CN112947579A - Man-machine unmanned aerial vehicle task allocation method based on cluster characteristic relation - Google Patents
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Abstract
The invention discloses a man-machine unmanned aerial vehicle task allocation method based on cluster characteristic relation. Step 1: primarily selecting all clusters of a cluster of one party participating in a task according to task requirements; step 2: classifying the machine group and the participating members in actual combat, and establishing a relationship characteristic architecture; and step 3: establishing a cluster characteristic relation table according to the difference of the unmanned units or the unmanned units in each cluster; and 4, step 4: establishing a characteristic relation gain function according to the characteristic relation table, the intra-cluster relation and the inter-cluster relation in the step 3; and 5: establishing a global gain function according to the gain function in the step 4; step 6: and (5) realizing the task allocation of the manned and unmanned aerial vehicles according to the global gain function in the step 5. The invention aims at the task allocation problem during cooperative combat of a man-machine cluster and an unmanned aerial vehicle cluster.
Description
Technical Field
The invention relates to the field of task allocation, in particular to a method for allocating tasks of a man-machine unmanned aerial vehicle based on cluster characteristic relation.
Background
The unmanned aerial vehicle participates in air combat deeply, is used as a manned assistant plane or accepts manned commands to form teams, realizes blocking, interference, trapping, fighting and the like by generating tactical formations, is an important means of air game or combat, and also becomes an important air combat mode in the future.
The man-machine can fully utilize the intelligence and comprehensive judgment capability of the man to carry out battlefield situation analysis, and can also utilize stronger maneuvering performance to finish antagonistic combat; the unmanned aerial vehicle can execute dangerous tasks under severe conditions by utilizing the advantages of lightness, flexibility, low cost and good stealth performance; the advantages of the two are complementary, the capability of coping with environmental changes can be effectively improved, and the efficiency of the battle cluster is greatly improved. The real-time and efficient task allocation during air combat is the key for fully utilizing respective advantages of a human machine and an unmanned aerial vehicle and improving the efficiency of an combat cluster.
At present, the distribution of the manned or unmanned aerial vehicle and tasks is carried out mainly according to the loss degree of the manned or unmanned aerial vehicle, the income value of the target, the size of the flight range and the like, and the overall efficiency is obtained by superposing the operational benefit indexes of the manned or unmanned aerial vehicle. The task allocation method does not consider the mutual influence relation between the people and the unmanned aerial vehicles in the specific task execution process, and has certain defects in the aspect of reflecting real battle scenes.
Disclosure of Invention
The invention provides a manned and unmanned aerial vehicle task allocation method based on cluster characteristic relation, which increases cluster relation income for global benefits of manned/unmanned aerial vehicles by establishing a cluster relation characteristic function for a cooperative combat cluster.
The invention is realized by the following technical scheme:
a man-machine unmanned aerial vehicle task allocation method based on cluster characteristic relationship is disclosed, the cooperative task allocation method comprises the following steps:
step 1: primarily selecting all clusters of a cluster of one party participating in a task according to task requirements;
step 2: classifying the machine group and the participating members in actual combat, and establishing a relationship characteristic architecture;
and step 3: establishing a cluster characteristic relation table according to the difference of the man-machine unit/the unmanned-machine unit in each cluster;
and 4, step 4: establishing a characteristic relation gain function according to the characteristic relation table, the intra-cluster relation and the inter-cluster relation in the step 3;
and 5: establishing a global gain function according to the gain function in the step 4;
step 6: and (5) realizing the task allocation of the manned and unmanned aerial vehicles according to the global gain function in the step 5.
Further, the step 1 specifically includes that the local cluster includes the number, serial number and performance characteristics of the man-machine and unmanned aerial vehicle units;
the task target set is MB ═ MB during cooperative combat1,MB2,…,MBmM targets, i.e. the set of our party clusters is defined as
N people/unmanned planes are arranged, and epsilon belongs to { M, U } to represent the model; and epsilon-M represents an unmanned plane, and epsilon-U represents an unmanned plane.
