CN113159687B - Workshop AGV-UAV (automated guided vehicle-unmanned aerial vehicle) coordinated material distribution path planning method and system - Google Patents
Workshop AGV-UAV (automated guided vehicle-unmanned aerial vehicle) coordinated material distribution path planning method and system Download PDFInfo
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Abstract
The invention discloses a material distribution path planning method and system for cooperation of an AGV-UAV in a workshop. The method comprises the following steps: determining initial parameters of a material distribution path; because the AGV and the UAV are both constrained by energy and can not timely supplement electric quantity in the driving process, the energy consumption of all the processes of the UAV and the AGV material distribution is taken as an optimization target, a path optimization model of the AGV-UAV cooperative material distribution is constructed, and corresponding constraint conditions are given; and solving an optimization model of path planning by adopting an improved genetic algorithm, so as to obtain an optimal path planning scheme. According to the invention, an optimization model is established, and an improved genetic algorithm is adopted for solving, so that the distribution efficiency is improved.
Description
Technical Field
The invention relates to the field of distribution path planning, in particular to a workshop AGV-UAV (automated guided vehicle-unmanned aerial vehicle) coordinated material distribution path planning method and system.
Background
This section merely sets forth background information related to the invention and does not necessarily constitute prior art.
The material distribution is an important part in the workshop and plays a very important role in the connection of various working procedures in the workshop. For the intelligent workshops which are studied actively nowadays, the method for carrying out material distribution by using the AGV trolley has a plurality of advantages compared with the traditional manual material conveying mode, the manual work can be saved by using the AGV trolley for distribution, the working efficiency can be improved, and the automation degree is high.
Along with the continuous maturity of unmanned aerial vehicle technique, unmanned aerial vehicle is continuously paid attention to by people and has put into use in a large number in the aspect of the commodity circulation, and we can see unmanned aerial vehicle's delivery and compare traditional on-vehicle transportation and have a lot of advantages again, unmanned aerial vehicle need not consider the influence of abominable topography in the transportation, and delivery time has also obviously reduced, and delivery efficiency is high.
Therefore, based on the application scene of workshops, the three-dimensional space of workshops is fully utilized, the working efficiency and the automation degree of material distribution are improved, and how to optimize the path of the AGV and the UAV for material distribution in a coordinated manner becomes a problem to be solved.
Disclosure of Invention
In order to solve the problems, the invention provides a workshop AGV-UAV (automated guided vehicle-unmanned aerial vehicle) cooperative material distribution path planning method and system, an optimization model is established, an improved genetic algorithm is adopted for solving, and distribution efficiency is improved.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a workshop AGV-UAV coordinated material distribution path planning method comprises the following steps:
acquiring initial parameters of a material distribution path by adopting an AGV and an UAV;
based on the initial parameters, taking energy consumption of all processes of the UAV and the AGV material distribution as an optimization target, and constructing a path optimization model of the AGV-UAV cooperative material distribution;
and according to the constraint conditions, solving a path planning optimization model by adopting an improved genetic algorithm to obtain an optimal path planning scheme.
As a further improvement of the present invention, the initial variables include the travel speed of each AGV and UAV, the processing and delivery processes of the material, the travel distance of the AGV and the flight distance of the UAV between the processing devices, the energy consumed within the unit distance of the AGV and UAV, the mass of the delivered material, and the 0-1 decision variables of the AGV and UAV; wherein a 0-1 decision variable indicates whether a certain dispensing procedure of the material selects the AGV or the UAV for dispensing.
As a further improvement of the invention, the minimum optimization objective of the AGV-UAV in coordination with material delivery is constructed from the energy consumed by the AGV and UAV in units of travel distance.
As a further improvement of the invention, the constraints are:
the distribution process of each material can be carried out on one AGV or UAV only;
AGV and UAV delivery time does not exceed the time limit of the processing procedure;
each material distribution requirement has a strict sequence;
an AGV or UAV can only deliver one delivery requirement of one material at a time;
the mass of the material dispensed by each AGV and UAV does not exceed the maximum load;
the total energy consumed by each AGV and UAV delivery cannot exceed the maximum energy that it is permitted to consume.
As a further improvement of the invention, the specific process of solving the path planning optimization model by adopting the improved genetic algorithm according to constraint conditions is as follows:
firstly, adopting a multilayer coding mode based on natural numbers to carry out coding and decoding;
taking the reciprocal of the total energy consumption of the distribution scheme represented by each chromosome as the fitness value of the genetic algorithm;
then initializing a population by adopting a hill climbing algorithm;
then selecting the initial population by adopting a selection method combining a tournament selection method and a roulette method;
finally, the CX crossing method is adopted to carry out crossing operation and mutation operation is carried out through random gene position exchange.
As a further improvement of the present invention, the inverse of the total energy consumption of the distribution scheme represented by each chromosome is taken as an fitness value of the present genetic algorithm, and specifically includes:
giving chromosomes, wherein the chromosomes are divided into three layers, the first layer is a main chromosome, the crossover and mutation operations are performed, the rest is a secondary chromosome, the crossover and mutation operations are not performed, and the secondary chromosome is recoded after crossover and mutation along with the main chromosome;
each gene position of the first layer represents a selected dispensing equipment number, namely a selected part of the dispensing equipment, and the upper layer of the gene position represents the sorting of the dispensing equipment, wherein the part is a known condition and does not participate in calculation, so that the gene position does not belong to the chromosome category, can be conveniently encoded, in the layer of the gene position, each number represents the number of materials, each number represents the total dispensing procedure number of the materials, and each number represents the dispensing sequence of the dispensing procedure, so that the gene position is sequentially arranged from the first dispensing procedure of the first material to the end of the last dispensing procedure of the last material;
the second layer is a combined chromosome, the length of the second layer is twice that of the first layer, namely twice that of the total distribution process, each gene position in the left half part sequentially corresponds to the distance required to be distributed by the distribution equipment selected by each process, and the distance from the last standby point of the equipment to the starting point of the distribution process is required to be calculated if the equipment is selected before;
the third layer of chromosomes is located immediately below the second layer and has a length consistent with the first layer and represents the energy consumption of the selected dispensing device to dispense the material per unit time;
scanning the first layer of chromosomes from left to right in turn, and determining the distribution equipment selected by each distribution process firstly by knowing the sequence of each distribution process; scanning the second layer of chromosomes in the same way in turn, and obtaining the distribution distance of each process on the corresponding distribution equipment from the left half part and the distance from the last standby point of the distribution equipment to the processing equipment at the starting point of the process from the left half part when scanning the second layer of chromosomes; finally scanning the third layer chromosome to obtain the data of unit energy consumption;
the problem is encoded in the form of chromosomes, each chromosome is a solution, and the total energy consumption of the expression scheme can be obtained for each chromosome, so that the fitness of the chromosome minimizing the total energy consumption should be the highest, and the inverse of the total energy consumption of the distribution scheme represented by each chromosome is used as the fitness value of the genetic algorithm.
As a further improvement of the present invention, the mutation operation includes:
two determined positions are randomly generated on the part needing chromosome calculation, and genes at the two positions are interchanged to obtain a new chromosome.
As a further improvement of the invention, a mountain climbing algorithm is adopted to initialize the population, and the specific process is as follows:
(1) Taking an individual from the population to perform mountain climbing operation;
(2) Decoding the extracted individual, and calculating the fitness of the individual;
(3) Randomly selecting a point in the chromosome part of the individual procedure, and mutating the value of the gene position of the point to obtain a new chromosome, wherein the new chromosome is used as a neighborhood of an original chromosome;
(4) Calculating a new chromosome as a fitness value, if the fitness value is superior to that of the original chromosome, replacing the original chromosome, otherwise, retaining the original chromosome;
(5) Repeating processes (3) and (4) until a maximum number of iterations is reached.
(6) And (3) carrying out mountain climbing operation on the individuals left in the population according to the method until all the individuals in the population carry out mountain climbing operation.
As a further improvement of the invention, the selection method combining the tournament selection method and the roulette method is adopted to select the initial population, and the specific steps are as follows:
(1) The individual with the highest fitness value in the initial population is determined as the excellent individual;
(2) Directly copying the excellent individuals to the next generation, and selecting the initial population by using a roulette method by the rest individuals;
(3) And when the next generation population is subjected to selection operation, comparing the fitness value of each individual in the population with the excellent individual. If the fitness value of each individual in the population is worse than the fitness value of the good individual, the good individual is directly copied to the next generation; if individuals with fitness values better than the fitness values of good individuals exist in the population, the individuals are directly reserved to the next generation, and the rest individuals are selected for roulette;
(4) Repeating the step (3) until the population iteration is finished.
A shop-AGV-UAV coordinated material delivery path planning system comprising:
the acquisition unit is used for acquiring initial parameters of a material distribution path by adopting the AGV and the UAV;
the modeling unit is used for constructing a path optimization model of the AGV-UAV collaborative material distribution by taking the energy consumption of all the processes of the UAV and the AGV material distribution as an optimization target based on the initial parameters;
and the solving unit is used for solving the path planning optimization model by adopting an improved genetic algorithm according to the constraint condition to obtain an optimal path planning scheme.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the AGV and the UAV are cooperated to carry out material distribution of the workshop, so that the distribution efficiency of the workshop is greatly improved, the three-position environment of the workshop is fully utilized, the workshop is more automated, and an improved genetic algorithm is adopted to solve the optimization model, so that the optimal solution can be found more quickly, and the distribution efficiency is improved.
In workshops, some topography is complex, and for places where the topography is complex and the AGVs are inconvenient to reach, unmanned aerial vehicles are matched to carry out material distribution, minimum energy consumption of the AGVs and the UAVs is used as an optimization model, and under corresponding constraint conditions, an improved genetic algorithm is adopted to carry out solving, so that accurate and rapid distribution of materials is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a method for planning a material delivery path by cooperation of an AGV-UAV in a workshop;
FIG. 2 is a diagram of a production line Cheng Gante of the individual material processing steps of one example of the process of the present invention;
FIG. 3 is a schematic representation of the encoding in the method of the present invention;
FIG. 4 is a schematic diagram of a population initialization process according to the present invention;
FIG. 5 is a schematic diagram of a selection operation flow in the present invention;
FIG. 6 is a diagram showing an example of the crossover operation in the present invention;
FIG. 7 is a diagram illustrating a variation operation in the present invention;
FIG. 8 is a schematic diagram of a plant AGV-UAV coordinated material delivery path planning system;
fig. 9 is a schematic diagram of an electronic device.
Detailed Description
The invention is further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. 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 present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
As shown in FIG. 1, the invention provides a material distribution path planning method cooperated with an AGV-UAV in a workshop, which comprises the following steps:
s1: determining initial parameters of a material delivery path: the automatic feeding device comprises the running speed of each AGV and each UAV, the processing procedure and the distribution procedure of materials, the running distance of each AGV between processing equipment and the flight distance of each UAV, and the energy consumed in unit distance of each AGV and each UAV.
The initial variables further include: the quality of the dispensed material, 0-1 decision variables for AGVs and UAVs; wherein a 0-1 decision variable indicates whether a certain dispensing procedure of the material selects the AGV or the UAV for dispensing.
For example, in this example, there are S materials P at a single material Base site Base0 of the plant, and given all process schedules for materials P, H AGVs and F UAVs are now employed to dispense S materials P from the material Base site Base0 onto the respective process equipment i, j that each material P needs to reach. And because the maximum loads of the AGVs and the UAVs are different, the RFID tags are used for collecting the quality data of each material, so that the UAVs and the AGVs can make certain distribution selection according to the quality data of the materials. And each dispensing process of the material P may select a different UAV or AGV device for dispensing.
Decision variables include:
s2: and taking the energy consumption of all the processes of the UAV and the AGV material distribution as an optimization target, and constructing a path optimization model of the AGV-UAV cooperative material distribution.
The path optimization model of material delivery is as follows:
the parameters and symbols required for the model are shown in the following table:
TABLE 1
Objective function:
constraint:
wherein L is a very large positive number, and the rest symbols have the meanings:
wherein the method comprises the steps ofIndicating the sum of the dispensing processes for all materials.
(2) Indicating that the dispensing process for each material can only be performed on one AGV or UAV.
(3) Representing the time constraints on the selected AGV.
(4) Representing time constraints on the selected UAV.
(5) (6) represents a demand sequencing constraint for each material delivery.
(7) (8) means that an AGV or UAV can only deliver one dispensing request for one material at a time.
(9) Indicating that the mass of material dispensed by each AGV does not exceed its maximum load.
(10) Indicating that the mass of material dispensed by each UAV does not exceed its maximum load.
(11) Indicating that the total energy consumed by each AGV delivery cannot exceed the maximum energy that it is permitted to consume.
(12) Indicating that the total energy consumed by each UAV delivery cannot exceed the maximum energy that it is permitted to consume.
(13) Indicating that the number of material dispensing procedures performed by each AGV or UAV cannot exceed a limit.
(14) Indicating that each variable parameter is positive.
As some possible implementations, the relevant assumptions are made as follows:
each AGV and UAV is in a full charge state before beginning dispensing.
The specifications of each AGV and the UAV are the same, namely the speed of each AGV is the same, the speed of each UAV is also the same, and the running speed of each UAV is not influenced by the quality of materials.
And the volume factor of the materials is not considered.
AGVs and UAVs operate independently without interfering with each other.
AGVs and UAVs travel at even speeds.
The travel process of AGVs and UAVs is considered an unobstructed pass.
At the same point in time, a material can only be dispensed by an AGV or UAV.
The distribution processes of one material have strict sequence, and the distribution processes of different materials have no strict sequence.
The priority of each material is the same.
AGVs and UAVs do not allow for a non-consequential stop to them during the dispensing process.
All AGVs and UAVs remain on standby at the target device after completing a material dispensing task.
Under the corresponding constraint condition, an improved genetic algorithm is adopted to solve the optimal solution of the path planning model, and the specific process is as follows:
the example shown in fig. 2 is first encoded using a natural number based multi-layer encoding scheme.
TABLE 2 dispensing procedures required for materials and energy consumption required for UAV and AGV dispensing
TABLE 3 distance traveled by AGV between processing tools (m)
TABLE 4 flight distance (m) of UAVs between processing devices
The AGV speed was given as 10m/min and the UAV flight speed was given as 15m/min. FIG. 3 shows a chromosome (within a rectangular box) for the coding given in the example, the chromosome is divided into three layers, the first layer is the master chromosome, the crossover and mutation operations are performed, the rest is the slave chromosome, the crossover and mutation operations are not performed, but they are recoded after crossover and mutation along with the master chromosome. Each gene of the first layer represents a selected dispensing device number, i.e., a selected portion of the dispensing device, on which a number of digits representing the ordering of the dispensing process, which is a known condition, does not participate in the calculation and therefore does not fall into the chromosome category, where other gene codes may be facilitated, where the number of digits represents the number of materials, the number of times each digit represents the total number of dispensing processes for the material, and the order of each digit represents the order of dispensing processes, e.g., the first "2" of the occurrence represents the material P 2 The second "2" appearing indicates the material P 2 The second distribution step O of (2) 2 13 . The gene positions are arranged in sequence from the beginning of the first delivery process of the first material to the end of the last delivery process of the last material, corresponding to the first layer of chromosomes, i.e. the selected part of the delivery device. Since this example has three materials and a total of seven dispensing steps, the chromosomal genes of the selected portion of the dispensing apparatus are arranged according to the corresponding dispensing stepsFor example, the right-most digit "4" of the layer represents the material P 2 The third distribution step O of (2) 2 32 The 4 th dispensing device, UAV2, is selected for dispensing. The second layer is a combined dye body, the length of the combined dye body is twice that of the first layer, namely twice that of the total distribution process, each gene position in the left half part sequentially corresponds to the distance required to be distributed by the distribution equipment selected by each process, the right half part is used for calculating the distance from the last standby point of the equipment to the starting point of the distribution process if the equipment is selected before, for example, the third gene position '2' in the chromosome of the first layer is used for being filled with the material P before 1 If the first dispensing step of the material P1 is selected, then a second selection of the dispensing device requires the addition of the device from the end point "machine 4" of the first dispensing step of the material P1 to the material P 3 Distance "13" from the start point "machine tool 2" of the first dispensing step. The third layer of chromosomes is located immediately below the second layer and is of a length consistent with the first layer, indicating the energy consumption per unit time of the selected dispensing device to dispense the material. The advantage of using such coding is that the readability of the chromosome is improved, and the key information of the whole problem is contained in the chromosome, so that the decoding process is clear.
Decoding: the first chromosome layer is scanned from left to right, and the distribution equipment selected by each distribution process can be determined first because the sequence of each distribution process is known. In the same manner, the second layer of chromosome is scanned in turn, and when the second layer of chromosome is scanned, the distance of the distribution device from the last standby point to the processing device at the start point of each process is obtained from the left half part, and the distance of the distribution device from the corresponding distribution device is obtained from the left half part. And finally scanning the third layer chromosome to obtain the data of unit energy consumption.
The problem is encoded in the form of chromosomes, each chromosome is a solution, and the total energy consumption of the expression scheme can be obtained for each chromosome, so that the fitness of the chromosome minimizing the total energy consumption should be the highest, and the inverse of the total energy consumption of the distribution scheme represented by each chromosome is used as the fitness value of the genetic algorithm. I.e.
Then initializing a population by adopting a hill climbing algorithm; the specific process is as follows:
taking an individual from the population to perform mountain climbing operation.
And decoding the extracted individual to calculate the fitness of the individual.
Randomly selecting a point in the chromosome part of the individual process, and mutating the gene position value of the point to obtain a new chromosome, wherein the new chromosome is used as the neighborhood of the original chromosome.
And calculating the new chromosome as a fitness value, replacing the original chromosome if the fitness value is superior to that of the original chromosome, otherwise, retaining the original chromosome.
The process is repeated until a maximum number of iterations is reached.
And (3) carrying out mountain climbing operation on the rest individuals of the population according to the method until all the individuals of the population carry out mountain climbing operation.
The specific flow chart is shown in fig. 4.
Then selecting the initial population by adopting a selection method combining a tournament selection method and a roulette method; the method comprises the following specific steps:
the individual with the highest fitness value in the initial population is determined as the excellent individual.
The good individuals are directly copied to the next generation and the remaining individuals use roulette to perform a selection operation on the initial population.
When the next generation population is selected, the fitness value of each individual in the population is compared with the fitness value of the good individual. If the fitness value of each individual in the population is worse than that of the good individual, the good individual is directly copied to the next generation; if individuals with fitness values better than the fitness values of good individuals exist in the population, the individuals are directly reserved to the next generation, and the rest individuals are subjected to roulette selection.
Repeating the steps until the population iteration is finished.
The concrete flow chart is shown in FIG. 5
Finally, the CX crossing method is adopted to carry out crossing operation and mutation operation is carried out through random gene position exchange.
A Cycle Cross (CX) Crossover method is adopted, which can retain the excellent gene of the parent and can also enable the offspring to have feasibility. First, two parent chromosomes P are selected 1 ，P 2 And then randomly selecting the same gene position from the two parent chromosomes, if the genes of the selected gene positions are identical, the selected gene positions need to be reselected, and then the selected gene positions are directly copied to the offspring O according to the original positions 1 ，O 2 Will P 2 Residual genes of (A) are put into O 1 ，P 1 Residual genes of the gene are put into O 2 An example illustration is shown in fig. 6.
Mutation operation: two defined positions are randomly generated on the part where the chromosome is required to be calculated, and genes at the two positions are interchanged to obtain a new chromosome, so that the chromosome can be ensured to be resolvable, and an example diagram is shown in figure 7.
Another object of the present invention, as shown in fig. 8, is to provide a material distribution path planning system cooperated with an AGV-UAV in a workshop, comprising:
the acquisition unit is used for acquiring initial parameters of a material distribution path by adopting the AGV and the UAV;
the modeling unit is used for constructing a path optimization model of the AGV-UAV collaborative material distribution by taking the energy consumption of all the processes of the UAV and the AGV material distribution as an optimization target based on the initial parameters;
and the solving unit is used for solving the path planning optimization model by adopting an improved genetic algorithm according to the constraint condition to obtain an optimal path planning scheme.
As shown in fig. 9, a third object of the present invention is to provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the shop floor AGV-UAV collaborative material distribution path planning method when executing the computer program.
A fourth object of the present invention is to provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the shop floor AGV-UAV collaborative material delivery path planning method.
It will be appreciated by those skilled in the art that 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 an entirely hardware embodiment, an entirely 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, CD-ROM, 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (8)
1. The method for planning the material distribution path by the cooperation of the AGV-UAV in the workshop is characterized by comprising the following steps of:
acquiring initial parameters of a material distribution path by adopting an AGV and an UAV;
based on the initial parameters, taking energy consumption of all processes of the UAV and the AGV material distribution as an optimization target, and constructing a path optimization model of the AGV-UAV cooperative material distribution;
the path optimization model of material delivery is as follows:
the parameters and symbols required for the model are as follows:
objective function:
constraint:
wherein L is a very large positive number, and the rest symbols have the meanings:
wherein the method comprises the steps ofRepresenting the sum of the distribution procedures of all materials;
(2) Indicating that the dispensing process for each material can only be performed on one AGV or UAV;
(3) Representing a time constraint on the selected AGV;
(4) Representing a time constraint on the selected UAV;
(5) (6) representing a sequential constraint for each material delivery requirement;
(7) (8) means that an AGV or UAV can only deliver one delivery request of one material at a time;
(9) Indicating that the mass of material dispensed by each AGV does not exceed its maximum load;
(10) Indicating that the mass of material dispensed by each UAV does not exceed its maximum load;
(11) Indicating that the total energy consumed by each AGV delivery cannot exceed the maximum energy that it is permitted to consume;
(12) Indicating that the total energy consumed by each UAV delivery cannot exceed the maximum energy that it is permitted to consume;
(13) Indicating that the number of material dispensing procedures performed by each AGV or UAV cannot exceed a limit;
(14) Indicating that each variable parameter is positive;
solving a path planning optimization model by adopting an improved genetic algorithm according to constraint conditions to obtain an optimal path planning scheme;
according to constraint conditions, the specific process of solving the path planning optimization model by adopting the improved genetic algorithm is as follows:
firstly, adopting a multilayer coding mode based on natural numbers to carry out coding and decoding;
taking the reciprocal of the total energy consumption of the distribution scheme represented by each chromosome as the fitness value of the genetic algorithm;
then initializing a population by adopting a hill climbing algorithm;
then selecting the initial population by adopting a selection method combining a tournament selection method and a roulette method;
finally, performing crossover operation by adopting CX crossover method and performing mutation operation by randomly exchanging gene positions;
the reciprocal of the total energy consumption of the distribution scheme represented by each chromosome is taken as the fitness value of the genetic algorithm, and specifically comprises the following steps:
giving chromosomes, wherein the chromosomes are divided into three layers, the first layer is a main chromosome, the crossover and mutation operations are performed, the rest is a secondary chromosome, the crossover and mutation operations are not performed, and the secondary chromosome is recoded after crossover and mutation along with the main chromosome;
each gene position of the first layer represents a selected distribution equipment number, namely a selected part of the distribution equipment, and the upper layer of the gene position represents the sequence of distribution procedures, and the part is a known condition; in the layer digital part, each number represents the number of the material, each number of occurrences represents the total distribution procedure number of the material, and each number of occurrences represents the distribution sequence of the distribution procedure, so that the gene positions are sequentially arranged from the first distribution procedure of the first material to the last distribution procedure of the last material corresponding to the first layer of the chromosome, namely the part selected by the distribution equipment;
the second layer is a combined chromosome, the length of the second layer is twice that of the first layer, namely twice that of the total distribution process, each gene position in the left half part sequentially corresponds to the distance required to be distributed by the distribution equipment selected by each process, and the distance from the last standby point to the starting point of the distribution process of the equipment is required to be calculated if the equipment is selected before;
the third layer of chromosomes is located immediately below the second layer and has a length consistent with that of the first layer and represents the energy consumption of the material dispensed per unit time of the selected dispensing device;
scanning the first layer of chromosomes in turn from left to right, and scanning the second layer of chromosomes in turn in the same way, wherein when the second layer of chromosomes is scanned, the left half part obtains the distribution distance of each process on the corresponding distribution equipment, and the left half part obtains the distance from the last standby point of the distribution equipment to the process starting point processing equipment; finally scanning the third layer chromosome to obtain the data of unit energy consumption;
encoding the questions in the form of chromosomes, each chromosome being a solution, for each chromosome, obtaining the total energy consumption of its representation; the fitness of the chromosome that minimizes the total energy consumption is highest, so the reciprocal of the total energy consumption of the distribution scheme represented by each chromosome is taken as the fitness value of the present genetic algorithm.
2. A method of material delivery path planning in conjunction with a shop AGV-UAV as set forth in claim 1,
the initial variables comprise the running speed of each AGV and UAV, the running distance of each AGV between processing equipment and the flight distance of each UAV, the consumed energy in the unit distance of each AGV and UAV, the quality of the distributed materials and the 0-1 decision variables of each AGV and UAV; wherein, the 0-1 decision variable indicates whether an AGV or UAV is selected for delivery in a certain delivery procedure of the material.
3. A method of material delivery path planning in conjunction with a shop AGV-UAV as set forth in claim 1,
the minimum optimal target for AGV-UAV co-material delivery is constructed from the energy consumed in AGVs and UAVs per distance travelled.
4. A method of material delivery path planning in conjunction with a shop AGV-UAV as set forth in claim 1,
the constraint conditions are as follows:
the distribution process of each material can be carried out on one AGV or UAV only;
AGV and UAV delivery time does not exceed the time limit of the processing procedure;
each material distribution requirement has a strict sequence;
an AGV or UAV can only deliver one delivery requirement of one material at a time;
the mass of the material dispensed by each AGV and UAV does not exceed the maximum load;
the total energy consumed by each AGV and UAV delivery cannot exceed the maximum energy that it is permitted to consume.
5. A method of material delivery path planning in conjunction with a shop AGV-UAV as set forth in claim 1,
the mutation operation comprises the following steps:
two determined positions are randomly generated on the part needing chromosome calculation, and genes at the two positions are interchanged to obtain a new chromosome.
6. A method of material delivery path planning in conjunction with a shop AGV-UAV as set forth in claim 1,
the mountain climbing algorithm is adopted to initialize the population, and the specific process is as follows:
1) Taking an individual from the population to perform mountain climbing operation;
2) Decoding the extracted individual, and calculating the fitness of the individual;
3) Randomly selecting a point in a chromosome part in an individual working procedure, changing the value of a gene position of the point to obtain a new chromosome, and taking the new chromosome as a neighborhood of an original chromosome;
4) Calculating a new chromosome as a fitness value, if the fitness value is superior to that of the original chromosome, replacing the original chromosome, otherwise, retaining the original chromosome;
5) Repeating processes 3) and 4) until a maximum number of iterations is reached;
6) And (3) carrying out mountain climbing operation on the individuals left in the population according to the method until all the individuals in the population carry out mountain climbing operation.
7. A method of material delivery path planning in conjunction with a shop AGV-UAV as set forth in claim 1,
selecting the initial population by adopting a selection method combining a tournament selection method and a roulette method, wherein the selection method comprises the following specific steps:
1) The individual with the highest fitness value in the initial population is determined as the excellent individual;
2) Directly copying the excellent individuals to the next generation, and selecting the initial population by using a roulette method by the rest individuals;
3) When the next generation population is selected, comparing the fitness value of each individual in the population with the excellent individuals; if the fitness value of each individual in the population is worse than that of the good individual, the good individual is directly copied to the next generation; if individuals with fitness values better than the fitness values of good individuals exist in the population, the individuals are directly reserved to the next generation, and the rest individuals are selected for roulette;
4) Repeating the step 3) until the population iteration is finished.
8. A shop floor AGV-UAV coordinated material delivery path planning system, based on the shop floor AGV-UAV coordinated material delivery path planning method of claim 1, comprising:
the acquisition unit is used for acquiring initial parameters of a material distribution path by adopting the AGV and the UAV;
the modeling unit is used for constructing a path optimization model of the AGV-UAV collaborative material distribution by taking the energy consumption of all the processes of the UAV and the AGV material distribution as an optimization target based on the initial parameters;
and the solving unit is used for solving the path planning optimization model by adopting an improved genetic algorithm according to the constraint condition to obtain an optimal path planning scheme.
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