CN115741703A - Method suitable for multi-station multi-robot optical measuring point task allocation - Google Patents
Method suitable for multi-station multi-robot optical measuring point task allocation Download PDFInfo
- Publication number
- CN115741703A CN115741703A CN202211471228.XA CN202211471228A CN115741703A CN 115741703 A CN115741703 A CN 115741703A CN 202211471228 A CN202211471228 A CN 202211471228A CN 115741703 A CN115741703 A CN 115741703A
- Authority
- CN
- China
- Prior art keywords
- robot
- station
- task
- measuring point
- measuring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000003287 optical effect Effects 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000001514 detection method Methods 0.000 claims abstract description 40
- 238000004519 manufacturing process Methods 0.000 claims abstract description 9
- 230000002068 genetic effect Effects 0.000 claims abstract description 8
- 238000012163 sequencing technique Methods 0.000 claims abstract description 5
- 238000011156 evaluation Methods 0.000 claims description 21
- 210000000349 chromosome Anatomy 0.000 claims description 20
- 239000011159 matrix material Substances 0.000 claims description 18
- 238000005259 measurement Methods 0.000 claims description 18
- 230000035772 mutation Effects 0.000 claims description 9
- 108090000623 proteins and genes Proteins 0.000 claims description 7
- 230000001502 supplementing effect Effects 0.000 claims description 4
- 230000001413 cellular effect Effects 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 239000012634 fragment Substances 0.000 claims description 3
- 230000008569 process Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Images
Classifications
-
- 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
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides a task allocation method suitable for multi-station multi-robot optical measuring point tasks, which relates to key steps of floating point number coding, elite selection strategy, secondary evolution and the like through improvement of a genetic algorithm, and considers coordination relations among three important links of stations, robots to which the stations belong and measuring point sequencing of measuring points in multi-station multi-robot task allocation in a comprehensive manner by combining reachability analysis of the robots. On the premise of meeting production requirements, the quality of task allocation of the optical measuring points and the detection efficiency are greatly improved.
Description
Technical Field
The invention relates to the technical field of robot task allocation schemes, in particular to an improved genetic algorithm suitable for multi-station multi-robot system non-contact optical measuring point task allocation.
Background
With the increasing intelligence degree of the automobile industry, higher requirements are provided for the quality of automobile products, the quality of an automobile body belongs to one of the requirements, and the manufacturing quality of the automobile body directly influences the quality of the final automobile product, so that the detection of the manufacturing quality of the automobile body is always highly regarded by the automobile industry at home and abroad.
By combining the characteristics of industrial robot such as high compatibility and expandability and taking the experience of non-contact operation (such as welding) in the past industrial field as reference, the detection of the manufacturing quality of the vehicle body is in the transition period from the traditional contact type measuring equipment (such as a three-coordinate measuring machine) to the non-contact type optical measuring head. Whether the detection efficiency and the detection precision can be effectively improved or not depends on a method for measuring point task allocation of the multi-station multi-robot system.
The traditional multi-station multi-robot task allocation mostly adopts a strategy of solving in steps, firstly, measuring points are grouped according to certain rule constraint, secondly, measuring point sets are allocated to all robots of all stations according to a nearby allocation principle and combining robot reachable information under the condition of meeting a specific constraint condition, then, the measuring point sets allocated to all robots of all stations are sequenced and optimized in path, and finally, evaluation indexes are established and corresponding algorithms are provided. When the method is used for an object with a large complex structure, such as an automobile body, the allocation strategy performed in sequence in different steps is often prone to focus on one link and cannot obtain an overall optimal solution, so that the allocation is unreasonable, the running time exceeds the detection period, the actual production requirements cannot be met, and even a better solution cannot be obtained within a specified time.
Disclosure of Invention
The invention aims to provide an improved genetic algorithm suitable for the task allocation of the non-contact optical measuring points of a multi-station multi-robot system to effectively solve the defects of the traditional strategy.