Further, the step 2 of establishing a relationship characteristic architecture specifically includes an intra-cluster relationship and an inter-cluster relationship;
the intra-cluster relationship: in the same machine group, a man-machine and an unmanned aerial vehicle cooperate with each other to jointly complete a certain task; the mutual promotion among the two is capable of improving the overall fighting efficiency and showing a cooperative relationship; if all members of the human-computer/unmanned-aerial-vehicle are isomorphic, the isomorphic individuals have no obvious promoting effect and are defined as unrelated.
The cluster-to-cluster relationship: among different clusters, due to the difference between deployment and command scheduling, the cost of a hybrid formation is higher than that of a single cluster formation; different individuals among the clusters show a competitive relationship or no relationship among the individuals.
Further, the step 3 is to use a relationship characteristic function phi of the ith member and the jth member in the characteristic relationship tableij，φij(ii) an effect on the ith member for the jth member; characteristic function phiijThe formula of (c) is expressed as follows:
wherein r is+Is a positive real number, r_Is a negative real number; based on the interaction between the two members, thenij＝φji(ii) a Become intoThe member itself has no influence on itself, then phiij＝0(i＝j)。
Further, the step 4 is specifically that the characteristic relationship gain function is used to represent the magnitude of the promoting or inhibiting effect of the human-computer units in the local cluster on the performances of each other; or the magnitude of the promotion or inhibition of each other's performance by the drone units; for the ith task target, it is recorded as MBiThe characteristic relation phi of the clusterklDown pair task MBiThe gain of (d) is expressed as S (phi)kl,MBi)。
Further, the step 5 of establishing a global gain function specifically includes obtaining an additional gain allocated to the collaborative task based on a characteristic relationship gain function;
based on the task value, the loss of the human-computer/unmanned aerial vehicle and the flight distance, obtaining respective benefits of executing the task when the human-computer unit and the unmanned aerial vehicle unit do not consider the cluster relationship;
global gains are derived based on execution gains for each manned/unmanned unit and additional gains due to fleet relationships.
Further, the obtaining of the respective benefits of the manned and unmanned units in executing the tasks without considering the fleet relationship based on the task value, the manned/unmanned loss and the range size is specifically that the benefit of the manned/unmanned unit in executing the ith target while excluding the fleet relationship characteristic is SYiThe formula, expressed as,
wherein HijA revenue function for the manned/unmanned aerial vehicle to perform the task; j is the jth member of manned/unmanned aerial vehicles; n is the total number of the unmanned aerial vehicles and the unmanned aerial vehicles; gamma rayijAssigning matrices to tasks
Element, gammaijIs represented as follows:
further, the obtaining of the additional benefit of the cooperative task allocation based on the characteristic relationship gain function is specifically that, when the ith target is executed, the additional value of the fleet relationship to the respective benefit can be expressed as Δ SYi
Wherein k and l are the serial numbers of the members of the manned or unmanned aerial vehicle, and gammaikIndicates whether the manned/unmanned k performs the task i, γilIndicates whether the manned/unmanned aerial vehicle l performs the task i, MB or notiFor the ith task object, S (phi)kl,MBi) For cluster characteristic relation phiklDown pair task MBiThe relationship of (1).
Further, the overall profit obtained based on the execution profit of each human-computer/unmanned aerial vehicle unit and the additional profit generated by the fleet relationship is specifically that the task allocation is based on the task demand and the task type, a group of human-computer and unmanned aerial vehicle sequences are selected for each task, so that the total benefit is maximum when the task is completed, and the overall profit index SY is established as follows:
wherein Δ SYiThe added value of the cluster relationship to the respective profit when the ith target is executed; SY (simple and easy) to useiThe income of the human machine/unmanned machine when the ith target is executed when the cluster relation characteristic is eliminated; and m is the total number of task targets.
Further, in step 8, specifically, the task allocation aims at obtaining a task allocation matrix Γ that maximizes the global gain index, that is:
wherein ΔSYiThe added value of the cluster relationship to the respective profit when the ith target is executed; SY (simple and easy) to useiThe income of the human machine/unmanned machine when the ith target is executed when the cluster relation characteristic is eliminated; and m is the total number of task targets.
