CN116880540B - Heterogeneous unmanned aerial vehicle group task allocation method based on alliance game formation - Google Patents
Heterogeneous unmanned aerial vehicle group task allocation method based on alliance game formationInfo
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Abstract
本发明公开了一种基于联盟形成博弈的异构无人机群任务分配方法，包括：步骤1：针对协同监测与协同评估两类任务，分别定义无人机群协同任务执行效能；步骤2：将无人机群协同任务执行问题建模为联盟形成博弈模型，其中博弈参与者为机群内各无人机；步骤3：利用基于部分合作的联盟形成算法进行求解，使得各无人机的任务执行效能最大化，得到各无人机的任务选择结果；步骤4：根据上述任务选择结果，分配无人机群任务，完成基于联盟形成博弈的异构无人机群任务分配。本发明利用博弈的方法，使得各无人机具备自主决策能力，具备时效性高、信息交互少的特点，可有效提高无人机群自主性，发挥机群任务执行效能。
The present invention discloses a method for allocating tasks for heterogeneous drone swarms based on an alliance formation game, comprising the following steps: Step 1: defining the collaborative task execution efficiency of drone swarms for two types of tasks, collaborative monitoring and collaborative evaluation; Step 2: modeling the drone swarm collaborative task execution problem as an alliance formation game model, where the game participants are the drones in the swarm; Step 3: solving the problem using an alliance formation algorithm based on partial cooperation to maximize the task execution efficiency of each drone and obtain a task selection result for each drone; Step 4: allocating drone swarm tasks based on the task selection result, thereby completing task allocation for heterogeneous drone swarms based on an alliance formation game. The present invention utilizes a game-based approach to enable each drone to have autonomous decision-making capabilities, with high timeliness and minimal information exchange. This method can effectively improve the autonomy of the drone swarm and maximize the task execution efficiency of the swarm.
Description
Technical Field
The invention relates to an unmanned aerial vehicle group task allocation method, in particular to a heterogeneous unmanned aerial vehicle group task allocation method based on alliance forming games.
Background
Unmanned aerial vehicles have been widely used in various fields with the advantages of high dynamic performance, dynamic deployment and the like. With the rapid development of unmanned aerial vehicle technology, cluster control and artificial intelligence, the heterogeneous unmanned aerial vehicle group collaborative task execution can effectively solve the problems of low single unmanned aerial vehicle task execution efficiency, poor robustness and the like, for example, the heterogeneous unmanned aerial vehicle group can execute multi-target monitoring tasks and evaluation tasks.
Currently, unmanned aerial vehicle group task allocation can be largely divided into centralized and distributed. The centralized method can well solve the problem of task allocation of the unmanned aerial vehicle group under multiple constraints, and has the advantage of giving a theoretical optimal solution under the problem. In addition, the centralized method needs global information, and is difficult to apply in a high-dynamic complex environment where information interaction between unmanned aerial vehicles cannot be guaranteed. The distributed method mainly comprises a part of algorithms in convex optimization and a multi-agent reinforcement learning algorithm, wherein the convex optimization algorithm is often limited by structural characteristics of problems, and the convergence, training time and storage constraint of the multi-agent reinforcement learning algorithm are difficult to guarantee. Therefore, the heterogeneous unmanned aerial vehicle task allocation method is a very challenging hot spot problem.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a task allocation method for heterogeneous unmanned aerial vehicle groups based on alliance forming games.
In order to solve the technical problems, the invention discloses a task allocation method for heterogeneous unmanned aerial vehicle groups based on alliance forming games, which comprises the following steps:
Step 1, respectively defining the execution efficiency of the unmanned aerial vehicle group cooperative task aiming at the task type of the unmanned aerial vehicle;
the unmanned aerial vehicle task types comprise two types of tasks of collaborative monitoring and collaborative evaluation.
