CN115471110A - Conditional probability-based multi-heterogeneous unmanned aerial vehicle system cooperative task allocation method considering multi-objective evolutionary algorithm - Google Patents

Conditional probability-based multi-heterogeneous unmanned aerial vehicle system cooperative task allocation method considering multi-objective evolutionary algorithm Download PDF

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CN115471110A
CN115471110A CN202211191094.6A CN202211191094A CN115471110A CN 115471110 A CN115471110 A CN 115471110A CN 202211191094 A CN202211191094 A CN 202211191094A CN 115471110 A CN115471110 A CN 115471110A
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王昕炜
王磊
高晓华
余馨咏
苏析超
彭海军
吕琛
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Beihang University
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Abstract

A multi-heterogeneous unmanned aerial vehicle system cooperative task allocation method based on conditional probability and considering a multi-objective evolutionary algorithm comprises the steps of firstly, setting an objective function based on the conditional probability and giving constraint conditions according to detected enemy target information, current battlefield situation, fighting risk and fighting resources of one party, and establishing a multi-objective optimization model for heterogeneous unmanned aerial vehicle cooperative task allocation. And secondly, selecting a corresponding genetic operator based on the actual combat situation, and solving a multi-objective optimization model for cooperative task allocation by using an improved multi-objective optimization algorithm. Thirdly, a decision maker selects a certain solution from the Pareto solution set by using a solution selection method according to the preference of the objective function, and takes a task allocation scheme corresponding to the solution as an execution scheme. The method is suitable for developing the multi-heterogeneous unmanned aerial vehicle cooperative task allocation under the condition of various combat resources, can provide a task allocation scheme suitable for actual combat situations for pre-combat task allocation, and has certain significance for the research of the multi-objective optimization problem of the multi-heterogeneous unmanned aerial vehicle cooperative task allocation.

Description

Multi-heterogeneous unmanned aerial vehicle system cooperative task allocation method based on conditional probability and considering multi-objective evolutionary algorithm
Technical Field
The invention belongs to the field of unmanned aerial vehicle task planning, and relates to a multi-heterogeneous unmanned aerial vehicle system cooperative task allocation method based on conditional probability and considering a multi-objective evolutionary algorithm.
Background
Under the present complicated battlefield environment, many heterogeneous unmanned aerial vehicle cooperate the operation to be the important means of coping with the complicated battlefield condition. In order to fully exert the advantages of cooperative combat of multiple heterogeneous unmanned aerial vehicles, based on the currently detected battlefield situation and combat resource conditions, it is very critical to allocate cooperative pre-combat tasks on the basis of comprehensively considering various combat risks (unmanned aerial vehicle damage, task execution failure and the like) to make an efficient combat scheme meeting the combat requirements. The heterogeneity of drones, the complexity of targets, and the increase in the number of drones and targets make the problem model more complex and larger in size. For complex task allocation problems, the characteristics of low dependence degree on the model and capability of rapidly solving large-scale problems of the group intelligent algorithm make the method more advantageous in solving the problem of cooperative task allocation than the traditional precise solving method. However, in previous studies, risks that may exist in actual combat were ignored in order to simplify the model, such as a drone being destroyed by an enemy, a mission not being completed, etc., and the combat resources were assumed to be sufficient. These settings can make the resulting task allocation scheme more difficult than is practical. Meanwhile, chromosome locking is a difficult problem to be solved when an evolutionary algorithm is used, and the efficiency of an unlocking method directly influences the optimization efficiency of the algorithm. In order to comprehensively improve the operational benefits, the minimized operational cost while the maximized operational benefits have important research values under the complex situation with risks at present, and the multi-objective optimization problem needs to be considered when the indexes with conflict relationships are optimized. Therefore, the multi-objective optimization of the collaborative task planning of the heterogeneous unmanned aerial vehicles is of great significance on the premise of comprehensively considering various risks and various combat resource conditions.
Disclosure of Invention
In order to carry out multi-heterogeneous unmanned aerial vehicle system cooperative task allocation based on multi-objective optimization on the basis of considering various risks, the invention provides a multi-heterogeneous unmanned aerial vehicle system cooperative task allocation method based on conditional probability and considering a multi-objective evolutionary algorithm. According to the method, a multi-heterogeneous unmanned aerial vehicle system task planning method considering various risks is constructed by introducing conditional probability and based on a multi-objective evolutionary algorithm capable of optimizing multiple conflict targets at the same time. In the method, a chromosome coding method is provided based on the characteristic of cooperative task allocation of the heterogeneous unmanned aerial vehicle, genetic operators are constructed, the problem of cooperative task allocation under various resource conditions can be solved by an improved multi-objective evolutionary algorithm, and a logic unlocking method for maintaining population randomness and quickly unlocking at a chromosome lock point is provided for the complex condition of chromosome lock. The multi-heterogeneous unmanned aerial vehicle system collaborative task allocation based on multi-objective optimization, which is developed under the condition of considering various risks, better conforms to the actual combat situation, can be suitable for collaborative task allocation under the condition of various resources, and the configuration of the logical unlocking method can greatly improve the allocation efficiency.
In order to achieve the purpose, the invention adopts the technical scheme that:
a multi-heterogeneous unmanned aerial vehicle system cooperative task allocation method considering a multi-objective evolutionary algorithm based on conditional probability includes the steps that firstly, objective functions and constraint conditions are set based on the conditional probability according to detected enemy target information, current battlefield situation, fighting risk and fighting resources of one party, and a multi-objective optimization model of heterogeneous unmanned aerial vehicle cooperative task allocation is established. And then, solving the constructed model by utilizing the constructed multi-objective evolutionary algorithm to obtain a Pareto solution set. And finally, selecting a certain solution from the Pareto solution set by using a solution selection method, and taking a task allocation scheme corresponding to the solution as an execution scheme. The calculation flow chart of the invention is shown in fig. 1, and comprises the following steps:
step 1: according to the detected enemy target information, the current battlefield situation, the fighting risk and the fighting resources of the party, setting a target function based on the conditional probability and giving constraint conditions, and establishing a multi-target optimization model for the cooperative task allocation of the heterogeneous unmanned aerial vehicle, which is concretely as follows:
step 1-1: collecting fundamental data of enemy targets and my unmanned aerial vehicles in mission planning
For convenience of description, order
Figure BDA0003869424570000021
Setting the detected enemy target to contain N T Target, jth target marked as T j The set of objects is denoted as
Figure BDA0003869424570000022
Each target contains three types of subtasks: the classification task, the attack task and the evaluation task are respectively marked as C, A and V. Different enemy targets have different values and risks, and high value targets are often accompanied by high risks. Setting a target T j The value of (A) is recorded as
Figure BDA0003869424570000023
The high value target was designated Hv and the low value target was designated Lv. In order to improve the success rate of the battle, the attack on the high-value target is set twice, and the attack on the low-value target is set once. Let A i Representing the ith attack task, target
Figure BDA0003869424570000024
Is recorded as a task set
Figure BDA0003869424570000025
Setting up
Figure BDA0003869424570000026
And
Figure BDA0003869424570000027
respectively represent the unmanned aerial vehicle to the target T j Subtasks C, A of 1 、A 2 And the starting execution time of V,
Figure BDA0003869424570000028
and
Figure BDA0003869424570000029
respectively represent the unmanned aerial vehicle to the target T j Subtasks C, A of 1 、A 2 And the execution time of V.
Setting N in unmanned aerial vehicle cluster U Erect unmanned aerial vehicle, the ith unmanned aerial vehicle marks as U i The set of drones is noted
Figure BDA00038694245700000210
The set cluster comprises three types of heterogeneous unmanned aerial vehicles: the reconnaissance plane, the fighter plane and the ammunition plane are respectively marked as s, c and m. The types of tasks executable by the s-type unmanned aerial vehicle are C and V, the types of tasks executable by the C-type unmanned aerial vehicle are C, A and V, and the type of tasks executable by the m-type unmanned aerial vehicle is A. Let unmanned aerial vehicle U i Is recorded as
Figure BDA00038694245700000211
The success rate and survival rate for different drones performing different tasks for different targets are different. Setting up
Figure BDA00038694245700000212
Express unmanned plane U i Execution target T j The success rate of the task type k of (c),
Figure BDA00038694245700000213
express unmanned plane U i Execution target T j The mortality rate of task type k, wherein,
Figure BDA00038694245700000214
k∈I 3 k =1,2,3 denotes task types C, a, and V, respectively. According to the capability of the unmanned aerial vehicle, when the unmanned aerial vehicle U i Is of type s
Figure BDA00038694245700000215
When unmanned plane U i Is m times
Figure BDA00038694245700000216
Let unmanned aerial vehicle U i Is recorded as a task set
Figure BDA00038694245700000217
Wherein
Figure BDA00038694245700000218
Presentation to unmanned aerial vehicle U i The number of tasks of (a) is,
Figure BDA00038694245700000219
express unmanned plane U i The ith executed task in the task set.
Step 1-2: constructing an objective function of a model
For target T j If some subtasks are not successfully executed, the target T is determined to be successful execution of the subtasks j Is not successfully executed. There are two risks that a drone may be destroyed while performing a task and that the task is not successfully completed. To achieve maximum operational benefit at minimum operational cost, consider as objective functions the value expectation of maximizing the targets that are successfully executed and the value expectation of minimizing the drone that is destroyed. The two objective functions are constructed by introducing conditional probabilities. Order to
Figure BDA00038694245700000220
Express unmanned plane U i Whether to slave target
Figure BDA00038694245700000221
Flying to target T j Performing T j If the value of the task type k is 1, execution is indicated, otherwise, execution is not performed. Wherein, V j Representing a target T j Of the position of (a).
(i) Maximizing value expectation of successfully executed targets
Figure BDA00038694245700000222
(ii) Minimizing value expectation of a crashed drone
Figure BDA0003869424570000031
Wherein,
Figure BDA0003869424570000032
to represent
Figure BDA0003869424570000033
The first task of the group (c) of (c),
Figure BDA0003869424570000034
order to
Figure BDA0003869424570000035
Based on the actual situation
Figure BDA0003869424570000036
Figure BDA0003869424570000037
And
Figure BDA0003869424570000038
(ii) minimizing the target (i) as follows
(iii) Minimizing value expectation of unsuccessfully executed targets
Figure BDA0003869424570000039
Let f = (f) 1 ,f 2 ) T Wherein f is 1 ＝J 3 ，f 2 ＝J 2 。
Step 1-3: constraint condition of construction model
Based on the actual situation of the collaborative task assignment and the above analysis, the following constraints are considered.
The payload of a drone is limited, and therefore, in actual combat mission allocation, the number of type a missions allocated to each drone cannot exceed the payload of that drone. The number of tasks of task types C and V allocated to the unmanned aerial vehicle capable of executing task types C and V is not limited.
Figure BDA00038694245700000310
Wherein, AM i Express unmanned plane U i The load capacity of (2). If the type of the unmanned aerial vehicle is s, the payload of the unmanned aerial vehicle is 0.
In order to shorten the stay time of the same unmanned aerial vehicle on the same target and reduce the risk of discovering and destroying the unmanned aerial vehicle, the attack frequency of the same unmanned aerial vehicle on the same target is assumed to be not more than 1.
Figure BDA00038694245700000311
Thus, different attack missions of high value targets may be assigned to different drones.
The tasks of each target need to be performed in order, i.e. timing constraints need to be met. For the same target task, task type a can only be executed after task type C is completed, and task type V can only be executed after task type a is completely completed. Thus, task allocation needs to satisfy the following constraints.
Figure BDA0003869424570000041
Wherein, if the target T j For a high value target, then
Figure BDA0003869424570000042
Otherwise
Figure BDA0003869424570000043
When each unmanned aerial vehicle executes the tasks in the task set, the tasks are required to be executed according to the sequence in which the tasks in the set are distributed, that is to say
Figure BDA0003869424570000044
Can only be performed after its previous task is completed.
Figure BDA0003869424570000045
Wherein,
Figure BDA0003869424570000046
represent
Figure BDA0003869424570000047
Step 1-4: multi-objective optimization model for constructing multi-heterogeneous unmanned aerial vehicle cooperative task allocation
Based on the objective function and the constraint condition constructed above, the multi-objective optimization of the cooperative task allocation of the heterogeneous unmanned aerial vehicle is to minimize the vector function f on the basis of satisfying the constraint (4) - (7).
min f＝(f 1 ,f 2 ) T
s.t.(4)-(7)
Step 2: solving a multi-objective optimization model for collaborative task allocation by using the constructed multi-objective evolutionary algorithm to obtain a Pareto solution set thereof, which is specifically as follows:
step 2-1: setting parameters for improved multiobjective optimization algorithms
Setting population size S, maximum iteration number G and cross probability P c And the probability of variation P m The value of (c).
Step 2-2: initializing a population
The coding of the chromosome is in such a way that the chromosome is constituted by genes, each of which represents the assignment of a certain task. The information in the gene includes a target number corresponding to the assigned task, the assigned task type, and a number of the unmanned aerial vehicle executing the task. The corresponding genome of the same unmanned aerial vehicle forms a sub-chromosome, and all the sub-chromosomes form a complete chromosome. Randomly generating a population P with the size of S g (g = 0). The initialization procedure for generating an initial population containing S chromosomes according to the above coding scheme is given below.
Step 2-2-1: inputting target parameters and population quantity of unmanned aerial vehicle and enemy
Inputting ammunition amount A of all unmanned aerial vehicles in array form m The unmanned aerial vehicle number sets comprise an enemy target number set TS, unmanned aerial vehicle number sets AU with types of c and m, and unmanned aerial vehicle number sets CVU with types of s and c. Let the number of chromosomes in the population equal 0.
Step 2-2-2: judging the chromosome number of the current population
And if the number of chromosomes in the population is equal to S, completing population initialization and exiting the current initialization process. Otherwise, executing step 2-2-3 to step 2-2-4
Step 2-2-3: an empty Chromosome is initialized and recorded as Chromosome
Step 2-2-4: encoding chromosomal Chromosome
Step 2-2-4-1: randomly selecting a target from TS, and recording as
Figure BDA0003869424570000048
Step 2-2-4-2: for the selected target
Figure BDA0003869424570000049
Subtask C and attack task A 1 Is distributed
The genes Gene1, gene2, gene3, gene4 are initialized. Firstly, randomly selecting an unmanned aerial vehicle from CVU (continuously variable Unit), and recording the unmanned aerial vehicle as
Figure BDA0003869424570000051
Order to
Figure BDA0003869424570000052
Chromosome = Chromosome @ Gene1. Then randomly selecting an unmanned aerial vehicle from AU (all-in-one) to be recorded as
Figure BDA0003869424570000053
Order to
Figure BDA0003869424570000054
Chromosome = Chromosome @ Gene2, and updates a m Instant command
Figure BDA0003869424570000055
Step 2-2-4-3: if it is
Figure BDA0003869424570000056
Is a high value target and A m Not equal to 0, then the target is checked
Figure BDA0003869424570000057
Attack task A of 2 Distributing, otherwise, turning to step 2-2-4
Randomly selecting an unmanned aerial vehicle from AU on the premise of satisfying constraint (5) and recording the unmanned aerial vehicle as
Figure BDA0003869424570000058
Order to
Figure BDA0003869424570000059
Chromosome = Chromosome ═ Gene3. Update A m Instant command
Figure BDA00038694245700000510
Step 2-2-4-4: for the selected target
Figure BDA00038694245700000511
Is allocated to the subtask V of
Randomly selecting an unmanned aerial vehicle from the CVU and recording the selected unmanned aerial vehicle as
Figure BDA00038694245700000512
Removing targets from TS
Figure BDA00038694245700000513
Order to
Figure BDA00038694245700000514
Chromosome＝Chromosome∪Gene4。
Step 2-2-4-5: judging whether the current chromosome is coded
If the length of the array TS is not 0, and A m If the vector is not zero, executing the step 2-2-4-1 to the step 2-2-4-5, otherwise, adding 1 to the number of chromosomes in the population, exiting the encoding process of the current chromosome, and turning to the step 2-2-2.
Step 2-3: calculating a population P according to the objective functions (2) and (3) g Fitness value of middle chromosome
Step 2-4: generation of S-size sub-populations
Step 2-4-1: from the population P using a roulette algorithm g Two crossed parents F are selected 1 ,F 2
Step 2-4-2: selecting corresponding crossover operators according to the conditions of total resources and total task quantity, and performing crossover operation according to crossover probability
Let L denote the number of high value targets. Order RM i Represents the parent F i Corresponding combinations of drones with residual ammunition, RT i Representing the parent F i A set of enemy targets that are not allocated. Let p be 1 And p 2 Express from crossA randomly selected cross-point in the fork parent,
Figure BDA00038694245700000515
and
Figure BDA00038694245700000516
respectively represent F 1 And F 2 Cross point p of 1 And p 2 A set of drones performing task types C and V,
Figure BDA00038694245700000517
and
Figure BDA00038694245700000518
respectively represent F 1 And F 2 Cross point p of 1 And p 2 A set of drones executing task type a. Let PT 1 And PT 2 Respectively represent F 1 And F 2 The set of allocated targets.
By the parent F 1 The interleaving procedure is given as an example. If it is
Figure BDA00038694245700000519
I.e. the ammunition is sufficient and has a residue of
Figure BDA00038694245700000520
In this case, the crossover process is step 2-4-2-1 to step 2-4-2-4.
Step 2-4-2-1: inputting information of intersection and intersection parent, i = p 1 -1
Step 2-4-2-2: judging whether the cross process is finished
If i satisfies i ≦ p 2 1, executing the step 2-4-2-3 to the step 2-4-2-4, otherwise, quitting the opposite parent F 1 The interleaving operation of (1).
Step 2-4-2-3: performing cross operation on the gene at the cross point i
The following operation was performed when the task type in the gene at the intersection i is A. If it is
Figure BDA00038694245700000521
And is
Figure BDA00038694245700000522
If the corresponding target in the gene at the cross point i is removed and is not an empty set, then
Figure BDA00038694245700000523
Randomly selecting an unmanned aerial vehicle, otherwise, selecting from RM 1 And (4) randomly selecting an unmanned aerial vehicle from the set after removing the corresponding target in the gene at the cross point i. Replacing information of the unmanned aerial vehicle in the current gene with the information of the selected unmanned aerial vehicle, and updating the RM 1 。
The following operation is performed when the task type in the gene at the intersection i is C or V. If it is
Figure BDA0003869424570000061
Then the slave is random
Figure BDA0003869424570000062
Randomly selecting an unmanned aerial vehicle, otherwise, never belonging to
Figure BDA0003869424570000063
Randomly selecting one unmanned aerial vehicle from the unmanned aerial vehicles of type s or c. And replacing the information of the unmanned aerial vehicle in the current gene by using the information of the unmanned aerial vehicle.
Step 2-4-2-4: let i = i +1, go to step 2-4-2
If it is
Figure BDA0003869424570000064
I.e. the ammunition is sufficient and has no residue, there is
Figure BDA0003869424570000065
The crossover process now goes from step 2-4-2-5 to step 2-4-2-8.
Step 2-4-2-5: inputting information of intersection and intersection parent, and making i = p 1 -1
Step 2-4-2-6: judging whether the cross process is finished
If i satisfies i ≦ p 2 1, executing the step 2-4-2-7 to the step 2-4-2-8, otherwise, quitting the opposite parent F 1 The interleaving operation of (1).
Step 2-4-2-7: performing crossover operation on the gene at the crossover point i
Order cross parent F 1 ,F 2 The targets corresponding to the genes at the cross points i of (A) are respectively referred to as
Figure BDA0003869424570000066
If it is
Figure BDA0003869424570000067
Then go to step 2-4-2-8, otherwise perform the following procedure.
If it is
Figure BDA0003869424570000068
All are low value targets or all are high value targets, then directly at F 1 In exchange for the corresponding gene
Figure BDA0003869424570000069
The information of (1). If it is
Figure BDA00038694245700000610
In order to be a high-value target,
Figure BDA00038694245700000611
for low-value targets, the numbers of targets in the corresponding genes are interchanged first, and then one target is randomly selected
Figure BDA00038694245700000612
And the target number of the gene is changed to
Figure BDA00038694245700000613
If it is
Figure BDA00038694245700000614
In order to be a low-value target,
Figure BDA00038694245700000615
for high value targets, the numbers of targets in the corresponding genes are then interchanged first, and one is then randomly selected
Figure BDA00038694245700000616
Attack the gene and change the target number of the gene to
Figure BDA00038694245700000617
Step 2-4-2-8: let F 1 Cross of (p) 1 And p 2 The number of genes between which the information is updated is denoted as l, and let i = i + l, go to step 2-4-2-6
If it is
Figure BDA00038694245700000618
That is, the ammunition amount is insufficient, there are
Figure BDA00038694245700000619
The crossover process now goes from step 2-4-2-9 to step 2-4-2-13.
Step 2-4-2-9: inputting information of intersection and intersection parent, i = p 1 -1
Step 2-4-2-10: judging whether the cross process is finished
If i satisfies i ≦ p 2 1, executing the step 2-4-2-11 to the step 2-4-2-13, otherwise, exiting the parent F 1 The interleaving operation of (1).
Step 2-4-2-11: performing crossover operation on the gene at the crossover point i
Let the target in the gene at cross point i be recorded
Figure BDA00038694245700000620
If it is
Figure BDA00038694245700000621
Then the slave RT 1 ∩PT 2 In the method, a target is randomly selected, otherwise, the target is selected from the RT 1 Randomly selects a target. Marking the selected target as
Figure BDA00038694245700000622
When in use
Figure BDA00038694245700000623
At the time of low value, if
Figure BDA00038694245700000624
For low value targets, then target
Figure BDA00038694245700000625
Target replacement in all corresponding genes
Figure BDA00038694245700000626
Otherwise, from F 1 Randomly selecting a low value target
Figure BDA00038694245700000627
Target object
Figure BDA00038694245700000628
And
Figure BDA00038694245700000629
target replacement in all corresponding genes
Figure BDA00038694245700000630
When in use
Figure BDA0003869424570000071
When it is a high-value target, if
Figure BDA0003869424570000072
For low value targets, then first from RT 1 In which a target is randomly selected
Figure BDA0003869424570000073
Target object
Figure BDA0003869424570000074
Target replacement in all corresponding genes
Figure BDA0003869424570000075
Then randomly selecting an object
Figure BDA0003869424570000076
And replacing the target of the gene with a target of the gene
Figure BDA0003869424570000077
Finally, add target
Figure BDA0003869424570000078
The genes of task types C and V of (a); if it is
Figure BDA0003869424570000079
For high value targets, the target
Figure BDA00038694245700000710
Target replacement in all corresponding genes
Figure BDA00038694245700000711
Step 2-4-2-12: updating RT 1 And PT 1
Step 2-4-2-13: let F 1 Cross of (p) 1 And p 2 The number of genes whose information is updated in between is denoted as l, and let i = i + l, go to step 2-4-2-10.
Step 2-4-3: performing mutation operation on the cross filial generation according to the mutation probability
The two cross filial generations obtained by the cross operation are marked as O 1 ，O 2 . With O 1 For example, the mutation process is given. Let q be 1 And q is 2 The starting point and the termination point of the gene segment needing variation. Let O be 1 The set of low-to-medium value targets is denoted CT, O 1 The collection of C and V genes is designated G cv ，O 1 The collection of genes of A in (A) is denoted as G a ，O 1 Corresponding set of drones with ammunition surplus andthe set of unassigned objects are denoted respectively
Figure BDA00038694245700000712
And
Figure BDA00038694245700000713
step 2-4-3-1: input variance and O 1 Let i = q 1 -1
Step 2-4-3-2: judging whether the mutation process is finished
If i satisfies i ≦ q 2 1, executing the step 2-4-3-3 to the step 2-4-3-4, otherwise, exiting from the pair O 1 The mutation operation of (3).
Step 2-4-3-3: performing mutation operation on the gene at the mutation point i
When the temperature is higher than the set temperature
Figure BDA00038694245700000714
If O is 1 Is C or V, O is removed from the CVU 1 Randomly selecting an unmanned aerial vehicle from the CVU according to the unmanned aerial vehicle in the gene at the variation point i; if O is 1 Is A, then
Figure BDA00038694245700000715
In which O is removed 1 The gene at the mutation point i, and then
Figure BDA00038694245700000716
Randomly selecting an unmanned aerial vehicle and updating
Figure BDA00038694245700000717
Replacing O with information of selected drone 1 The variation point i of (a) is determined.
When the temperature is higher than the set temperature
Figure BDA00038694245700000718
First from
Figure BDA00038694245700000719
Randomly selecting a target, denoted T m Then find all targets and O 1 The gene at the mutation point i of (1) is targeted to the same gene, and the target is replaced with T m . If T m Is a high value target, and O 1 Is a low value target, then O 1 The target in the gene at the variation point i of (a) is removed from the CT, then one target is randomly selected from the CT and all the gene information is removed, and finally ammunition is distributed to the T m (ii) a If T is m Is a low value target, and O 1 Is a high value target, then T is randomly deleted m A gene of the attack task of (1).
When in use
Figure BDA00038694245700000720
If O is 1 The type of task in the gene at the mutation point i of (3) is C or V, then from G cv In which O is removed 1 The gene at the mutation point i of (3), and then randomly from G cv Selecting a gene; otherwise, from G a In which O is removed 1 The gene at the mutation point i of (3), and then randomly from G a One gene is selected. Contacting the selected gene with O 1 The unmanned aerial vehicle information of the gene at the variation point i is exchanged.
Step 2-4-3-4: let O be 1 Variation point q of (2) 1 And q is 2 The number of genes whose information is updated in between is denoted l, and let i = i + l, go to step 2-4-3-2
Step 2-4-4: repeating steps 2-4-1 to 2-4-3 until a sub-population of size S is obtained
Figure BDA0003869424570000081
Step 2-5: judging whether chromosomes in the sub-population obtained in the step 2-4 are locked or not, and unlocking the locked chromosomes
The following gives the judgment process and unlocking method for a chromosome deadlock situation.
Step 2-5-1: transforming chromosomes into a form consisting of daughter chromosomes
And arranging the genes in the chromosome from small to large according to the unmanned aerial vehicle number, and obtaining the task executed by each unmanned aerial vehicle and the execution sequence according to the sub chromosomes.
Step 2-5-2: set of established representations of completed tasks, denoted CS
Step 2-5-3: if the dimension of CS is equal to the dimension of chromosome, terminating the unlocking process, otherwise, executing the step 2-5-4 to the step 2-5-6
Step 2-5-4: determining whether the task in the current gene of each daughter chromosome can be executed
Each unmanned aerial vehicle starts to execute from the first task in the task set, if the task type is C, the task is directly executed, the task is deleted from the task set, and the task is added into the CS; if the task type is A, judging whether a task type C of a target corresponding to the task is contained in the CS, if so, directly executing, deleting the task from the task set and adding the task into the CS, otherwise, the task cannot be executed, and the unmanned aerial vehicle is in a waiting state; if the task type is V, judging whether a task type C of a target corresponding to the task and all attack tasks of the target are contained in the CS, if so, directly executing, deleting the task from a task set and adding the task into the CS, otherwise, the task cannot be executed, and the unmanned aerial vehicle is in a waiting state.
Step 2-5-5: judging whether the chromosome is locked
And judging the states of all the current unmanned aerial vehicles according to the step 2-5-4, and if all the current unmanned aerial vehicles are in the waiting state, locking the chromosome.
Step 2-5-6: fixed point unlocking at deadlocking point
And if the type of the task to be executed by the unmanned aerial vehicle at the deadlock point is A, randomly selecting the task with the type of C from the rest tasks of the unmanned aerial vehicle and exchanging the sequence with the current task. This is performed for all drones in the waiting state. And (5) turning to the step 2-5-3.
Step 2-6: binding population P g And
Figure BDA0003869424570000082
obtaining a population Q with the scale of 2S g
Step 2-7: calculating a population Q according to the objective functions (2) and (3) g Fitness value of middle chromosome
Step 2-8: slave population Q based on non-dominated quick sorting method and elite retention strategy g S chromosomes are selected to form a parent population P of the next iteration q
Step 2-9: let g = g +1,q = g
Step 2-10: if G is less than G, turning to the step 2-3, otherwise outputting a Pareto solution set
And step 3: selecting a certain solution from the Pareto solution set by using a solution selection method, and taking a task allocation scheme corresponding to the solution as an execution scheme
Order to
Figure BDA0003869424570000083
Representing the number of non-dominated solutions on the Pareto optimal front,
Figure BDA0003869424570000084
representing the ith non-dominated solution. First, calculate
Figure BDA0003869424570000085
Wherein alpha is 1 And alpha 2 Is a weight and satisfies alpha 1 +α 2 =1. And the decision maker takes the value of the weight according to the preference degree of the objective function. Then, for the calculated S i And (6) sorting. Finally, the smallest S is selected i A task allocation scheme of a non-dominant solution to which the value corresponds.
The invention has the beneficial effects that:
the invention provides the enemy target information and various resource information of our party which are currently detected, and the method can comprehensively consider various risks in actual combat, such as the fact that the unmanned aerial vehicle is destroyed and the task is not successfully completed. On the basis, the method can perform cooperative task allocation on the multi-heterogeneous unmanned aerial vehicle system according to different combat resource conditions, can simultaneously optimize a plurality of objective functions with conflict relationships, and can enable the unmanned aerial vehicle cluster to obtain a more efficient and practical task execution scheme. And various risks and various battle resource conditions are considered, so that the distribution method is more perfect. Meanwhile, the logical unlocking method constructed aiming at the dying condition of the chromosome greatly improves the optimization efficiency of the algorithm.
Drawings
FIG. 1 is a flow chart of the calculation of the present invention.
Fig. 2 is an optimized Pareto optimal front end in the embodiment of the present invention.
FIG. 3 is a diagram of an objective function J according to an embodiment of the present invention 3 The optimum value in each iteration varies with the number of iterations.
FIG. 4 is a diagram of an objective function J according to an embodiment of the present invention 2 The optimum value in each iteration varies with the number of iterations.
Fig. 5 shows the CPU running time of 15 task allocation experiments performed by using the cooperative task allocation method in the embodiment of the present invention.
Fig. 6 is the CPU run time for 15 unlocking experiments using the constructed logical unlocking method in the embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following examples.
Considering the situation that the ammunition amount is sufficient, 5 heterogeneous unmanned planes perform tasks on 5 targets. Let unmanned aerial vehicle U i Is recorded as a task set
Figure BDA0003869424570000091
Unmanned plane U i Is recorded as
Figure BDA0003869424570000092
Unmanned plane U i The loading of the ammunition is recorded as AM i . Let the high value target mark be Hv, the low value target mark be Lv, target T j The value of (A) is recorded as
Figure BDA0003869424570000093
Task set notation of objectsDo not like
Figure BDA0003869424570000094
Let unmanned aerial vehicle U i Execution target T j The success rate of task type k of is recorded as
Figure BDA0003869424570000095
Unmanned plane U i Execution target T j The mortality rate of task type k is recorded as
Figure BDA0003869424570000096
The invention provides a multi-heterogeneous unmanned aerial vehicle system task allocation method based on conditional probability and considering a multi-objective evolutionary algorithm, which comprises the following steps of:
step 1: according to the detected enemy target information, the current battlefield situation, the fighting risk and the fighting resources of the party, setting a target function based on the conditional probability and giving constraint conditions, and establishing a multi-target optimization model for the cooperative task allocation of the heterogeneous unmanned aerial vehicle
Step 1-1: collecting fundamental data of enemy targets and my unmanned aerial vehicles in mission planning
Collecting the type of the unmanned aerial vehicle,
Figure BDA0003869424570000097
AM i The type of object,
Figure BDA0003869424570000098
And
Figure BDA0003869424570000099
the information of (a) is shown in tables 1 to 3.
Table 1 information of unmanned aerial vehicle
Figure BDA00038694245700000910
TABLE 2 information of the objects
Figure BDA0003869424570000101
TABLE 3 success rate and destroyed rate when unmanned aerial vehicle executes task
Figure BDA0003869424570000102
Step 1-2: constructing an objective function of a model
Order to
Figure BDA0003869424570000103
Express unmanned plane U i Whether to slave target
Figure BDA0003869424570000104
Flying to target T j Performing T j If the value of the task type k is 1, the execution is indicated, otherwise, the execution is not performed. Wherein, V j Representing a target T j The position of (a). To obtain the maximum operational benefit at the minimum operational cost, the following objective function is constructed.
(i) Maximizing value expectation of successfully executed targets
Figure BDA0003869424570000105
(ii) Minimizing value expectation of a crashed drone
Figure BDA0003869424570000106
Wherein,
Figure BDA0003869424570000107
represents M Ui The first task of the group (c) of (c),
Figure BDA0003869424570000108
order to
Figure BDA0003869424570000109
Based on the actual situation have
Figure BDA00038694245700001010
Figure BDA00038694245700001011
And
Figure BDA0003869424570000111
(ii) minimizing the target (i) as follows
(iii) Minimizing value expectation of unsuccessfully executed targets
Figure BDA0003869424570000112
Let f = (f) 1 ,f 2 ) T Wherein f is 1 ＝J 3 ，f 2 ＝J 2 。
Step 1-3: constraint condition of construction model
Based on the characteristics of the collaborative task allocation and the above analysis, the following constraints are considered.
The payload of a drone is limited, and therefore, in the actual combat mission allocation, the number of type a missions allocated to each drone cannot exceed the payload of that drone. The number of tasks of task types C and V assigned by the drones capable of executing task types C and V is not limited.
Figure BDA0003869424570000113
Wherein, AM i Express unmanned plane U i The amount of loading of. If the type of the unmanned plane is s, the loading capacity is0。
In order to shorten the stay time of the same unmanned aerial vehicle on the same target and reduce the risk of discovering and destroying the unmanned aerial vehicle, the attack frequency of the same unmanned aerial vehicle on the same target is assumed to be not more than 1.
Figure BDA0003869424570000114
Thus, different attack missions of high value targets may be assigned to different drones.
The tasks of each target need to be performed in order, i.e. timing constraints need to be met. For tasks of the same goal, task type A can only be executed after task type C is completed, whereas task type V can only be executed after task type A is completed. Thus, task allocation needs to satisfy the following constraints.
Figure BDA0003869424570000115
Wherein,
Figure BDA0003869424570000116
when each unmanned aerial vehicle executes the tasks in the task set, the tasks are required to be executed according to the sequence in which the tasks in the set are distributed, that is to say
Figure BDA0003869424570000117
Can only be performed after its previous task is completed.
Figure BDA0003869424570000118
Wherein,
Figure BDA0003869424570000119
to represent
Figure BDA00038694245700001110
Step 1-4: multi-objective optimization model for constructing multi-heterogeneous unmanned aerial vehicle cooperative task allocation
Based on the objective function and the constraint conditions constructed above, the multi-objective optimization of the cooperative task allocation of the heterogeneous unmanned aerial vehicles is to minimize a vector function f on the basis of satisfying the constraints (11) - (14).
min f＝(f 1 ,f 2 ) T
s.t.(11)-(14)
And 2, step: solving a multi-objective optimization model of cooperative task allocation by using a constructed multi-objective evolutionary algorithm to obtain a Pareto solution set thereof
Step 2-1: setting improved multi-objective optimization algorithm parameters
The population size S =100, the maximum number of iterations G =200, and the crossover probability P are set c =0.8 and mutation probability P m ＝0.2。
Step 2-2: initializing a population
The encoding of the chromosome is such that the chromosome is constructed by genes, each of which represents the assignment of a certain task. The information in the gene includes a target number corresponding to the assigned task, the assigned task type, and the number of the unmanned aerial vehicle executing the task. Randomly generating a population P with the size of 100 g (g = 0). The initialization procedure for generating an initial population containing 100 chromosomes according to the above coding scheme is given below.
Step 2-2-1: inputting unmanned aerial vehicle and enemy target parameters
Inputting ammunition amount A of all unmanned aerial vehicles in array form m The unmanned aerial vehicle number sets comprise an enemy target number set TS, unmanned aerial vehicle number sets AU with types of c and m, and unmanned aerial vehicle number sets CVU with types of s and c. Let the number of chromosomes in the population equal 0.
Step 2-2-2: judging the chromosome number of the current population
And if the number of chromosomes in the population is equal to 100, finishing population initialization and exiting the current initialization process. Otherwise, executing step 2-2-3 to step 2-2-4
Step 2-2-3: an empty Chromosome is initialized and recorded as Chromosome
Step 2-2-4: encoding chromosomal Chromosome
Step 2-2-4-1: randomly selecting a target from TS, and recording as
Figure BDA0003869424570000121
Step 2-2-4-2: for the selected target
Figure BDA0003869424570000122
Subtask C and attack task A of (1) 1 Is distributed
The genes Gene1, gene2, gene3, gene4 are initialized. Firstly, randomly selecting an unmanned aerial vehicle from the CVU, and recording the unmanned aerial vehicle as
Figure BDA0003869424570000123
Order to
Figure BDA0003869424570000124
Chromosome = Chromosome ═ Gene1. Then, randomly selecting an unmanned aerial vehicle from AU (all-purpose aircraft) to be recorded as
Figure BDA0003869424570000125
Order to
Figure BDA0003869424570000126
Chromosome = Chromosome U Gene2, and renews A m Instant command
Figure BDA0003869424570000127
Step 2-2-4-3: if it is
Figure BDA0003869424570000128
Is a high value target and A m Not equal to 0, then the target is checked
Figure BDA0003869424570000129
Attack task A of 2 Distributing, otherwise, switching to step 2-2-4-4 to randomly select an unmanned aerial vehicle from AU on the premise of satisfying the constraint (12) and recording as
Figure BDA00038694245700001210
Order to
Figure BDA00038694245700001211
Chromosome = Chromosome @ Gene3. Update A m Instant command
Figure BDA00038694245700001212
Step 2-2-4-4: for the selected target
Figure BDA00038694245700001213
Is allocated to the subtask V of
Randomly selecting an unmanned aerial vehicle from the CVU and recording the selected unmanned aerial vehicle as
Figure BDA0003869424570000131
Removing targets from TS
Figure BDA0003869424570000132
Order to
Figure BDA0003869424570000133
Chromosome＝Chromosome∪Gene4。
Step 2-2-4-5: judging whether the current chromosome is coded
If the length of the array TS is not 0, and A m If the vector is not zero, executing the step 2-2-4-1 to the step 2-2-4-5, otherwise, adding 1 to the number of chromosomes in the population, exiting the encoding process of the current chromosome, and turning to the step 2-2-2.
Step 2-3: calculating a population P according to the objective functions (10) and (9) g Fitness value of mesochromosome
Step 2-4: generation of a size 100 sub-population
Step 2-4-1: from the population P using a roulette algorithm g Two crossed parents F are selected 1 ,F 2
Step 2-4-2: selecting corresponding crossover operators according to the conditions of total resources and total task quantity, and performing crossover operation according to crossover probability 0.8
Order RM i Represents the parent F i Corresponding combinations of drones with residual ammunition, RT i Represents the parent F i A set of enemy targets that are not allocated. Let p be 1 And p 2 Representing randomly selected intersection points from the intersecting parents,
Figure BDA0003869424570000134
and
Figure BDA0003869424570000135
respectively represent F 1 And F 2 Cross point p of 1 And p 2 A set of drones performing task types C and V,
Figure BDA0003869424570000136
and
Figure BDA0003869424570000137
respectively represent F 1 And F 2 Cross point p of 1 And p 2 A set of drones executing task type a. Let PT 1 And PT 2 Respectively represent F 1 And F 2 The set of allocated targets.
By the father generation F 1 The interleaving procedure is given as an example.
Figure BDA0003869424570000138
I.e. the ammunition is sufficient and has a residue of
Figure BDA0003869424570000139
In this case, the crossover process is step 2-4-2-1 to step 2-4-2-5.
Step 2-4-2-1: inputting information of intersection and intersection parent, i = p 1 -1
Step 2-4-2-2: judging whether the cross process is finished
If i satisfies i ≦ p 2 1, executing the step 2-4-2-3 to the step 2-4-2-4, otherwise, quitting the opposite parent F 1 The interleaving operation of (3).
Step 2-4-2-3: performing cross operation on the gene at the cross point i
The following operation was performed when the task type in the gene at the intersection i is A. If it is
Figure BDA00038694245700001310
And is provided with
Figure BDA00038694245700001311
If the corresponding target in the gene at the cross point i is removed and is not an empty set, then
Figure BDA00038694245700001312
Randomly selecting an unmanned aerial vehicle, otherwise, selecting from RM 1 And (4) randomly selecting an unmanned aerial vehicle from the set after removing the corresponding target in the gene at the cross point i. Replacing information of the unmanned aerial vehicle in the current gene with the information of the selected unmanned aerial vehicle, and updating the RM 1 。
The following operation is performed when the task type in the gene at the intersection i is C or V. If it is
Figure BDA00038694245700001313
Then randomly get from
Figure BDA00038694245700001314
Randomly selecting an unmanned aerial vehicle, otherwise, never belonging to
Figure BDA00038694245700001315
Randomly selecting one unmanned aerial vehicle from the unmanned aerial vehicles of type s or c. And replacing the information of the unmanned aerial vehicle in the current gene by using the information of the unmanned aerial vehicle.
Step 2-4-2-4: let i = i +1, go to step 2-4-2
Step 2-4-3: performing mutation operation on the cross filial generation according to the mutation probability of 0.2
The two cross filial generations obtained by the cross operation are marked as O 1 ，O 2 . With O 1 For example, the mutation process is given. Let q be 1 And q is 2 Starting and ending of gene segments requiring mutationAnd (6) stopping the point. Let O be 1 The set of low and medium value targets is denoted CT, O 1 The collection of genes in C and V is denoted G cv ，O 1 The collection of genes of A in (A) is denoted as G a ，O 1 The corresponding set of drones with ammunition remaining and the set of unassigned targets are denoted respectively
Figure BDA0003869424570000141
And
Figure BDA0003869424570000142
step 2-4-3-1: input variation points and O 1 Let i = q 1 -1
Step 2-4-3-2: judging whether the variation process is finished
If i satisfies i ≦ q 2 1, executing the step 2-4-3-3 to the step 2-4-3-4, otherwise, exiting from the pair O 1 The mutation operation of (3).
Step 2-4-3-3: performing mutation operation on the gene at the mutation point i
If O is 1 Is C or V, O is removed from the CVU 1 Randomly selecting an unmanned aerial vehicle from the CVU according to the unmanned aerial vehicle in the gene at the variation point i; if O is 1 Is A, then
Figure BDA0003869424570000143
In which O is removed 1 The gene at the mutation point i, and then
Figure BDA0003869424570000144
Randomly selecting an unmanned aerial vehicle and updating
Figure BDA0003869424570000145
Replacing O with information of selected drone 1 The variation point i of (a) is determined.
Step 2-4-3-4: let O be 1 Variation point q of 1 And q is 2 The number of genes between which information is updated is denoted as lAnd let i = i + l go to step 2-4-3-2
Step 2-4-4: repeating steps 2-4-1 to 2-4-3 until a sub-population of size 100 is obtained
Figure BDA0003869424570000146
Step 2-5: judging whether chromosomes in the sub-population obtained in the step 2-4 are locked or not, and unlocking the locked chromosomes
The following gives the judgment process and unlocking method for a chromosome deadlock situation.
Step 2-5-1: transforming chromosomes into a form consisting of daughter chromosomes
And arranging the genes in the chromosome from small to large according to the unmanned aerial vehicle number, and obtaining the task executed by each unmanned aerial vehicle and the execution sequence according to the sub chromosomes.
Step 2-5-2: constructing an empty set of completed tasks, denoted CS
Step 2-5-3: if the dimension of CS is equal to the dimension of chromosome, terminating the unlocking process, otherwise, executing the step 2-5-4 to the step 2-5-6
Step 2-5-4: determining whether the task in the current gene of each sub-chromosome can be executed
Each unmanned aerial vehicle starts to execute from the first task in the task set, if the task type is C, the unmanned aerial vehicle directly executes, deletes the task from the task set and adds the task into the CS; if the task type is A, judging whether a task type C of a target corresponding to the task is contained in the CS, if so, directly executing, deleting the task from the task set and adding the task into the CS, otherwise, the task cannot be executed, and the unmanned aerial vehicle is in a waiting state; if the task type is V, whether the task type C of the target corresponding to the task and all attack tasks of the target are contained in the CS is judged, if yes, the task is directly executed, the task is deleted from the task set and added into the CS, otherwise, the task cannot be executed, and the unmanned aerial vehicle is in a waiting state.
Step 2-5-5: judging whether the chromosome is locked
And judging the states of all the current unmanned aerial vehicles according to the step 2-5-4, and if all the current unmanned aerial vehicles are in the waiting state, locking the chromosome.
Step 2-5-6: fixed point unlocking at lock dead point
And if the type of the task to be executed by the unmanned aerial vehicle at the deadlock point is A, randomly selecting the task with the type of C from the rest tasks of the unmanned aerial vehicle and exchanging the sequence with the current task. This is performed for all drones in the waiting state. And (5) turning to step 2-5-3.
Step 2-6: binding population P g And
Figure BDA0003869424570000147
obtaining a population Q of size 200 g
Step 2-7: calculating a population Q according to the objective functions (10) and (9) g Fitness value of middle chromosome
Step 2-8: slave population Q based on non-dominated quick sorting method and elite retention strategy g 100 chromosomes are selected to form a parent population P of the next iteration q 。
Step 2-9: let g = g +1,q = g
Step 2-10: if g is less than 200, turning to step 2-3, otherwise outputting a Pareto solution set
And 3, step 3: selecting a certain solution from the Pareto solution set by using a solution selection method, and taking a task allocation scheme corresponding to the solution as an execution scheme
Order to
Figure BDA0003869424570000151
Representing the number of non-dominant solutions on the Pareto optimal frontier,
Figure BDA0003869424570000152
representing the ith non-dominant solution. First, calculate
Figure BDA0003869424570000153
Wherein the weight takes the value of alpha 1 =0.5 and α 2 =0.5. Then, for the calculated S i And (6) sorting. Finally, selectingSelecting the minimum S i A task allocation scheme of a non-dominant solution to which the value corresponds.
The Pareto optimal front end obtained by optimization is shown in fig. 2, and fig. 3 and fig. 4 show the change of the optimal values of two objective functions in each iteration along with the number of iterations. Based on the solution selection strategy, the 15 th non-dominated solution on the Pareto frontier is obtained as the selected solution, and the corresponding solution information and the specific task allocation scheme are shown in table 4. To test the computational efficiency of the method, 15 experiments were performed with CPU run times as shown in fig. 5.
TABLE 4 Pareto optimal frontier information of the 15 th non-dominated solution and corresponding task assignment scheme
Figure BDA0003869424570000154
To verify the validity of the structured logical unlocking method, 100 chromosomes were randomly generated without considering timing constraints. These 100 chromosomes were subjected to 15 experiments to determine whether to lock and unlock using the constructed logical unlocking method. The unlocked CPU run time is shown in fig. 6. As can be seen from the figure, the logic unlocking method can effectively solve the locking condition.
The invention provides a multi-heterogeneous unmanned aerial vehicle system cooperative task allocation method considering a multi-objective evolutionary algorithm based on conditional probability. Based on the method, on the premise of comprehensively considering the risk of being destroyed by the unmanned aerial vehicle and the risk of incomplete task, cooperative task allocation can be carried out on the heterogeneous unmanned aerial vehicle group under various battle resource situations, and a plurality of conflict targets can be simultaneously optimized. In addition, the logic unlocking mode constructed in the method greatly improves the solving efficiency. The multi-heterogeneous unmanned aerial vehicle system task allocation method considering the operational risk and various operational situations provides a task allocation scheme more suitable for actual operational situations for pre-war task allocation, and can effectively improve the solving efficiency of task allocation.
The above-mentioned embodiments only express the embodiments of the present invention, but not should be understood as the limitation of the scope of the invention patent, it should be noted that, for those skilled in the art, many variations and modifications can be made without departing from the concept of the present invention, and these all fall into the protection scope of the present invention.

Claims (5)

1. A multi-heterogeneous unmanned aerial vehicle system task allocation method based on conditional probability and considering a multi-objective evolutionary algorithm is characterized by comprising the following steps: firstly, setting an objective function and constraint conditions based on conditional probability according to detected enemy target information, current battlefield situation, fighting risk and fighting resources of one party, and establishing a multi-objective optimization model for heterogeneous unmanned aerial vehicle cooperative task allocation; secondly, solving the constructed model by utilizing a constructed multi-objective evolutionary algorithm to obtain a Pareto solution set; and finally, selecting a certain solution from the Pareto solution set by using a solution selection method, and taking a task allocation scheme corresponding to the solution as an execution scheme.
2. The conditional probability-based multi-heterogeneous unmanned aerial vehicle system task allocation method considering multi-objective evolutionary algorithm according to claim 1, is characterized by comprising the following steps:
step 1: according to the detected enemy target information, the current battlefield situation, the fighting risk and the fighting resources of the party, setting a target function based on the conditional probability and giving constraint conditions, and establishing a multi-target optimization model for the cooperative task allocation of the heterogeneous unmanned aerial vehicle, which is concretely as follows:
step 1-1: collecting fundamental data of enemy targets and unmanned aerial vehicles of my party in mission planning;
order to
Figure FDA0003869424560000011
The enemy target set to be detected comprises N T Target, jth target marked as T j The set of objects is denoted as
Figure FDA0003869424560000012
Each target contains three types of subtasks: classification task, attack task, assessment taskAffairs, respectively marked as C, A, V; different enemy goals have different values and risks; setting a target T j Is recorded as
Figure FDA0003869424560000013
Making the high-value target mark as Hv and the low-value target mark as Lv; in order to improve the success rate of the battle, the attack on a high-value target is set twice, and the attack on a low-value target is set once; let A i Representing the ith attack task, target
Figure FDA0003869424560000014
The task set of (a) is written as:
Figure FDA0003869424560000015
setting up
Figure FDA0003869424560000016
And
Figure FDA0003869424560000017
respectively represent the unmanned aerial vehicle to the target T j Subtasks C, A of 1 、A 2 And the starting execution time of V,
Figure FDA0003869424560000018
and
Figure FDA0003869424560000019
respectively represent the unmanned aerial vehicle to the target T j Subtasks C, A of 1 、A 2 And the execution time of V;
setting N in unmanned aerial vehicle cluster U Erect unmanned aerial vehicle, the ith unmanned aerial vehicle marks as U i The set of drones is noted
Figure FDA00038694245600000110
Setting three heterogeneous unmanned planes in clusterMachine: scout plane, fighter plane and ammunition plane, which are respectively marked as s, c and m; the type of the executable task of the s-type unmanned aerial vehicle is C and V, the type of the executable task of the C-type unmanned aerial vehicle is C, A and V, and the type of the executable task of the m-type unmanned aerial vehicle is A; let unmanned aerial vehicle U i The value of (A) is recorded as
Figure FDA00038694245600000111
The success rate and survival rate of different unmanned aerial vehicles executing different tasks of different targets are different; setting up
Figure FDA00038694245600000112
Express unmanned plane U i Execution target T j The success rate of the task type k of (c),
Figure FDA00038694245600000113
express unmanned plane U i Execution target T j The mortality rate of task type k, wherein,
Figure FDA00038694245600000114
k∈I 3 k =1,2,3 denotes task types C, a, and V, respectively;
according to the capability of the unmanned aerial vehicle, when the unmanned aerial vehicle U i Is of type s
Figure FDA0003869424560000021
When unmanned plane U i Is m times
Figure FDA0003869424560000022
Let unmanned aerial vehicle U i Is recorded as a task set
Figure FDA0003869424560000023
Wherein
Figure FDA0003869424560000024
Presentation to unmanned aerial vehicle U i The number of tasks of (a) is,
Figure FDA0003869424560000025
express unmanned plane U i The ith executed task in the task set;
step 1-2: constructing an objective function of the model;
for target T j If some subtasks are not successfully executed, the target T is determined to be successful execution of the subtasks j Is not successfully executed; when the unmanned aerial vehicle executes a task, two risks exist, namely that the unmanned aerial vehicle is possibly destroyed and the task is not successfully completed; considering as an objective function the value expectation of maximizing the targets that are successfully executed and the value expectation of minimizing the drone that is destroyed in order to obtain the maximum operational benefit at the minimum operational cost; constructing the two objective functions by introducing conditional probabilities; order to
Figure FDA0003869424560000026
Express unmanned plane U i Whether to slave target
Figure FDA0003869424560000027
Flying to target T j Performing T j If the value of the task type k is 1, the execution is indicated, otherwise, the execution is not performed; wherein, V j Representing a target T j The position of (a);
(i) Maximizing the value expectation of a successfully executed target;
Figure FDA0003869424560000028
(ii) Minimizing the value expectation of a crashed drone;
Figure FDA0003869424560000029
wherein,
Figure FDA00038694245600000210
to represent
Figure FDA00038694245600000211
The first task of the group (c) of (c),
Figure FDA00038694245600000212
order to
Figure FDA00038694245600000213
Based on the actual situation, the method comprises the following steps:
Figure FDA00038694245600000214
Figure FDA00038694245600000215
and
Figure FDA00038694245600000216
target (i) is minimized as follows:
(iii) Minimizing the value expectation of objects that are not successfully executed;
Figure FDA0003869424560000031
let f = (f) 1 ,f 2 ) T Wherein, f 1 ＝J 3 ，f 2 ＝J 2 ；
Step 1-3: constructing constraint conditions of the model;
in actual combat task allocation, the number of tasks of type A allocated to each unmanned aerial vehicle cannot exceed the payload of the unmanned aerial vehicle; the number of tasks of the task types C and V allocated by the unmanned aerial vehicle capable of executing the task types C and V is not limited;
Figure FDA0003869424560000032
wherein, AM i Express unmanned plane U i The loading capacity of (d); if the type of the unmanned aerial vehicle is s, the missile loading amount of the unmanned aerial vehicle is 0;
the attack frequency of the same unmanned aerial vehicle on the same target is not more than 1 time;
Figure FDA0003869424560000033
thus, different attack tasks of the high-value target may be assigned to different drones;
the tasks of each target need to be executed in sequence, namely, the time sequence constraint needs to be met; for the tasks with the same target, the task type A can be executed only after the task type C is finished, and the task type V can be executed only after the task type A is completely finished; thus, task allocation needs to satisfy the following constraints;
Figure FDA0003869424560000034
wherein, if the target T j Is a high value target, then
Figure FDA0003869424560000035
Otherwise
Figure FDA0003869424560000036
When each unmanned aerial vehicle executes the tasks in the task set, the tasks are required to be executed according to the sequence in which the tasks in the set are distributed, that is to say
Figure FDA0003869424560000037
Can only be usedIs executed after its previous task is completed;
Figure FDA0003869424560000038
wherein,
Figure FDA0003869424560000039
represent
Figure FDA00038694245600000310
Step 1-4: constructing a multi-objective optimization model for multi-heterogeneous unmanned aerial vehicle cooperative task allocation;
based on the constructed objective function and constraint conditions, the multi-objective optimization of the cooperative task allocation of the multi-heterogeneous unmanned aerial vehicle is to minimize a vector function f on the basis of meeting the constraints (4) - (7);
min f＝(f 1 ,f 2 ) T
s.t.(4)-(7)
step 2: solving a multi-objective optimization model for collaborative task allocation by using the constructed multi-objective evolutionary algorithm to obtain a Pareto solution set thereof, which is specifically as follows:
step 2-1: setting parameters of the improved multi-objective optimization algorithm, including population size S, maximum iteration number G and cross probability P c And the probability of variation P m A value of (d);
step 2-2: initializing a population;
the coding of the chromosome adopts a mode of forming the chromosome by genes, wherein each gene represents the distribution condition of a certain task; the information in the genes comprises target numbers corresponding to the distributed tasks, the distributed task types and the numbers of the unmanned aerial vehicles executing the tasks; the corresponding genome of the same unmanned aerial vehicle forms a sub-chromosome, and all the sub-chromosomes form a complete chromosome; randomly generating a population P with the size S g (g＝0)；
Step 2-3: calculating a population P according to the objective functions (2) and (3) g Adaptation of mesochromosomesA value of the metric;
step 2-4: generating a sub-population of size S;
step 2-5: judging whether chromosomes in the sub-population obtained in the step 2-4 are locked or not, and unlocking the locked chromosomes;
step 2-6: binding population P g And
Figure FDA0003869424560000041
obtaining population Q with the scale of 2S g ；
Step 2-7: calculating population Q according to the objective functions (2) and (3) g Fitness value of the mesochromosome;
step 2-8: slave population Q based on non-dominated quick sorting method and elite retention strategy g S chromosomes are selected to form a parent population P of the next iteration q ；
Step 2-9: let g = g +1,q = g;
step 2-10: if G is less than G, turning to the step 2-3, otherwise outputting a Pareto solution set;
and step 3: selecting a certain solution from the Pareto solution set by using a solution selection method, and taking a corresponding task allocation scheme as an execution scheme, wherein the method specifically comprises the following steps:
order to
Figure FDA0003869424560000042
Representing the number of non-dominant solutions on the Pareto optimal frontier,
Figure FDA0003869424560000043
represents the ith non-dominant solution; first, calculate
Figure FDA0003869424560000044
Wherein alpha is 1 And alpha 2 Is a weight and satisfies alpha 1 +α 2 =1; the decision maker takes values of the weights according to the preference degree of the objective function; then, for the calculated S i Sorting is carried out; finally, the smallest S is selected i A task allocation scheme of a non-dominant solution to which the value corresponds.
3. The multi-heterogeneous unmanned aerial vehicle system task allocation method based on conditional probability and considering multi-objective evolutionary algorithm according to claim 1, wherein in the step 2-2, an initialization process for generating an initial population including S chromosomes according to the coding method specifically comprises the following steps:
step 2-2-1: inputting target parameters and population quantity of the unmanned aerial vehicle and the enemy;
inputting ammunition amount A of all unmanned aerial vehicles in array form m An enemy target number set TS, unmanned aerial vehicle number sets AU with types of c and m, and unmanned aerial vehicle number sets CVU with types of s and c; making the number of chromosomes in the population equal to 0;
step 2-2-2: judging the chromosome number of the current population;
if the number of chromosomes in the population is equal to S, completing population initialization, and exiting the current initialization process; otherwise, executing the step 2-2-3 to the step 2-2-4;
step 2-2-3: initializing an empty Chromosome which is recorded as Chromosome;
step 2-2-4: encoding a chromosomal Chromosome;
step 2-2-4-1: randomly selecting a target from TS, and recording as
Figure FDA0003869424560000051
Step 2-2-4-2: for the selected target
Figure FDA0003869424560000052
Subtask C and attack task A of (1) 1 Distributing;
initializing genes Gene1, gene2, gene3, gene4; firstly, randomly selecting an unmanned aerial vehicle from CVU (continuously variable Unit), and recording the unmanned aerial vehicle as
Figure FDA0003869424560000053
Order to
Figure FDA0003869424560000054
Chromosome = Chromosome ═ Gene1; then randomly selecting an unmanned aerial vehicle from AU (all-in-one) to be recorded as
Figure FDA0003869424560000055
Order to
Figure FDA0003869424560000056
Chromosome = Chromosome @ Gene2, and updates a m Instant command
Figure FDA0003869424560000057
Step 2-2-4-3: if it is
Figure FDA0003869424560000058
Is a high value target and A m Not equal to 0, then target is checked
Figure FDA0003869424560000059
Attack task A of 2 Distributing, otherwise, turning to the step 2-2-4-4;
randomly selecting an unmanned aerial vehicle from AU on the premise of satisfying constraint (5) and recording the unmanned aerial vehicle as
Figure FDA00038694245600000510
Order to
Figure FDA00038694245600000511
Chromosome = Chromosome ═ Gene3; update A m Instant command
Figure FDA00038694245600000512
Step 2-2-4-4: for the selected target
Figure FDA00038694245600000513
The subtask V of (2) is allocated;
randomly selecting an unmanned aerial vehicle from the CVU and recording the selected unmanned aerial vehicle as
Figure FDA00038694245600000514
Removing targets from TS
Figure FDA00038694245600000515
Order to
Figure FDA00038694245600000516
Chromosome＝Chromosome∪Gene4；
Step 2-2-4-5: judging whether the current chromosome is coded or not;
if the length of the array TS is not 0, and A m If the vector is not zero, executing the step 2-2-4-1 to the step 2-2-4-5, otherwise, adding 1 to the number of chromosomes in the population, exiting the encoding process of the current chromosome, and turning to the step 2-2-2.
4. The multi-heterogeneous unmanned aerial vehicle system task allocation method based on conditional probability and considering multi-objective evolutionary algorithm is characterized in that the specific steps of generating the sub-population with the size S in the steps 2-4 are as follows:
step 2-4-1: from population P using roulette algorithm g Two crossed parents F are selected 1 ,F 2 ；
Step 2-4-2: selecting corresponding crossover operators according to the conditions of the total resources and the total task quantity, and performing crossover operation according to the crossover probability;
let L represent the number of high value targets; order RM i Representing the parent F i Corresponding unmanned aerial vehicle combinations with residual ammunition, RT i Representing the parent F i A set of enemy targets that are not allocated; let p be 1 And p 2 Representing randomly selected intersection points from the intersecting parents,
Figure FDA00038694245600000517
and
Figure FDA00038694245600000518
respectively represent F 1 And F 2 Cross point p of 1 And p 2 A set of drones performing task types C and V,
Figure FDA00038694245600000519
and
Figure FDA00038694245600000520
respectively represent F 1 And F 2 Cross point p of 1 And p 2 A set of drones executing task type a in between; let PT 1 And PT 2 Respectively represent F 1 And F 2 The set of targets assigned in (a);
by the parent F 1 The crossover process is given for the example; if it is
Figure FDA0003869424560000061
I.e. the ammunition is sufficient and has a residue of
Figure FDA0003869424560000062
At this time, the crossing process is from step 2-4-2-1 to step 2-4-2-4;
step 2-4-2-1: inputting information of intersection and intersection parent, and making i = p 1 -1；
Step 2-4-2-2: judging whether the crossing process is finished or not;
if i satisfies i ≦ p 2 1, executing the step 2-4-2-3 to the step 2-4-2-4, otherwise, quitting the opposite parent F 1 The cross operation of (2);
step 2-4-2-3: performing cross operation on the genes at the cross point i;
performing the following operation when the task type in the gene at the cross point i is A; if it is
Figure FDA0003869424560000063
And is provided with
Figure FDA0003869424560000064
If the corresponding target in the gene at the cross point i is removed and is not an empty set, then
Figure FDA0003869424560000065
Randomly selecting an unmanned aerial vehicle, otherwise, selecting from RM 1 Randomly selecting an unmanned aerial vehicle from the set after removing the corresponding target in the gene at the cross point i; replacing information of the unmanned aerial vehicle in the current gene with the information of the selected unmanned aerial vehicle, and updating the RM 1 ；
Performing the following operation when the task type in the gene at the cross point i is C or V; if it is
Figure FDA0003869424560000066
Then the slave is random
Figure FDA0003869424560000067
Selecting an unmanned aerial vehicle at random, otherwise, never belonging to
Figure FDA0003869424560000068
Randomly selecting one unmanned aerial vehicle from the unmanned aerial vehicles of the type s or c; replacing the information of the unmanned aerial vehicle in the current gene by using the information of the unmanned aerial vehicle;
step 2-4-2-4: let i = i +1, go to step 2-4-2-2;
if it is
Figure FDA0003869424560000069
I.e. the ammunition is sufficient and has no residue, there is
Figure FDA00038694245600000610
The crossing process is from step 2-4-2-5 to step 2-4-2-8;
step 2-4-2-5: inputting information of intersection and intersection parent, and making i = p 1 -1；
Step 2-4-2-6: judging whether the crossing process is finished or not;
if i satisfies i ≦ p 2 1, executing the step 2-4-2-7 to the step 2-4-2-8, otherwise, exiting the parent F 1 The cross operation of (2);
step 2-4-2-7: performing cross operation on the gene at the cross point i;
let cross parent F 1 ,F 2 The targets corresponding to the genes at the cross points i of (A) are respectively referred to as
Figure FDA00038694245600000611
If it is
Figure FDA00038694245600000612
Turning to the step 2-4-2-8, otherwise executing the following process;
if it is
Figure FDA00038694245600000613
All are low value targets or all are high value targets, then directly at F 1 In exchange for the corresponding gene
Figure FDA00038694245600000614
The information of (a); if it is
Figure FDA00038694245600000615
In order to be a high-value target,
Figure FDA00038694245600000616
for low value targets, the numbers of targets in the corresponding genes are then interchanged first, and one is then randomly selected
Figure FDA00038694245600000617
Attack the gene and change the target number of the gene to
Figure FDA00038694245600000618
If it is
Figure FDA00038694245600000619
In order to be a low-value target,
Figure FDA00038694245600000620
is a high value target, thatHow to exchange the numbers of the targets in the respective genes first and then randomly select one
Figure FDA0003869424560000071
And the target number of the gene is changed to
Figure FDA0003869424560000072
Step 2-4-2-8: let F 1 Cross of (p) 1 And p 2 The number of genes between which information is updated is denoted as l, and let i = i + l, go to step 2-4-2-6;
if it is
Figure FDA0003869424560000073
That is, the amount of ammunition is insufficient, have
Figure FDA0003869424560000074
The crossing process is from step 2-4-2-9 to step 2-4-2-13;
step 2-4-2-9: inputting information of intersection and intersection parent, and making i = p 1 -1；
Step 2-4-2-10: judging whether the crossing process is finished or not;
if i satisfies i ≦ p 2 1, executing the step 2-4-2-11 to the step 2-4-2-13, otherwise, quitting the opposite parent F 1 The interleaving operation of (2);
step 2-4-2-11: performing cross operation on the genes at the cross point i;
let the target in the gene at cross point i be recorded
Figure FDA0003869424560000075
If it is
Figure FDA0003869424560000076
Then the slave RT 1 ∩PT 2 Randomly selects a target, otherwise, from RT 1 Randomly selecting a target; marking the selected target as
Figure FDA0003869424560000077
When in use
Figure FDA0003869424560000078
When the target is low value, if
Figure FDA0003869424560000079
For low value targets, then target
Figure FDA00038694245600000710
Target replacement in all corresponding genes
Figure FDA00038694245600000711
Otherwise, from F 1 Randomly selecting a low value target
Figure FDA00038694245600000712
Target object
Figure FDA00038694245600000713
And
Figure FDA00038694245600000714
target replacement in all corresponding genes
Figure FDA00038694245600000715
When the temperature is higher than the set temperature
Figure FDA00038694245600000716
At the time of high value, if
Figure FDA00038694245600000717
For low value targets, then first from RT 1 In randomly selecting an object
Figure FDA00038694245600000718
Target object
Figure FDA00038694245600000719
Target replacement in all corresponding genes
Figure FDA00038694245600000720
Then randomly selecting an object
Figure FDA00038694245600000721
Attack the gene, and target replacement of the gene
Figure FDA00038694245600000722
Finally, add target
Figure FDA00038694245600000723
The genes of task types C and V of (1); if it is
Figure FDA00038694245600000724
For high value targets, the target
Figure FDA00038694245600000725
Target replacement in all corresponding genes
Figure FDA00038694245600000726
Step 2-4-2-12: updating RT 1 And PT 1 ；
Step 2-4-2-13: let F 1 Cross of (a) p 1 And p 2 The number of genes between which information is updated is denoted as l, and let i = i + l, go to step 2-4-2-10;
step 2-4-3: carrying out mutation operation on the cross filial generation according to the mutation probability;
the two cross filial generations obtained by the cross operation are marked as O 1 ，O 2 (ii) a With O 1 For example, the mutation process is given; let q be 1 And q is 2 Is a group requiring variationStarting point and ending point of the segment; let O be 1 The set of low-to-medium value targets is denoted CT, O 1 The collection of genes in C and V is denoted G cv ，O 1 The collection of genes of A in (A) is denoted as G a ，O 1 The corresponding set of drones with ammunition surplus and the set of unassigned targets are denoted respectively
Figure FDA00038694245600000727
And
Figure FDA00038694245600000728
step 2-4-3-1: input variation points and O 1 Let i = q 1 -1；
Step 2-4-3-2: judging whether the variation process is finished or not;
if i satisfies i ≦ q 2 1, executing the step 2-4-3-3 to the step 2-4-3-4, otherwise, exiting from the pair O 1 Mutation operation of (3);
step 2-4-3-3: carrying out mutation operation on the gene at the mutation point i;
when the temperature is higher than the set temperature
Figure FDA0003869424560000081
If O is 1 Is C or V, O is removed from the CVU 1 Randomly selecting an unmanned aerial vehicle from the CVU according to the unmanned aerial vehicle in the gene at the variation point i; if O is 1 Is A, then
Figure FDA0003869424560000082
In which O is removed 1 The gene at the mutation point i, and then
Figure FDA0003869424560000083
Randomly selecting an unmanned aerial vehicle and updating
Figure FDA0003869424560000084
Use and selectInformation replacement of selected drones O 1 The unmanned aerial vehicle information in the gene at the mutation point i;
when in use
Figure FDA0003869424560000085
First from
Figure FDA0003869424560000086
Randomly selecting a target, denoted T m Then find all targets and O 1 The gene at the mutation point i of (1) is targeted to the same gene, and the target is replaced with T m (ii) a If T m Is a high value target, and 1 is a low value target, then O 1 The target in the gene at the variation point i of (a) is removed from the CT, then one target is randomly selected from the CT and all the gene information is removed, and finally ammunition is distributed to the T m (ii) a If T is m Is a low value target, and 1 is a high value target, then T is deleted randomly m A gene of the attack task of (1);
when the temperature is higher than the set temperature
Figure FDA0003869424560000087
If O is 1 Is C or V, then from G cv In which O is removed 1 The gene at the mutation point i of (3), and then randomly from G cv Selecting a gene; otherwise, from G a In which O is removed 1 The gene at the mutation point i of (3), and then randomly from G a Selecting a gene; contacting the selected gene with O 1 Exchanging the unmanned aerial vehicle information of the gene at the variation point i;
step 2-4-3-4: let O be 1 Variation point q of 1 And q is 2 The number of genes between which information is updated is denoted as l, and let i = i + l, go to step 2-4-3-2;
step 2-4-4: repeating steps 2-4-1 to 2-4-3 until a sub-population of size S is obtained
Figure FDA0003869424560000088
5. The method for allocating the system tasks of the heterogeneous unmanned aerial vehicle based on the conditional probability and considering the multi-objective evolutionary algorithm is characterized in that in the step 2 to the step 5, the judgment process and the unlocking method for the locking condition of one chromosome are as follows:
step 2-5-1: converting the chromosome into a form consisting of daughter chromosomes;
arranging genes in the chromosome from small to large according to the unmanned aerial vehicle number, and obtaining the task executed by each unmanned aerial vehicle and the execution sequence according to the sub chromosomes;
step 2-5-2: establishing a set representing completed tasks, and recording the set as CS;
step 2-5-3: if the dimension of the CS is equal to the dimension of the chromosome, terminating the unlocking process, otherwise, executing the steps 2-5-4 to 2-5-6;
step 2-5-4: judging whether the task in the current gene of each sub-chromosome can be executed or not;
each unmanned aerial vehicle starts to execute from the first task in the task set, if the task type is C, the task is directly executed, the task is deleted from the task set, and the task is added into the CS; if the task type is A, judging whether a task type C of a target corresponding to the task is contained in the CS, if so, directly executing, deleting the task from the task set and adding the task into the CS, otherwise, the task cannot be executed, and the unmanned aerial vehicle is in a waiting state; if the task type is V, judging whether a task type C of a target corresponding to the task and all attack tasks of the target are contained in the CS, if so, directly executing, deleting the task from a task set and adding the task into the CS, otherwise, the task cannot be executed, and the unmanned aerial vehicle is in a waiting state;
step 2-5-5: judging whether the chromosome is locked;
judging the states of all the current unmanned aerial vehicles according to the step 2-5-4, and if all the current unmanned aerial vehicles are in the waiting state, locking the chromosome;
step 2-5-6: unlocking at a dead lock point at a fixed point;
if the type of the task to be executed by the unmanned aerial vehicle at the deadlock position is A, randomly selecting the task with the type of C from the rest tasks of the unmanned aerial vehicle and exchanging the sequence with the current task; this operation is performed for all the drones in the waiting state; and (5) turning to step 2-5-3.
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CN115952942A (en) * 2023-03-13 2023-04-11 季华实验室 Multi-target inspection scheduling planning method and device, electronic equipment and storage medium
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