The invention has the beneficial effects that:
the invention establishes a relation characteristic function, and can describe and describe the influence of other people/unmanned aerial vehicles on the self efficiency when the same task is executed. The global benefit function and the task allocation model are established based on the relation characteristic function, the scheduling or competition loss in the mixed compiling of different clusters can be reduced according to the inter-cluster relation and the intra-cluster relation of the clusters, the global benefit is improved by utilizing the cooperation relation among different units of the manned machine/unmanned machine, and the maximization of the manned/unmanned machine cooperative combat benefit is realized.
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FIG. 1 is a schematic structural diagram of the present invention.
Fig. 2 is a schematic diagram of a cluster relationship in embodiment 2 of the present invention.
Fig. 3 is a schematic diagram illustrating cluster characteristic relationships between two clusters according to embodiment 2 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
A man-machine unmanned aerial vehicle task allocation method based on cluster characteristic relationship is disclosed, the cooperative task allocation method comprises the following steps:
step 1: primarily selecting all clusters of a cluster of one party participating in a task according to task requirements;
step 2: classifying the machine group and the participating members in actual combat, and establishing a relationship characteristic architecture;
and step 3: establishing a cluster characteristic relation table according to the difference of the man-machine unit/the unmanned-machine unit in each cluster;
and 4, step 4: establishing a characteristic relation gain function according to the characteristic relation table, the intra-cluster relation and the inter-cluster relation in the step 3;
and 5: establishing a global gain function according to the gain function in the step 4;
step 6: and (5) realizing the task allocation of the manned and unmanned aerial vehicles according to the global gain function in the step 5.
Further, the step 1 specifically includes that the local cluster includes the number, serial number and performance characteristics of the man-machine and unmanned aerial vehicle units; the local party is the own party, and the own party and the enemy are the opposite organizations, groups and the like in the battle. In actual combat, each party has a certain number of clusters to participate in the combat.
The machine group is a combat unit consisting of a plurality of manned machines and unmanned machines. The manned/unmanned aerial vehicle is an actual participant and an executor in cooperative combat, and is the minimum unit in the combat. The different relationships are heterogeneous because of differences in cost, hit probability, and the like.
The task target set is MB ═ MB during cooperative combat1,MB2,…,MBmM targets, i.e. the set of our party clusters is defined as
N people/unmanned planes are arranged, and epsilon belongs to { M, U } to represent the model; and epsilon-M represents an unmanned plane, and epsilon-U represents an unmanned plane.
Further, the step 2 of establishing a relationship characteristic architecture specifically includes an intra-cluster relationship and an inter-cluster relationship;
the intra-cluster relationship: in the same machine group, a man-machine and an unmanned aerial vehicle cooperate with each other to jointly complete a certain task; the mutual promotion among the two is capable of improving the overall fighting efficiency and showing a cooperative relationship; if all members of the human-computer/unmanned-aerial-vehicle are isomorphic, the isomorphic individuals have no obvious promoting effect and are defined as unrelated.
A cooperative relationship may be defined when there are the following features between different manned/unmanned units within the same fleet:
the functions of the units are heterogeneous, so that better effects can be exerted in cooperation;
information sharing exists among the units, and task execution efficiency and accuracy are higher during cooperation;
a plurality of units belong to the same control center;
fourthly, a plurality of units belong to the same cluster and are subjected to tactical drilling in advance;
the unit is controlled by drivers, and drivers are skillfully matched.
The cluster-to-cluster relationship: among different clusters, due to the difference between deployment and command scheduling, the cost of a hybrid formation is higher than that of a single cluster formation; thus, in most cases, different individuals within a fleet exhibit a competitive relationship or, in few cases, no relationship between individuals. The cluster relationship described is shown in FIG. 2.
Different manned/unmanned units among different clusters may be defined as competing relationships when the following features exist:
the units have the same function, and the efficiency redundancy exists in the cooperation process;
the units are different in scheduling, deployment and cooperation and need to be mutually ground;
the information among the units is not smooth, and the control rights are different, so that the units become barrier constraints for executing tasks mutually;
and fourthly, the units are easy to compete with each other, and the individual behaviors which are not beneficial to unified tactics are avoided.
