CN117917313A

CN117917313A - Robot polishing track generation method, system, device and medium

Info

Publication number: CN117917313A
Application number: CN202311801997.6A
Authority: CN
Inventors: 张琬琦; 程刚; 王磊
Original assignee: Hangzhou Lingxi Robot Intelligent Technology Co ltd
Current assignee: Hangzhou Lingxi Robot Intelligent Technology Co ltd
Priority date: 2023-12-26
Filing date: 2023-12-26
Publication date: 2024-04-23

Abstract

The application discloses a robot polishing track generation method, a system, a device and a medium, comprising the following steps: determining a polishing area according to target point cloud data acquired in advance; converting the target point cloud data of the polishing area into a target coordinate system to obtain three-dimensional target point cloud data; constructing a grid map based on the three-dimensional target point cloud data, dividing the grid map to obtain a plurality of sub-areas, determining the path of each sub-area based on the grid map, and obtaining the shortest grinding path according to the connection sequence of the sub-areas; and converting the shortest grinding path into a world coordinate system to obtain a target grinding path, and determining a target grinding track according to the target grinding path. The application can reduce the complexity of data conversion and preprocessing, and can flexibly adapt to various shapes and geometric changes. A smooth and accurate polishing processing track is created for the robot, and efficient automatic polishing is realized.

Description

Robot polishing track generation method, system, device and medium

Technical Field

The application relates to the technical field of robot machining, in particular to a method, a system, a device and a medium for generating a robot polishing track.

Background

Currently, industrial robots are widely used for polishing the surfaces of products in the industries of automobiles, furniture, bathrooms and the like, so as to replace human beings to execute most of tedious and repeated tasks. The quality of the generated polishing track has decisive effects on the quality of products, the processing period, the material waste rate and the like. However, the robotic automated sanding trajectory planning problem has been a bottleneck in the above-described industry sanding process.

Traditional manual polishing methods are time-consuming, labor-consuming, error-prone and low in productivity, and damage caused by dust and noise cannot be avoided. Generating a grinding path of a manufacturing process using a robot program requires additional programming skills and expertise. Even if the robot operator has specialized programming skills, the grinding path generation still takes a lot of time to teach. In addition, problems may arise in that Computer Aided Design (CAD) files are not available or are inaccurate in generating the track. Therefore, in order to reduce the cost and improve the product quality, it is necessary to develop an automatic robot polishing track planning method to replace the manual teaching generation track planning method.

Disclosure of Invention

The application aims to provide a robot polishing track generation method, a system, a device and a medium, which at least solve the problems that how to realize automatic planning of a robot polishing track in the related technology, and the manual polishing method is time-consuming and labor-consuming, easy to make mistakes and low in productivity.

The first aspect of the application provides a robot polishing track generation method, which comprises the following steps:

determining a polishing area according to target point cloud data acquired in advance;

Converting the target point cloud data of the polishing area into a target coordinate system to obtain three-dimensional target point cloud data, wherein the target coordinate system takes a central point of the polishing area as an origin;

Constructing a grid map based on the three-dimensional target point cloud data, dividing the grid map to obtain a plurality of sub-areas, determining the path of each sub-area based on the grid map, and obtaining the shortest grinding path according to the connection sequence of the sub-areas;

And converting the shortest grinding path into a world coordinate system to obtain a target grinding path, and determining a target grinding track according to the target grinding path.

In one embodiment, the acquiring of the target point cloud data includes:

Acquiring original point cloud data, and converting the original point cloud data into a world coordinate system to obtain the original point cloud data in the world coordinate system;

determining a region of interest according to the initial point cloud data;

And determining the target point cloud data through outlier filtering according to the region of interest.

In one embodiment, the determining the polishing area according to the pre-acquired target point cloud data includes:

And determining a polishing area by adopting a polygonal frame selection method according to the pre-acquired target point cloud data.

In one embodiment, the constructing a grid map based on the three-dimensional target point cloud data, dividing the grid map to obtain a plurality of sub-areas, determining a path of each sub-area based on the grid map, and obtaining a shortest grinding path according to a connection sequence of different sub-areas, including:

constructing a grid map based on the three-dimensional target point cloud data, and dividing the grid map to obtain a plurality of sub-areas;

Determining the path of each subarea by adopting a cattle-tillage type full-coverage path planning method based on the subareas and a preset polishing radius;

determining the connection sequence of different subareas by adopting a travel provider planning algorithm according to the path;

And determining the shortest grinding path according to the path and the connection sequence.