In order to achieve the purpose, the invention provides a method suitable for multi-station multi-robot optical measuring point task allocation, which comprises the following steps:
step 1: determining initialization information comprising position and structure parameters of the station robot, measuring point information and vehicle body point cloud;
step 2: distributing the measuring points to a left detecting task set and a right detecting task set by taking the axis of the production line as a reference, selecting one side with more measuring points to plan first, and then planning the tasks of the measuring points on the other side by combining the result of the planned one side;
and 3, step 3: clustering the left and right detection task sets in the step 2, grouping the measuring points with the same characteristics into a group, and storing the grouped same characteristic measuring point sets into a cellular array to ensure that the measuring points with the same characteristics can be detected on the same station, thereby meeting the detection task of a factory;
and 4, step 4: determining the accessibility of different robots to each measuring point based on the kinematics theory of the robots, and constructing an accessibility matrix and an optimal feasible detection angle set;
and 5: and (3) further improving the information of the measuring points in the step 2: supplementing a number index, a characteristic number index, reachable information and joint angle information of the reachable robot, and then rejecting information of the unreachable point based on the reachability matrix obtained in the step 4;
and 6: based on the same characteristic measuring point set and the reachability matrix, executing floating point number encoding operation on the measuring point set to be distributed: the integer number represents station information of the measuring points, the decile number represents robot information of the measuring points, and the percentile number represents a detected sequence number of the measuring points in the current set;
and 7: establishing a fitness function as an evaluation index, calculating the path length of measuring points in different task subsets, detecting time and the distance of clustering centers among the different task subsets, selecting a certain weight proportion, taking a summation form as the evaluation index of population fitness after each evolution, and carrying out comprehensive evaluation and sequencing on chromosomes in a population;
and step 8: using an improved genetic algorithm: the allocation task of the vehicle body measuring points is effectively realized by an elite selection strategy, double crossing, double variation and quadratic evolution;
and step 9: coding validity check must be carried out after each operation for step 8 to ensure that the integer parts of the measured point gene segments with the same characteristics are the same; constraining the ten-tenth position of the measuring point gene fragment according to the accessibility matrix, and ensuring that the number of the ten-tenth position is consistent with the number of the accessible robot of the measuring point;
step 10: and (4) sequentially executing the steps 6-9 within the specified iteration times to complete the distribution tasks of the measuring points, and decoding the obtained optimal detection task set distributed by each train number and each robot to obtain the optimal measuring point set distributed by each station and each robot.
Furthermore, in the step 2, a concept of 'few obeying to most' is adopted, firstly, a measuring point set at the side with more measuring points is planned, and the measuring points with the same characteristics can be detected on the same station, so that the detection task of a factory is met.
And step 4, calculating a reachability matrix of the measuring point and a corresponding best feasible detection angle set according to the position information of the robot in the station, the D-H parameter table and other conditions, and using the reachability matrix as a basis for task allocation later.
Further, in step 5, the information of the measuring points which are inaccessible by all robots is removed, and the information of the single measuring point is supplemented as follows: measuring point position information x, y and z; measuring point first and second vector information i 1 ,j 1 ,k 1 ,i 2 ,j 2 ,k 2 (ii) a The accessibility of the measuring point is represented by using a 0/1 variable, wherein 0 represents that the measuring point is inaccessible to the corresponding robot, and 1 represents that the measuring point is accessible to the corresponding robot; index numbers of the measuring points; and (6) measuring point feature numbering.
