CN113291847A - Intelligent bulk material stacking and taking method based on three-dimensional imaging - Google Patents
Intelligent bulk material stacking and taking method based on three-dimensional imaging Download PDFInfo
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- CN113291847A CN113291847A CN202110349022.9A CN202110349022A CN113291847A CN 113291847 A CN113291847 A CN 113291847A CN 202110349022 A CN202110349022 A CN 202110349022A CN 113291847 A CN113291847 A CN 113291847A
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- 239000013590 bulk material Substances 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000003384 imaging method Methods 0.000 title claims abstract description 13
- 239000000463 material Substances 0.000 claims abstract description 48
- 239000000428 dust Substances 0.000 claims abstract description 11
- 238000001914 filtration Methods 0.000 claims abstract description 7
- 238000005070 sampling Methods 0.000 claims description 5
- 238000009499 grossing Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 239000003595 mist Substances 0.000 claims description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 3
- 229910000831 Steel Inorganic materials 0.000 description 2
- 239000003245 coal Substances 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 206010047531 Visual acuity reduced Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
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- 238000005457 optimization Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G65/00—Loading or unloading
- B65G65/005—Control arrangements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Abstract
The invention provides a three-dimensional imaging-based intelligent bulk material stacking and taking method, which comprises the following steps: 1. acquiring three-dimensional point cloud coordinates of the bulk material based on x positioning equipment and a laser scanner; 2. performing space-time registration on the x coordinate of the bulk material according to the nearest neighbor principle, forming three-dimensional point cloud data of the bulk material pile together with y and z coordinates of the laser scanner, and unifying the coordinates of the scanner to a coordinate system of the bulk material pile; 3. carrying out cubic B-spline curve interpolation on the coordinates of the laser scanner and the millimeter wave radar to compensate data lost by the scanner due to dust; 4. filtering point cloud discrete data based on mean filtering, and removing clutter points of the laser scanner; 5. fitting a local profile of the stockpile based on a least square method, and compensating scanning point loss caused by local shadow; 6. carrying out gridding layering processing on the three-dimensional point cloud data in the x, y and z directions; 7. based on the thought of layered material piling and taking and slope material piling and taking, the material piling and taking points are calculated and generated according to the principle that high-rise bulk materials are preferably grabbed and the material piling and taking equipment runs in sequence.
Description
Technical Field
The invention relates to an intelligent material piling and taking strategy for bulk materials in a stock yard, in particular to the unmanned automatic grabbing requirement for the bulk materials in the bulk stock yard.
Background
The material piling and taking mode of the bulk cargo material yard mainly depends on manual operation equipment, but because the material yard, especially a closed dry coal shed, has high dust concentration and poor vision of a driver in a high-dust environment, safe and efficient operation cannot be ensured, and the body health of the driver is seriously influenced by bulk cargo dust. The instability of manual material stacking and taking and long-term fatigue driving cannot guarantee the continuity of the material stacking and taking, the efficiency of the material stacking and taking is affected, the automation and the intelligence of the material stacking and taking equipment are realized, and a new technical scheme is needed to solve the problems.
Disclosure of Invention
The invention aims to provide a bulk material intelligent stacking and taking method based on three-dimensional imaging. The invention can realize the acquisition of three-dimensional point cloud data of bulk materials based on an x positioning system and a laser scanner and the intelligent generation of optimal material piling and taking points based on the three-dimensional point cloud data. The defects that the automation operation degree of a bulk cargo stock ground is low, the labor intensity of workers is high, and all-weather continuous operation cannot be realized are overcome.
The invention mainly comprises the following steps:
the first step is as follows: and installing and fixing an x positioning system, a laser scanner and a millimeter wave radar. And acquiring sensor data in real time, acquiring three-dimensional point cloud data of the bulk materials, and performing nearest neighbor registration on the x coordinate.
The second step is that: filtering and interpolation fitting are carried out on the bulk material point cloud data, and scanner data loss caused by dust and the like is removed to smooth the point cloud data.
The third step: the point cloud data are subjected to hierarchical gridding treatment, so that the optimal material piling and taking points can be generated conveniently through subsequent calculation;
the fourth step: based on the principle of sequential traveling of the material piling and taking equipment, the high-level preferential grabbing of the bulk materials is performed, the comprehensive attribute weight of each grid point is calculated, then the weighted values are sequenced, and the optimal material piling and taking point is calculated and generated.
The fifth step: updating the point cloud data of the bulk materials, repeating the fourth step, and iteratively generating the next optimal material piling and taking point.
The invention realizes the intelligent unmanned material taking operation of the bulk cargo stock ground based on three-dimensional imaging, ensures the requirement of all-weather material taking of the bulk cargo stock ground and improves the bulk cargo material taking efficiency of the stock ground. The invention is not only used in large ports and docks, open warehouses, but also in large mine material yards, steel enterprises and other bulk material factories.
Drawings
FIG. 1 is a diagram of a coal pile bulk material;
FIG. 2 is a schematic diagram of a bulk three-dimensional point cloud data acquisition device;
FIG. 3 is a schematic diagram of a mesh division of bulk material point cloud data;
FIG. 4 is a flow chart 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.
The technical scheme of the invention is further illustrated by combining specific examples.
Referring to fig. 1 to 4, the present invention provides a bulk material intelligent stacking and reclaiming method based on three-dimensional imaging, which includes the following steps:
s1, mounting and fixing an x positioning device, a laser scanner and a millimeter wave radar;
s2, acquiring three-dimensional point cloud x, y and z data of the bulk material in real time, carrying out nearest neighbor sampling on an x coordinate, and gridding the x coordinate of the bulk material;
s3, carrying out interpolation fitting processing on the three-dimensional coordinates of the bulk material pile to compensate the problem of scanner data loss caused by stock yard dust;
s4, filtering out point cloud outliers based on a mean filtering algorithm;
s5, fitting the local coordinates of the point cloud based on a least square method, and smoothing the point cloud;
s6, layering and meshing the three-dimensional point cloud data of the bulk material pile;
and S7, updating the point cloud data of the bulk material according to the ideas of layered stacking and reclaiming and slope stacking and reclaiming, and iteratively calculating the next optimal stacking and reclaiming point.
The specific operation is as follows:
the laser scanner and x-coordinate positioning system are mounted in the manner of fig. 2.
Step 1, collecting x coordinates of bulk materials in real time through an x coordinate positioning device, firstly carrying out gridding processing on the x coordinates, namely carrying out uniform distribution processing on the x coordinates, and interpolating the x according to a nearest neighbor principle. The specific implementation method comprises the following steps: assuming that the spatial resolution of the X coordinate of the bulk material is dx, that is, the X coordinate distribution of the bulk material should be X0, X0+ dx, X0+2 × dx, …, X + n × dx, and the current actual sampling value of the X position of the bulk material is X, min { dis (X-X0 + K × dx) }, K ═ 1,2,3, …, n is solved. Where dis represents the distance between two points. And finding out the K value meeting the condition, wherein the position is the current nearest neighbor coordinate of the bulk material: x' X0+ K dx. The X coordinate of the current row of the bulk material is considered to be X'.
And 2, the penetration capacity of the scanner is limited, and the echoes of the scanner are likely to be largely lost in a high-dust environment. The invention realizes that the point cloud data of the bulk materials can be effectively collected under the high-dust environment by adopting a cubic spline interpolation compensation mode of millimeter wave radar data and scanner data. Embodied as the known scanner scanning range end point coordinates a (y0, z0), B (ym, z1), and the points of the millimeter wave radar and the points of the laser scanner C (yi, zi) distributed between the end points, where y0<＝yi<Then the cubic spline interpolation point y can be obtained0，y1，…，yn，ym∈[y0，y1]And then obtaining a cubic spline curve according to a cubic spline interpolation formula. And then, according to the distribution condition of the points in one row, interpolating new points in sparse places, and randomly eliminating points in dense places to ensure that the number of the points in each row is consistent.
And 3, due to the influence of mirror reflection or water mist, dust and the like, the echo of the scanner has peak clutter. The method uses a least square method to fit local point cloud straight lines containing clutter and smooth point cloud data. Suppose a pile row of data points X { (X)0,y0),(x1,y1),...,(xn,yn) Constructing a loss function
Optimization objective
And solving to obtain parameters a and b, then obtaining a linear equation of y ═ ax + b, and re-fitting the local coordinates of the bulk cargo according to the linear equation to finally achieve the purpose of smoothing the point cloud.