In one embodiment, the constructing a grid map based on the three-dimensional target point cloud data, dividing the grid map to obtain a plurality of sub-areas includes:

according to a preset grid size, carrying out grid division on the range of the three-dimensional target point cloud data to obtain the grid map;

Determining intra-column connectivity between adjacent grids according to each column in the grid map;

determining adjacent column connectivity between adjacent columns according to the intra-column connectivity;

and dividing the grid map to obtain a plurality of sub-areas according to the intra-column connectivity and the adjacent column connectivity.

In one embodiment, the dividing the grid map according to the intra-column connectivity and the adjacent-column connectivity to obtain a plurality of sub-areas includes:

if the intra-column connectivity or the adjacent column connectivity is connected, merging the current grid or the column line segment;

if the intra-column connectivity or the adjacent column connectivity is not connected, decomposing the current grid or the column line segment;

And obtaining a plurality of sub-areas until all the grid maps are traversed.

In one embodiment, the determining the target grinding track according to the target grinding path includes:

And determining the target polishing track through a polynomial spline interpolation algorithm according to the target polishing path.

A second aspect of the present application provides a robotic grinding track generation system, the system comprising:

The polishing area determining module is used for determining a polishing area according to the cloud data of the target point acquired in advance;

The coordinate system conversion module is used for converting the target point cloud data of the polishing area into a target coordinate system to obtain three-dimensional target point cloud data, and the target coordinate system takes the central point of the polishing area as an origin;

The shortest grinding path determining module is used for constructing a grid map based on the three-dimensional target point cloud data, dividing the grid map to obtain a plurality of sub-areas, determining the path of each sub-area based on the grid map, and obtaining the shortest grinding path according to the connection sequence of different sub-areas;

And the target grinding track determining module is used for converting the shortest grinding path into a world coordinate system to obtain a target grinding path, and determining a target grinding track according to the target grinding path.

The third aspect of the present application provides a robot polishing track generation device, including a memory and one or more processors, where the memory stores executable codes, and the one or more processors are configured to implement the above-mentioned robot polishing track generation method when executing the executable codes.

A fourth aspect of the present application provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the robot grinding track generation method described above.

The method, the system, the device and the medium for generating the robot polishing track have at least the following technical effects.

And determining a polishing area by acquiring the cloud data of the target point, converting the cloud data of the target point in the area into a proper coordinate system, and constructing a grid map. Then, the regions are decomposed, and the path of each sub-region, and the connection order between the different sub-regions are determined based on the obtained sub-regions. By such a process, the shortest grinding path can be obtained. And then, carrying out coordinate system conversion on the shortest grinding path to finally obtain the target grinding track. The application can reduce the complexity of data conversion and preprocessing, and can flexibly adapt to various shapes and geometric changes. By the aid of the method, smooth and accurate polishing processing tracks can be created for the robot, and efficient automatic polishing is achieved.

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

fig. 1 is a schematic flow chart of a method for generating a polishing track of a robot according to an embodiment of the present application;

Fig. 2 is a schematic flow chart of acquiring cloud data of a target point according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of obtaining a shortest grinding path according to an embodiment of the present application;

FIG. 4 is a schematic flow chart of decomposing a plurality of sub-regions according to an embodiment of the present application;

FIG. 5 is a block diagram of a robot grinding track generation system provided by an embodiment of the present application;

fig. 6 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application.

Detailed Description

The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses. 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 is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.

Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in connection with the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.

The embodiment of the application provides a method, a system, a device and a medium for generating a robot polishing track.

In a first aspect, an embodiment of the present application provides a method for generating a polishing track of a robot, and fig. 1 is a schematic flow chart of the method for generating a polishing track of a robot, as shown in fig. 1, where the method includes the following steps:

And step S101, determining a polishing area according to the cloud data of the target point acquired in advance.

In one embodiment, the polishing area is determined by a polygon framing method according to pre-acquired target point cloud data.

Specifically, the point cloud data in the corresponding frame is cut based on PCL to obtain the polishing area. The realization of the clipping algorithm is as follows: 1. calculating the relation between a certain point and the edge of the polygon, and judging whether the relation is included or not; 2. and (3) projecting the point cloud and the polygon on a plane, filtering out indexes I= {1,2, …, n } of points in the polygon, wherein n is the number of the points, and according to the obtained index I, obtaining the point coordinates of the corresponding indexes from the original point cloud to obtain a point cloud data set omega _n of the region to be polished. And the area to be polished can be accurately determined, so that the situation of mistaken polishing or missed polishing is avoided. Meanwhile, the working strength is effectively reduced, so that the working efficiency is improved. The operation is simple and easy, and the polishing task can be completed rapidly.

Fig. 2 is a schematic flow chart of acquiring cloud data of a target point according to an embodiment of the present application, as shown in fig. 2, based on the flow chart shown in fig. 1, including the following steps:

Step S201, acquiring original point cloud data, and converting the original point cloud data into a world coordinate system to obtain the original point cloud data in the world coordinate system.

Importing a pcd format file of a workpiece to be polished into a point cloud reading program based on a PCL library, and expressing the obtained original point cloud data as: m= { P _i(x_i,y_i,z_i), i=1, 2, …, n }, where P _i represents the i-th point of the workpiece to be polished and n is the number of points. And converting the original point cloud data from the camera coordinate system to the world coordinate system to obtain the original point cloud data under the world coordinate system.

Step S202, a region of interest is determined according to the initial point cloud data.

The region of interest is determined using ROI cropping, which generally refers to selecting a particular region or object in an image and then cropping it from the original image for analysis, identification, or other manipulation in a subsequent processing step. And establishing a minimum bounding box lambda { [ x _min,x_max]×[y_min,y_max]×[z_min,z_max ] } with each side parallel to the coordinate axis direction and capable of containing all initial point cloud data, cutting the initial point cloud data by utilizing lambda, and only keeping the point cloud data positioned in the minimum bounding box to obtain the region of interest.

And step S203, determining target point cloud data through outlier filtering according to the region of interest.

For point cloud data in a region of interest obtained by clipping to have noise, outliers or outliers in the data set need to be detected and removed in order to more accurately analyze the main trends and features of the data. Specifically, for point cloud data Ω s in a region of interest obtained by clipping, a StatisticalOutlierRemoval outlier filter in a PCL library is used to process, a statistical analysis is performed on the neighborhood of each point, the average distance from each point to other points in the neighborhood is calculated, and it is assumed that these distances conform to gaussian distribution. And judging whether each point accords with the Gaussian distribution according to the distance statistical information of the points in the neighborhood, and marking the neighborhood distance of a certain point as an outlier if the neighborhood distance of the certain point does not accord with the Gaussian distribution under the global distance. For the point cloud subset Ω _k marked as an outlier, it is removed from the total point cloud data Ω _s, resulting in an outlier filtered target point cloud data set Ω, where Ω=Ω _s-Ω_k.

With continued reference to fig. 1, step S102 is performed after step S101, as follows.

And S102, converting the target point cloud data of the grinding area into a target coordinate system to obtain three-dimensional target point cloud data, wherein the target coordinate system takes the central point of the grinding area as an origin.

Specifically, a PCA principal component analysis method is adopted to convert cloud data of a target point of a grinding area into a target coordinate system. The PCA principal component analysis method is a method for performing dimension reduction and feature extraction on point cloud data in a three-dimensional space, and aims to find a main direction in the data. Calculating a center point P _c (x, y, z) of the point cloud data according to the target point cloud data of the polishing area, and calculating a major axis, a center axis and a minor axis three-direction characteristic value eta ₁,η₂,η₃ and a characteristic vector corresponding to the characteristic value by decomposing the semi-positive definite covariance matrixFurther obtain feature vector matrix/>Wherein/>The purpose of this step is to obtain byAnd P _c (x, y, z) calculate a transformation matrix T of the original coordinate system into a target coordinate system composed of principal components, wherein/>And projecting the target point cloud data of the polishing area to a target coordinate system, namely, obtaining three-dimensional target point cloud data by remaining the target point cloud data of the polishing area with a transformation matrix T.

And step S103, constructing a grid map based on the three-dimensional target point cloud data, dividing the grid map to obtain a plurality of sub-areas, determining the path of each sub-area based on the grid map, and obtaining the shortest grinding path according to the connection sequence of different sub-areas.

Fig. 3 is a schematic flow chart of obtaining a shortest grinding path according to an embodiment of the present application, as shown in fig. 3, on the basis of the flow chart shown in fig. 1, step S103 includes the following steps:

Step S301, a grid map is constructed based on three-dimensional target point cloud data, and the grid map is divided to obtain a plurality of sub-areas.

Fig. 4 is a schematic flow chart of decomposing a plurality of sub-areas according to an embodiment of the present application, as shown in fig. 4, based on the flow chart shown in fig. 3, step S301 includes the following steps:

and S401, carrying out grid division on the range of the cloud data of the three-dimensional target point according to the preset grid size to obtain a grid map.

When generating the grid map, all three-dimensional target point cloud data needs to be traversed first, and minimum and maximum coordinate values are found to determine the boundary and the range of the grid map. Next, the grid size is preset, and this decision will affect the resolution of the map, i.e. the size of the grid cells. Smaller grid sizes may provide finer detail, but may also result in larger generated maps. Therefore, application scenarios and computing resources need to be considered when presetting the grid size.

For each point, its coordinates are projected into the corresponding grid cell by dividing it by the grid size and rounding it in order to map the three-dimensional target point cloud data to a specific location on the grid map. Subsequently, for each grid cell, an average or other statistical characteristic of the grid may be calculated from the characteristics (e.g., height, color, etc.) of the points contained therein. In this way, each grid may be assigned a respective value to distinguish between free areas and obstacle areas in the map. Finally, a grid map can be generated through the series of steps.

Step S402, determining the intra-column connectivity between adjacent grids according to each column in the grid map.

Step S403, determining adjacent column connectivity between adjacent columns according to the intra-column connectivity.

And step S404, dividing the grid map to obtain a plurality of sub-areas according to the intra-column connectivity and the adjacent column connectivity.

In one embodiment, if intra-column connectivity or adjacent column connectivity is connected, the current grid or column line segments are merged.

If the column connectivity or the adjacent column connectivity is not connected, the current grid or column line segment is decomposed.

Until all the grid maps are traversed, a plurality of sub-areas are obtained.

In steps S402 to S404, specifically, in a given grid map, 0 is used for each grid to represent an obstacle, and 1 represents a communication area. The connected regions are divided into different column line segments. For example, for data [0,0,0,0,1,1,1,0,1,0,0,0,1,1,0,1,1,0], the division results in 4 column line segments: (4,7), (8,9), (12,14), (15,17). This means that the columns in the first column line segment go from index 4 to index 7, the second column line segment goes from index 8 to index 9, and so on.

Next, connectivity between adjacent columns needs to be determined. For each pair of adjacent column line segments, it is checked whether each column line segment of the left column is adjacent to each column line segment of the right column. If the end index of the column line segment of the left column and the start index of the column line segment of the right column differ by 1, they are adjacent and the corresponding matrix element is set to 1; otherwise, they are not adjacent, and the matrix element is 0. Finally, the following connectivity matrix is obtained: [1, 0], [0, 1], the element (i, j) in the matrix indicates whether or not communication is established between the i-th column segment of the left column and the j-th column segment of the right column. For example, (0, 1) indicates that the column segment of the first column is adjacent to and communicates with the column segment of the second column, so that the corresponding matrix element is 1; (1, 2) represents that the column segment of the second column is adjacent to and communicates with the column segment of the third column, and thus the corresponding matrix element is 1.

And determining the intra-column connectivity of each column and the adjacent column connectivity between adjacent columns, triggering an IN event when the connectivity is increased, and decomposing two corresponding grids or two column line segments. When connectivity is reduced, an OUT event is triggered, merging the corresponding two grids or two column line segments. After the current column is traversed, the next column is continued, and the steps are repeated until all columns are traversed, and the grid map is decomposed into a plurality of sub-areas.

It should be noted that, the division of the grid map may also use the brute cell decomposition method (Boustrophedon) or the Morse method (Morse).

Since the decomposed pattern is a very simple geometry, there is only the direction of each side of the geometry in the choice of direction. The starting point is also only the vertex of each sub-region. The coverage direction with the least number of turns is the optimal solution for a single subarea, but the wiring distance between the subareas also needs to be considered for the global. From this point of view, the quality of the partitioning scheme can be measured using the following formula:

Where m is the total number of sub-regions resolved, y _max,,i is the maximum vertex of the y-coordinate of the i-th resolved region, y _min,i is the minimum vertex of the y-coordinate of the i-th resolved region, and ω is the sum of the minimum directions of the resolved regions.

With continued reference to fig. 3, step S302 is performed after step S301, as follows.

Step S302, determining the path of each sub-area by adopting a cattle-tillage type full-coverage path planning method based on the sub-areas and the preset polishing radius.

A grid of starting positions is selected based on the sub-areas that have been decomposed and marked as covered. From the start position, adjacent grids that are not marked as covered are accessed sequentially in a predefined order (e.g., left to right, top to bottom). For each neighboring grid, it is determined whether it has been marked as covered. If an adjacent grid is not marked as covered, it is marked as covered. If a neighboring grid has been marked as covered, then access continues to the next neighboring grid. Until all grids are covered.

It should be noted that if the width of a sub-region is smaller than the preset grinding radius R, the sub-region cannot generate a path. In addition, spiral coverage or Z-coverage methods can be used to determine the path of each sub-region.

Step S303, determining the connection sequence of different subareas by adopting a travel provider planning algorithm according to the path.

The embodiment of the application converts the connection sequence of determining different subareas into solving a Traveller (TSP), and aims to find a path so that the traveller accesses each city once along the path and finally returns to the starting city, and the total length of the path is minimum. In other words, in the embodiment of the present application, each sub-area is regarded as a target point, and the distance between each sub-area is calculated as a path according to the generated path. Different calculation methods, such as Euclidean distance, manhattan distance, etc., can be selected according to practical situations. Find the shortest path connecting all the target points. Existing TSP solving algorithms (e.g., greedy algorithm, dynamic programming, etc.) are used to solve the shortest path. And finally, determining the connection sequence of each sub-area according to the shortest path obtained by solving.

It should be noted that determining the connection order of the different sub-areas further includes using a neural network algorithm or a heuristic algorithm.

And step S304, determining the shortest grinding path according to the path and the connection sequence.

And connecting the discrete point data on the paths according to the connection sequence to form a broken line, so as to obtain the shortest grinding path.

With continued reference to fig. 1, step S104 is performed after step S103, as follows.

And step S104, converting the shortest grinding path into a world coordinate system to obtain a target grinding path, and determining a target grinding track according to the target grinding path.

In one embodiment, the target grinding trajectory is determined by a polynomial spline interpolation algorithm based on the target grinding path.

And converting the shortest grinding path into a world coordinate system to obtain a target grinding path, and fitting the target grinding path by using a smooth curve fitting algorithm such as a multi-pattern interpolation algorithm, an S-curve or Bezier curve track planning to obtain a smooth curve. And uniformly sampling on the smooth curve obtained by fitting to obtain the final target polishing track. Preferably, the speed and acceleration information of each point on the target polishing track is obtained through three times of B spline interpolation, so that accurate motion control data are provided for the robot to execute polishing tasks.

In summary, according to the method for generating the robot polishing track provided by the embodiment of the application, the polishing area is determined by acquiring the target point cloud data, the point cloud data in the area is converted into a proper coordinate system, and the grid map is constructed. Then, the regions are decomposed, and the path of each sub-region, and the connection order between the different sub-regions are determined based on the obtained sub-regions. By such a process, the shortest grinding path can be obtained. And then, carrying out coordinate system conversion on the shortest grinding path to finally obtain the target grinding track. The application can reduce the complexity of data conversion and preprocessing, and can flexibly adapt to various shapes and geometric changes. By the aid of the method, smooth and accurate polishing processing tracks can be created for the robot, and efficient automatic polishing is achieved.

It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.

In a second aspect, an embodiment of the present application provides a system for generating a polishing track of a robot, which is used to implement the foregoing embodiments and preferred embodiments, and will not be described again. As used below, the terms "module," "unit," "sub-unit," and the like may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

Fig. 5 is a block diagram of a robot polishing track generation system according to an embodiment of the present application, as shown in fig. 5, the system includes:

the polishing area determining module 501 is configured to determine a polishing area according to pre-acquired cloud data of a target point.

The coordinate system conversion module 502 is configured to convert the target point cloud data of the polishing area into a target coordinate system, to obtain three-dimensional target point cloud data, where the target coordinate system uses a center point of the polishing area as an origin.

The shortest grinding path determining module 503 is configured to construct a grid map based on the three-dimensional target point cloud data, divide the grid map to obtain a plurality of sub-areas, determine a path of each sub-area based on the grid map, and connect the different sub-areas in sequence, so as to obtain the shortest grinding path point cloud data.

The target grinding track determining module 504 is configured to convert the shortest grinding path point cloud data into a world coordinate system, obtain target grinding path point cloud data, and determine a target grinding track according to the target grinding path point cloud data.

The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.

In a third aspect, an embodiment of the present application provides a robot polishing track generating device, including a memory and one or more processors, where the memory stores executable codes, and the one or more processors are configured to implement the steps in any one of the method embodiments described above when executing the executable codes.

Optionally, the robot polishing track generating device may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.

It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and this embodiment is not repeated herein.

In a fourth aspect, in combination with the method for generating a polishing track by a robot in the foregoing embodiment, an embodiment of the present application may be implemented by providing a storage medium. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements any of the robot grinding track generation methods of the above embodiments.

In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a robot grinding track generation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

In one embodiment, fig. 6 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, as shown in fig. 6, and an electronic device, which may be a server, and an internal structure diagram of which may be shown in fig. 6, is provided. The electronic device includes a processor, a network interface, an internal memory, and a non-volatile memory connected by an internal bus, where the non-volatile memory stores an operating system, computer programs, and a database. The processor is used for providing computing and control capability, the network interface is used for communicating with an external terminal through network connection, the internal memory is used for providing environment for the operation of an operating system and a computer program, the computer program is executed by the processor to realize a robot polishing track generation method, and the database is used for storing data.

It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the electronic device to which the present inventive arrangements are applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

It should be understood by those skilled in the art that the technical features of the above-described embodiments may be combined in any manner, and for brevity, all of the possible combinations of the technical features of the above-described embodiments are not described, however, they should be considered as being within the scope of the description provided herein, as long as there is no contradiction between the combinations of the technical features.

The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for generating a robot grinding track, the method comprising:

2. The method for generating a grinding track of a robot according to claim 1, wherein the acquiring of the target point cloud data includes:

determining a region of interest according to the initial point cloud data;

3. The method for generating a grinding track by a robot according to claim 1, wherein the determining a grinding area according to the cloud data of the target point acquired in advance includes:

4. The method for generating a grinding track of a robot according to claim 1, wherein the constructing a grid map based on the three-dimensional target point cloud data, dividing the grid map to obtain a plurality of sub-areas, determining a path of each sub-area based on the grid map, and obtaining a shortest grinding path according to a connection sequence of the sub-areas, comprises:

5. The method for generating a grinding track of a robot according to claim 4, wherein the constructing a grid map based on the three-dimensional target point cloud data, dividing the grid map into a plurality of sub-areas, includes:

6. The method for generating a grinding track of a robot according to claim 5, wherein the dividing the grid map into a plurality of sub-areas according to the intra-column connectivity and the adjacent-column connectivity comprises:

And obtaining a plurality of sub-areas until all the grid maps are traversed.

7. The method of claim 1, wherein the determining the target grinding track from the target grinding path point cloud data comprises:

8. A robotic grinding trajectory generation system, the system comprising:

9. A robotic grinding track generation device comprising a memory and one or more processors, the memory having executable code stored therein, the one or more processors, when executing the executable code, for implementing the robotic grinding track generation method of any one of claims 1-7.

10. A computer-readable storage medium, having stored thereon a program which, when executed by a processor, implements the robot grinding track generation method of any one of claims 1-7.