The method for defining the performance of the unmanned aerial vehicle group collaborative task comprises the following steps:
Step 1-1, when the unmanned aerial vehicle group executes the cooperative monitoring task type, calculating task execution efficiency P R(Sm of the unmanned aerial vehicle group), the specific method is as follows:
When the unmanned aerial vehicle group S m executes the cooperative task m and the task is of the cooperative monitoring task type, the task execution performance P R(Sm) of the unmanned aerial vehicle group is defined as:
Wherein, the The probability of completion of the unmanned plane n to the monitoring task m is represented by n epsilon S m, wherein the unmanned plane n belongs to the unmanned plane group S m,Vm and is a value coefficient of the cooperative task m, and R m is a threat coefficient of the cooperative task m.
Step 1-2, when the unmanned aerial vehicle group executes the cooperative evaluation task type, calculating the task execution efficiency P C(Sm of the unmanned aerial vehicle group), the specific method is as follows:
When the unmanned aerial vehicle group S m executes the cooperative task m and the task is a cooperative evaluation task type, the task execution performance P C(Sm) of the unmanned aerial vehicle group is defined as:
Wherein, the Is the probability of completion of the evaluation task m by the drone n.
Modeling the unmanned aerial vehicle group collaborative task execution problem as a coalition to form a game model, wherein a game participant is each unmanned aerial vehicle in the unmanned aerial vehicle group;
modeling the unmanned aerial vehicle group cooperative task execution problem as a alliance to form a game model, wherein the game model The following are provided:
Wherein, the Representing the number of N drones in the cluster performing the task, Representing a set of M tasks that the drone needs to perform,The method comprises the steps of collecting actions of N unmanned aerial vehicles, wherein a n is the action selection of the unmanned aerial vehicle N; Is a utility function set of N unmanned aerial vehicles, wherein u n is a utility function of unmanned aerial vehicle N, The coalition state formed for coalition partition, i.e. unmanned aerial vehicle group.
The utility function of the unmanned aerial vehicle is as follows:
Using the average allocation criterion, the utility function u n of unmanned plane n is defined as:
Where S m is the number of drones performing task m.
Step 3, solving by utilizing a alliance forming algorithm based on partial cooperation, so that the task execution efficiency of each unmanned aerial vehicle is maximized, and obtaining a task selection result of each unmanned aerial vehicle, wherein the method comprises the following specific steps of:
step 3-1, initializing, namely randomly selecting a task by each unmanned aerial vehicle to form an initial alliance partition
Step 3-2, randomly selecting one unmanned aerial vehicle n, keeping task selection unchanged by other unmanned aerial vehicles, and calculating the current task execution efficiency U n of the unmanned aerial vehicle n, wherein the specific method is as follows:
Wherein u j is the utility function of the unmanned aerial vehicle j, For the joined coalition of drone n,The federation was not added to unmanned n. Wherein the symbol "\" indicates that the set element is to be excluded from the set.
Step 3-3, unmanned plane n randomly selects a task different from task a n Calculating and changing the task execution efficiency U' n of the unmanned plane n after task selection according to the step 3-2;
step 3-4, if U n<U′n, the unmanned plane n changes the task selection from the task a n to a' n, if U n≥U′n, the unmanned plane n keeps the task selection a n unchanged;
And 3-5, outputting the task selection and task execution efficiency of each unmanned aerial vehicle when the stable alliance partition is converged, otherwise, repeatedly executing the step 3-2 until the stable alliance partition is converged.
The convergence to a stable alliance partition, i.e. the sum of the task execution performance of all unmanned aerial vehiclesRemains unchanged during the iteration.
And 4, distributing unmanned aerial vehicle group tasks according to the task selection result to complete heterogeneous unmanned aerial vehicle group task distribution based on alliance forming games.
The beneficial effects are that:
by applying the alliance game, each unmanned aerial vehicle has autonomous decision making capability, is more suitable for the problem of task allocation of large-scale unmanned aerial vehicle groups, and meanwhile, global information interaction is not needed, so that the calculation complexity can be effectively reduced. Compared with other distributed optimization methods, the game-based optimization method has high timeliness and less information interaction, can effectively improve the autonomy of the unmanned aerial vehicle group, and exerts the task execution efficiency of the unmanned aerial vehicle group.
Drawings
The foregoing and/or other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings and detailed description.
FIG. 1 is a schematic diagram of the overall structure of the task allocation method of the present invention.
FIG. 2 is a flow chart of a part-collaboration-based federation formation algorithm in accordance with the present invention.
FIG. 3 is a diagram of the result of a convergence simulation of a partially collaborative-based federation formation algorithm according to the present invention.
Detailed Description
The invention provides a task allocation method for heterogeneous unmanned aerial vehicle groups based on alliance forming games, which enables heterogeneous unmanned aerial vehicle groups to cooperate with heterogeneous tasks through local information interaction and exert cluster combat efficacy.
The technical scheme is as follows:
A heterogeneous unmanned aerial vehicle group task allocation method based on alliance forming game comprises the following steps:
And step 1, aiming at the two tasks of collaborative monitoring and collaborative evaluation, respectively defining the execution efficiency of the collaborative tasks of the unmanned aerial vehicle group.
And 2, modeling the unmanned aerial vehicle group collaborative task execution problem as a alliance to form a game model, wherein game participants are unmanned aerial vehicles in the unmanned aerial vehicle group.
And 3, solving by utilizing a alliance forming algorithm based on partial cooperation so as to maximize the task execution efficiency of each unmanned aerial vehicle.
In the step 1, when the unmanned aerial vehicle group S m executes the collaborative monitoring task m, the task execution performance P R(Sm) is defined as:
Wherein, the The probability of task completion of the unmanned aerial vehicle n on the task m is monitored, n epsilon S m indicates that the unmanned aerial vehicle n belongs to the cluster S m,Vm and is a value coefficient of the task m, and the threat coefficient of the task m is R m. Similarly, when the unmanned aerial vehicle group S m performs the collaborative evaluation task m, the task performance P C(Sm) is defined as:
Wherein, the The evaluation task completion probability of the unmanned plane n to the task m.
In the step 2, modeling the unmanned aerial vehicle group cooperative task execution problem as a coalition to form a game model, wherein the game model is defined as:
Wherein, the Representing the number of N drones in the cluster performing the task, Representing a set of M tasks that the drone needs to perform,A n is a task selection of the unmanned plane n; is a utility function, wherein u n is a utility function of the drone n, The coalition state formed for coalition partition, i.e. unmanned aerial vehicle group. Using the average allocation criterion, defining the utility function of the unmanned plane n as:
Where S m is the number of drones performing task m.
In the step 3, a coalition forming algorithm based on partial cooperation is provided, so that the task execution efficiency of each unmanned aerial vehicle is maximized, and the specific algorithm is as follows:
step 3-1, initializing, namely randomly selecting a task by each unmanned aerial vehicle to form an initial alliance partition
Step 3-2, randomly selecting one unmanned aerial vehicle n, keeping task selection unchanged by other unmanned aerial vehicles, and calculating the current task execution efficiency U n of the unmanned aerial vehicle n according to the following formula:
Wherein u j is the utility function of the unmanned aerial vehicle j, For the joined coalition of drone n,The federation was not added to unmanned n. Wherein the symbol "\" indicates that the set element is to be excluded from the set.
Step 3-3, unmanned plane n randomly selects tasks different from a n And calculating the performance U' n of the n task of the unmanned aerial vehicle after the task selection is changed according to the formula.
Step 3-4, if U n<U′n, unmanned plane n changes its task selection from a n to a' n, and if U n≥U′n, unmanned plane n keeps its task selection a n unchanged.
And 3-5, outputting the task selection and task execution efficiency of each unmanned aerial vehicle when the stable alliance partition is converged, otherwise, repeatedly executing the step 3-2 until the stable alliance partition is converged.
The convergence to the stable alliance partition indicates that no unmanned aerial vehicle is willing to change the task selection of the unmanned aerial vehicle, and the current alliance structure is fixed. The judgment standard is that the total of the task execution efficiency of all unmanned aerial vehiclesRemains unchanged over multiple iterations.
The principle of the invention is as follows:
the invention comprehensively considers the task value and the task threat coefficient, and defines the task execution efficiency oriented to different tasks. By adopting the idea of game theory, each unmanned aerial vehicle in the group is used as a game participant, the problem of unmanned aerial vehicle group cooperative task execution is solved by using the alliance forming game, and a part-cooperation-based alliance forming algorithm is provided for solving the game model, so that the maximization of the task execution efficiency of each unmanned aerial vehicle is realized.
Examples:
the invention will now be described in further detail with reference to the drawings and to specific examples.
The invention provides a heterogeneous unmanned aerial vehicle group task allocation method based on alliance forming game, which is shown in fig. 1 and comprises the following steps:
And step 1, respectively defining the execution efficiency of the collaborative tasks of the unmanned plane group aiming at two types of tasks, namely the collaborative monitoring task and the collaborative evaluating task.
When the unmanned aerial vehicle group S m executes the cooperative monitoring task m, the task execution performance P R(Sm) is defined as:
Wherein, the The probability of task completion of the unmanned aerial vehicle n on the task m is monitored, n epsilon S m indicates that the unmanned aerial vehicle n belongs to the cluster S m,Vm and is a value coefficient of the task m, and the threat coefficient of the task m is R m. Similarly, when the unmanned aerial vehicle group S M performs the collaborative evaluation task m, the task performance P C(Sm) is defined as:
Wherein, the The evaluation task completion probability of the unmanned plane n to the task m.
And 2, modeling the unmanned aerial vehicle group collaborative task execution problem as a alliance to form a game model, wherein game participants are unmanned aerial vehicles in the unmanned aerial vehicle group.
The gaming model is defined as:
Wherein, the Representing the number of N drones in the cluster performing the task, Representing a set of M tasks that the drone needs to perform,A n is a task selection of the unmanned plane n; is a utility function, wherein u n is a utility function of the drone n, The coalition state formed for coalition partition, i.e. unmanned aerial vehicle group. Using the average allocation criterion, defining the utility function of the unmanned plane n as:
Where S m is the number of drones performing task m.
And 3, solving by utilizing a alliance forming algorithm based on partial cooperation so as to maximize the task execution efficiency of each unmanned aerial vehicle.
As shown in fig. 2, the partial collaboration based federation formation algorithm steps are as follows:
step 3-1, initializing, namely randomly selecting a task by each unmanned aerial vehicle to form an initial alliance partition
Step 3-2, randomly selecting one unmanned aerial vehicle n, keeping task selection unchanged by other unmanned aerial vehicles, and calculating the current task execution efficiency U n of the unmanned aerial vehicle n according to the following formula:
Wherein u j is the utility function of the unmanned aerial vehicle j, For the joined coalition of drone n,The federation was not added to unmanned n. Wherein the symbol "\" indicates that the set element is to be excluded from the set.
Step 3-3, unmanned plane n randomly selects tasks different from a n And calculating the performance U' n of the n task of the unmanned aerial vehicle after the task selection is changed according to the formula.
Step 3-4, if U n<U′n, unmanned plane n changes its task selection from a n to a' n, and if U n≥U′n, unmanned plane n keeps its task selection a n unchanged.
And 3-5, outputting the task selection and task execution efficiency of each unmanned aerial vehicle when the stable alliance partition is converged, otherwise, repeatedly executing the step 3-2 until the stable alliance partition is converged.
The heterogeneous unmanned aerial vehicle group task allocation method based on alliance forming game, which is designed by the invention, is applied to a specific example, so that the execution efficiency of unmanned aerial vehicle group tasks can be effectively improved, and the specific application is as follows:
Consider a scenario comprising 2 monitoring tasks, 2 assessment tasks, where different tasks are monitored or assessed by a group of 10 capability heterogeneous drones. The method comprises the steps of enabling the probability of completing a monitoring task to be 0 if the unmanned aerial vehicle is only a monitoring unmanned aerial vehicle, enabling the probability of completing the monitoring task to be uniformly distributed on the basis of 0.4 and 0.8, enabling the probability of completing the monitoring task to be 0 if the unmanned aerial vehicle is only the monitoring unmanned aerial vehicle, enabling the probability of completing the monitoring task to be uniformly distributed on the basis of 0.4 and 0.8 if the unmanned aerial vehicle is only the monitoring unmanned aerial vehicle, and enabling the probability of completing the monitoring task and the probability of completing the monitoring task to be uniformly distributed on the basis of 0.4 and 0.8 if the unmanned aerial vehicle is a double-task integrated unmanned aerial vehicle. In addition, the task value coefficient matrix is [9.5,8.4,9.1,7.6], and the task threat coefficient matrix is [2.7,2.2,1.2,2.8]. FIG. 3 shows the experimental results.
As shown in fig. 3, a comparison diagram of the performance convergence of the task execution performance of the unmanned aerial vehicle group under the application of the proposed alliance formation algorithm based on partial cooperation and the conventional alliance formation algorithm based on pareto criterion and selfish criterion shows that the convergence rate corresponding to the method designed by the invention is the fastest, and the performance of the task performance of the unmanned aerial vehicle group is obviously higher than that of the other two conventional methods.
In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, where the computer storage medium is capable of storing a computer program, where the computer program when executed by the data processing unit may perform part or all of the steps of the method for task allocation of a heterogeneous unmanned aerial vehicle group, where the method is provided by the present application and especially forms a game based on a coalition. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory, RAM), or the like.
It will be apparent to those skilled in the art that the technical solutions in the embodiments of the present invention may be implemented by means of a computer program and its corresponding general hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied essentially or in the form of a computer program, i.e. a software product, which may be stored in a storage medium, and include several instructions to cause a device (which may be a personal computer, a server, a single-chip microcomputer, MUU or a network device, etc.) including a data processing unit to perform the methods described in the embodiments or some parts of the embodiments of the present invention.
The invention provides a thought and a method for distributing tasks of a heterogeneous unmanned aerial vehicle group based on alliance forming game, and the method and the way for realizing the technical scheme are numerous, the above is only a preferred embodiment of the invention, and it should be pointed out that a plurality of improvements and modifications can be made by those skilled in the art without departing from the principle of the invention, and the improvements and the modifications are also regarded as the protection scope of the invention. The components not explicitly described in this embodiment can be implemented by using the prior art.
Claims (7)
1. A heterogeneous unmanned aerial vehicle group task allocation method based on alliance forming game is characterized by comprising the following steps:
Step 1, respectively defining the execution efficiency of the unmanned aerial vehicle group cooperative task aiming at the task type of the unmanned aerial vehicle;
modeling the unmanned aerial vehicle group collaborative task execution problem as a coalition to form a game model, wherein a game participant is each unmanned aerial vehicle in the unmanned aerial vehicle group;
step 3, solving by utilizing a alliance forming algorithm based on partial cooperation, so that the task execution efficiency of each unmanned aerial vehicle is maximized, and obtaining a task selection result of each unmanned aerial vehicle;
step 4, distributing unmanned aerial vehicle group tasks according to the task selection result, and completing heterogeneous unmanned aerial vehicle group task distribution based on alliance forming games;
The task types of the unmanned aerial vehicle in the step1 comprise two types of tasks of collaborative monitoring and collaborative evaluation;
The step 1 of defining the performance of the unmanned aerial vehicle group cooperative task respectively specifically includes:
Step 1-1, calculating task execution efficiency P R(Sm of the unmanned aerial vehicle group when the unmanned aerial vehicle group executes the collaborative monitoring task type;
step 1-2, calculating task execution efficiency P C(Sm of the unmanned aerial vehicle group when the unmanned aerial vehicle group executes the cooperative evaluation task type;
The task execution performance P R(Sm of the unmanned aerial vehicle group in step 1-1) is calculated as follows:
When the unmanned aerial vehicle group S m executes the cooperative task m and the task is of the cooperative monitoring task type, the task execution performance P R(Sm) of the unmanned aerial vehicle group is defined as:
Wherein, the The probability of completion of the unmanned plane n to the monitoring task m is represented by n epsilon S m, wherein the unmanned plane n belongs to the unmanned plane group S m,Vm and is a value coefficient of the cooperative task m, and R m is a threat coefficient of the cooperative task m.
2. The method for task allocation of heterogeneous unmanned aerial vehicle groups based on alliance game formation according to claim 1, wherein the task execution performance P C(Sm of the unmanned aerial vehicle groups in step 1-2 is as follows:
When the unmanned aerial vehicle group S m executes the cooperative task m and the task is a cooperative evaluation task type, the task execution performance P C(Sm) of the unmanned aerial vehicle group is defined as:
Wherein, the Is the probability of completion of the evaluation task m by the drone n.
3. The method for assigning tasks to heterogeneous unmanned aerial vehicle groups based on coalition-based game according to claim 2, wherein in step 2, the problem of executing cooperative tasks of unmanned aerial vehicle groups is modeled as a coalition-based game model, and the game modelThe following are provided:
Wherein, the Representing the number of N drones in the cluster performing the task, Representing a set of M tasks that the drone needs to perform,The method comprises the steps of collecting actions of N unmanned aerial vehicles, wherein a n is the action selection of the unmanned aerial vehicle N; Is a utility function set of N unmanned aerial vehicles, wherein u n is a utility function of unmanned aerial vehicle N, The coalition state formed for coalition partition, i.e. unmanned aerial vehicle group.
4. The method for assigning tasks to heterogeneous unmanned aerial vehicle groups based on coalition game according to claim 3, wherein the unmanned aerial vehicle utility function in step 2 is as follows:
Using the average allocation criterion, the utility function u n of unmanned plane n is defined as:
Where S m is the number of drones performing task m.
5. The method for assigning tasks to heterogeneous unmanned aerial vehicle groups based on coalition-based game according to claim 4, wherein the coalition-based partial cooperation algorithm in step 3 comprises the following specific steps:
step 3-1, initializing, namely randomly selecting a task by each unmanned aerial vehicle to form an initial alliance partition
Step 3-2, randomly selecting one unmanned aerial vehicle n, keeping task selection unchanged by other unmanned aerial vehicles, and calculating the current task execution efficiency U n of the unmanned aerial vehicle n;
Step 3-3, unmanned plane n randomly selects a task different from task a n Wherein \represents that the set element a n is derived from the setPerforming medium rejection, namely calculating and changing the task execution efficiency U' n of the unmanned plane n after task selection according to the step 3-2;
step 3-4, if U n<U′n, the unmanned plane n changes the task selection from the task a n to a' n, if U n≥U′n, the unmanned plane n keeps the task selection a n unchanged;
And 3-5, outputting the task selection and task execution efficiency of each unmanned aerial vehicle when the stable alliance partition is converged, otherwise, repeatedly executing the step 3-2 until the stable alliance partition is converged.
6. The method for assigning tasks to heterogeneous unmanned aerial vehicle groups based on alliance game formation according to claim 5, wherein the method for calculating the current task execution performance U n of unmanned aerial vehicle n in step 3-2 comprises the following steps:
Wherein u j is the utility function of the unmanned aerial vehicle j, For the joined coalition of drone n,The federation was not added to unmanned n.
7. The method for assigning tasks to heterogeneous unmanned aerial vehicle groups based on coalition-based game as claimed in claim 6, wherein the convergence to stable coalition partition in step 3-5 is the sum of task execution performance of all unmanned aerial vehiclesRemains unchanged during the iteration.
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