Grab bucket grabbing point calculation method and system based on terrain distribution

Abstract

The invention relates to the technical field of material carrying, in particular to a grab bucket grabbing point calculation method and system based on terrain distribution. The method comprises the following steps that point cloud data of materials in a cabin are obtained through radar equipment, and the point cloud data are divided into a plurality of grabbing areas according to the structure of the cabin; carrying out grid processing on the point cloud data in the grabbing area to obtain grid point cloud data; obtaining a grid value of a grid standard point according to the grid point cloud data; and constructing a target grabbing point screening model, and screening a target grabbing point in the grid point cloud data by combining the target grabbing point screening model, the grid value and the grab bucket parameter. According to the method, the point cloud data of the material terrain distribution are obtained through the radar equipment, and then the point cloud data are subjected to data analysis to obtain the optimal grabbing point, so that the problem of improving the material grabbing efficiency is solved.

Description

Technical Field

[0001] The invention relates to the technical field of material handling, and in particular to a method and system for calculating grabbing points of a grab bucket based on terrain distribution. Background Art

[0002] In the port material handling scenario, mechanical grabs are important loading and unloading tools, and their operating efficiency and accuracy are directly related to the overall production efficiency. However, the traditional grab grab point calculation method is often based on a fixed material density and grab strategy, without considering the terrain distribution of the material in the cabin, or using manual operation to control the grab, resulting in low efficiency. Therefore, how to design a method and system that can improve material grabbing efficiency is a problem that needs to be solved urgently. Summary of the invention

[0003] In view of the shortcomings of existing methods and the needs of practical applications, in order to improve the intelligent recognition ability of material grabbing points and solve the problem of improving material grabbing efficiency. On the one hand, the present invention provides a grabbing point calculation method based on terrain distribution, comprising the following steps: The point cloud data of the materials in the cabin is obtained by radar equipment, and the point cloud data is divided into multiple grabbing areas according to the cabin structure; the point cloud data in the grabbing area is gridded to obtain grid point cloud data; the grid value of the grid standard point is obtained according to the grid point cloud data; a target grab point screening model is constructed, and the target grab point is screened in the grid point cloud data by combining the target grab point screening model, the grid value and the grab bucket parameters. The present invention obtains point cloud data with the terrain distribution of materials in the cabin through radar equipment, and then performs data analysis on the point cloud data to obtain the optimal grab point, thereby solving the problem of improving the efficiency of material grabbing.

[0004] Optionally, the method for calculating grabbing points of grab buckets based on terrain distribution further includes the following steps: Based on the captured area, the point cloud data is smoothed and denoised; the grid point cloud data is interpolated. The present invention is helpful to prevent the interference of rain, snow, fog and dust on the original scattered point cloud data by smoothing, denoising and interpolating the data, improve the reliability and quality of the data, and further improve the accuracy of the present invention.

[0005] Optionally, the smoothing noise reduction process satisfies the following formula: ,in, Indicates The point cloud data is smoothed and denoised. Indicates The point cloud data set of the capture area where the point cloud data is located. Indicates Point cloud data and The similarity weight of the point cloud data, Indicates The coordinate information of the point cloud data.

[0006] Optionally, the similarity weight satisfies the following formula: ,in, Indicates Point cloud data and The similarity weight of the point cloud data, Indicates Point cloud data and The distance of the point cloud data, The standard deviation parameter representing the degree of smoothness control. The present invention utilizes the distance between point cloud data to construct its similarity weight, realizes accurate data relationship control, and further helps to improve data smoothing and noise reduction effects.

[0007] Optionally, the distance between point cloud data satisfies the following formula: ,in, Indicates Point cloud data and The distance of the point cloud data, Indicates The coordinate information of the point cloud data, Indicates The coordinate information of the point cloud data.

[0008] Optionally, the interpolation processing of the grid point cloud data satisfies the following formula: ,in, Represents the standard grid points after interpolation The grid value of represents the number of discrete points near the standard grid point, Indicates The grid value of discrete points, Indicates The horizontal distance from a discrete point to the standard grid point.

[0009] Optionally, the grid value of the grid standard point is obtained according to the grid point cloud data, satisfying the following formula: ,in, Represents standard grid points The grid value of Indicates The inverse distance weight of discrete points, Indicates The height value of the discrete point. The grid value obtained by the present invention through processing the grid data can represent all the information of the grid, which is conducive to improving the calculation efficiency of the present invention.

[0010] Optionally, the inverse distance weight satisfies the following formula: ,in, Indicates The inverse distance weight of discrete points, represents the coordinates of the standard grid points, Indicates The coordinates of the discrete points.

[0011] Optionally, the constructed target grasping point screening model satisfies the following formula: ,in, Represents standard grid points The target value of Indicates the length of the grab when it is fully opened. Indicates the width of the grab when it is fully opened. It represents a conditional function that takes 1 if the condition is met and takes 0 if the condition is not met. Represents standard grid points The grid value of The maximum height of the gripping area, The invention traverses the point cloud data according to the grab parameters to obtain the optimal target grab point, which is conducive to improving the applicability and reliability of the invention.

[0012] In the second aspect, in order to be able to efficiently execute the method for calculating grab grab points based on terrain distribution provided by the present invention, the present invention also provides a system for calculating grab grab points based on terrain distribution, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method for calculating grab grab points based on terrain distribution as described in the first aspect of the present invention. The system for calculating grab grab points based on terrain distribution of the present invention has a compact structure and stable performance, and can stably execute the method for calculating grab grab points based on terrain distribution provided by the present invention, further improving the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flow chart of a method for calculating grabbing points of a grab bucket based on terrain distribution provided by an embodiment of the present invention; Figure 2A framework diagram of a grabbing point calculation system based on terrain distribution provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a grab bucket grab point calculation device based on terrain distribution provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0015] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0016] See also Figure 1 In order to improve the intelligent recognition capability of material grabbing points and solve the problem of improving material grabbing efficiency. The present invention provides a method for calculating grabbing points of grab buckets based on terrain distribution, such as Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Obtain point cloud data of materials in the cabin through radar equipment, and divide the point cloud data into multiple grabbing areas according to the cabin structure.

[0017] In the embodiment, the distribution of materials in the cabin is scanned by a multi-line laser radar, the cabin surface data is extracted, the cabin position is separated, and then the material point cloud data is segmented by density clustering to obtain the point cloud data of the materials in the cabin; further, according to the length and width of the cabin, the point cloud data is equally divided into 4 grabbing areas. In the embodiment, the grabbing area can also be divided according to the actual situation of the goods.

[0018] In other embodiments, the point cloud data may be smoothed and denoised based on the divided capture areas.

[0019] Furthermore, the smoothing noise reduction process satisfies the following formula: ,in, Indicates The point cloud data is smoothed and denoised. Indicates The point cloud data set of the capture area where the point cloud data is located. Indicates Point cloud data and The similarity weight of the point cloud data, Indicates The coordinate information of the point cloud data.

[0020] Furthermore, the similarity weight satisfies the following formula: ,in, Indicates Point cloud data and The similarity weight of the point cloud data, Indicates Point cloud data and The distance of the point cloud data, represents the standard deviation parameter for controlling the degree of smoothness. In this embodiment, ; The distance between point cloud data satisfies the following formula: ,in, Indicates Point cloud data and The distance of the point cloud data, Indicates The coordinate information of the point cloud data, Indicates The coordinate information of the point cloud data.

[0021] S2. Gridding the point cloud data in the captured area to obtain grid point cloud data.

[0022] Specifically, the point cloud data in the captured area is gridded, and the scattered point cloud is converted into a DEM grid format as grid point cloud data.

[0023] Using the Delaunay triangulation algorithm or other triangulation algorithms, point cloud data are connected according to their projection relationship on the horizontal plane to generate an irregular triangulated network. TIN can well represent the ups and downs and detailed features of the terrain and is the basis for generating DEM. Based on TIN, point cloud data is converted into regular raster data, namely DEM grid, through interpolation algorithms such as linear interpolation, bilinear interpolation, cubic convolution interpolation, etc.

[0024] S3. Obtaining grid values ​​of grid standard points according to the grid point cloud data.

[0025] In an embodiment, the step S3 obtains the grid value of the grid standard point according to the grid point cloud data, satisfying the following formula: ,in, Represents standard grid points The grid value of Indicates The inverse distance weight of discrete points, Indicates The height value of the discrete point.

[0026] Furthermore, the inverse distance weight satisfies the following formula: ,in, Indicates The inverse distance weight of discrete points, represents the coordinates of the standard grid points, Indicates The coordinates of the discrete points.

[0027] In some other embodiments, the grid point cloud data may be interpolated to satisfy the following formula: ,in, Represents the standard grid points after interpolation The grid value of represents the number of discrete points near the standard grid point, Indicates The grid value of discrete points, Indicates The horizontal distance from a discrete point to a standard grid point. It can be understood that It can be adjusted according to the actual situation. When the discrete degree is large, a larger value can be taken, and when the discrete degree is small, a smaller value can be taken. In the embodiment You can take 6-10.

[0028] In the embodiment, the discrete points refer to points other than the standard grid points in the point cloud.

[0029] S4. Construct a target grab point screening model, and screen the target grab points in the grid point cloud data by combining the target grab point screening model, the grid value and the grab bucket parameter.

[0030] In the embodiment, the constructed target grab point screening model satisfies the following formula: ,in, Represents standard grid points The target value of Indicates the length of the grab when it is fully opened. Indicates the width of the grab when it is fully opened. It represents a conditional function that takes 1 if the condition is met and takes 0 if the condition is not met. Represents standard grid points The grid value of The maximum height of the gripping area, Indicates the maximum operating grab depth of the grab bucket.

[0031] Specifically, in combination with the target grabbing point screening model, the grid value and the grab bucket parameters, the target grabbing point is screened in the grid point cloud data, and is calculated in sequence according to the grabbing area. According to the maximum height H of the current area, the maximum operating grabbing depth h of the grab bucket, and the length L and width W of the grab bucket when it is fully opened, all grids in the target grabbing area are traversed according to the L*W rectangular area, and the sum of all values ​​of each grid in the L*W area around the grid that meet the height greater than Hh is calculated, which is the target value of the standard grid point, and the standard grid point corresponding to the maximum target value is selected as the target grabbing point in the area. In the embodiment, the grabbing area can be preferentially selected according to the actual situation.

[0032] See also Figure 2 In an embodiment, in order to efficiently execute the method for calculating grabbing points based on terrain distribution provided by the present invention, the present invention also provides a system for calculating grabbing points based on terrain distribution, including: an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory contains program instructions, and the program instructions are used for the steps of the method for calculating grabbing points based on terrain distribution. The system for calculating grabbing points based on terrain distribution of the present invention has a compact structure and stable performance, and can stably execute the method for calculating grabbing points based on terrain distribution of the present invention, further improving the overall applicability and practical application ability of the present invention.

[0033] In an embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. An input device may be used to obtain data information. An output device may be used to output the results obtained by storing program instructions contained in a computer program in a memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0034] In yet another alternative embodiment, see Figure 3 In order to efficiently execute the method for calculating grabbing points of a grab bucket based on terrain distribution provided by the present invention, this embodiment further provides a device for calculating grabbing points of a grab bucket based on terrain distribution, such as Figure 3 As shown, including: The memory 10 is used to store computer programs; the processor 20 is used to execute the computer programs to implement the above-mentioned method for calculating grabbing points of grab buckets based on terrain distribution. The memory 10, the processor 20, the communication interface 31 and the communication bus 32. The memory 10, the processor 20 and the communication interface 31 all communicate with each other through the communication bus 32.

[0035] In an embodiment, the memory 10 is used to store one or more program instructions. The memory 10 may store program instructions for implementing the following functions: The point cloud data of the materials in the cabin are obtained by radar equipment, and the point cloud data are divided into multiple grabbing areas according to the cabin structure; the point cloud data in the grabbing area are gridded to obtain grid point cloud data; the grid value of the grid standard point is obtained according to the grid point cloud data; a target grabbing point screening model is constructed, and the target grabbing point is screened in the grid point cloud data by combining the target grabbing point screening model, the grid value and the grab bucket parameters.

[0036] In one possible implementation, the memory 10 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory 10 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0037] The processor 20 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic device, and the processor 20 may be a microprocessor or any conventional processor, etc. The processor 20 may call a program stored in the memory 10. The communication interface 31 may be an interface of a communication module, and is used to connect with other devices or systems.

[0038] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the grabbing point calculation device based on terrain distribution in this embodiment. In actual applications, the grabbing point calculation device based on terrain distribution may include: Figure 3 More or fewer components than shown, or combinations of certain components.

[0039] The embodiment further provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for calculating grabbing points of a grab bucket based on terrain distribution are implemented.

[0040] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0041] In summary, the present invention obtains point cloud data of material terrain distribution through radar equipment, and then performs data analysis on the point cloud data to obtain the optimal grasping point, thereby solving the problem of improving material grasping efficiency.

[0042] Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope recorded in the present invention.

Claims

1. A method for calculating grabbing points of grab buckets based on terrain distribution, characterized in that: The method for calculating grabbing points of grab buckets based on terrain distribution comprises the following steps: Obtaining point cloud data of materials in the cabin through radar equipment, and dividing the point cloud data into multiple grabbing areas according to the cabin structure; Performing gridding processing on the point cloud data in the grabbing area to obtain grid point cloud data; According to the grid point cloud data, a grid value of a grid standard point is obtained; Constructing a target grab point screening model, combining the target grab point screening model, the grid value and the grab bucket parameter, and screening the target grab point in the grid point cloud data; The constructed target grab point screening model satisfies the following formula: ,in, Represents standard grid points The target value of Indicates the length of the grab when it is fully opened. Indicates the width of the grab when it is fully opened. It represents a conditional function that takes 1 if the condition is met and takes 0 if the condition is not met. Represents standard grid points The grid value of The maximum height of the gripping area, Indicates the maximum operating grab depth of the grab bucket; In combination with the target grabbing point screening model, the grid value and the grab bucket parameters, the target grabbing points are screened in the grid point cloud data, including: calculating in sequence according to the grabbing area, according to the maximum height H of the current area, the maximum operating grabbing depth h of the grab bucket, and the length L and width W of the grab bucket when it is fully opened, sliding through all grids in the target grabbing area according to the L*W rectangular area, calculating each grid in the L*W area around the grid, and adding all values ​​that meet the height greater than Hh, which is the target value of the standard grid point, and selecting the standard grid point corresponding to the maximum target value as the target grabbing point in the area.

2. The method for calculating grabbing points of grab buckets based on terrain distribution according to claim 1, characterized in that: The following steps are also included: Based on the captured area, smoothing and denoising the point cloud data; An interpolation process is performed on the grid point cloud data.

3. The method for calculating grabbing points of grab buckets based on terrain distribution according to claim 2, characterized in that: The smoothing noise reduction process satisfies the following formula: ,in, Indicates The point cloud data is smoothed and denoised. Indicates The point cloud data set of the capture area where the point cloud data is located. Indicates Point cloud data and The similarity weight of the point cloud data, Indicates The coordinate information of the point cloud data.

4. The method for calculating grabbing points of grab buckets based on terrain distribution according to claim 3 is characterized in that: The similarity weight satisfies the following formula: ,in, Indicates Point cloud data and The similarity weight of the point cloud data, Indicates Point cloud data and The distance of the point cloud data, Represents the standard deviation parameter that controls the degree of smoothing.

5. The method for calculating grabbing points of grab buckets based on terrain distribution according to claim 4 is characterized in that: The distance between point cloud data satisfies the following formula: ,in, Indicates Point cloud data and The distance of the point cloud data, Indicates The coordinate information of the point cloud data, Indicates The coordinate information of the point cloud data.

6. The method for calculating grabbing points of grab buckets based on terrain distribution according to claim 2, characterized in that: The interpolation processing of the grid point cloud data satisfies the following formula: ,in, Represents the standard grid points after interpolation The grid value of represents the number of discrete points near the standard grid point, Indicates The grid value of discrete points, Indicates The horizontal distance from a discrete point to the standard grid point.

7. The method for calculating grabbing points of grab buckets based on terrain distribution according to claim 1, characterized in that: The grid value of the grid standard point is obtained according to the grid point cloud data, satisfying the following formula: ,in, Represents standard grid points The grid value of Indicates The inverse distance weight of discrete points, Indicates The height value of the discrete point.

8. The method for calculating grabbing points of grab buckets based on terrain distribution according to claim 7, characterized in that: The inverse distance weight satisfies the following formula: ,in, Indicates The inverse distance weight of discrete points, represents the coordinates of the standard grid points, Indicates The coordinates of the discrete points.

9. A grabbing point calculation system based on terrain distribution, characterized in that: The grab grab point calculation system based on terrain distribution includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the grab grab point calculation method based on terrain distribution according to any one of claims 1-8.

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