A distributed mission planning method for multiple aircraft based on potential game

Abstract

The present invention discloses a multi-aircraft distributed task planning method based on potential game, which is used to solve the task planning problem of a multi-aircraft system under the constraints of factors such as the aircraft itself, task requirements and flight environment. First, it is necessary to obtain the position status information and task target information of each aircraft and broadcast them to each aircraft. Then, a multi-aircraft task planning model is constructed based on the aircraft position status information and task target information. Secondly, based on the potential game theory, the multi-aircraft task planning model is mapped to the potential game model; on this basis, all feasible strategy sets for multi-aircraft to perform tasks are obtained, and then the Nash equilibrium solution of the task planning scheme is solved in the potential game model; finally, through iterative calculation, the optimal Nash equilibrium solution is obtained to obtain the final task planning scheme. The present invention is used for the task planning problem of multiple aircraft under complex environmental conditions, and provides a new method for solving the task planning problem.

Description

Technical Field

[0001] The invention relates to a distributed task planning method based on potential game, which is used to solve the problem of multi-aircraft task planning under complex environmental conditions and belongs to the field of multi-aircraft task planning. Technical Background

[0002] Multi-aircraft systems are increasingly being used to perform various complex tasks, including intelligence reconnaissance and battlefield surveillance, natural disaster relief, search and rescue, forest fire fighting, agricultural irrigation, weather forecasting, etc. In order to adapt to more complex and changeable mission environments, it is necessary to improve the autonomous planning capabilities of multi-aircraft systems.

[0003] In the actual execution of the mission, due to the constraints of factors such as the aircraft itself, mission requirements and flight environment, the autonomous planning and intelligent decision-making of multiple aircraft is a very complex problem. It is mainly reflected in the following aspects: the flight environment of aircraft is becoming increasingly complex. Due to its own advantages and broad development prospects, aircraft have been increasingly valued by the military of various countries. Therefore, its flight environment has become more and more complex. In addition to conventional threats and obstacles, the dynamics and uncertainty of the flight environment need to be considered. Moreover, due to its own performance constraints, unmanned aerial vehicles often cannot obtain accurate and real-time global situation information and cannot respond to changes in the situation in a timely manner; the heterogeneity of aircraft makes it difficult to make accurate and real-time global situation information and intelligent decision-making in the mission decision-making and planning process. In the process of planning, the execution of specific tasks often requires the aircraft to have specific functions, so it is necessary to consider the performance differences and functional differences of the aircraft itself; the complexity of the task itself, in the process of task planning, it is necessary to consider factors such as the time-varying nature of the task benefit value, the coupling between tasks, and the uncertainty of the task; the limitations of distributed communication, due to the distribution of multiple aircraft, in the process of task planning, their data interaction often relies on distributed communication links, and the communication links need to consider their own communication bandwidth, communication frequency and other limitations. Therefore, the information obtained by each unmanned aerial vehicle during the planning process may be asynchronous and inconsistent, which brings great difficulties to the task planning problem of multiple unmanned aerial vehicles.

[0004] Through the above analysis of the characteristics of mission planning, it can be seen that in order to ensure effective coordination and cooperation between aircraft to ensure high efficiency and high success rate of mission execution, it is necessary to implement highly autonomous task allocation and planning strategies, which puts forward great demands on the autonomous mission planning system of multi-aircraft systems. Using the idea of ​​game theory, each aircraft is regarded as an intelligent individual to participate in mission planning, which can greatly improve the autonomy of distributed mission planning of multi-aircraft systems.

[0005] The present invention studies a multi-aircraft mission planning method based on potential game in response to the above-mentioned needs, which has important theoretical significance and engineering value for further improving the autonomous mission planning capability of multiple aircraft. Summary of the invention

[0006] Purpose of the Invention

[0007] In order to solve the above technical problems, the purpose of the present invention is to provide a multi-aircraft distributed task planning method based on potential game to cope with various constraints in the task planning environment, so as to ensure that the multi-aircraft system can still perform tasks collaboratively with high efficiency and high accuracy under the constraints of factors such as the aircraft itself, task requirements and flight environment.

[0008] Technical Solution

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] The present invention is a multi-aircraft distributed mission planning method based on potential game, and the method specifically comprises the following steps:

[0011] Step 1: Obtain the position status information and mission target information of each aircraft and broadcast it to each aircraft;

[0012] Step 2: construct a multi-aircraft mission planning model based on the aircraft position status information and mission target information;

[0013] Step 3, mapping the multi-aircraft mission planning model into a potential game model;

[0014] Step 4: Calculate all feasible strategy sets for multiple aircraft to perform tasks based on the potential game model, and solve the Nash equilibrium solution of the task planning scheme in the potential game model;

[0015] Step 5: Iterate the calculation to obtain the optimal Nash equilibrium solution and thus obtain the final task planning plan.

[0016] Further, taking multiple spacecraft as an example, the step 1 specifically includes the following process:

[0017] Step 1.1, obtaining the initial position, fuel load, and onboard equipment information of each spacecraft in a multi-spacecraft system consisting of n spacecraft, as well as the position information, functional requirements, and time window information corresponding to m mission targets;

[0018] Step 1.2, broadcast the information and requirements involved in step 1.1 to each spacecraft.

[0019] Furthermore, the step 2 specifically includes the following process:

[0020] Step 2.1: mathematically describe the data information obtained in step 1 using the quaternion<T,A,C,R> To represent the task planning problem, where T = {t1,…,t j ,…,tm} is the task set, t j represents the jth task; A={a1,…,a i ,…,a n} is the set of spacecraft, a i represents the i-th spacecraft; C is the constraint set, which represents the various constraints that need to be met in the distributed task planning process of the multi-spacecraft system; R represents the total task revenue corresponding to the task performed by the multi-spacecraft system;

[0021] The model for task planning is as follows:

[0022]

[0023] Where χ is the decision variable matrix, χ=[χ ij ] n×m , χ ij ∈{0,1},χ i,j =1 means that the i-th spacecraft chooses to perform the j-th mission, otherwise the spacecraft i does not perform mission j; R ij represents the benefit of spacecraft i performing mission j, and to obtain R ij The value of R ij =δ ij R j0 ,δ ij ∈[0,1],R j0 represents the initial benefit of task j, δ ij represents the capability coefficient of spacecraft i to perform mission j, C ij is a set of constraints that affect the mission benefits of spacecraft i performing mission j;

[0024] Step 2.2, mathematically modeling the constraints described in step 2.1, including functional constraints, time constraints, and fuel consumption constraints for task execution;

[0025] Functional constraints for task execution: Use F = [F ij ] n×m To indicate whether spacecraft i has the capability to perform mission j, where F ij ∈{0,1} is used to identify whether spacecraft i has the ability to perform mission j;

[0026] Time constraint for task execution: Task j needs to be completed within a specific time window, expressed as in, Indicates the opening time of the task window. represents the end time of the task window; the start time of task j is determined by the latest arrival time of all spacecraft participating in task j, that is:

[0027]

[0028] The spacecraft that arrives the latest at this time to perform mission j is defined as:

[0029]

[0030] Fuel consumption constraint for mission execution: The larger the orbital altitude r, the smaller the corresponding speed v. The energy consumption generated by spacecraft i during the execution of mission j is numerically defined as:

[0031]

[0032] Among them, d ij is the orbital height difference between spacecraft i and the target position of mission j, r i is the orbital height of spacecraft i from the earth, is the attitude adjustment angle of spacecraft i during mission j, κ, λ, γ correspond to orbit height difference coefficient, orbit height coefficient and attitude adjustment angle coefficient respectively;

[0033] Assume that the cumulative energy consumed by a single spacecraft to perform a mission does not exceed its energy load:

[0034]

[0035] Among them, q ri (i) is the initial energy of spacecraft i; q mi (i) Energy threshold for safe operation of spacecraft;

[0036] Step 2.3, based on the constraint information obtained in step 2.2, define the benefit of executing task j as:

[0037]

[0038] Among them, k∈[0,1], e is a natural constant, η is the time discount coefficient, and ρ is the fuel consumption discount coefficient.

[0039] Furthermore, the step three specifically includes the following process:

[0040] Step 3.1, mapping the multi-aircraft mission planning model described in step 2 into the following finite strategy set game model:

[0041] G=(A,S,{U i}) (7)

[0042] Where S = S1 ×… × S i ×…×S n represents the strategy space of the multi-spacecraft system, S i represents the set of all strategies that spacecraft i can execute, and sets the strategy of spacecraft i itself to si ,s i ∈S i , the strategy set of other spacecraft is s -i ,s -i =(s1,…s i-1 ,s i+1 ,…,s n ), then the strategy set of the multi-spacecraft system can be expressed as s = (s i ,s -i );U i represents the strategic payoff function of spacecraft i;

[0043] Step 3.2, define the current situation in the game model as a strategy set s = (s i ,s -i ), the corresponding situation benefit function is expressed as:

[0044] U(s i ,s -i )=∑ j∈s R j (8)

[0045] Among them, U i (s i ,s -i ) represents the strategic benefit of spacecraft i performing the mission in the corresponding situation, U i (s i ,s -i ) value and s i and -i Related, expressed as:

[0046]

[0047] Among them, U(0,s -i ) represents the situation benefit corresponding to the execution strategy of spacecraft other than spacecraft i; the corresponding strategy benefit function when updating the strategy under the current situation is as follows:

[0048]

[0049] Among them, s′ i is the strategy executed by spacecraft i after the strategy is updated;

[0050] Step 3.3, define the finite strategy set game model G = (A, S, {U i}), if there exists a function φ:S→R such that for all s,s'∈S i ,satisfy:

[0051] U i (s′ i ,s -i)-U i (s i ,s -i )=φ(s′ i ,s -i )-φ(s i ,s -i ) (11)

[0052] Then G is a potential game, φ is the corresponding potential function; define φ as φ = ∑ j∈s R j , i.e., the situation benefit; satisfy:

[0053]

[0054] It is expressed as:

[0055]

[0056] The corresponding policy set It is a Nash equilibrium.

[0057] Furthermore, the step 4 specifically includes the following process:

[0058] Step 4.1, design an algorithm to obtain all feasible strategy sets for the multi-flight system to perform tasks, including the following steps:

[0059] (1) Initialize the parameters of the aircraft and mission data;

[0060] (2) The aircraft selects a set of feasible strategies and sets task packages 1, 2, 3, ..., where task package 1 records the strategy with 1 executable task, task package 2 records the strategy with 2 executable tasks based on task package 1, and task package 3 records the strategy with 3 executable tasks based on task package 2, until all feasible strategy sets of the aircraft are obtained;

[0061] (3) Go to the next aircraft to obtain the corresponding feasible strategy set until the feasible strategy sets of all aircraft are obtained;

[0062] Step 4.2, based on the feasible strategy set of all aircraft, obtain the mission planning scheme through the multi-aircraft mission planning algorithm based on potential game, including the following steps:

[0063] (1) Set the number of iterations and the initial situation and initialize the global benefit and task planning scheme;

[0064] (2) Randomly select the aircraft to choose the strategy that maximizes the current situation, and update the mission planning plan at the same time, recording the mission time, fuel consumption value and global benefit value;

[0065] (3) Repeat the previous step until the global benefit cannot be increased and the Nash equilibrium solution is reached, and then the next round of iterative calculation is performed;

[0066] (4) Select the maximum value from the obtained Nash equilibrium solutions to obtain the final task planning solution.

[0067] The beneficial effects of the present invention are:

[0068] (1) The present invention takes into account the task planning problem of a multi-aircraft system under the constraints of factors such as the aircraft itself, mission requirements and flight environment. The designed task planning method ensures the autonomy of each aircraft and a high success rate in executing the mission.

[0069] (2) The design introduces potential game theory and maps the multi-aircraft distributed mission planning model into the game model.

[0070] (3) The designed feasible strategy set calculation algorithm and multi-aircraft distributed task planning algorithm based on potential game ensure that each aircraft can obtain the optimal task execution strategy while considering multiple constraints, thereby obtaining the optimal task planning solution.

[0071] (4) It has great practical significance and application prospects in multi-aircraft distributed mission planning.

[0072] Description of the accompanying tables and graphs

[0073] Figure 1 This is a flowchart of a multi-aircraft distributed mission planning method based on potential game;

[0074] Figure 2 This is a schematic diagram of a multi-aircraft distributed mission planning model architecture in an embodiment of the present invention;

[0075] Figure 3 A schematic diagram of a Hohmann transfer orbit in an embodiment of the present invention;

[0076] Figure 4 It is a flowchart of a distributed task planning algorithm based on potential game in an embodiment of the present invention;

[0077] Figure 5 Schematic diagram of the flight trajectory of each aircraft in the embodiment of the present invention. DETAILED DESCRIPTION

[0078] The task planning method of the present invention is further explained in conjunction with the attached diagrams.

[0079] The present application is a multi-aircraft distributed mission planning method based on potential game, and the method specifically includes the following steps:

[0080] Step 1: Obtain the position status information and mission target information of each aircraft and broadcast it to each aircraft;

[0081] Step 2: construct a multi-aircraft mission planning model based on the aircraft position status information and mission target information;

[0082] Step 3, mapping the multi-aircraft mission planning model into a potential game model;

[0083] Step 4: Calculate all feasible strategy sets for multiple aircraft to perform tasks based on the potential game model, and solve the Nash equilibrium solution of the task planning scheme in the potential game model;

[0084] Step 5: Iterate the calculation to obtain the optimal Nash equilibrium solution and thus obtain the final task planning plan.

[0085] Furthermore, taking multiple spacecraft as an example, this planning method includes the following steps:

[0086] (a) Multi-vehicle system Taking a multi-spacecraft system as an example, consider n spacecrafts performing m tasks, using the quaternion<T,A,C,R> Represents the task planning problem, where T = {t1,…,t j ,…,t m} is the task set, t j represents the jth task; A={a1,…,a i ,…,a n} is the set of spacecraft, a i represents the i-th spacecraft; C is the constraint set, which represents the various constraints that need to be met in the distributed task planning process of the multi-spacecraft system; R represents the total task revenue corresponding to the multi-spacecraft system executing the task, which can be used to judge the quality of the task planning results, and then obtain the task planning model:

[0087]

[0088] Where χ is the decision variable matrix, χ=[χ ij ] n×m , χ ij ∈{0,1},χ i,j =1 means that the i-th spacecraft chooses to perform the j-th mission, otherwise the spacecraft i does not perform mission j; R ij represents the benefit of spacecraft i performing mission j, and to obtain R ij The value of R ij =δ ij R j0 ,δ ij ∈[0,1],R j0 represents the initial benefit of task j, δ ij represents the capability coefficient of spacecraft i to perform mission j, Cij is a set of constraints that affect the mission benefit of spacecraft i performing mission j, including:

[0089] Time constraint of task execution: Considering the timeliness of task execution, that is, task j needs to be completed within a specific time window, expressed as in, Indicates the opening time of the task window. represents the end time of the task window. Since task j may be executed by multiple spacecraft in collaboration, the start time of task j is determined by the latest arrival time of all spacecraft participating in task j, that is:

[0090]

[0091] In a multi-spacecraft system, Figure 3 Taking Hohmann transfer as an example, the shortest time required for spacecraft i to perform task j is set as:

[0092]

[0093] Among them minh ij represents the shortest time required for spacecraft i to reach the target position of mission j, r1 represents the orbital height of the spacecraft, r2 represents the orbital height of the mission target position, μ represents the gravitational constant of the central celestial body, Is a vector and The angle of For the axis, the low circular track The angle is positive. α and β represent the spacecraft orbital operation and orbital maneuvering time coefficients respectively.

[0094] Fuel consumption constraints for mission execution: In a multi-spacecraft system, the fuel consumption required for spacecraft to perform missions can be divided into two situations: attitude adjustment and orbital maneuvering. The fuel consumption generated by spacecraft i during the execution of mission j is numerically defined as:

[0095]

[0096] Among them, d ij is the orbital height difference between spacecraft i and the target position of mission j, r i is the orbital height of spacecraft i from the earth, is the attitude adjustment angle of spacecraft i during mission j. The attitude adjustment angle is expressed as the angle between the initial orbital plane and the final orbital plane during the spacecraft orbital maneuver. κ, λ, and γ correspond to the orbital height difference coefficient, orbital height coefficient, and attitude adjustment angle coefficient, respectively. The cumulative energy consumed by a single spacecraft to perform a mission cannot exceed its fuel load:

[0097]

[0098] Among them, q ri (i) is the initial energy of spacecraft i; q mi (i) The fuel threshold required to ensure safe operation of the spacecraft.

[0099] Considering the time constraints and fuel consumption constraints as well as the conflicts that may arise in the task allocation process, we define:

[0100]

[0101] Then the task t j The execution benefit can be expressed as:

[0102]

[0103] Where k∈[0,1], e is a natural constant, η is the time discount coefficient, and ρ is the energy consumption discount coefficient. The task planning model can be further expressed as

[0104]

[0105] (b) Considering the autonomy of each aircraft in the multi-aircraft system, the idea of ​​game theory is introduced, and each aircraft is regarded as an intelligent individual participating in the game. On the basis of the mission planning model, a corresponding game model is established:

[0106] G=(A,S,{U i}) (9)

[0107] Where S = S1 ×… × S i ×…×S n represents the strategy space of the multi-spacecraft system, S i represents the set of all strategies that spacecraft i can execute, and let the strategy of spacecraft i itself be s i ,s i ∈S i , the strategy set of other spacecraft is s -i ,s -i =(s1,…s i-1 ,s i+1 ,…,s n ), the strategy set of multiple spacecraft systems can be expressed as s = (s i ,s -i ). i Represents the policy profit function of aircraft i.

[0108] Define the situation as the set of strategies s=(s i ,s -i ), the situation benefit function is expressed as:

[0109]

[0110] Among them U i (s i ,s -i ) represents the benefit of spacecraft i performing the mission in the corresponding situation, U i (s i ,s -i ) is not only related to s i Also related to s -i Related, expressed as:

[0111]

[0112] Where U(0,s -i ) represents the situation benefits corresponding to the execution strategy of spacecraft other than spacecraft i.

[0113] In the game process, in order to express the benefits brought by the spacecraft when the strategy is updated, the strategy benefit function is defined as:

[0114]

[0115] where s′ i It is the strategy executed by spacecraft i after the strategy is updated.

[0116] According to the potential game theory, the finite strategy set game model G = (A, S, {U i}), if there exists a function φ:S→R, Make

[0117] U i (s′ i ,s -i )-U i (s i ,s -i )-φ(s′ i ,s -i )-φ(s i ,s -i ) (13)

[0118] Then G is a potential game, and φ is the corresponding potential function. It is defined as φ = ∑ j∈s R j , that is, the situation benefit, in s -iWhen φ is determined, it can ensure that when formula (13) holds, the optimal benefit of each spacecraft is consistent with the optimal benefit of the system. When the spacecraft achieves its maximum benefit by continuously iteratively adjusting its own strategy, the global benefit of the system is also maximized. Until each spacecraft cannot obtain a higher benefit by adjusting its own benefit, the game is said to reach Nash equilibrium. That is, for satisfy:

[0119]

[0120] It can also be expressed as:

[0121]

[0122] The corresponding policy set It is a Nash equilibrium solution.

[0123] Since the aircraft will choose the strategy that maximizes its own benefits, some tasks will not be executed, thus falling into a local optimal situation. In order to ensure that the result obtained in the above potential game model is the global optimal solution, a bias that has a functional relationship with the number of tasks executed is added to the model so that the distribution result satisfies that all tasks can be executed. At the same time, a random operator is added to randomly select individual update strategies at each decision moment, and the optimal task planning solution is obtained by comparing different Nash equilibria. The strategy benefit function is optimized as follows:

[0124]

[0125] Among them, m0 is the number of task points in the situation before the strategy update, m1 is the number of task points in the situation after the strategy update, and x is the bias of the number of task executions.

[0126] (c) Based on the potential game model, an algorithm is designed to obtain all feasible strategy sets of the aircraft and the final mission planning scheme, wherein the algorithm for calculating all feasible strategy sets of the aircraft includes the following steps:

[0127] (1) Initialize the parameters of the aircraft and mission data;

[0128] (2) The aircraft selects a set of feasible strategies and sets task packages 1, 2, 3, ..., where task package 1 records the strategy with 1 executable task, task package 2 records the strategy with 2 executable tasks based on task package 1, and task package 3 records the strategy with 3 executable tasks based on task package 2, until all feasible strategy sets of the aircraft are obtained;

[0129] (3) Go to the next aircraft to obtain the corresponding feasible strategy set until the feasible strategy sets of all aircraft are obtained.

[0130] On the basis of obtaining all feasible strategy sets, a multi-spacecraft mission planning algorithm based on potential game is designed. Figure 4 As shown, the following steps are included:

[0131] (1) Set the number of iterations and the initial situation and initialize the global benefit and task planning scheme;

[0132] (2) Randomly select the aircraft to choose the strategy that maximizes the current situation, and update the mission planning plan at the same time, recording the mission time, fuel consumption value and global benefit value;

[0133] (3) Repeat the previous step until the global benefit cannot be increased and the Nash equilibrium solution is reached, and then the next round of iterative calculation is performed;

[0134] Select the maximum value from the obtained Nash equilibrium solutions to obtain the final task planning solution.

[0135] The effectiveness of the present invention is verified by performing simulation below:

[0136] The values ​​of the position information data of each spacecraft in the multi-aircraft system are shown in the following table:

[0137] Table 1

[0138] Aircraft Data Information Table

[0139] Aircraft <![CDATA[Coordinate (10 6 m)]]> α β Q K λ δ 1 9.8 3.4 2.5 3 0.03 3 9 0.5 0.4 4 2 3.2 5.7 5.8 5 0.05 2 15 0.4 0.7 4 3 6.4 5.3 4.2 2 0.04 3 12 0.8 0.8 6 4 14.4-1.5 6.1 4 0.04 3 9 0.2 0.3 6 5 3.6 5.9-2.8 3 0.05 4 25 0.4 0.2 8 6 -4.5 -6.4 -2.1 5 0.02 4 14 0.6 0.5 4

[0140] A multi-vehicle system consisting of 6 spacecraft is set to complete the mission planning for 8 mission objectives.

[0141] The information data values ​​of each task target in the task planning problem are shown in the following table:

[0142] Table 2

[0143] Task data information table

[0144] Task <![CDATA[Coordinate (10 6 m)]]> h(min) <![CDATA[r j ]]> k η ρ 1 4.6-4.5 6.5 0 4 10 0.4 0.05 0.2 2 1.2-8.3 8.9 2 5 8 0.5 0.07 0.3 3 8.2 13.2 4.9 4 15 9 0.5 0.0.8 0.3 4 -6.3 7.2 2.5 1 5 9 0.6 0.06 0.4 5 -4.4 -6.5 -6.6 6 14 8 0.4 0.06 0.2 6 3.7 5.6 4.5 3 7 7 0.6 0.05 0.1 7 7.3 5.4 2.4 1 4 10 0.8 0.06 0.3 8 5.1 -3.3 -5.4 5 20 7 0.7 0.04 0.2

[0145] Based on the aircraft information and mission information, Figure 4 The algorithm flow shown in the figure first obtains the feasible strategy set for all aircraft as shown in the following table:

[0146] Table 3

[0147] Aircraft feasible strategy set information table

[0148]

[0149] After obtaining all feasible strategy set information, set the number of games, initialize the task planning scheme, use the distributed task planning algorithm based on potential game, iteratively calculate and record the Nash equilibrium solution until the upper limit of the number of games is reached and multiple Nash equilibrium solutions are obtained. At this time, the optimal task planning scheme can be compared as shown in the following table:

[0150] Table 4 Nash equilibrium solution information table

[0151] Aircraft Solution 1 Solution 2 Solution 3 Solution 4 Solution 5 1 2 1 2 1 1 2 3 6 5 6 3 5 6 5 3 8 8 8 8 8 4 6 7 7 6 5 1 5 3 1 5 3 7 3 6 7 4 4 7 4 Global benefits 76.2126 74.6439 86.1987 66.8272 75.4935

[0152] In the embodiment of the present invention, five Nash equilibrium solutions are obtained by simulation, among which the optimal Nash equilibrium solution is solution 3, and the trajectories of the corresponding aircraft are shown as follows: Figure 5 As shown, the simulation results show that the multi-aircraft distributed task planning method designed by the present invention taking into account multiple constraints can obtain good planning results under the constraints of factors such as the aircraft itself, task requirements and flight environment.

[0153] In summary, for the mission planning problem of multiple aircraft under various constraints, the method of the present invention can effectively obtain the optimal mission planning solution.

[0154] The above is only a preferred embodiment of the present application, and the present application is not limited to the above embodiments. It is understood that other improvements and changes directly derived or associated by those skilled in the art without departing from the spirit and concept of the present application should be considered to be included in the protection scope of the present application.

Claims

1. A multi-aircraft distributed mission planning method based on potential game, characterized in that: The method specifically comprises the following steps: Step 1: Obtain the position status information and mission target information of each aircraft and broadcast it to each aircraft; Step 2: construct a multi-aircraft mission planning model based on the aircraft position status information and mission target information; the step 2 specifically includes the following process: Step 2.1: mathematically describe the data information obtained in step 1 using the quaternion<T,A,C,R> To represent the task planning problem, where T = {t1,…,t j ,…,t m } is the task set, t j represents the jth task; A={a1,…,a i ,…,a n } is the spacecraft set, a i represents the i-th spacecraft; C is the constraint set, which represents the various constraints that need to be met in the distributed task planning process of the multi-spacecraft system; R represents the total task revenue corresponding to the task performed by the multi-spacecraft system; The model for task planning is as follows: Where X is the decision variable matrix, χ = [χ ij ] n×m , χ ij ∈{0,1},χ i,j =1 means that the i-th spacecraft chooses to perform the j-th mission, otherwise the spacecraft i does not perform mission j; R ij represents the benefit of spacecraft i performing mission j, and to obtain R ij The value of R ij =δ ij R j0 ,δ ij ∈[0,1],R j0 represents the initial benefit of task j, δ ij represents the capability coefficient of spacecraft i to perform mission j, C ij is a set of constraints that affect the mission benefits of spacecraft i performing mission j; Step 2.2, mathematically modeling the constraints described in step 2.1, including functional constraints, time constraints, and fuel consumption constraints for task execution; Functional constraints for task execution: Use F = [F ij ] n×m To indicate whether spacecraft i has the capability to perform mission j, where F ij ∈{0,1} is used to identify whether spacecraft i has the ability to perform mission j; Time constraint for task execution: Task j needs to be completed within a specific time window, denoted as h j ∈[h j start ,h j end ], where h j start Indicates the opening time of the task window, h j end represents the end time of the task window; the start time of task j is determined by the latest arrival time of all spacecraft participating in task j, that is: The spacecraft that arrives the latest at this time to perform mission j is defined as: Fuel consumption constraint for mission execution: The larger the orbital altitude r, the smaller the corresponding speed v. The energy consumption generated by spacecraft i during the execution of mission j is numerically defined as: Among them, d ij is the orbital height difference between spacecraft i and the target position of mission j, r i is the orbital height of spacecraft i from the earth, is the attitude adjustment angle of spacecraft i during mission j, κ, λ, γ correspond to orbit height difference coefficient, orbit height coefficient and attitude adjustment angle coefficient respectively; Assume that the cumulative energy consumed by a single spacecraft to perform a mission does not exceed its energy load: Among them, q ri (i) is the initial energy of spacecraft i; q mi (i) Energy threshold for safe operation of spacecraft; Step 2.3, based on the constraint information obtained in step 2.2, define the benefit of executing task j as: Among them, k∈[0,1], e is a natural constant, η is the time discount coefficient, and ρ is the fuel consumption discount coefficient; Step three, mapping the multi-aircraft mission planning model into a potential game model; the step three specifically includes the following process: Step 3.1, mapping the multi-aircraft mission planning model described in step 2 into the following finite strategy set game model: G=(A,S,{U i }) (7) Where S = S1 ×… × S i ×…×S n represents the strategy space of the multi-spacecraft system, S i represents the set of all strategies that spacecraft i can execute, and sets the strategy of spacecraft i itself to s i ,s i ∈S i , the strategy set of other spacecraft is s -i ,s -i =(s1,…s i-1 ,s i+1 ,…,s n ), then the strategy set of the multi-spacecraft system can be expressed as s = (s i ,s -i );U i represents the strategic payoff function of spacecraft i; Step 3.2, define the current situation in the game model as a strategy set s for a multi-spacecraft system = (s i ,s -i ), the corresponding situation benefit function is expressed as: U(s i ,s -i )=∑ j∈s R j (8) Among them, U i (s i ,s -i ) represents the strategic benefit of spacecraft i performing the mission in the corresponding situation, U i (s i ,s -i ) value and s i and -i Related, expressed as: Among them, U(0,s -i ) represents the situation benefit corresponding to the execution strategy of spacecraft other than spacecraft i; the corresponding strategy benefit function when updating the strategy under the current situation is as follows: Among them, s′ i is the strategy executed by spacecraft i after the strategy is updated; Step 3.3, define the finite strategy set game model G = (A, S, {U i }), if there exists a function φ:S→R such that for all s,s'∈S i ,satisfy: IN i (with' i ,with -i )-IN i (with i ,with -i )=φ(s′ i ,with -i )-φ(s i ,with -i ) (11) Then G is a potential game, φ is the corresponding potential function; define φ as φ = ∑ j∈s R j , i.e., the situation benefit; satisfy: It is expressed as: The corresponding policy set is a Nash equilibrium; Step 4: Calculate all feasible strategy sets for multiple aircraft to perform tasks based on the potential game model, and solve the Nash equilibrium solution of the task planning scheme in the potential game model; Step 5: Iterate the calculation to obtain the optimal Nash equilibrium solution and thus obtain the final task planning plan.

2. The multi-aircraft distributed mission planning method based on potential game according to claim 1 is characterized in that: The aircraft is a spacecraft.

3. The multi-aircraft distributed mission planning method based on potential game according to claim 2 is characterized in that: The step 1 specifically includes the following process: Step 1.1, obtaining the initial position, fuel load, and onboard equipment information of each spacecraft in a multi-spacecraft system consisting of n spacecraft, as well as the position information, functional requirements, and time window information corresponding to m mission targets; Step 1.2, broadcast the information and requirements involved in step 1.1 to each spacecraft.

4. The multi-aircraft distributed mission planning method based on potential game according to claim 2 is characterized in that , the step 4 specifically includes the following process: Step 4.1, design an algorithm to obtain all feasible strategy sets for the multi-flight system to perform tasks, including the following steps: (1) Initialize the parameters of the aircraft and mission data; (2) The aircraft selects a set of feasible strategies and sets task packages 1, 2, 3, ..., where task package 1 records the strategy with 1 executable task, task package 2 records the strategy with 2 executable tasks based on task package 1, and task package 3 records the strategy with 3 executable tasks based on task package 2, until all feasible strategy sets of the aircraft are obtained; (3) Go to the next aircraft to obtain the corresponding feasible strategy set until the feasible strategy sets of all aircraft are obtained; Step 4.2, based on the feasible strategy set of all aircraft, obtain the mission planning scheme through the multi-aircraft mission planning algorithm based on potential game, including the following steps: (1) Set the number of iterations and the initial situation and initialize the global benefit and task planning scheme; (2) Randomly select an aircraft to choose the strategy that maximizes the current situation, and update the mission planning plan at the same time, recording the mission time, fuel consumption value and global benefit value; (3) Repeat the previous step until the global benefit cannot be increased and the Nash equilibrium solution is reached, and then the next round of iterative calculation is performed; (4) Select the maximum value from the obtained Nash equilibrium solutions to obtain the final task planning solution.

Images