CN111496786A - Point cloud model-based mechanical arm operation processing track planning method - Google Patents
Point cloud model-based mechanical arm operation processing track planning method Download PDFInfo
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- CN111496786A CN111496786A CN202010294469.6A CN202010294469A CN111496786A CN 111496786 A CN111496786 A CN 111496786A CN 202010294469 A CN202010294469 A CN 202010294469A CN 111496786 A CN111496786 A CN 111496786A
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012545 processing Methods 0.000 title claims abstract description 37
- 230000000007 visual effect Effects 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000005498 polishing Methods 0.000 claims description 4
- 238000005507 spraying Methods 0.000 claims description 4
- 238000005520 cutting process Methods 0.000 claims description 3
- 238000000227 grinding Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 7
- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000003860 storage Methods 0.000 abstract description 3
- 230000009471 action Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012850 discrimination method Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Abstract
The invention discloses a method for planning an operation processing track of a mechanical arm based on a point cloud model, and relates to the technical field of robot processing. The method for planning the mechanical arm operation processing track based on the point cloud model can realize the purpose of instantly obtaining a workpiece point cloud model by using vision, automatically plans the mechanical arm operation processing track based on the point cloud model, avoids manual teaching, well avoids the gridding treatment of the point cloud or the reconstruction process of a parameter curved surface, is time-consuming and complex in calculation and lacks robustness, and simultaneously compared with a grid, the point cloud does not need a topological relation, thereby greatly simplifying the storage and the representation of a three-dimensional model.
Description
Technical Field
The invention relates to the technical field of robot processing, in particular to a method for instantly obtaining a point cloud model of a workpiece by using vision, automatically planning a mechanical arm operation processing track based on the point cloud model and avoiding manual teaching.
Background
Along with high-speed iteration and updating of products, higher requirements are provided for the robot processing industry, the operation efficiency of the original manual teaching mode is low, the product iteration period is higher than the manual teaching period, and the current market development requirements cannot be met.
The ideal model based on the workpiece can only generate a simple track and cannot meet the actual requirement; at present, the operation tracks (spraying and polishing) required by the robot are generally taught manually. The robot trajectory planning of manual teaching has high technical requirement on workers, high labor intensity and low efficiency, the teaching capability does not depend on the technical level of operators seriously, the teaching period is long, and the existing teaching means hardly meets the high-efficiency and high-precision machining requirements of complex components of high-speed iteration and updated products in the fields of 3C, automobile industry and the like. Accordingly, there is a need in the art for further optimization of a robot operation trajectory scheme so as to meet the processing requirements of the robot for high-speed iteration of products and short, more efficient and high-precision trajectory generation requirements involved in the generation of new generations.
With the wide application of three-dimensional imaging equipment in mechanical arm operation, point clouds serve as an efficient data format containing three-dimensional object description information. The invention provides a method for directly planning and extracting a processing track based on a point cloud model, which avoids the gridding treatment of the point cloud or the reconstruction process of a parameter curved surface, the process is time-consuming and complex in calculation and lacks robustness, and simultaneously compared with a grid, the point cloud does not need a topological relation, thereby greatly simplifying the storage and the representation of a three-dimensional model; compared with a parameter surface, the method does not need excessive pretreatment, for example, B-splines require interpolation points to be tensor lattices, so that the flow of geometric treatment is greatly simplified, the operation processing track of the mechanical arm can be directly planned and extracted on the point cloud, and the method has great theoretical and application significance for strengthening teaching-free operation of the mechanical arm.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a method for planning the operation processing track of a mechanical arm based on a point cloud model, and provides a method for instantly obtaining a workpiece point cloud model by vision, automatically planning the operation processing track of the mechanical arm based on the point cloud model and avoiding manual teaching.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for planning mechanical arm operation processing tracks based on a point cloud model specifically comprises the following steps:
s1, instantly acquiring a workpiece point cloud model by using visual acquisition equipment;
s2, equally dividing the model obtained in the step S1 in three-dimensional (x, y, z) direction into blocks in a minimum bounding box mode;
s3, obtaining a plurality of small cubes after cutting in the step S2, defining surface serial numbers of the cubes according to a uniform direction, and separating information of each surface of the model through an adjacent algorithm;
s4, extracting the information of the effective surface in each cube, and arranging and combining the information according to a certain rule to form a complete mechanical arm operation curve track;
and S5, combining the operation curve track of the produced mechanical arm with a corresponding processing technology, and automatically planning the operation track which can be executed within the reach range of the mechanical arm under the coordination of the positioner.
Preferably, the point cloud model obtained in step S1 is point cloud pcd data or point cloud ply data.
Preferably, in step S1, the vision collecting device is a vision detecting device with model EKT-VT-680D for collecting vision data.
Preferably, the minimum bounding box in step S2 is an AABB bounding box or an OBB bounding box.
Preferably, in step S3, each cube defines a surface number according to a sequence in a uniform direction (1, 2, 3 … n).
Preferably, in step S4, the alignment and combination are performed according to the rule of the x direction or the downward spiral direction.
Preferably, the corresponding processing technology in step S5 is spraying, grinding or polishing.
(III) advantageous effects
The invention provides a method for planning an operation processing track of a mechanical arm based on a point cloud model. Compared with the prior art, the method has the following beneficial effects: the method for planning the mechanical arm operation processing track based on the point cloud model specifically comprises the following steps: s1, a workpiece point cloud model is obtained instantly by using visual collection equipment, S2, the model obtained in the step S1 is cut into blocks in a three-dimensional (x, y, z) direction in a minimum bounding box mode, S3, after the model is cut in the step S2, a plurality of small cubes are obtained, surface serial numbers are defined according to a unified direction for each cube, then information of each surface of the model is separated out through a proximity algorithm, S4, information of effective surfaces in each cube is extracted and is combined into a complete mechanical arm operation curve track according to a certain rule arrangement, S5, the produced mechanical arm operation curve track is combined with a corresponding processing technology, an executable operation track in a mechanical arm reach range is automatically planned under the cooperation of a positioner, the workpiece point cloud model can be obtained instantly by using visual, the mechanical arm operation processing track is automatically planned based on the point cloud model, the method has the advantages that manual teaching is omitted, the gridding treatment of point clouds or the reconstruction process of a parameter curved surface is well omitted, the calculation is time-consuming and complex, robustness is lacked, meanwhile, compared with a grid, the point clouds are free of topological relations, storage and representation of a three-dimensional model are greatly simplified, compared with a parameter curved surface, excessive preprocessing is not needed, for example, B-splines require interpolation points to be tensor type lattices, the flow of geometric processing is greatly simplified, the operation processing track of the mechanical arm can be directly planned and extracted on the point clouds, and great theoretical and application significance is achieved for strengthening teaching-free operation of the mechanical arm.
Drawings
Fig. 1 is a working principle diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a technical solution: a method for planning mechanical arm operation processing tracks based on a point cloud model specifically comprises the following steps:
s1, a visual acquisition device is used for acquiring a workpiece point cloud model in real time, the acquired point cloud model is point cloud pcd data or point cloud ply data, the visual acquisition device selects a EKT-VT-680D visual detector for visual data acquisition, the visual detector adopts gray scale image detection, the gray scale refers to the gradation level of different brightness from the lightest to the darkest of a display image, the more the gray scale level, the more exquisite the image effect is, the judgment requirement for the picture is to judge whether the electronic book normally displays the picture without calculating the gray scale level, can intercept part of the picture for analysis and processing, can adopt line scanning and boundary discrimination method in the aspect of software algorithm to determine that the picture presents a linear boundary, determining the change of the gray value presentation rule of the picture by calculating the line scanning gray value, thereby quickly judging whether the picture is a gray-scale picture;
s2, equally dividing the model obtained in the step S1 in the three-dimensional (x, y, z) direction into blocks in the form of a minimum bounding box, wherein the minimum bounding box adopts an AABB bounding box or an OBB bounding box;
s3, after cutting in the step S2, obtaining a plurality of small cubes, defining surface serial numbers of each cube according to a uniform direction, separating information of each surface of the model through a proximity algorithm, defining the surface serial numbers of each cube according to the sequence of the uniform direction (1, 2 and 3 … n), wherein the proximity algorithm adopts a kNN classification algorithm, the KNN algorithm can be used for classification and regression, the attribute of a sample can be obtained by finding out k nearest neighbors of the sample and assigning the average value of the attributes of the neighbors to the sample, and more useful, different weight values are given to the influence of the neighbors with different distances on the sample, for example, the weight values are in inverse proportion to the distances;
s4, extracting the information of the effective surface in each cube, arranging and combining the information into a complete mechanical arm operation curve track according to a certain rule, and arranging and combining the information according to the rule of the x direction or the lower spiral direction;
and S5, combining the operation curve track of the produced mechanical arm with a corresponding processing technology, and automatically planning the operation track which can be executed within the reach range of the mechanical arm under the coordination of the positioner, wherein the corresponding processing technology is spraying, grinding or polishing.
To sum up the above
The invention can realize that a workpiece point cloud model is obtained in real time by using vision, and the mechanical arm operation processing track is automatically planned based on the point cloud model, so that manual teaching is avoided, and the gridding treatment of the point cloud or the reconstruction process of a parameter curved surface are well avoided.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A method for planning mechanical arm operation processing tracks based on a point cloud model is characterized by comprising the following steps: the method specifically comprises the following steps:
s1, acquiring a workpiece point cloud model by using visual acquisition equipment;
s2, equally dividing the model obtained in the step S1 in three-dimensional (x, y, z) direction into blocks in a minimum bounding box mode;
s3, obtaining a plurality of small cubes after cutting in the step S2, defining surface serial numbers of the cubes according to a uniform direction, and separating information of each surface of the model through an adjacent algorithm;
s4, extracting the information of the effective surface in each cube, and arranging and combining the information according to a certain rule to form a complete mechanical arm operation curve track;
and S5, combining the operation curve track of the produced mechanical arm with a corresponding processing technology, and automatically planning the operation track which can be executed within the reach range of the mechanical arm under the coordination of the positioner.
2. The method for planning the mechanical arm operation processing track based on the point cloud model according to claim 1, wherein the method comprises the following steps: the point cloud model obtained in step S1 is point cloud pcd data or point cloud ply data.
3. The method for planning the mechanical arm operation processing track based on the point cloud model according to claim 1, wherein the method comprises the following steps: in the step S1, the vision collecting device is a vision detecting device with model EKT-VT-680D for collecting vision data.
4. The method for planning the mechanical arm operation processing track based on the point cloud model according to claim 1, wherein the method comprises the following steps: the minimum bounding box in the step S2 is an AABB bounding box or an OBB bounding box.
5. The method for planning the mechanical arm operation processing track based on the point cloud model according to claim 1, wherein the method comprises the following steps: in the step S3, each cube defines a surface number according to the order of the uniform direction (1, 2, 3 … n).
6. The method for planning the mechanical arm operation processing track based on the point cloud model according to claim 1, wherein the method comprises the following steps: in step S4, the combination is performed according to the rule of the x direction or the downward spiral direction.
7. The method for planning the mechanical arm operation processing track based on the point cloud model according to claim 1, wherein the method comprises the following steps: the corresponding processing technology in the step S5 is spraying, grinding or polishing.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010294469.6A CN111496786A (en) | 2020-04-15 | 2020-04-15 | Point cloud model-based mechanical arm operation processing track planning method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112179602A (en) * | 2020-08-28 | 2021-01-05 | 北京邮电大学 | Mechanical arm collision detection method |
| CN112207833A (en) * | 2020-10-16 | 2021-01-12 | 深圳市华成工业控制股份有限公司 | Method and system for planning movement path, host and storage medium |
| CN112666890A (en) * | 2020-12-30 | 2021-04-16 | 西安中科微精光子制造科技有限公司 | Curved surface workpiece machining track planning method |
| CN114170314A (en) * | 2021-12-07 | 2022-03-11 | 深圳群宾精密工业有限公司 | 3D glasses process track execution method based on intelligent 3D vision processing |
-
2020
- 2020-04-15 CN CN202010294469.6A patent/CN111496786A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112179602A (en) * | 2020-08-28 | 2021-01-05 | 北京邮电大学 | Mechanical arm collision detection method |
| CN112207833A (en) * | 2020-10-16 | 2021-01-12 | 深圳市华成工业控制股份有限公司 | Method and system for planning movement path, host and storage medium |
| CN112207833B (en) * | 2020-10-16 | 2021-08-17 | 深圳市华成工业控制股份有限公司 | Method and system for planning movement path, host and storage medium |
| CN112666890A (en) * | 2020-12-30 | 2021-04-16 | 西安中科微精光子制造科技有限公司 | Curved surface workpiece machining track planning method |
| CN112666890B (en) * | 2020-12-30 | 2022-09-16 | 西安中科微精光子科技股份有限公司 | Curved surface workpiece machining track planning method |
| CN114170314A (en) * | 2021-12-07 | 2022-03-11 | 深圳群宾精密工业有限公司 | 3D glasses process track execution method based on intelligent 3D vision processing |
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