned path. And generating a detection track plan of the workpiece to be detected through a preset track planning algorithm according to the characteristic parameters and the position information, wherein the preset track planning algorithm comprises one of a shortest path algorithm, a genetic algorithm and an ant colony algorithm. For example, the shortest path algorithm takes the position information as a starting point, and searches different paths to find the detection track plan with highest efficiency so as to achieve the aim of optimization. By using simpler algorithms such as a shortest path algorithm, a genetic algorithm, an ant colony algorithm and the like, the deep learning algorithm is not needed, so that the implementation of a detection track planning method for generating complex workpieces is simpler and easier to implement, the generated track is more interpretable by the detection track planning of the workpieces to be detected, which is generated according to the characteristic parameters analyzed by geometric shape data, and the reliability and feasibility of the detection track are improved.
In one embodiment, as shown in FIG. 4, the step S140 includes steps S141-S142;
s141, comparing the characteristic parameters with preset standard characteristic parameters to obtain a comparison result;
s142, generating a detection track plan of the workpiece to be detected through the preset track planning algorithm according to the comparison result and the position information.
In this embodiment, before receiving a working instruction, a CAD model is used to analyze the geometry and characteristic parameters of a standard complex workpiece as preset standard characteristic parameters, and a worker selects in advance data of the workpiece to be detected, such as position distance information between two points, parallelism roundness of a place, and the like, after receiving the working instruction, the worker obtains the characteristic parameters according to the analyzed geometry of point cloud data obtained by a sensor or a camera when running a program, and compares the characteristic parameters to be detected with the preset standard characteristic parameters, and if the workpiece to be detected selected by the worker has a slight deviation from the preset standard characteristic parameters after comparing the characteristic parameters to be detected, a detection track plan for the characteristic parameters is generated according to a preset detection track planning algorithm with the position information as a starting point. And according to the comparison result and the position information, and according to the detection track planning of the workpiece to be detected, which is generated by the preset track planning algorithm, the detection track planning is simpler and easier to implement.
In one embodiment, as shown in fig. 4, the step S140 further includes steps S143 to S144;
s143, performing collision detection according to the detection track plan to obtain a collision detection result;
s144, feeding back and optimizing the detection track planning according to the collision detection result.
In this embodiment, collision detection is performed on the generated detection track plan to prevent the workpiece to be detected from touching the detection device in the detection process, for example, to prevent the workpiece to be detected from touching the mechanical arm, optimization of the detection track plan is completed through the collision detection, and an optimal detection track plan of the workpiece to be detected is determined according to the optimized detection track of the workpiece to be detected. After determining the optimal detection track planning of the workpiece to be detected, the generated detection track planning is applied to a robot or other automatic equipment, so that the detection task of the complex workpiece can be completed. The detection task of the complex workpiece can be executed by a common track execution method such as PID control, motion planning and the like. And collision detection is carried out on the detection track planning to obtain a collision detection result, and the optimized detection track planning of the workpiece to be detected is fed back, so that the track planning process can be optimized in a self-adaptive manner, and the production efficiency and the product quality of the complex workpiece are improved.
Fig. 5 is a schematic block diagram of a workpiece inspection trajectory planning device 200 according to an embodiment of the present application. As shown in fig. 5, the application further provides a workpiece detection track planning device corresponding to the workpiece detection track planning method. The workpiece inspection trajectory planning device comprises a unit for executing the workpiece inspection trajectory planning method, and the device can be configured in a desktop computer, a tablet computer, a portable computer, and the like. Specifically, referring to fig. 5, the workpiece inspection trajectory planning device includes an acquisition unit 210, a preprocessing unit 220, an analysis unit 230, and a generation unit 240.
The acquiring unit 210 is configured to acquire geometric data and positional information of the workpiece to be measured.
In an embodiment, the obtaining unit 210 includes an obtaining subunit and a converting unit.
The acquisition subunit is used for acquiring the position information to be converted of the workpiece to be detected;
the conversion unit is used for converting the position information to be converted through a matrix to obtain the position information of the workpiece to be detected.
And a preprocessing unit 220, configured to preprocess the geometry data to obtain preprocessed geometry data.
In one embodiment, the preprocessing unit 220 includes a filtering unit and a denoising unit.
The filtering unit is used for filtering the geometric shape data according to a preset filtering algorithm to obtain the filtered geometric shape data; and/or
And the denoising unit is used for denoising the geometric shape data according to a preset denoising algorithm to obtain the denoised geometric shape data.
And the analysis unit 230 is configured to perform analysis processing on the geometry data according to a preset geometry analysis algorithm to determine a characteristic parameter of the workpiece to be measured.
The generating unit 240 is configured to generate a detection track plan of the workpiece to be detected according to the feature parameter and the position information through a preset track planning algorithm.
In one embodiment, the generating unit 240 includes a comparing unit, a generating subunit, a detecting unit, and a detecting unit.
The comparison unit is used for comparing the characteristic parameters with preset standard characteristic parameters to obtain comparison results;
the generation subunit is used for generating a detection track plan of the workpiece to be detected through the preset track planning algorithm according to the comparison result and the position information;
the detection unit is used for carrying out collision detection according to the detection track plan to obtain a collision detection result;
and the optimizing unit is used for feeding back and optimizing the detection track planning according to the collision detection result.
It should be noted that, as will be clearly understood by those skilled in the art, the specific implementation process of the workpiece inspection trajectory planning device 200 and each unit may refer to the corresponding description in the foregoing method embodiments, and for convenience and brevity of description, the description is omitted here.
The workpiece inspection trajectory planning device described above may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 6.
Referring to fig. 6, fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a terminal or a server, where the terminal may be an electronic device with a communication function, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster formed by a plurality of servers.
With reference to FIG. 6, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a method of workpiece inspection trajectory planning.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the non-volatile storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to perform a method of workpiece inspection trajectory planning.
The network interface 505 is used for network communication with other devices. It will be appreciated by those skilled in the art that the architecture shown in fig. 6 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting of the computer device 500 to which the present inventive arrangements may be implemented, as a particular computer device 500 may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
Wherein the processor 502 is adapted to run a computer program 5032 stored in a memory for implementing the steps of the above method.
It should be appreciated that in an embodiment of the application, the processor 502 may be a Central processing unit (Central ProcessingUnit, CPU), and the processor 502 may also be other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Those skilled in the art will appreciate that all or part of the flow in a method embodying the above described embodiments may be accomplished by computer programs instructing the relevant hardware. The computer program comprises program instructions, and the computer program can be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present application also provides a storage medium. The storage medium may be a computer readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. The program instructions, when executed by a processor, cause the processor to perform the steps of the method as described above.
The storage medium may be a U-disk, a removable hard disk, a Read-only memory (ROM), a magnetic disk, or an optical disk, or other various computer-readable storage media that may store program codes.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed.
The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the application can be combined, divided and deleted according to actual needs. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The integrated unit may be stored in a storage medium if implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a terminal, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. A method for planning a detection track of a workpiece is characterized by comprising the following steps:
acquiring geometric shape data and position information of a workpiece to be measured;
preprocessing the geometric shape data to obtain preprocessed geometric shape data;
analyzing and processing the geometric shape data according to a preset geometric shape analysis algorithm to determine characteristic parameters of the workpiece to be detected;
and generating a detection track plan of the workpiece to be detected through a preset track planning algorithm according to the characteristic parameters and the position information.
2. The method of claim 1, wherein the step of obtaining positional information of the workpiece to be measured comprises:
acquiring position information to be converted of the workpiece to be detected;
and converting the position information to be converted through a matrix to obtain the position information of the workpiece to be tested.
3. The method of claim 1, wherein the step of preprocessing the geometry data to obtain preprocessed geometry data comprises:
filtering the geometric shape data according to a preset filtering algorithm to obtain filtered geometric shape data; and/or
Denoising the geometric shape data according to a preset denoising algorithm to obtain the denoised geometric shape data.
4. The method of claim 1, wherein the pre-set geometry analysis algorithm is any one of curvature calculation, normal vector calculation, area calculation, convex hull calculation.
5. The method according to claim 1, wherein the preset trajectory planning algorithm is any one of a shortest path algorithm, a genetic algorithm, and an ant colony algorithm.