Further, the fitness function in step 7 is:
wherein, M represents the number of work stations of the detection line, i represents the work station index, n represents the number of robots on one work station in the detection line, j represents the robot index, M i Set of tasks representing the ith workstation, M ij Represents the task set of the jth robot at the ith station, M ijs A set of tasks representing the j-th robot at the i-th station arranged according to a sequence s, d (M) i ) Representing each adjacent task set M on the ith station ij Sum of Euclidean distances of center point coordinates, t (M) ij ) Indicating the detection time of the task set of the jth robot at the ith station, d (M) ijs ) Indicating the path length, ω, of the j-th robot at the i-th station in a sequence s of task sets 1 、ω 2 、ω 3 Respective weight coefficient, ω 1 Is negative, ω 2 、ω 3 Is positive. The following is d (M) i )、t(M ij )、d(M ijs ) The specific expression of (A):
wherein, delta jj′ Represents a 0/1 variable, 0 represents that the jth robot and the j ' robot are not adjacent, 1 represents that the jth robot and the j ' robot are adjacent, and j, j ' is equal to {1,2, \ 8230;, n }, c ij (M ij ) Representing the center coordinates of all measuring points in a task set of the jth robot on the ith station;
wherein, t 01 Represents the moving time of the mechanical arm from the initial pose to the first measuring point,
the moving time of the mechanical arm returning to the initial pose from the last measuring point is represented, s and t respectively represent two different measuring points in the task set of the jth robot on the ith station, and x st Representing a 0/1 variable, 0 representing that two measurement points are not adjacent, 1 representing that two measurement points are adjacent, t st Represents the moving time from the measuring point s to the measuring point t, num represents the number of the measuring points in the task set of the jth robot on the ith station, and t M Representing the detection time of a single measuring point;
and k' represent indexes of task sets of j robots on i-th stations of measuring points.
Further, the step 8 of selecting the elite refers to selecting chromosomes 10% before the current population evaluation value as the elite layer and reserving the elite layer to the first 10% of the new generation; the rest 90 percent of the non-elite layer is randomly selected as the first 20 to 70 percent of the next generation; while the last remaining 30% of the next generation is replaced by random encoding; the double crossing refers to selecting two chromosomes of a specific hierarchy, then randomly selecting crossing positions on the chromosomes, and finally performing crossing operation aiming at units, tens and percentiles respectively; double mutation is similar to double crossover operation, and performs mutation operation for integer bits and decimal bits respectively; the secondary evolution is to re-order the points in the elite hierarchy.
Compared with the prior art, the invention has the advantages that:
1) A floating point number coding mode is provided, integer digits represent station information of measuring points, decile digits represent robot information of the measuring points, and percentile digits represent detected sequence numbers of the measuring points in a current set, so that one measuring point is abstracted into a floating point type number and is regarded as a gene segment in a chromosome, and a whole measuring point set forms a chromosome in a population, and a foundation is laid for subsequent operation. The coding mode takes station, robot and measuring point sequence information into consideration, and theoretically, an optimal solution exists.
2) And (4) building a comprehensive evaluation function to make the final result evolve towards the optimal direction. The evaluation function is composed of three parts, which are respectively: the detection time of the measurement point set of the jth robot at the ith station, the path length of the measurement point set of the jth robot at the ith station and the sum of the center distances of the measurement point sets of the adjacent robots at the ith station are multiplied by respective weight coefficients to obtain a final evaluation function, the smaller the evaluation function value is, the better the evaluation function value is, the evaluation function value acts on chromosomes in the evolution process of the past generation, and the evaluation results are sequenced, so that the choice of elite is facilitated.
3) In the middle process, a series of operations such as an elite selection strategy, double crossing, double variation and the like are adopted to perfect the population evolution mechanism in the genetic algorithm. Selecting elite, namely selecting chromosomes 10% before the current population evaluation value as an elite layer and reserving the elite layer to the first 10% of a new generation; the rest 90 percent of the non-elite layer is randomly selected as the first 20 to 70 percent of the next generation; while the last remaining 30% of the next generation is replaced by random encoding. The double crossing refers to selecting two chromosomes of a specific hierarchy, then randomly selecting crossing positions on the chromosomes, and finally performing crossing operation aiming at units, tens and percentiles respectively. Double mutation is similar to double crossover operations in that the mutation operations are performed on integer bits and fractional bits, respectively. After the three operations, a new generation of population is evolved, and the fitness of the new generation of population is better due to the limitation of evaluation indexes.
4) Checking the correctness of the codes, which is mainly to check whether the coding condition accords with the reality after each stepping is finished, and mainly aims at checking integer digits and decile digits, wherein the rule is that for the integer digits, the measuring points with the same characteristics are required to be detected at the same station; for deciles, the robot reachable information of the stations must be satisfied.
5) And (4) secondary evolution, when the first evolution falls into a local optimal solution, performing secondary crossing, mutation and rearrangement of measuring point sequence based on the chromosomes which are sequenced in the first 10% after the first evolution, namely the elite layer. And selecting a proper threshold value according to experience to enable the result of the first evolution to jump out of local optimum and enable the final result to be more convergent.
Drawings
FIG. 1 is a flowchart of a task allocation method for multi-robot optical measurement point detection according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating steps of a task allocation method according to an embodiment of the present invention
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be further described below.
As shown in fig. 1 and fig. 2, the present invention provides a method for multi-station multi-robot optical measurement point task allocation, which includes the following steps:
step 1: determining initialization information comprising a position and structure parameters of a station robot, measuring point information and a vehicle body point cloud;
and 2, step: distributing the measuring points to a left detecting task set and a right detecting task set by taking the axis of the production line as a reference, selecting one side with more measuring points to plan first, and then planning the tasks of the measuring points on the other side by combining the result of the planned one side;
according to the idea of 'few obeys most', firstly, a measuring point set on the side with more measuring points is planned, and the measuring points with the same characteristics can be detected on the same station, so that the detection task of a factory is met.
And 3, step 3: clustering the left and right detection task sets in the step 2, grouping the measuring points with the same characteristics into a group, and storing the grouped same characteristic measuring point sets into a cellular array to ensure that the measuring points with the same characteristics can be detected on the same station, thereby meeting the detection task of a factory;
and 4, step 4: determining the accessibility of different robots to each measuring point based on a robot kinematics theory, and constructing an accessibility matrix and an optimal feasible detection angle set;
and calculating the reachability matrix of the measuring point and the corresponding best feasible detection angle set according to the position information of the robot in the station, the D-H parameter table and other conditions, and taking the reachability matrix as the basis of the task allocation.
And 5: and (3) further improving the information of the measuring points in the step 2: supplementing a number index, a feature number index, reachable information and joint angle information of the reachable robot, and then removing the information of the unreachable points based on the reachability matrix obtained in the step 4;
eliminating the measuring point information which is inaccessible to all robots, and supplementing the information of a single measuring point as follows: measuring point position information x, y and z; measuring point first and second vector information i 1 ,j 1 ,k 1 ,i 2 ,j 2 ,k 2 (ii) a The accessibility of the measuring point is represented by using a variable 0/1, wherein 0 represents that the measuring point is inaccessible to the corresponding robot, and 1 represents that the measuring point is accessible to the corresponding robot; index numbers of the measuring points; and (6) measuring point feature numbers.
And 6: based on the same characteristic measuring point set and the reachability matrix, executing floating point number encoding operation on the measuring point set to be distributed: integer bits represent station information of the measuring points, decile bits represent robot information of the measuring points, and percentile bits represent detected sequence numbers of the measuring points in the current set;
and 7: establishing a fitness function as an evaluation index, calculating the path length of measuring points in different task subsets, detecting time and the distance of clustering centers among the different task subsets, selecting a certain weight proportion, taking a summation form as the evaluation index of population fitness after each evolution, and carrying out comprehensive evaluation and sequencing on chromosomes in a population;
the fitness function is:
wherein M represents the number of stations of the detection line, i represents the station index, n represents the number of robots on one station in the detection line, j represents the robot index, M i Set of tasks representing the ith workstation, M ij Represents the task set of the jth robot at the ith station, M ijs A set of tasks representing the j-th robot at the i-th station arranged according to a sequence s, d (M) i ) Representing each adjacent task set M on the ith station ij Sum of Euclidean distances of center point coordinates, t (M) ij ) Indicating the detection time of the task set of the jth robot at the ith station, d (M) ijs ) Indicating the path length, ω, of the j-th robot at the i-th station in a sequence s of task sets 1 、ω 2 、ω 3 Respective weight coefficient, ω 1 Is negative, ω 2 、ω 3 Is positive. The following is d (M) i )、t(M ij )、d(M ijs ) The specific expression of (1):
wherein, delta jj′ Represents a 0/1 variable, 0 represents that the jth robot and the j ' robot are not adjacent, 1 represents that the jth robot and the j ' robot are adjacent, and j, j ' is equal to {1,2, \ 8230;, n }, c ij (M ij ) Representing the center coordinates of all measuring points in a task set of the jth robot on the ith station;
wherein, t 01 Showing the arm fromThe moving time of the initial pose moving to the first measuring point,
the moving time of the mechanical arm returning to the initial pose from the last measuring point is represented, s and t respectively represent two different measuring points in the task set of the jth robot on the ith station, and x st Represents the variable 0/1, 0 represents that two measuring points are not adjacent, 1 represents that two measuring points are adjacent, t st Represents the moving time from the measuring point s to the measuring point t, num represents the number of the measuring points in the task set of the jth robot on the ith station, and t M Representing the detection time of a single measuring point;
and k' represent indexes of task sets of j robots on i-th stations of measuring points.
And 8: using an improved genetic algorithm: the allocation task of the vehicle body measuring points is effectively realized by the elite selection strategy, double crossing, double variation and secondary evolution;
selecting elite, namely selecting chromosomes 10% before the current population evaluation value as an elite layer and reserving the elite layer to the first 10% of a new generation; the rest 90 percent of the non-elite layer is randomly selected as the first 20 to 70 percent of the next generation; while the last remaining 30% of the next generation is replaced by random codes; the double crossing refers to selecting two chromosomes of a specific hierarchy, then randomly selecting crossing positions on the chromosomes, and finally performing crossing operation aiming at units, tens and percentiles respectively; double mutation is similar to double crossover operation, and mutation operation is performed for integer bits and decimal bits respectively; the secondary evolution is to re-order the measuring points of the elite level.
And step 9: a coding validity check must be performed after each operation for step 8 to ensure that the integer portions of the survey point gene segments with the same characteristics are the same; constraining the ten-tenth position of the measuring point gene fragment according to the accessibility matrix, and ensuring that the number of the ten-tenth position is consistent with the number of the accessible robot of the measuring point;
step 10: and (4) sequentially executing the steps 6-9 within the specified iteration times to complete the distribution tasks of the measuring points, and decoding the obtained optimal detection task set distributed by each train number and each robot to obtain the optimal measuring point set distributed by each station and each robot. And stored in the following format:
| 1 st robot | No. 2 robot | 3 rd robot | ...... | |
| Station 1 | ||||
| 2 nd station | ||||
| Station 3 | ||||
| ...... |
The invention provides an improved genetic algorithm suitable for non-contact optical measuring point task allocation of a multi-station multi-robot system aiming at the problem of non-contact optical measuring point task allocation of a white automobile body, and three important links of stations, robots and sequencing of measuring points in the multi-station multi-robot task allocation are comprehensively considered in a floating point number coding mode. On the premise of meeting the production requirements, the quality of task allocation and the detection efficiency are greatly improved.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A method suitable for multi-station multi-robot optical measuring point task allocation is characterized by comprising the following steps:
step 1: determining initialization information comprising position and structure parameters of the station robot, measuring point information and vehicle body point cloud;
step 2: distributing the measuring points to a left detecting task set and a right detecting task set by taking the axis of the production line as a reference, selecting one side with more measuring points to plan first, and then planning the tasks of the measuring points on the other side by combining the result of the planned one side;
and step 3: clustering the left and right detection task sets in the step 2, grouping the measuring points with the same characteristics into a group, and storing the grouped same characteristic measuring point sets into a cellular array to ensure that the measuring points with the same characteristics can be detected on the same station, thereby meeting the detection task of a factory;
and 4, step 4: determining the accessibility of different robots to each measuring point based on the kinematics theory of the robots, and constructing an accessibility matrix and an optimal feasible detection angle set;
and 5: and (3) further improving the information of the measuring points in the step 2: supplementing a number index, a feature number index, reachable information and joint angle information of the reachable robot, and then removing the information of the unreachable points based on the reachability matrix obtained in the step 4;
step 6: based on the same characteristic measuring point set and the reachability matrix, executing floating point number encoding operation on the measuring point set to be distributed: the integer number represents station information of the measuring points, the decile number represents robot information of the measuring points, and the percentile number represents a detected sequence number of the measuring points in the current set;
and 7: establishing a fitness function as an evaluation index, calculating the path lengths of the measuring points in different task subsets, the detection time and the distance of the clustering centers among the different task subsets, selecting a certain weight proportion, taking a summation form as the evaluation index of the fitness of the population after each evolution, and comprehensively evaluating and sequencing chromosomes in the population;
and 8: using an improved genetic algorithm: the allocation task of the vehicle body measuring points is effectively realized by the elite selection strategy, double crossing, double variation and secondary evolution;
and step 9: coding validity check must be carried out after each operation for step 8 to ensure that the integer parts of the measured point gene segments with the same characteristics are the same; constraining the ten-tenth of the measuring point gene fragment according to the accessibility matrix, and ensuring that the number of the ten-tenth is consistent with the number of the accessible robot of the measuring point;
step 10: and (4) sequentially executing the steps 6-9 within the specified iteration times to complete the distribution tasks of the measuring points, and decoding the obtained optimal detection task set distributed by each train number and each robot to obtain the optimal measuring point set distributed by each station and each robot.
2. The method for task allocation of the optical measurement points of the multiple robots at multiple stations according to claim 1, wherein in the step 2, a concept of 'few obeying to most' is adopted, firstly, a measurement point set on the side with more measurement points is planned, and it is ensured that the measurement points with the same characteristics can be detected at the same station, so as to meet the detection task of a factory.
3. The method for task allocation of multiple stations and multiple robots to optical measurement points according to claim 1, wherein the step 4 is to calculate the reachability matrix of the measurement points and the corresponding best feasible detection angle set according to the position information of the robots in the stations and the conditions such as the D-H parameter table, and use the reachability matrix as the basis for task allocation later.
4. The method for task allocation of optical measurement points of multiple robots at multiple stations according to claim 1, wherein measurement point information that all robots are unreachable is removed in step 5, and information of a single measurement point is supplemented by: measuring point position information x, y and z; measuring point first and second vector information i 1 ,j 1 ,k 1 ,i 2 ,j 2 ,k 2 (ii) a The accessibility of the measuring point is represented by using a 0/1 variable, wherein 0 represents that the measuring point is inaccessible to the corresponding robot, and 1 represents that the measuring point is accessible to the corresponding robot; index numbers of the measuring points; and (6) measuring point feature numbering.
5. The method for task assignment of multi-station multi-robot optical measurement points according to claim 1, wherein the fitness function in step 7 is:
wherein M represents the number of stations of the detection line, i represents the station index, n represents the number of robots on one station in the detection line, j represents the robot index, M i Set of tasks representing the ith workstation, M ij Task set, M, representing jth robot at ith station ijs A set of tasks representing the j-th robot at the i-th station arranged according to a sequence s, d (M) i ) Representing each adjacent task set M on the ith station ij Sum of Euclidean distances of center point coordinates, t (M) ij ) Indicating the detection time of the task set of the jth robot at the ith station, d (M) ijs ) Path length, ω, representing task set arranged in sequence s for jth robot at ith station 1 、ω 2 、ω 3 Respective weight coefficient, ω 1 Is negative, ω 2 、ω 3 Is positive. The following is d (M) i )、t(M ij )、d(M ijs ) The specific expression of (A):
wherein, delta jj′ Represents a 0/1 variable, 0 represents that the jth robot and the j ' robot are not adjacent, 1 represents that the jth robot and the j ' robot are adjacent, j ' is in the {1,2 ij (M ij ) Representing the center coordinates of all measuring points in a task set of the jth robot on the ith station;
wherein, t 01 Represents the moving time of the mechanical arm from the initial pose to the first measuring point,
representing the moving time of the mechanical arm returning to the initial pose from the last measuring point, s and t respectively represent two different measuring points in the task set of the jth robot on the ith station, and x st Represents the variable 0/1, 0 represents that two measuring points are not adjacent, 1 represents that two measuring points are adjacent, t st Represents the moving time from the measuring point s to the measuring point t, num represents the number of the measuring points in the task set of the jth robot on the ith station, and t M Representing the detection time of a single measuring point;
and k' represent indexes of task sets of j robots on i-th stations of measuring points.
6. The method for task allocation of the multi-station multi-robot optical measurement points according to claim 1, wherein the elite selection in the step 8 is to select a chromosome which is 10% of the current population evaluation value before, as an elite layer, and reserve the chromosome to the first 10% of a new generation; the rest 90 percent of the non-elite layer is randomly selected as the first 20 to 70 percent of the next generation; while the last remaining 30% of the next generation is replaced by random codes; the double crossing refers to selecting two chromosomes of a specific hierarchy, then randomly selecting crossing positions on the chromosomes, and finally performing crossing operation aiming at units, tens and percentiles respectively; double mutation is similar to double crossover operation, and performs mutation operation for integer bits and decimal bits respectively; the secondary evolution is to re-order the points in the elite hierarchy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211471228.XA CN115741703A (en) | 2022-11-23 | 2022-11-23 | Method suitable for multi-station multi-robot optical measuring point task allocation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211471228.XA CN115741703A (en) | 2022-11-23 | 2022-11-23 | Method suitable for multi-station multi-robot optical measuring point task allocation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115741703A true CN115741703A (en) | 2023-03-07 |
Family
ID=85335589
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211471228.XA Pending CN115741703A (en) | 2022-11-23 | 2022-11-23 | Method suitable for multi-station multi-robot optical measuring point task allocation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115741703A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117610899A (en) * | 2024-01-24 | 2024-02-27 | 纳博特南京科技有限公司 | Multi-robot task allocation method based on priority |
-
2022
- 2022-11-23 CN CN202211471228.XA patent/CN115741703A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117610899A (en) * | 2024-01-24 | 2024-02-27 | 纳博特南京科技有限公司 | Multi-robot task allocation method based on priority |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110069880B (en) | Multi-target equipment layout and production schedule collaborative optimization method based on simulation | |
| CN111113409A (en) | Multi-robot multi-station cooperative spot welding operation planning method based on step-by-step optimization | |
| CN106779153B (en) | Intelligent stereoscopic warehouse goods space allocation optimization method | |
| CN107506956A (en) | Based on improvement particle cluster algorithm supply chain production and transport coordinated dispatching method and system | |
| CN104835026B (en) | Automatic stereowarehouse picking scheduling modeling optimization method based on Petri network and improved adaptive GA-IAGA | |
| CN106610654A (en) | Improved genetic algorithm for flexible workshop scheduling | |
| CN109934364B (en) | Tool on-time distribution method based on genetic simulated annealing algorithm | |
| CN112232545B (en) | AGV task scheduling method based on simulated annealing algorithm | |
| CN115741703A (en) | Method suitable for multi-station multi-robot optical measuring point task allocation | |
| CN108460463A (en) | High-end equipment flow line production dispatching method based on improved adaptive GA-IAGA | |
| CN114330870B (en) | Vehicle path planning method with time window based on multiple swarm evolution algorithm | |
| CN114707294A (en) | Multi-target scheduling method for job shop with limited transportation capacity constraint | |
| CN113762899A (en) | Mixed algorithm-based three-dimensional cargo boxing method | |
| CN111975767A (en) | Multi-robot visual detection system collaborative motion planning method based on multi-stage task allocation | |
| CN108108883B (en) | Clustering algorithm-based vehicle scheduling network elastic simplification method | |
| CN109917754B (en) | Multi-population distribution estimation algorithm-based robot assembly unit multi-target layout optimization method | |
| Hani et al. | Simulation based optimization of a train maintenance facility | |
| CN112990716B (en) | Double-resource constraint flexible workshop scheduling and layout integrated optimization method and system | |
| CN104599026A (en) | Supplier selection method | |
| CN113408988A (en) | Scheduling optimization method of warehousing system, electronic equipment and storage medium | |
| CN110705650B (en) | Sheet metal layout method based on deep learning | |
| CN113112121A (en) | Workshop layout scheduling optimization method based on multi-objective non-dominated sorting | |
| Drira et al. | Facility layout problems: A literature analysis | |
| Forghani et al. | A genetic algorithm for solving integrated cell formation and layout problem considering alternative routings and machine capacities | |
| CN103246923A (en) | Self-adaptive genetic algorithm-based flexible workshop dispatching technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |