CN114570551A - Method and system for planning multi-color spraying path - Google Patents

Method and system for planning multi-color spraying path Download PDF

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Publication number
CN114570551A
CN114570551A CN202210254129.XA CN202210254129A CN114570551A CN 114570551 A CN114570551 A CN 114570551A CN 202210254129 A CN202210254129 A CN 202210254129A CN 114570551 A CN114570551 A CN 114570551A
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color
spraying
spray
sequence
block
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CN114570551B (en
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马昕
单益飞
李贻斌
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Faoyiwei Suzhou Robot System Co ltd
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Shandong University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
    • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
    • B05B12/00—Arrangements for controlling delivery; Arrangements for controlling the spray area
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
    • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
    • B05B12/00—Arrangements for controlling delivery; Arrangements for controlling the spray area
    • B05B12/02—Arrangements for controlling delivery; Arrangements for controlling the spray area for controlling time, or sequence, of delivery
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
    • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
    • B05B12/00—Arrangements for controlling delivery; Arrangements for controlling the spray area
    • B05B12/14—Arrangements for controlling delivery; Arrangements for controlling the spray area for supplying a selected one of a plurality of liquids or other fluent materials or several in selected proportions to a spray apparatus, e.g. to a single spray outlet
    • B05B12/149—Arrangements for controlling delivery; Arrangements for controlling the spray area for supplying a selected one of a plurality of liquids or other fluent materials or several in selected proportions to a spray apparatus, e.g. to a single spray outlet characterised by colour change manifolds or valves therefor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
    • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
    • B05B13/00—Machines or plants for applying liquids or other fluent materials to surfaces of objects or other work by spraying, not covered by groups B05B1/00 - B05B11/00
    • B05B13/02—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work
    • B05B13/04—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work the spray heads being moved during spraying operation
    • B05B13/0431—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work the spray heads being moved during spraying operation with spray heads moved by robots or articulated arms, e.g. for applying liquid or other fluent material to 3D-surfaces
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

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  • Robotics (AREA)
  • Image Generation (AREA)
  • Spray Control Apparatus (AREA)

Abstract

The invention belongs to the field of spray path planning, and provides a method and a system for planning a multi-color spray path. The method comprises the steps of identifying a generated pattern of a workpiece to be sprayed, and acquiring color block information of the pattern; constructing a cluster traveling salesman problem model according to the color block information, and determining the spraying sequence among the color clusters and the color block spraying sequence in the color clusters; determining a spraying path in color blocks by taking the minimum moving distance of a spray gun between the color blocks as a target according to the spraying sequence among the color clusters and the color block spraying sequence in the color clusters; and obtaining the spraying path of the spray gun based on the spraying sequence among the fixed color clusters, the color block spraying sequence in the color clusters and the spraying path in the color blocks.

Description

Method and system for planning multi-color spraying path
Technical Field
The invention belongs to the field of spray path planning, and particularly relates to a method and a system for planning a multi-color spray path.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The spraying robot replaces workers to perform spraying operation, so that the spraying quality can be improved, the occupational injury of the workers is reduced, the coating is saved, and the spraying robot is more environment-friendly. The spray path planning is a key link of the spray robot for spray operation. In spray path planning, the current technical means of monochromatic spray path planning are relatively mature, and the method is successfully applied to the spray operation fields of vehicles, furniture, airplanes and the like, but the personalized pattern multicolor spray path planning is still in the starting stage, and the multicolor spray path planning problem is widely concerned by the academic and industrial fields. Therefore, there is currently a lack of specific implementation for the multi-color spray path planning problem. The current situation of how to plan the multi-color spraying path, and how to plan the multi-color spraying path are not left to the right and how to provide theoretical guidance.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a method and a system for planning a multicolor spray coating path, which decompose the Problem of planning the multicolor spray coating path into a Problem of upper-layer Cluster Traveler (CTSP) and a Problem of lower-layer route scheme selection, and can realize the effect of full-coverage multicolor spray coating by a hierarchical optimization method.
In order to achieve the purpose, the invention adopts the following technical scheme:
a first aspect of the present invention provides a method of multi-color spray path planning.
A method of multi-color spray path planning, comprising:
identifying the generated pattern of the workpiece to be sprayed to acquire color block information of the pattern;
constructing a cluster traveling salesman problem model according to the color block information, and determining the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
determining a spraying path in color blocks by taking the minimum moving distance of a spray gun between the color blocks as a target according to the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
and obtaining the spraying path of the spray gun based on the spraying sequence among the fixed color clusters, the spraying sequence of the color blocks in the color clusters and the spraying path in the color blocks.
A second aspect of the invention provides a system for multi-color spray path planning.
A system for multi-color spray path planning, comprising:
an acquisition module configured to: identifying the generated pattern of the workpiece to be sprayed to acquire color block information of the pattern;
an order determination module configured to: constructing a cluster traveling salesman problem model according to the color block information, and determining the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
an intra-block path planning module configured to: determining a spraying path in color blocks by taking the minimum moving distance of a spray gun between the color blocks as a target according to the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
an output module configured to: and obtaining the spraying path of the spray gun based on the spraying sequence among the fixed color clusters, the color block spraying sequence in the color clusters and the spraying path in the color blocks.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of multi-color spray path planning as described above in relation to the first aspect.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps in the method of multi-color spray path planning as described in the first aspect above.
Compared with the prior art, the invention has the beneficial effects that:
the invention solves the problem of multi-color spray path planning by decomposing the problem of multi-color spray path planning into the problem of upper-layer cluster traveling salesmen and the problem of lower-layer route scheme selection and adopting a hierarchical optimization method, thereby realizing the effect of full-coverage multi-color spray and improving the accuracy of multi-color spray path planning.
The invention solves the problem that the multi-color spray path planning has no operation and theoretical guidance by decomposing the full-coverage multi-color spray path planning task.
The invention has the advantages of fast and efficient path planning, reduced labor intensity of operators, and improved working efficiency and spraying quality.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart illustrating a method of multi-color spray path planning in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a cluster traveler problem in a multi-color spray path planning according to an embodiment of the present invention;
FIG. 3 shows 4 cases of routes when the number of turns is even according to an embodiment of the present invention;
FIG. 4 shows 4 cases of routes when the number of turns is odd according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of chromosome definition according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a same color sequential interleaving operation according to an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a homochromatic exchange mutation operation according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a pattern of a color block for spraying according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a feasible patch spray sequence scheme derived by Genetic Algorithm (GA) according to an embodiment of the present invention;
FIG. 10 is a graph illustrating the convergence of a genetic algorithm according to an embodiment of the present invention;
fig. 11 is a graph illustrating a taboo Search algorithm (TS) convergence curve according to an embodiment of the present invention;
fig. 12 is a schematic diagram of a pattern of a spraying patch with a route case selection according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a segment, or a portion of code, which may comprise one or more executable instructions for implementing the logical function specified in the respective embodiment. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Example one
As shown in fig. 1, the present embodiment provides a method for planning a multi-color spray path, and the present embodiment is illustrated by applying the method to a server, it is to be understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and implemented by interaction between the terminal and the server. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network server, cloud communication, middleware service, a domain name service, a security service CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. In this embodiment, the method includes the steps of:
identifying the generated pattern of the workpiece to be sprayed to acquire color block information of the pattern;
constructing a cluster traveling salesman problem model according to the color block information, and determining the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
determining a spraying path in color blocks by taking the minimum moving distance of a spray gun between the color blocks as a target according to the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
and obtaining the spraying path of the spray gun based on the spraying sequence among the fixed color clusters, the color block spraying sequence in the color clusters and the spraying path in the color blocks.
The technical scheme of the embodiment can be realized according to the following contents:
1. multi-color spray path planning problem
In order to finish the spraying of the personalized patterns on the surface of the vehicle as soon as possible, the spraying sequence of the color blocks to be sprayed and the entering positions and the exiting positions of the color blocks to be sprayed are required to be sequenced, so that the moving path length of the spray gun is reduced, and the time required for finishing the spraying is shortened. The multi-color spray path planning problem can be broken down into two smaller sub-problems: there are an upper (global) sub-problem and a lower (local) sub-problem. An upper (global) sub-problem, which is described herein as a cluster traveler problem, with the purpose of defining the spray order between color clusters and the spray order of color patches within color clusters; one lower (partial) sub-problem is with respect to routing case selection, with the purpose of finding the locations where the start of spraying enters a patch to be sprayed and the end of spraying leaves that patch. And (4) finding the position of starting spraying in the spraying color block by using a zigzag path planning method in the spraying color block, and determining the position of finishing spraying in the spraying color block.
1.1 CTSP
The upper sub-problem is the cluster traveler problem. In multi-color spray path planning, the cluster traveler problem can be defined in the form as follows. A set of spraying color blocks V ═ V is on an undirected weighted complete map G ═ (V, E)0,v1,v2,...,vn}, set of edges
Figure BDA0003548145970000061
The set of color blocks V is divided into disjoint color clusters C according to the colors of the color blocks themselves0,C1,...,Cm. In the actual spraying problem, we set the starting position v of the spray gun0Virtually a color block in a color cluster. Each side (v)i,vj) E has an nonnegative cost dijThe edges are divided into two types according to whether the nodes are in the same cluster: if the nodes defining the edge are in the same cluster, the edge is called an intra-cluster edge; if the nodes defining an edge are in different clusters, the edge is referred to as an inter-cluster edge. The goal of the cluster traveler problem is to find a minimum cost Hamiltonian circle in G and the sprayed color dots within the same cluster must be continuously visited. Note that if there is only one cluster or only one painted patch in each cluster, the cluster traveler Problem becomes the traveler Problem (TSP). The cluster traveler problem is said to be an extension of the traveler problem.
FIG. 2 shows 4 clusters (C)0,C1,C2And C and3) And 7 Cluster traveler problem form of sprayed color lump, wherein C0＝{0}，C1＝{1,2}，C2{3,4} and C 35, 6. The possible routes in fig. 2 are: 0 → 1 → 2 → 4 → 3 → 6 → 5 → 0. The solid line between the sprayed patch 1 and the sprayed patch 2 indicates intra-cluster connection, and the dotted line between the sprayed patch 2 and the sprayed patch 4 indicates inter-cluster connection. In the multi-color spray path planning problem, spray color blocks of the same color are set as the same cluster, and a starting point and an end point of the moving spray gun are independently set as one color block in one cluster. The starting and ending point positions are not the color patches to be painted, and the starting and ending points of the movement of the spray gun are set to achieve a more generalized CTSP problem. And the starting and ending positions of the lance movement may be the same. The spray gun starts from the starting color block 0 and finally returns to the starting color block 0.
1.2 selection of starting point for color lump spraying
The lower sub-problem is the routing case selection problem. In rectangular spraying of color blocks, the color blocks are sprayed by using a zigzag path planning method, and a spray gun can start spraying at the positions of four vertexes of the color blocks. As shown in fig. 3 and 4, at viThere are 4 starting points (h) of spraying in the color patch1,h2,h3,h4) And (4) selecting. When the spray break is even, the four route case scenario is shown in fig. 3. When the spray break is odd, four situations are shown in fig. 4. For a patch, the spray path is typically planned along the length of the patch to reduce the number of corners and thus the spray time. Based on the zigzag path planning method and the spray diameter of the spray gun, it can be determined whether the spray break within the color patch is odd or even.