6. The method according to claim 1, wherein the step of generating a detection trajectory plan of the workpiece to be detected by a preset trajectory planning algorithm according to the characteristic parameter and the position information comprises:
comparing the characteristic parameters with preset standard characteristic parameters to obtain a comparison result;
and generating a detection track plan of the workpiece to be detected through the preset track planning algorithm according to the comparison result and the position information.
7. The method according to claim 6, wherein after the step of generating the detection track plan of the workpiece to be detected by the preset track planning algorithm according to the comparison result and the position information, the method further comprises:
performing collision detection according to the detection track plan to obtain a collision detection result;
and optimizing the detection track planning according to the collision detection result feedback.
8. A workpiece inspection trajectory planning device, comprising:
the acquisition unit is used for acquiring geometric shape data and position information of the workpiece to be detected;
the preprocessing unit is used for preprocessing the geometric shape data to obtain preprocessed geometric shape data;
the analysis unit is used for analyzing and processing the geometric shape data according to a preset geometric shape analysis algorithm to determine characteristic parameters of the workpiece to be detected;
and the generating unit is used for generating a detection track plan of the workpiece to be detected through a preset track planning algorithm according to the characteristic parameters and the position information.
9. A computer device, characterized in that it comprises a memory on which a computer program is stored and a processor which, when executing the computer program, implements the method according to any of claims 1-7.
10. A storage medium storing a computer program comprising program instructions which, when executed by a processor, implement the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310675734.9A CN116755402A (en) | 2023-06-08 | 2023-06-08 | Workpiece detection track planning method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310675734.9A CN116755402A (en) | 2023-06-08 | 2023-06-08 | Workpiece detection track planning method, device, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116755402A true CN116755402A (en) | 2023-09-15 |
Family
ID=87958256
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310675734.9A Pending CN116755402A (en) | 2023-06-08 | 2023-06-08 | Workpiece detection track planning method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116755402A (en) |
-
2023
- 2023-06-08 CN CN202310675734.9A patent/CN116755402A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110599544B (en) | Workpiece positioning method and device based on machine vision | |
| US20180307203A1 (en) | Machining defect factor estimation device | |
| KR102649930B1 (en) | Systems and methods for finding and classifying patterns in images with a vision system | |
| CN115035120B (en) | Machine tool control method and system based on Internet of things | |
| JP2018136896A (en) | Information processor, system, information processing method, and manufacturing method of article | |
| CN112017232A (en) | Method, device and equipment for positioning circular pattern in image | |
| CN111582186A (en) | Object edge identification method, device, system and medium based on vision and touch | |
| CN115993804B (en) | Cutter parameter adjustment method based on numerical control machine tool and related equipment | |
| CN115781673A (en) | Part grabbing method, device, equipment and medium | |
| CN105405122A (en) | Circle detection method based on data stationarity | |
| CN114179069A (en) | Automatic path correction method and system for industrial robot | |
| CN112906215B (en) | Pipe tool path generation method based on SolidWorks secondary development | |
| CN116909208B (en) | Shell processing path optimization method and system based on artificial intelligence | |
| JP5868005B2 (en) | Imaging apparatus, imaging condition setting method, program | |
| CN116755402A (en) | Workpiece detection track planning method, device, equipment and medium | |
| CN116700149A (en) | Self-adaptive filtering optimization method for numerical control machining tool path | |
| CN116604212A (en) | Robot weld joint identification method and system based on area array structured light | |
| CN111275758A (en) | Hybrid 3D visual positioning method and device, computer equipment and storage medium | |
| TWM531354U (en) | Machining device | |
| CN111633337B (en) | Reflection eliminating method and device for laser welding seam measurement | |
| CN115861368A (en) | Multi-class contour tracking method, device and medium for multi-class images | |
| CN112614172A (en) | Plane and/or curved surface dividing method and system based on three-dimensional vision | |
| CN109655884B (en) | Method and system for removing speed pickup abnormal points | |
| CN110826504A (en) | Visual identification method and device combined with fine-grained classification and computer equipment | |
| CN116343131B (en) | Intelligent monitoring and early warning method, device, equipment and storage medium for camera set |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |