A collaborative task allocation method for multi-UAV systems based on overlapping alliance formation game theory

Abstract

This invention relates to a collaborative task allocation method for multi-UAV systems based on overlapping alliance formation game theory, comprising: modeling the multi-UAV system task allocation problem under communication energy constraints as a system total utility maximization problem, and establishing an optimization problem model; establishing an overlapping alliance formation game model; based on the overlapping alliance formation game model, employing a joint optimization algorithm of alliance formation and transmission power to solve the optimization problem model, obtaining the optimal alliance structure and optimized UAV transmission power; and assigning corresponding tasks to UAVs and setting transmission power according to the optimal alliance structure and optimized UAV transmission power. Compared with existing technologies, this invention can improve the utilization rate and decision-making flexibility of UAVs, and enhance task execution efficiency and system total utility.

Description

Technical Field

[0001] This invention relates to the field of multi-agent cooperative control technology, and in particular to a method for cooperative task allocation of multi-UAV systems based on overlapping alliance game theory. Background Technology

[0002] Compared to the limited sensing and communication capabilities of a single drone, multi-drone systems offer advantages such as higher mission completion rates, better environmental adaptability, wider spatial distribution, and autonomous collaboration when facing multi-tasking scenarios with widely distributed mission areas. Multi-drone systems can execute multiple tasks more efficiently and comprehensively through cooperation, making them particularly suitable for complex collaborative mission scenarios. Drones, equipped with communication devices, can sense, collect, and process data from ground-based wireless sensor networks or IoT devices, transmitting the collected data to a base station. With the continuous innovation of wireless communication technology, the increasing intelligence of drones, and the sustained growth in demand for intelligent applications, multi-drone systems are widely used in various fields, such as environmental monitoring and search and rescue. In particular, reasonable task allocation and communication resource optimization can better leverage the advantages of multi-drone systems in efficiently completing missions.

[0003] In multi-UAV system collaborative task scenarios, multiple UAVs need to be allocated for each task. The collaboration between UAVs reflects the process of forming cooperative alliances for corresponding tasks. Alliance formation game theory is a widely used game theory model in multi-UAV task allocation, where the goal of alliance formation is to complete the allocated tasks as much as possible and maximize the overall system utility. For example, Chinese patent CN116090342A proposes a large-scale distributed UAV task allocation method based on alliance formation game theory. This method uses communication between UAVs and multiple leader UAVs to replace global communication between all UAVs, updating the task allocation results through communication between UAVs and leader UAVs, and between leader UAVs themselves.

[0004] However, existing multi-drone task allocation methods based on alliance-forming game theory have the following problems:

[0005] 1) Most existing work assumes that each drone can only join one alliance due to its limited individual capabilities. However, advancements in drone technology have enhanced its mission execution capabilities and onboard energy storage, allowing a single drone to be deployed to perform multiple tasks. For example, a drone with sufficient energy or resources can participate in multiple different cooperative alliances, thereby improving the utilization rate of onboard energy and the overall efficiency of mission completion. However, allowing drones to participate in multiple alliances leads to alliance overlap, thus requiring game theory modeling based on overlapping alliance formation.

[0006] 2) Minimizing the total task execution time is one of the keys to improving the overall utility of task performance. However, most existing work focuses on reducing task execution time through collaboration, with little consideration given to reducing task data transmission time. Therefore, a more efficient multi-machine collaborative transmission mechanism needs to be introduced.

[0007] 3) The optimization of UAV transmit power in the case of overlapping alliances is not considered. Therefore, it is necessary to solve for the optimal transmit power in different alliances for UAVs that have joined multiple alliances. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a collaborative task allocation method for multi-UAV systems based on overlapping alliance game theory, which can improve the utilization rate and decision-making flexibility of UAVs and enhance task execution efficiency.

[0009] The objective of this invention can be achieved through the following technical solution: a method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory, comprising the following steps:

[0010] S1. The task allocation problem of a multi-UAV system under communication energy constraints is modeled as the total utility maximization problem of the system, and an optimization problem model is established.

[0011] S2. Establish overlapping alliances to form a game theory model;

[0012] S3. Based on the overlapping alliance formation game model, a joint optimization algorithm for alliance formation and transmission power is adopted to solve the optimization problem model and obtain the optimal alliance structure and the optimized UAV transmission power.

[0013] S4. Based on the optimal alliance structure and the optimized UAV launch power, assign corresponding tasks to the UAVs and set their launch power.

[0014] Furthermore, step S1 specifically involves establishing an optimization problem model based on the task scenario, task attributes, and UAV attributes, combined with the UAV energy consumption model, and considering transmission power and communication energy constraints.

[0015] Furthermore, the task scenario, task attributes, and UAV attributes in step S1 are specifically as follows:

[0016] consider A drone alliance was formed to execute A task, a task set is used It indicates that drones are used in combination. The task attribute is represented as ,in Indicates the value of the task. Indicates the amount of task data;

[0017] Drone attributes include location and the ability to execute each task Multiple drones form an alliance to perform a mission. Each drone in the alliance works together to complete the mission and transmits data to the base station. Furthermore, a drone can join more than one alliance to complete multiple missions.

[0018] Furthermore, the specific process of establishing the optimization problem model in step S1 is as follows: representing the alliance structure as... The constraints on the formation of overlapping alliances are: Drones can join more than one alliance, that is ;

[0019] The expression for task time, based on the channel model, is as follows:

[0020]

[0021]

[0022] in, For task data collection time, For task data transmission time, The average time required to collect a unit of data. For the task The amount of data, For the power gain of the UAV wireless transmission channel, The received channel power gain is at a reference distance of 1 m. The distance between the drone and the mission. This is the path loss factor. Let Variance be the variance of the channel noise. For the drone's transmission power, For bandwidth;

[0023] Based on the drone energy consumption model, the expression for energy cost is as follows:

[0024]

[0025] in, For task execution time, For task data transmission time, This refers to the propulsion power during drone hovering mode. This refers to the drone's transmission power.

[0026] The constraints on the UAV's transmit power and communication energy are as follows:

[0027]

[0028]

[0029] in, For communication energy;

[0030] Based on the mission completion capabilities of each drone ,alliance Execute the task The overall completion rate is expressed as:

[0031]

[0032] The utility function of a consortium is defined as the difference between the task's reward and its cost:

[0033]

[0034] The first item is the revenue generated by the alliance in executing tasks, the second item is the time cost of the alliance in executing tasks, and the third item is the energy cost of the alliance in executing tasks. For task completion rate, For the value of the task, and These are the weighting coefficients;

[0035] The optimization objective of the multi-UAV system task allocation problem is transformed into the problem of maximizing the total system utility, and the optimization problem model is established as follows:

[0036]

[0037] The goal is to maximize total utility by optimizing the launch power of drones across multiple alliances under communication energy constraints.

[0038] Further, step S2 includes the following steps:

[0039] S21. Determine the three elements of a game, including the game participants, the strategy space, and the utility function;

[0040] S22. Define switching actions, including leave actions and join actions;

[0041] S23. Define the preference for alliance structure and the rules for altruistic preference in multiple alliances.

[0042] Furthermore, the three elements of the game determined in step S21 are specifically: the participants are... Each drone has a strategy for it. Choose the alliance to join, i.e., the task to be performed. Alliance participants The utility function is:

[0043] .

[0044] Furthermore, in step S22, the leave operation is defined as: given an alliance structure Participants Leaving an alliance it had already joined The resulting new alliance structure is ;

[0045] Define the join operation as follows: given a federation structure Participants Join an alliance it is not a party to The resulting new alliance structure is .

[0046] Furthermore, in step S23, the preference relationship of participants for the alliance structure is defined as follows: for participants and any two alliance structures and The alliance structure Superior Represented as , indicating participants Prefers alliance structure Alliance choices;

[0047] Define the multi-alliance altruistic preference rule as follows:

[0048]

[0049] in, Indicates participants In alliance structure The effect of the following Represents the collection of members of an alliance. Indicates participants The new alliance they joined Indicates participants The original alliance that withdrew, Indicates participants Maintain a group of participating alliances.

[0050] Further, step S3 includes the following steps:

[0051] S31. Initialize the attribute parameters of each task and the attribute parameters of the UAV, and initialize the alliance structure for each UAV to join the alliance corresponding to the task within the communication range.