Further, the step 3 is to use a relationship characteristic function phi of the ith member and the jth member in the characteristic relationship tableijRepresenting the effect of the jth member on the ith member; characteristic function phiijThe formula of (c) is expressed as follows:
wherein r is+Is a positive real number, r_Is a negative real number; based on the interaction between the two members, thenij＝φji(ii) a The member itself has no influence on itself, then phiij＝0(i＝j)。
Further, the step 4 is specifically that the characteristic relationship gain function is used to represent the magnitude of the promoting or inhibiting effect of the human-computer units in the local cluster on the performances of each other; or the magnitude of the promotion or inhibition of each other's performance by the drone units; for the ith task target, it is recorded as MBiThe characteristic relation phi of the clusterklDown pair task MBiThe gain of (d) is expressed as S (phi)kl,MBi)。
Further, the step 5 of establishing a global gain function specifically includes obtaining an additional gain allocated to the collaborative task based on a characteristic relationship gain function;
based on the task value, the loss of the human-computer/unmanned aerial vehicle and the flight distance, obtaining respective benefits of executing the task when the human-computer unit and the unmanned aerial vehicle unit do not consider the cluster relationship;
global gains are derived based on execution gains for each manned/unmanned unit and additional gains due to fleet relationships.
Further, the obtaining of the respective benefits of the manned and unmanned units in executing the tasks without considering the fleet relationship based on the task value, the manned/unmanned loss and the range size is specifically that the benefit of the manned/unmanned unit in executing the ith target while excluding the fleet relationship characteristic is SYiThe formula, expressed as,
wherein HijA revenue function for the manned/unmanned aerial vehicle to perform the task; i is the ith member; j is the jth member; n is the total number of the unmanned aerial vehicles and the unmanned aerial vehicles; gamma rayijAssigning matrices to tasks
Element, gammaijIs represented as follows:
further, the obtaining of the additional benefit of the cooperative task allocation based on the characteristic relationship gain function is specifically that, when the ith target is executed, the additional value of the fleet relationship to the respective benefit can be expressed as Δ SYi
Wherein k and l are the serial numbers of the members of the manned or unmanned aerial vehicle, and gammaikIndicates whether the manned/unmanned k performs the task i, γilIndicates whether the manned/unmanned aerial vehicle l performs the task i, MB or notiFor the ith task object, S (phi)kl,MBi) For cluster characteristic relation phiklDown pair task MBiThe relationship of (1).
Further, the overall profit obtained based on the execution profit of each human-computer/unmanned aerial vehicle unit and the additional profit generated by the fleet relationship is specifically that the task allocation is based on the task demand and the task type, a group of human-computer and unmanned aerial vehicle sequences are selected for each task, so that the total benefit is maximum when the task is completed, and the overall profit index SY is established as follows:
wherein Δ SYiThe added value of the cluster relationship to the respective profit when the ith target is executed; SY (simple and easy) to useiThe income of the human machine/unmanned machine when the ith target is executed when the cluster relation characteristic is eliminated; and m is the total number of task targets.
Further, in step 8, specifically, the task allocation aims at obtaining a task allocation matrix Γ that maximizes the global gain index, that is:
wherein Δ SYiThe added value of the cluster relationship to the respective profit when the ith target is executed; SY (simple and easy) to useiThe income of the human machine/unmanned machine when the ith target is executed when the cluster relation characteristic is eliminated; and m is the total number of task targets.
The traditional research on the task allocation problem of the manned/unmanned aerial vehicle is only limited to respectively considering the benefits of the manned/unmanned aerial vehicle for respectively executing the tasks, and the invention also simultaneously considers the mutual influence relationship between the manned/unmanned aerial vehicle in the specific task executing process, and completes the task allocation of the manned/unmanned aerial vehicle according to the relationship characteristics.
The invention establishes a relation characteristic function, and can describe and describe the influence of other people/unmanned aerial vehicles on the self efficiency when the same task is executed. The global benefit function and the task allocation model are established based on the relation characteristic function, the scheduling or competition loss in the mixed compiling of different clusters can be reduced according to the inter-cluster relation and the intra-cluster relation of the clusters, the global benefit is improved by utilizing the cooperation relation among different units of the manned machine/unmanned machine, and the maximization of the manned/unmanned machine cooperative combat benefit is realized.
Example 2
If we consider that i are two clusters, assume that the cluster a includes a-frame manned/unmanned aerial vehicles, the cluster B includes B-frame manned/unmanned aerial vehicles, and the total number N of manned/unmanned aerial vehicles is a + B. Cluster relationship feature matrix
Can be expressed as:
or
From the relational feature schema and the relational feature function definitions, phi is knownA、ΦBThe relationship between cluster A and cluster B, phiABAnd phiBAIs a cluster A, B cluster relationship and has the following properties:
(1)
φij＝0(i＝j)
(2)ΦBA＝ΦAB T
(3)
(4)
a cluster characteristic relationship table illustrating two clusters can be shown in fig. 3, where the cluster characteristic relationship function in fig. 3 is as follows:
example 3
Relation gain function S (phi)kl,MBi) Can be preset to be a constant value, and can also be defined as a nonlinear function related to cluster relation characteristics and tasks according to the limit of task benefits.
The fourth concrete implementation mode: step five, the income function H of the unmanned aerial vehicle/the manned vehicle for executing the taskijIs composed of
Hij＝(Cij-Gij+Eij)(1-βij)
In the formula, CijIs a task value revenue function, which can be expressed as
Wherein FijAs a function of task fitness, ηiIs the value coefficient, τ, of task iiTime discount coefficient for task i value, tiThe duration is executed for task i.
GijIs a function of fuel consumption and can be expressed as
Gij＝μjti
Wherein mujIs the manned/unmanned j fuel consumption coefficient.
EijIs a value loss function of the manned/unmanned aerial vehicle and can be expressed as
Wherein WjIs the value coefficient of the unmanned/unmanned j, rhoijTo execute task i, the value loss coefficient of manned/unmanned j.
βijRisk coefficients for the manned/unmanned j to perform task i.
Claims (10)
1. A man-machine unmanned aerial vehicle task allocation method based on cluster characteristic relationship is characterized in that the cooperative task allocation method comprises the following steps:
step 1: primarily selecting all clusters of a cluster of one party participating in a task according to task requirements;
step 2: classifying the machine group and the participating members in actual combat, and establishing a relationship characteristic architecture;
and step 3: establishing a cluster characteristic relation table according to the difference of the man-machine unit/the unmanned-machine unit in each cluster;
and 4, step 4: establishing a characteristic relation gain function according to the characteristic relation table, the intra-cluster relation and the inter-cluster relation in the step 3;
and 5: establishing a global gain function according to the gain function in the step 4;
step 6: and (5) realizing the task allocation of the manned and unmanned aerial vehicles according to the global gain function in the step 5.
2. The manned and unmanned aerial vehicle task allocation method based on cluster characteristic relationship according to claim 1, wherein the step 1 is specifically that our cluster includes the number, number and performance characteristics of the man-machine and unmanned aerial vehicle units;
the task target set is MB ═ MB during cooperative combat1,MB2,…,MBmM targets, i.e. the set of our party clusters is defined as
N people/unmanned planes are arranged, and epsilon belongs to { M, U } to represent the model; and epsilon-M represents an unmanned plane, and epsilon-U represents an unmanned plane.
3. The method for allocating manned, unmanned aerial vehicle tasks according to claim 1, wherein the step 2 of establishing a relationship feature architecture specifically includes an intra-cluster relationship and an inter-cluster relationship;
the intra-cluster relationship: in the same machine group, a man-machine and an unmanned aerial vehicle cooperate with each other to jointly complete a certain task; the mutual promotion among the two is capable of improving the overall fighting efficiency and showing a cooperative relationship; if all members of the human-computer/unmanned-aerial-vehicle are isomorphic, the isomorphic individuals have no obvious promoting effect and are defined as unrelated.
The cluster-to-cluster relationship: among different clusters, due to the difference between deployment and command scheduling, the cost of a hybrid formation is higher than that of a single cluster formation; different individuals among the clusters show a competitive relationship or no relationship among the individuals.
4. The method as claimed in claim 1, wherein the step 3 is specifically that the relationship characteristic function phi of the ith member and the jth member in the characteristic relationship tableijRepresenting the effect of the jth member on the ith member; characteristic function phiijIs expressed as follows:
wherein r is+Is a positive real number, r_Is a negative real number; based on the interaction between the two members, thenij＝φji(ii) a The member itself has no influence on itself, then phiij＝0(i＝j)。
5. The manned and unmanned aerial vehicle task allocation method based on fleet characteristic relationships according to claim 1, wherein the step 4 is specifically that the characteristic relationship gain function is used to characterize the magnitude of the promotion or inhibition effect of manned units in the fleet on the performance of each other; or the magnitude of the promotion or inhibition of each other's performance by the drone units; for the ith task target, it is recorded as MBiThe characteristic relation phi of the clusterklDown pair task MBiThe gain of (d) is expressed as S (phi)kl,MBi)。
6. The method according to claim 1, wherein the step 5 of establishing a global gain function specifically obtains additional gains for cooperative task allocation based on a characteristic relationship gain function;
based on the task value, the loss of the human-computer/unmanned aerial vehicle and the flight distance, obtaining respective benefits of executing the task when the human-computer unit and the unmanned aerial vehicle unit do not consider the cluster relationship;
global gains are derived based on execution gains for each manned/unmanned unit and additional gains due to fleet relationships.
7. The method according to claim 6, wherein the task assignment method based on task value, manned/unmanned aerial vehicle loss, and the like,The obtained gains of the unmanned and manned units when the fleet relationship is not considered are SY, and the yield of the unmanned/manned unit when the fleet relationship is eliminated is SYiThe formula, expressed as,
wherein HijA revenue function for the manned/unmanned aerial vehicle to perform the task; j is the jth member of manned/unmanned aerial vehicles; n is the total number of the unmanned aerial vehicles and the unmanned aerial vehicles; gamma rayijAssigning matrices to tasks
Element, gammaijIs represented as follows:
8. the method as claimed in claim 6, wherein the additional gains for collaborative task allocation obtained based on the gain function of the characteristic relationship are obtained by that when the ith objective is executed, the additional value of the respective gains of the fleet relationship is represented as Δ SYi
Wherein k and l are the serial numbers of the members of the manned or unmanned aerial vehicle, and gammaikIndicates whether the manned/unmanned k performs the task i, γilIndicates whether the manned/unmanned aerial vehicle l performs the task i, MB or notiFor the ith task object, S (phi)kl,MBi) For cluster characteristic relation phiklDown pair task MBiThe relationship of (1).
9. The human-machine-unmanned-aerial-vehicle task allocation method based on the fleet characteristic relationship as claimed in claim 6, wherein the global benefits obtained based on the execution benefits of each human-machine-unmanned-vehicle unit and the additional benefits generated by the fleet relationship are specifically that the task allocation is based on the task demand and the task type, a group of human-machine-unmanned-vehicle sequences and unmanned-aerial-vehicle sequences are selected for each task, so that the total benefit is maximized when the task is completed, and the global benefits index SY is established as follows:
wherein Δ SYiThe added value of the cluster relationship to the respective profit when the ith target is executed; SY (simple and easy) to useiThe income of the human machine/unmanned machine when the ith target is executed when the cluster relation characteristic is eliminated; and m is the total number of task targets.
10. The method for allocating manned, unmanned aerial vehicle tasks according to claim 1, wherein the step 8 is specifically that the task allocation is targeted to obtain a task allocation matrix Γ that maximizes the global gain index, that is:
wherein Δ SYiThe added value of the cluster relationship to the respective profit when the ith target is executed; SY (simple and easy) to useiThe income of the human machine/unmanned machine when the ith target is executed when the cluster relation characteristic is eliminated; and m is the total number of task targets.
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