1.3 mathematical model
Spray gun from v0Starting, returning to v after finishing all color block spraying tasks0. To make the model more generalized, this embodiment virtualizes the starting position of the spray gun as a sprayed patch in a color cluster. Only when the spray gun is spraying colour block viUsing spraying route case tSpraying color block vjSpraying route of (1) case w
Figure BDA0003548145970000081
Otherwise
Figure BDA0003548145970000082
Indicating colour patch v from sprayingiCase t to spraying color block vjThe cost of route case w. The mathematical formula is as follows:
the objective function (1) minimizes the distance the spray gun moves between the sprayed color patches:
Figure BDA0003548145970000083
wherein, i, j represents the serial numbers of the ith spraying color block and the jth spraying color block, i, j belongs to {0,1,2,. multidot.n }, i ≠ j, t, w represents the t-th zigzag route scheme in the color block, the w-th zigzag route scheme, t, w belongs to {1,2,3,4}, t ≠ w, z is a target function, the smaller the value of z represents the shorter the distance moved by the spray gun between the color blocks,
Figure BDA0003548145970000084
indicating that the spray gun is spraying colour block viCase t to spray color Block vjThe cost of the case w of (a),
Figure BDA0003548145970000085
is a variable of 0 to 1 when the spray gun is spraying the color patch viCase t to spray color Block vjCase w when
Figure BDA0003548145970000086
Otherwise
Figure BDA0003548145970000087
Namely, it is
Figure BDA0003548145970000088
The constraints are as follows:
each sprayed patch can only be sprayed once:
Figure BDA0003548145970000089
Figure BDA00035481459700000810
where V denotes a set of sprayed patches V ═ V0,v1,v2,...,vn}。
The sprayed patches within each color cluster must be sprayed continuously:
Figure BDA0003548145970000091
wherein, CcRepresents the C-th color cluster, | CcAnd | represents the number of all sprayed color blocks in the c-th color cluster.
And (3) eliminating the sub-loop:
Figure BDA0003548145970000092
Figure BDA0003548145970000093
this embodiment employs the use of the MTZ method to eliminate the sub-loop.
Only one of the four spray route schemes can be selected for use in each color block:
Figure BDA0003548145970000094
Figure BDA0003548145970000095
indicating the ith sprayColor block viThe number of the selected route schemes t, that is, the number of the route schemes in the selected paint color block is equal to 1, and the number of the other schemes is equal to 0. For example, the ith paint patch v is selectediThe 2 nd route scheme of
Figure BDA0003548145970000096
2. Hierarchical multicolor spray path planning algorithm
And for the problem of the upper (global) sub-problem cluster traveling salesman, solving the CTSP by adopting an improved genetic algorithm, and determining the spraying sequence of the color clusters and the color blocks in the color clusters. For the case selection problem of the spraying route in the next-level (local) sub-problem color block, a Greedy Method (GM) and a tabu search algorithm are adopted to determine the spraying route condition in the color block.
2.1 genetic Algorithm design
According to the characteristics of the cluster traveler problem in the multi-color spray path planning, a coding scheme for solving the problem is designed. Meanwhile, proper selection, crossover and mutation operators are designed to ensure that a better algorithm operation effect is achieved.
A. Chromosomal coding
Chromosomal coding is an important fundamental work in genetic algorithms. It affects the performance of the algorithm. For the cluster traveler problem in multi-color spray path planning, the present embodiment uses a single-chromosome segment coding scheme. The single chromosome segment coding scheme is that the serial numbers of the spraying color blocks with the same color are arranged in the same segment. The sequence between the code segments of the color patches of different colors is obtained by population initialization and subsequent evolutionary computation.
In this example, the lance movement start point and movement end point positions 0 do not participate in the chromosome coding. As shown in fig. 5, chromosome coding of 10 sprayed color patches in total of 3 colors is defined. 3 black spraying color blocks are defined, and the number sequence of the color blocks is as follows: 1 to 3. 3 white spraying color blocks are defined, and the number sequence of the color blocks is as follows: 4 to 6. 4 gray spraying color blocks are defined, and the number sequence of the color blocks is as follows: 7 to 10. As shown in fig. 5, the color sequence is: black-white-gray, the sprayed color block sequence is: 1 → 3 → 2 → 6 → 5 → 4 → 8 → 7 → 9 → 10.
B. Population initialization
The genetic algorithm starts an iterative search using the initial population. There are generally two methods of generating the starting population: (1) generating a population using a random method; (2) and generating an initial population meeting the requirements according to some prior knowledge.
The embodiment initializes the population by using a method of partial population greedy initialization. The method divides the initial population into three types of initial sub-populations. The first type of initial sub-population uses a greedy algorithm to generate an initial population. The first population accounted for 10% of the total initial population. The second type of initial sub-population randomly selects one of the two closest color blocks to be sprayed at a time as the next color block to be sprayed, using a method similar to a greedy algorithm. The second population accounts for 10% of the total number of the initial population. The third type of initial sub-population uses a random method to generate the population. The third population accounts for 80% of the total initial population. Note that the first color patch to be painted by all individuals is chosen using a random method, independent of the location of the start of the spray gun. The greedy algorithm is used in the method for greedy initialization of part of population, so that beneficial search information can be given to genetic algorithm. Meanwhile, the random method is used for generating 80% of individuals in the initial population total quantity, so that the search space is ensured to be large enough.
C. Fitness calculation
The fitness function provides key information for genetic algorithm evolution search. The higher the fitness of the individual, the easier the individual is to select through the tournament algorithm, and the information carried by the individual is easier to enter the next search. maxPath is defined as the maximum path length of an individual in the population, minPath is defined as the minimum path length of the individual in the population, and currentPath is defined as the path length of the current individual. The fitness function is:
Figure BDA0003548145970000111
D. selection operation
The selection operation is to directly reserve the individuals with high fitness to the next generation or generate new individuals through pairing and then inherit the new individuals to the next generation. The present embodiment employs a combination of elite reservation and tournament selection strategies. The elite retention strategy is to copy the most 5% fitness individual at a time directly to the next generation population. The tournament selection method is to randomly select 4 individuals from a population and select an individual with the highest fitness value from the 4 individuals as a parent 1 of the subsequent crossover and mutation operation. The parent 2 repeats the tournament selection process operation described above.
E. Crossover operation
Crossover operation is the exchange of part of the gene by two parents and the creation of a new individual. The purpose of the interleaving is to improve the search capability of the genetic algorithm. There are various interleaving methods, and the present embodiment uses the same-color sequential interleaving method, as shown in fig. 6. The method generates a starting point number startn and an ending point number endn for each spray color. The gene segment between startn and endn of parent 1 is copied to the same gene position of the offspring. Among the genes following endn in parent 2, genes that do not duplicate with the offspring were selected and filled in the offspring. And (4) selecting genes which are not repeated with the offspring from the father generation 2 to fill in the gaps of the offspring genes.
F. Mutation operations
Mutation operation is a method for realizing population diversity, and can expand the search direction. Mutation operations may break through local optima. The present embodiment adopts the same color-exchange variation method. As shown in fig. 7, among the genes of each color segment, the genes at two positions were randomly exchanged.
2.2 greedy Algorithm and taboo search Algorithm design
And (3) obtaining the solution of the upper (global) subproblem cluster traveler problem through a genetic algorithm. And obtaining the spraying sequence of the spraying color blocks of all colors. The lower (partial) sub-problem objective is to select a case of the route of the spray gun within each sprayed color block. The route case is determined by the zigzag spray route. In the embodiment, a greedy algorithm and a tabu search algorithm are used for solving the case that the spray gun selects the route in the color block.
G. Greedy algorithm initialization
And obtaining the optimal color block spraying sequence through a genetic algorithm. From the spray gunStarting from the starting position 0, the spraying starting point of the spraying route scheme closest to the starting position 0 in the first color block is searched. Then, the subsequent color block searches for the route case with the starting point of the color block route case closest to the end point of the previous color block route case. And on the basis of the optimal color block spraying sequence, obtaining spraying route cases in all color blocks by using a greedy algorithm. Obtaining the sequence S of the spraying route cases in all the color blocks on the basis of the optimal color block spraying sequence0。
H. Tabu search algorithm optimization
With S0For the initial solution, the route cases on one color block at a time are randomly selected. By replacing the route cases on the color blocks, different neighborhood solutions are generated. And putting the changed spraying color block route case into a taboo table. The length of the taboo is set as
Figure BDA0003548145970000121
And rounded to the nearest integer. n is the total number of all sprayed color blocks. The first half of the total path length minimum is taken as a candidate solution in the generated neighborhood solution. The length of the route case within a color block does not participate in the calculation. In the privileged criterion, if a better solution than the current optimal solution is not found, the non-taboo optimal state is selected as the new current solution among the candidate solutions. And simultaneously adding the corresponding object into a taboo table. And stopping the tabu search process after the iteration times are reached. The reason why the length of the route case in the color block does not participate in the calculation is as follows: and the zigzag path planning is used, the moving distance of the spray gun in the color block is a fixed value, the spray gun finishes the spraying in the color block, and when the next color block is selected, the moving distance of the spray gun is different.
3. Results of the experiment
To verify the implementation effect of the embodiment, we designed a personalized spray pattern to verify the correctness and performance of the algorithm we used.
3.1 genetic Algorithm and tabu search Algorithm Convergence analysis
This embodiment sets a pattern having 30 sprayed patches of 3 colors. There were 11 black spray color blocks, 9 white spray color blocks, and 10 gray spray color blocks in the pattern. Each sprayed color block is numbered in advance according to the color. The initial position of the spray gun is set as (0, 0). The order of spraying the colors and the order of spraying the color patches in each color are determined using a genetic algorithm. The evolution iteration number of the genetic algorithm is set to 1000, the cross probability is 0.9, the variation probability is 0.1, and the population number is 100. A greedy algorithm and a tabu search algorithm are used to determine the routing case for each sprayed color patch. The number of iterations of the tabu search algorithm is 100. The pattern of the sprayed color patches is shown in fig. 8, and the numbers represent the numbers of the color patches.
FIG. 9 shows a feasible color block spraying sequence scheme by genetic algorithm. The solid lines indicate movement of the spray gun between clusters of different colors, i.e., between color patches sprayed with different colors, and the dashed lines indicate movement of color patches of the same color. :
0→1→4→7→9→10→8→5→2→3→6
→11→20→16→12→15→19→13→17→14
→18→30→27→24→22→23→26→29
→28→25→21→0
fig. 10 shows that the convergence performance of the genetic algorithm is good, and fig. 11 shows that the convergence performance of the tabu search algorithm is good. Fig. 12 indicates the spray start position of the spray gun within the color patch using '@' and indicates the spray end position of the spray gun within the color patch using '@'.
4. Conclusion
In order to solve the problem of multi-color spray path planning, the problem is decomposed into an upper-layer cluster traveler problem and a lower-layer route scheme selection problem, and the problems are solved through a hierarchical optimization method. Can realize full-coverage multicolor spraying effect. The feasibility of the new method was demonstrated by calculating an example with 30 paint patches of 3 colors.
Example two
The present embodiments provide a system for multi-color spray path planning.
A system for multi-color spray path planning, comprising:
an acquisition module configured to: identifying the generated pattern of the workpiece to be sprayed to acquire color block information of the pattern;
an order determination module configured to: constructing a cluster traveling salesman problem model according to the color block information, and determining the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
an intra-block path planning module configured to: determining a spraying path in color blocks by taking the minimum moving distance of a spray gun between the color blocks as a target according to the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
an output module configured to: and obtaining the spraying path of the spray gun based on the spraying sequence among the fixed color clusters, the color block spraying sequence in the color clusters and the spraying path in the color blocks.
It should be noted here that the obtaining module, the order determining module, the intra-block path planning module and the output module are the same as the example and the application scenario realized by the steps in the first embodiment, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method of multi-color spray path planning as described in the first embodiment above.
Example four
The present embodiment provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for multi-color spray path planning as described in the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method of multi-color spray path planning, comprising:
identifying the generated pattern of the workpiece to be sprayed to acquire color block information of the pattern;
constructing a cluster traveling salesman problem model according to the color block information, and determining the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
determining a spraying path in color blocks by taking the minimum moving distance of a spray gun between the color blocks as a target according to the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
and obtaining the spraying path of the spray gun based on the spraying sequence among the fixed color clusters, the color block spraying sequence in the color clusters and the spraying path in the color blocks.
2. The method of multi-color spray path planning according to claim 1, wherein the cluster traveler problem model is defined as an undirected weighted complete graph G ═ (V, E) with a set of spray color patches V ═ { V, E) thereon0,v1,v2,...,vn}, set of edges
Figure FDA0003548145960000011
Wherein the color block set V is divided into disjoint color clusters C according to the colors of the color blocks themselves0,C1,...,Cm。。
3. The method of multicolor spray path planning according to claim 1, wherein the objective of the cluster traveler problem model is to find a minimum cost hamiltonian circle in G and the spray color blocks within the same cluster must be continuously visited.
4. The method of multicolor spray path planning according to claim 1, comprising, after constructing a cluster traveler problem model: solving the cluster traveling salesman problem model by adopting a genetic algorithm to obtain the optimal color block spraying sequence, wherein the method comprises the following steps:
chromosomal coding: the serial numbers of the color blocks with the same color are arranged in the same segment to obtain a single chromosome segment coding sequence;
population initialization: initializing the population by adopting greedy initialization of part of the population;
and (3) fitness calculation: acquiring key information required by genetic algorithm evolution by adopting a fitness function;
selecting operation: adopting a strategy of combining elite reservation and championship selection, directly reserving the individuals with high fitness to the next generation or generating new individuals through pairing and then inheriting the new individuals to the next generation;
and (3) cross operation: adopting a same-color sequence crossing method to improve the searching capability of the genetic algorithm;
mutation operation: and (3) randomly exchanging the genes at two positions in the genes of each color segment by adopting a homochromatic exchange variation method to obtain the varied single-chromosome segmented coding sequence.
5. The method of multicolor spray path planning according to claim 4, wherein said determining spray paths within color patches with the goal of minimizing the distance that the spray gun moves between spray color patches comprises: determining a spraying path in color blocks by adopting a greedy algorithm and a tabu search algorithm aiming at the minimum moving distance of a spray gun between the sprayed color blocks; the specific process of adopting the greedy algorithm comprises the following steps: according to the optimal color block spraying sequence, starting from the initial position of a spray gun, searching a spraying starting point of a spraying route scheme closest to the initial position in the first color block; and the subsequent color blocks find the route case with the starting point of the color block route case closest to the end point of the previous color block route case, and the sequence of the spraying route cases in all the color blocks is obtained on the basis of the optimal color block spraying sequence.
6. The method for multi-color spray path planning according to claim 5, wherein the specific process using tabu search algorithm comprises: taking the sequence of all the color block inner spraying route cases on the basis of the optimal color block spraying sequence as an initial solution, randomly selecting a route case on a color block each time, generating different neighborhood solutions by replacing the route case on the color block, putting the changed color block spraying route case into a taboo table, taking the first half of the minimum value of the total route path length in the generated neighborhood solutions as a candidate solution, and not taking part in the calculation of the length of the route case in the color block; in the privilege criterion, if no solution better than the current optimal solution is found, the optimal state of non-taboo is selected as the new current solution in the candidate solutions, and the corresponding object is added into the taboo table.
7. The method of multicolor spray path planning according to claim 1, wherein said determining spray paths within color patches specifically comprises: determining the position of a spray gun entering a color lump for starting spraying, determining whether the spraying turning in the color lump is an odd number or an even number according to a zigzag path planning method and the spraying diameter of the spray gun, and determining the position of the spray gun leaving the color lump for finishing spraying according to the position for starting spraying and the spraying turning number; wherein, the starting position of the spray gun is virtualized into a color block in a color cluster.
8. A system for multi-color spray path planning, comprising:
an acquisition module configured to: identifying the generated pattern of the workpiece to be sprayed to acquire color block information of the pattern;
an order determination module configured to: constructing a cluster traveling salesman problem model according to the color block information, and determining the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
an intra-block path planning module configured to: determining a spraying path in color blocks by taking the minimum moving distance of a spray gun between the color blocks as a target according to the spraying sequence among the color clusters and the color block spraying sequence in the color clusters;
an output module configured to: and obtaining the spraying path of the spray gun based on the spraying sequence among the fixed color clusters, the color block spraying sequence in the color clusters and the spraying path in the color blocks.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps in the method of multicolor spray path planning as claimed in any of claims 1 to 7.
10. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, carries out the steps in the method of multicolor spray path planning according to any one of claims 1-7.
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