And 4, performing down-sampling gridding division on the smoothed point cloud data. The material pile is thought to be covered by a large net, and the layering treatment is also carried out in the depth direction, the layering step length is h, so that the whole material pile is divided into block structures, and each block grid is a cube with the length, width and height (dx, dy, h). And calculating the attribute of each block structure, and calculating the maximum gradient and the full load rate of the depth layer k, x and y to which the block structure belongs for each block structure. The full load rate is defined as follows: assuming that the bulk material gripper grabs the bulk material fully, the volume is V0, and the bulk material volume of the current block structure is V, the full loading rate is ρ ═ V/V0. Defining the highest layer of the bulk material as 0 layer, the next highest layer as 1 layer, and so on, and the lowest layer as K-1 layer.
And 5, according to the thought of preferential gripping of the high-level bulk materials, starting from the 0 th layer, reading the maximum gradient and the full load rate in the x and y directions of each grid point in the maximum opening coverage range of the bulk material gripper. Assuming that the bulk gripper covers m grid points in the current k-layer height layer, the attributes of each grid point are sorted according to the following priorities: and the gradient in the x direction > the full load rate > the gradient in the y direction, and one grid point which is the most front after sequencing is taken as an optimal material taking point.
And 6, updating the bulk material point cloud data height by taking the material taking point generated in the step 5 as a central point. And repeating the steps 5 and 6 to generate the next material taking point until the number of the material taking points reaches the specified number.
The invention realizes the intelligent unmanned material taking operation of the bulk cargo stock ground based on three-dimensional imaging, ensures the requirement of all-weather material taking of the bulk cargo stock ground and improves the bulk cargo material taking efficiency of the stock ground. The invention is not only used in large ports and docks, open warehouses, but also in large mine material yards, steel enterprises and other bulk material factories.
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 (6)
1. A bulk material intelligent stacking and taking method based on three-dimensional imaging comprises the following steps:
s1, mounting and fixing an x positioning device, a laser scanner and a millimeter wave radar;
s2, acquiring three-dimensional point cloud x, y and z data of the bulk material in real time, carrying out nearest neighbor sampling on an x coordinate, and gridding the x coordinate of the bulk material;
s3, carrying out interpolation fitting processing on the three-dimensional coordinates of the bulk material pile to compensate the problem of scanner data loss caused by stock yard dust;
s4, filtering out point cloud outliers based on a mean filtering algorithm;
s5, fitting the local coordinates of the point cloud based on a least square method, and smoothing the point cloud;
s6, layering and meshing the three-dimensional point cloud data of the bulk material pile;
and S7, updating the point cloud data of the bulk material according to the ideas of layered stacking and reclaiming and slope stacking and reclaiming, and iteratively calculating the next optimal stacking and reclaiming point.
2. The bulk material intelligent stacking and taking method based on three-dimensional imaging is characterized in that: and installing and fixing the laser scanner and the x positioning equipment, acquiring the three-dimensional coordinates of the bulk materials in real time, and performing nearest neighbor sampling on the x coordinates.
3. The bulk material intelligent stacking and taking method based on three-dimensional imaging is characterized in that: interpolation fitting is carried out on the three-dimensional point cloud data of the bulk material, and data loss of a scanner caused by dust or water mist is compensated, so that the measured data is closer to the real appearance of the bulk material.
4. The bulk material intelligent stacking and taking method based on three-dimensional imaging is characterized in that: the gridding layering processing is carried out on the point cloud data of the bulk materials, so that the optimal material piling and taking points can be conveniently selected by subsequent calculation.
5. The bulk material intelligent stacking and taking method based on three-dimensional imaging is characterized in that: based on the principle of sequential traveling of the material piling and taking equipment, the high-rise bulk material is grabbed, the comprehensive attribute weight of each grid point is calculated, then the weighted values are sequenced, and the optimal material piling and taking point is selected.
6. The bulk material intelligent stacking and taking method based on three-dimensional imaging is characterized in that: updating the point cloud data of the bulk material, recalculating the attribute weight of the next grid point, selecting the second optimal material grabbing point, repeating the steps and generating all the material piling and taking points.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116524010A (en) * | 2023-04-25 | 2023-08-01 | 北京云中未来科技有限公司 | Unmanned crown block positioning method, system and storage medium for bulk material storage |
| CN116835268A (en) * | 2023-09-01 | 2023-10-03 | 测控人(天津)科技有限公司 | Remote control method and system for round stacker-reclaimer |
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| CN116524010A (en) * | 2023-04-25 | 2023-08-01 | 北京云中未来科技有限公司 | Unmanned crown block positioning method, system and storage medium for bulk material storage |
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Application publication date: 20210824 |