[0052] S32, Current Unmanned Aerial Vehicles Select a league you have already joined. The new alliance structure resulting from the execution of the departure action is denoted as . First, determine whether the total communication energy of the drone operating at maximum transmission power across multiple alliances exceeds the upper limit constraint. If it does, the transmission power needs to be optimized. Then, calculate the drone's communication energy based on the optimized transmission power. exist The utility of the drone is considered, and decisions are made based on the multi-alliance altruistic preference rule, if the drone prefers... If the alliance structure and drone launch power are updated, the decision-making process for the next drone will begin; otherwise, step S33 will be executed.

[0053] S33. If the exit action fails to update the alliance structure, then further attempt to join the action. The current drone Choose an alliance you are not participating in. The new alliance structure resulting from the execution of the join action is denoted as . First, determine whether the total communication energy of the drone operating at maximum transmission power across multiple alliances exceeds the upper limit constraint. If it does, the transmission power needs to be optimized. Then, calculate the drone's power based on the optimized transmission power. exist The utility of the drone is considered, and decisions are made based on the multi-alliance altruistic preference rule, if the drone prefers... Then update the alliance structure and drone launch power;

[0054] S34. Iterate through steps S32 to S33 until the preset iteration termination condition is met, and finally obtain a stable alliance structure and optimized UAV launch power.

[0055] Furthermore, the preset iteration termination condition in step S34 is specifically convergence to a stable alliance structure, that is, the total utility of the system no longer changes during the iteration process.

[0056] Compared with the prior art, the present invention has the following advantages:

[0057] This invention models the task allocation problem of a multi-UAV system under communication energy constraints as a system total utility maximization problem, establishing an optimization problem model. Then, an overlapping alliance formation game model is established, and a joint optimization algorithm for alliance formation and transmission power is used to solve the optimization problem model, obtaining the optimal alliance structure and optimized UAV transmission power. This enables UAVs to select tasks and form alliances based on the overlapping alliance formation game model, allowing them to collaboratively execute tasks and transmit data. Simultaneously, it effectively optimizes UAV transmission power, significantly improving UAV utilization and decision-making flexibility, and enhancing task execution efficiency.

[0058] This invention, based on task scenarios, task attributes, and UAV attributes, combined with a UAV energy consumption model and considering transmit power and communication energy constraints, establishes an optimization problem model. It models the task allocation problem of a multi-UAV system under communication energy constraints as a system total utility maximization problem, enabling multiple UAVs to form an alliance to execute a single task. Each UAV within the alliance collaborates to complete the task and transmits data to the base station. Furthermore, a UAV can join more than one alliance to complete multiple tasks. This invention considers that UAVs with sufficient energy or resources can participate in multiple tasks. The designed game-theoretic method based on overlapping alliance formation improves UAV utilization and system total utility. In addition, by introducing a multi-UAV cooperative transmission mechanism, data transmission time is reduced, system total utility is improved, and decision-making flexibility and UAV utilization are effectively enhanced.

[0059] This invention employs a joint optimization algorithm for alliance formation and transmission power, taking into account the transmission power and communication energy constraints of UAVs in the case of overlapping alliances. It can simultaneously optimize alliance formation and UAV transmission power, thereby improving mission execution performance and overall system utility. Attached Figure Description

[0060] Figure 1 This is a flowchart of the method of the present invention;

[0061] Figure 2 This is the final alliance structure diagram in the embodiment;

[0062] Figure 3 This is a graph showing the total utility convergence in the embodiments.

[0063] Figure 4 This is a graph showing the total utility of the algorithm under different task scales in the embodiments;

[0064] Figure 5 The graph shows the total utility of the algorithm in the embodiment under different drone scales. Detailed Implementation

[0065] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0066] Example

[0067] like Figure 1 As shown, a method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game includes the following steps:

[0068] S1. The task allocation problem of a multi-UAV system under communication energy constraints is modeled as the total utility maximization problem of the system, and an optimization problem model is established.

[0069] S2. Establish overlapping alliances to form a game theory model;

[0070] S3. Based on the overlapping alliance formation game model, a joint optimization algorithm for alliance formation and transmission power is adopted to solve the optimization problem model and obtain the optimal alliance structure and the optimized UAV transmission power.

[0071] S4. Based on the optimal alliance structure and the optimized UAV launch power, assign corresponding tasks to the UAVs and set their launch power.

[0072] This embodiment applies the above scheme, and in a mission scenario including 10 mission points and 6 drones, the following results are obtained: Figure 2 The stable alliance structure shown, Figure 2 The solid dots in the image represent drones. Figure 2 The asterisks in the text represent task points. Figure 2 The connecting lines represent the correspondence between drones and their assigned tasks, thus yielding the final task allocation result. Without loss of generality, the positions of all drones and task points are randomly generated within the currently defined area.

[0073] The specific application process of this embodiment:

[0074] Step 1: Based on the task scenario, task attributes, and UAV attributes, and combined with the UAV energy consumption model, considering the constraints of transmission power and communication energy, establish an optimization problem model, and model the multi-UAV system task allocation problem under communication energy constraints as a system total utility maximization problem.

[0075] consider A drone alliance was formed to execute In the above task scenarios, the task is... Task set It indicates that drones are used in combination. The task attribute is represented as... ,in Indicates the value of the task. This indicates the amount of data required for the mission. Drone attributes include location. and the ability to execute each task .

[0076] Alliance structure is represented as The constraints on the formation of overlapping alliances are: Drones can join more than one alliance, that is .

[0077] The expression for task time, based on the channel model, is as follows:

[0078]

[0079] in, Indicates the task data collection time. Indicates the time taken for task data transmission. This represents the average time required to collect one unit of data. Indicates task The amount of data. For ease of formula expression, define... In the formula This indicates the power gain of the drone's wireless transmission channel. This represents the received channel power gain at a reference distance of 1 m. Indicates the distance between the drone and the mission. Indicates the path loss factor. This represents the variance of the channel noise. The UAV's transmit power is denoted as... , Indicates bandwidth.

[0080] Since the energy consumption of UAVs performing missions mainly includes flight energy consumption and communication energy consumption, the expression for energy cost based on the UAV energy consumption model is as follows:

[0081]

[0082] in, Indicates the task execution time. Indicates the time taken for task data transmission. This indicates the propulsion power in the drone's hovering mode. This indicates the drone's launch power.

[0083] The drone's transmit power is constrained to The energy constraints of UAV communication are Of which, communication energy is .

[0084] Based on the mission completion capabilities of each drone ,alliance Execute the task The overall completion rate is expressed as:

[0085]

[0086] The utility function of a consortium is defined as the difference between the task's reward and its cost:

[0087]

[0088] in, Indicates the task completion rate. Indicates the value of the task. and These are the weighting coefficients.

[0089] The optimization objective of the task allocation problem for multi-UAV systems in the above task scenarios is to maximize the total system utility, and the optimization problem model is established as follows:

[0090]

[0091] Step 2: Establish overlapping alliances to form a game theory model.

[0092] Identify the three fundamental elements of a game. The three basic elements of game theory include the players, the strategy space, and the utility function. The players are... Each drone has a strategy for it. Choose the alliance to join (i.e., the mission to be performed). The utility function of the participants within the alliance is:

[0093]

[0094] Define switching actions, including leave actions and join actions.

[0095] Define the leave operation as follows: given a federation structure Participants Leaving one of its leagues The resulting new alliance structure is .

[0096] Define the join operation as follows: given a federation structure Participants Join an alliance that it does not belong to The resulting new alliance structure is .

[0097] Define the preference relationship of participants for alliance structure as follows: For participants and any two alliance structures and Alliance structure Superior Represented as , indicating participants Prefers alliance structure Alliance choices;

[0098] Define the multi-alliance altruistic preference rule as follows:

[0099]

[0100] in, Indicates participants In alliance structure The effect of the following Represents the collection of members of an alliance. Indicates participants The new alliance they joined Indicates participants The original alliance that withdrew, Indicates participants Maintain a group of participating alliances.

[0101] Step 3: Using a joint optimization algorithm for alliance formation and transmission power based on overlapping alliance formation game theory, solve the established optimization problem to obtain a stable alliance structure and optimized UAV transmission power. The specific algorithm process is as follows:

[0102] 1) Initialize the attribute parameters of each task and the attribute parameters of the UAV, and initialize the alliance structure for each UAV to join the alliance corresponding to the task within the communication range;

[0103] 2) Current drones Select a league you have already joined. The new alliance structure resulting from the execution of the departure action is denoted as . First, determine whether the total communication energy of the drone operating at maximum transmission power across multiple alliances exceeds the upper limit constraint. If it does, the transmission power needs to be optimized. Then, calculate the drone's communication energy based on the optimized transmission power. exist The utility of the drone is considered, and decisions are made based on the multi-alliance altruistic preference rule, if the drone prefers... If the alliance structure and drone launch power are updated, the decision-making process for the next drone will begin; otherwise, step 3 will be executed.

[0104] 3) If the exit action fails to update the alliance structure, then further attempt to join the action. (Current drone) Choose an alliance you are not participating in. The new alliance structure resulting from the execution of the join action is denoted as . First, determine whether the total communication energy of the drone operating at maximum transmission power across multiple alliances exceeds the upper limit constraint. If it does, the transmission power needs to be optimized. Then, calculate the drone's power based on the optimized transmission power. exist The utility of the drone is considered, and decisions are made based on the multi-alliance altruistic preference rule, if the drone prefers... Then update the alliance structure and drone launch power;

[0105] 4) Iterate through steps 2) to 3) until convergence to a stable coalition structure, meaning the total system utility no longer changes during the iteration process, ultimately yielding a stable coalition structure (e.g., Figure 2 (as shown) and the optimized drone launch power.

[0106] Step 4: Based on the above algorithm results, assign tasks to the UAVs and set the transmission power to complete the collaborative task allocation of the multi-UAV system based on the game of overlapping alliance formation.

[0107] This embodiment uses the simulation parameters shown in Table 1 for simulation experiments and analysis.

[0108] Table 1 Simulation Parameters

[0109]

[0110] To verify the effectiveness of this scheme, this embodiment compares it with traditional non-overlapping alliance formation game algorithms in an experiment. Figure 3 This invention demonstrates a multi-UAV system collaborative task allocation method based on overlapping alliance formation game theory. Figure 3 (The blue dashed line indicates that) it can quickly converge to a stable state, and it forms a game theory algorithm based on traditional non-overlapping alliances ( Figure 3 Compared to (shown by the solid red line in the middle), the overall system utility of the present invention is improved by 47.7%.

[0111] Figure 4 The results show that when the number of tasks is set to 6, as the number of drones increases from 4 to 20, the total system utility obtained by the algorithm designed in this invention shows an increasing trend, and the results of the algorithm designed in this invention are better than those of the traditional non-overlapping alliance forming game algorithm.

[0112] Figure 5 The results show that when the number of drones is set to 6 and the number of tasks increases from 2 to 20, the total system utility obtained by the algorithm designed in this invention is consistently higher than that of the traditional non-overlapping alliance forming game algorithm. The more tasks there are, the more significant the advantage of the algorithm designed in this invention becomes.

[0113] In summary, the multi-UAV system collaborative task allocation method based on overlapping alliance formation game proposed in this scheme allows UAVs to participate in the execution of multiple tasks, improving the flexibility of decision-making and the utilization rate of UAVs. By enabling multiple UAVs to collaboratively execute tasks and transmit data, and by jointly optimizing alliance formation and UAV launch power, the system can improve task execution effectiveness and overall system utility.

Claims

1. A method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory, characterized in that, Includes the following steps: S1. The task allocation problem of a multi-UAV system under communication energy constraints is modeled as the total utility maximization problem of the system, and an optimization problem model is established. S2. Establish overlapping alliances to form a game theory model; S3. Based on the overlapping alliance formation game model, a joint optimization algorithm for alliance formation and transmission power is adopted to solve the optimization problem model and obtain the optimal alliance structure and the optimized UAV transmission power. S4. Based on the optimal alliance structure and the optimized UAV launch power, assign corresponding tasks to the UAVs and set the launch power. The specific process of establishing the optimization problem model in S1 is as follows: Representing the alliance structure as The constraints formed by overlapping alliances are: Drones can join more than one alliance, that is ; The expression for task time, based on the channel model, is as follows: in, For task data collection time, For task data transmission time, The average time required to collect a unit of data. For the task The amount of data, For the power gain of the UAV wireless transmission channel, The received channel power gain is at a reference distance of 1 m. The distance between the drone and the mission. This is the path loss factor. Let Variance be the variance of the channel noise. For the drone's transmission power, For bandwidth; Based on the drone energy consumption model, the expression for energy cost is as follows: in, For task execution time, For task data transmission time, This refers to the propulsion power in drone hovering mode. This refers to the drone's transmission power. The constraints on the UAV's transmit power and communication energy are as follows: in, For communication energy; Based on the mission completion capabilities of each drone ,alliance Execute the task The overall completion rate is expressed as: The utility function of a consortium is defined as the difference between the task's reward and its cost: The first item is the revenue generated by the alliance in executing tasks, the second item is the time cost of the alliance in executing tasks, and the third item is the energy cost of the alliance in executing tasks. For task completion rate, For the value of the task, and These are the weighting coefficients; The optimization objective of the multi-UAV system task allocation problem is transformed into the problem of maximizing the total system utility, and the optimization problem model is established as follows: The goal is to maximize total utility by optimizing the launch power of drones across multiple alliances under communication energy constraints.

2. The method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory as described in claim 1, characterized in that, Specifically, step S1 involves establishing an optimization problem model based on the task scenario, task attributes, and UAV attributes, combined with the UAV energy consumption model, and considering transmission power and communication energy constraints.

3. The method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory as described in claim 2, characterized in that, The specific details of the task scenario, task attributes, and UAV attributes in step S1 are as follows: consider A drone alliance was formed to execute A task, a task set is used It indicates that drones are used in combination. The task attribute is represented as ,in Indicates the value of the task. Indicates the amount of task data; Drone attributes include location and the ability to execute each task Multiple drones form an alliance to perform a mission. Each drone in the alliance works together to complete the mission and transmits data to the base station. Furthermore, a drone can join more than one alliance to complete multiple missions.

4. The method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory as described in claim 1, characterized in that, Step S2 includes the following steps: S21. Determine the three elements of a game, including the game participants, the strategy space, and the utility function; S22. Define switching actions, including leave actions and join actions; S23. Define the preference for alliance structure and the rules for altruistic preference in multiple alliances.

5. The method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory as described in claim 4, characterized in that, The three elements of the game determined in step S21 are specifically: the participants are... Each drone has a strategy for each drone. Choose the alliance to join, i.e., the mission to be performed. Alliance participants The utility function is: 。 6. The method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory as described in claim 4, characterized in that, In step S22, the leave operation is defined as follows: given an alliance structure Participants Leaving an alliance it had already joined The resulting new alliance structure is ; Define the join operation as follows: given a federation structure Participants Join an alliance it is not a party to The resulting new alliance structure is .

7. The method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory as described in claim 4, characterized in that, In step S23, the preference relationship of participants for the alliance structure is defined as follows: for participants and any two alliance structures and Alliance structure Superior Represented as , indicating participants Prefers alliance structure Alliance choices below; Define the multi-alliance altruistic preference rule as follows: in, Indicates participants In alliance structure The effect of the following Represents the collection of members of an alliance. Indicates participants The new alliance they joined Indicates participants The original alliance that withdrew, Indicates participants Maintain a group of participating alliances.

8. A method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory as described in claim 4, characterized in that, Step S3 includes the following steps: S31. Initialize the attribute parameters of each task and the attribute parameters of the UAV, and initialize the alliance structure for each UAV to join the alliance corresponding to the task within the communication range. S32, Current Unmanned Aerial Vehicles Select a league you have already joined. The new alliance structure resulting from the execution of the departure action is denoted as . First, determine whether the total communication energy of the drone operating at maximum transmission power across multiple alliances exceeds the upper limit constraint. If it does, the transmission power needs to be optimized. Then, calculate the drone's communication energy based on the optimized transmission power. exist The utility of the drone is considered, and decisions are made based on the multi-alliance altruistic preference rule, if the drone prefers... If the alliance structure and drone launch power are updated, the decision-making process for the next drone will begin; otherwise, step S33 will be executed. S33. If the exit action fails to update the alliance structure, then further attempt to join the action. The current drone Choose an alliance you are not participating in. The new alliance structure resulting from the execution of the join action is denoted as . First, determine whether the total communication energy of the drone operating at maximum transmission power across multiple alliances exceeds the upper limit constraint. If it does, the transmission power needs to be optimized. Then, calculate the drone's power based on the optimized transmission power. exist The utility of the drone is considered, and decisions are made based on the multi-alliance altruistic preference rule, if the drone prefers... Then update the alliance structure and drone launch power; S34. Iterate through steps S32 to S33 until the preset iteration termination condition is met, and finally obtain a stable alliance structure and optimized UAV launch power.

9. A method for collaborative task allocation in a multi-UAV system based on overlapping alliance formation game theory as described in claim 8, characterized in that, The preset iteration termination condition in step S34 is specifically convergence to a stable alliance structure, that is, the total utility of the system no longer changes during the iteration process.

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