A multi-cluster aircraft target allocation fault-tolerant decision method based on clustering negotiation

Abstract

The application discloses a kind of multi-cluster aircraft target allocation fault-tolerant decision methods based on clustering negotiation, comprising: considering aircraft, target, no-fly zone information and task constraint condition, establish multi-cluster aircraft cluster communication topology, on this basis, construct aircraft cluster benefit function, design the optimization performance index of aircraft cluster, realize the description of multi-cluster aircraft target allocation problem;Based on the optimization performance index of cluster, establish aircraft cluster target selection rule, target transfer rule and cluster-distributed clustering negotiation model;Based on aircraft cluster target selection rule and target transfer rule, combined with heuristic local optimization algorithm, design multi-cluster cluster-distributed target allocation fault-tolerant decision algorithm, obtain target allocation result.The application can solve the problem of multi-cluster aircraft cluster target allocation, with small communication load, strong fault-tolerant ability and other advantages.

Description

Technical Field

[0001] This invention relates to a fault-tolerant decision-making method for target allocation in a multi-cluster aircraft swarm, belonging to the field of multi-agent task allocation. Background Technology

[0002] Swarm aircraft are systems composed of a large number of different types of aircraft with simple structures. Through functional complementarity and cooperation among the aircraft, swarm aircraft can adapt to complex and diverse mission scenarios such as emergency rescue and reconnaissance, improving mission efficiency and success rate, and have broad application prospects.

[0003] Target allocation is a core module of the decision-making layer for swarmed unmanned aerial vehicles (UAVs). By comprehensively analyzing the performance of the UAVs, the characteristics of the mission objectives, and environmental factors, it achieves optimal matching between the UAVs and the objectives, providing crucial information for subsequent trajectory planning and individual UAV control. However, due to the large scale of swarmed UAVs and the frequent failures of individual UAVs, it is difficult for operators to directly allocate and adjust targets for each UAV. Therefore, research on fault-tolerant decision-making algorithms for dynamic target allocation in multi-cluster UAV swarms is of great significance for expanding swarm size and improving swarm reliability.

[0004] The cluster-based dynamic target allocation fault-tolerant decision-making algorithm needs to meet the following requirements: (1) Local information availability. That is, each individual aircraft in a cluster can only obtain information about some related aircraft and cannot grasp the information of the entire cluster. (2) Independent decision-making by the central node of the aircraft cluster. That is, the aircraft cluster needs to select targets based on the obtained local information and perform local allocation of the targets matched by each aircraft in the cluster. (3) Convergence of the algorithm. That is, through the target selection and target transfer of the aircraft cluster, the target allocation result of the cluster can tend to remain unchanged. (4) Fault tolerance of the algorithm. That is, when some individual aircraft fail, can the cluster autonomously optimize and adjust the target allocation result of the cluster through the independent decision-making of each aircraft cluster? The existing target allocation algorithms are difficult to meet the above requirements. Therefore, this invention designs a multi-cluster aircraft target allocation fault-tolerant decision-making method based on cluster negotiation to meet the above requirements. Summary of the Invention

[0005] This invention discloses a fault-tolerant decision-making method for target allocation of multi-cluster aircraft based on cluster negotiation, which relates to the field of multi-agent task allocation. It can solve the problem of real-time dynamic target allocation of multi-cluster aircraft clusters, reduce cluster communication load, and improve cluster reliability.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] (1) Based on the aircraft and target information, clarify the target conflict constraints and target matching constraints of the cluster aircraft, and establish the communication topology of the multi-cluster aircraft;

[0008] (2) Based on the information of aircraft, target and no-fly zone, construct the mission execution efficiency function of aircraft to target, and then design the benefit function of aircraft cluster and establish the optimization performance index of aircraft cluster.

[0009] (3) Based on the aircraft cluster revenue function and cluster optimization performance index, design the target selection rules and target transfer rules of the aircraft cluster, and establish a distributed clustering negotiation model;

[0010] (4) Based on the target selection rules and target transfer rules of aircraft clusters, and combined with the heuristic local optimization algorithm, a fault-tolerant decision algorithm for distributed dynamic target allocation of multi-cluster aircraft clusters is designed.

[0011] This invention relates to the field of collaborative control technology for swarm aircraft, specifically a method for distributed dynamic target allocation in multi-formation swarm aircraft clusters that combines chaotic simulated annealing particle swarm optimization with contract net protocol. First, considering information on aircraft, targets, no-fly zones, and mission constraints, a reasonable multi-cluster aircraft communication topology is constructed, laying the foundation for subsequent fault-tolerant decision-making. Second, based on the communication topology, a mission execution efficiency function for each individual aircraft is constructed, and a cluster benefit function is designed accordingly, establishing cluster optimization performance indicators. These indicators aim to reflect the benefits of swarm aircraft in mission execution, providing a quantitative basis for cluster target allocation. Then, based on the cluster optimization performance indicators, this invention further establishes target selection and target transfer rules for aircraft sub-formations and introduces a distributed clustering negotiation model, enabling the aircraft cluster to determine the affiliation of each target through inter-cluster communication negotiation, while adhering to certain rules. Finally, by combining the target selection rules of the aircraft cluster, the target transfer rules, and the heuristic local optimization algorithm, this invention designs a cluster-based distributed dynamic target allocation fault-tolerant decision algorithm, which reduces communication load while realizing the dynamic adjustment and optimization of the target allocation strategy.

[0012] This invention not only solves the target allocation problem in multi-cluster aircraft swarms, but also boasts advantages such as low communication load and strong fault tolerance. Through a clustering negotiation mechanism for targets among aircraft clusters and an efficient algorithm design, it significantly improves the collaborative task execution efficiency of swarm aircraft, demonstrating high application value and promising prospects for widespread adoption. Attached Figure Description

[0013] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the method for calculating the performance of an aircraft in performing a target mission, according to an embodiment of the present invention.

[0015] Figure 2 This is a flowchart of the cluster-distributed dynamic target allocation algorithm according to an embodiment of the present invention;

[0016] Figure 3 This is a flowchart of the heuristic local optimization algorithm for aircraft clusters according to an embodiment of the present invention;

[0017] Figure 4 To simulate the distribution of aircraft, targets, and no-fly zones in each sub-formation of the experimental platform;

[0018] Figure 5 To simulate the target allocation results of the cluster under fault-free conditions on the experimental platform;

[0019] Figure 6 To simulate the change curve of cluster revenue under fault-free conditions on the experimental platform;

[0020] Figure 7 This is the result of target allocation for the cluster under simulated aircraft failure conditions on the experimental platform;

[0021] Figure 8 To illustrate the change curve of cluster revenue under simulated aircraft failure scenarios on the experimental platform;

[0022] Table 1 shows the simulated aircraft maneuverability of each sub-formation on the experimental platform;

[0023] Table 2 shows the importance coefficients of each target in the experimental platform simulation;

[0024] Table 3 shows the center coordinates, radius, and risk level of each no-fly zone simulated by the experimental platform. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Embodiments of the present invention will be described in detail below, examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used for explaining the present invention, and cannot be construed as limitations on the present invention. Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present invention pertains. It should also be understood that terms such as those defined in general dictionaries should be construed to have meanings consistent with their meanings in the context of the prior art, and unless defined as herein, they will not be construed in an idealized or overly formal sense.

[0026] The target assignment scenario considered in the present invention is as Figure 1 , including aircraft, targets and no-fly zones, wherein:

[0027] Aircraft: there are m aircraft, the coordinate information of aircraft i∈{1,…,m} is denoted as the maneuverability of aircraft i is denoted as ρ i , ρ i is a positive constant less than 1, all aircraft form p clusters, p<min{m,n}, each aircraft cluster is regarded as a sub-formation, sub-formation k∈{1,…,p} consists of aircraft, the set of aircraft in sub-formation k

[0028] Targets: there are n targets, the coordinate information of target j∈{1,…,n} is denoted as the target importance coefficient is denoted as w j , w j is a positive constant less than 1, which represents the importance of executing the target task.

[0029] No-fly zones: there are q no-fly zones, the center coordinate of no-fly zone l∈{1,…,q} is denoted as the coverage radius is denoted as R l the risk degree is denoted as β l , β l is a positive constant less than 1.

[0030] An embodiment of the present invention provides a fault-tolerant decision-making method for dynamic target allocation of multi-cluster aircraft based on clustering negotiation, and the specific implementation is as follows:

[0031] Step 1: Define the mission constraints of the swarm aircraft and establish a multi-cluster swarm communication topology.

[0032] Step 1.1, Decision variable matrix.

[0033] This invention considers assigning n targets to m aircraft. To characterize the matching relationship between targets and aircraft, a decision variable matrix X = [x ij ] m×n , where x ij x ∈{0,1} represents the matching relationship between aircraft i∈{1,…,m} and target j∈{1,…,n}, x ij =1 indicates that aircraft i and target j are successfully matched; otherwise, aircraft i and target j fail to match.

[0034] Step 1.2, Task Constraints.

[0035] Considering that each aircraft can match at most one target, the following target conflict constraints are established:

[0036]

[0037] Multiple sub-teams independently select one or more targets, and the target allocation reward function for each sub-team needs to be calculated based on the target task execution performance. To facilitate the calculation of the task execution performance of each sub-team, it is necessary to restrict each target to be selected by at most one sub-team, establishing the following target matching constraint:

[0038]

[0039] in, It is the set of targets selected by aircraft within the sub-formation k∈{1,…,p}.

[0040] Step 1.3, Cluster communication topology.

[0041] Considering that an aircraft swarm consists of multiple sub-formations, an undirected graph G = (V, E) is constructed to represent the communication topology of the multi-cluster aircraft swarm, where the node set V = {v...} i |i=1,…,m} represents the set of all aircraft, and the edge set E represents the set of communication links between aircraft. To facilitate fast and accurate calculation of target allocation benefits within each sub-formation, each sub-formation needs to use a star topology for internal communication. A central node is designated for each sub-formation to collect information from other aircraft within the sub-formation and calculate target allocation benefits. Therefore, the communication topology of sub-formations k∈{1,…,p} is represented as a subgraph G of graph G. k =(V k E k ),in, This represents the collection of aircraft within the sub-formation. Characterization of G k The central node, This represents the set of communication links within the sub-formation that are connected to the central node.

[0042] Considering that the entire cluster's communication node set consists of each sub-group node, we get:

[0043]

[0044] To conserve communication resources and reduce interference, the aircraft cluster must communicate only through the central node. Therefore, the entire cluster's communication link set consists of the communication links within each sub-formation and the communication links between the formation's central node, resulting in:

[0045]

[0046] Step 2: Construct the aircraft cluster revenue function and establish aircraft cluster optimization performance indicators.

[0047] Step 2.1, Spacecraft mission execution performance function.

[0048] The mission performance of aircraft i∈{1,…,m} against target j∈{1,…,n} is related to the aircraft's penetration capability in each no-fly zone, while the penetration capability of aircraft i against no-fly zones l∈{1,…,q} is related to the aircraft's maneuverability ρ. i ∈(0,1), Risk level β of no-fly zone l l ∈(0,1) and the distance d that the straight-line flight path of the aircraft overlaps with the no-fly zone l. il Based on the above relationship, the penetration performance function of aircraft i against no-fly zone l is established as follows:

[0049]

[0050] To obtain the mission performance of aircraft i against target j, it is necessary to calculate the distance at which the line connecting aircraft i and target j overlaps with the no-fly zone l (see...). Figure 1 )as follows:

[0051]

[0052] in, It is the vertical distance from the center of the no-fly zone l to the line connecting aircraft i and target j. It is the straight-line distance from aircraft i to the center of the no-fly zone l. ε is the straight-line distance from target j to the center of the no-fly zone l, and ε is a very small positive number. The above formula shows that when R... l <h ijlWhen the distance between the line connecting aircraft i and target j and the no-fly zone l is 0, meaning the aircraft does not pass through the no-fly zone l; otherwise...

[0053] Based on the penetration capability of aircraft i against no-fly zone l and the distance of overlap between the line connecting aircraft i and target j and no-fly zone l, the mission execution performance function of aircraft i against target j is established, that is, the probability function of aircraft i successfully executing the mission against target j is as follows:

[0054]

[0055] Step 2.2, Aircraft Cluster Revenue Function.

[0056] Since multiple aircraft within a sub-formation k∈{1,…,p} need to cooperate to execute one or more target tasks, the benefit of this sub-formation in executing target tasks is related to the allocation of targets by each aircraft within the sub-formation. This is addressed using a decision matrix. Characterizes the target allocation of each aircraft within sub-formation k, where X k X = [x ij ] m×n The subarray. Based on the aircraft i∈M k Task performance function and decision matrix X for target j k , thus obtaining the target j∈N k The probability of a task being successfully executed is as follows:

[0057]

[0058] Then, based on the target importance coefficient w j For ∈(0,1), the payoff function is as follows:

[0059]

[0060] Step 2.3, optimize cluster performance metrics.

[0061] The optimization objective of the entire aircraft swarm is to maximize the sum of the gains of all sub-formations in the swarm. The combination of the swarm optimization performance index and the aircraft selection objective of each sub-formation is {N}. k The cluster is related to |k=1,…,p}, therefore, the optimization objective for establishing the cluster is as follows:

[0062]

[0063] Among them, u ij w represents the mission performance of aircraft i against target j. j The importance coefficient characterizing target j.

[0064] Step 3: Determine the target selection rules and target transfer rules for the aircraft cluster, and establish a distributed clustering negotiation model.

[0065] Step 3.1, Aircraft Cluster Target Selection Rules.

[0066] definition The target set selected by the aircraft within the sub-formation k∈{1,…,p} at time t is represented by the target. The center of the target set of the sub-formation at time t is defined. The set of targets not selected by any sub-formation at time t. It is the decision matrix of sub-formation k at time t. The value of sub-formation k at time t is represented.

[0067] Based on the above relationships and the aircraft cluster payoff function, the target selection rule for aircraft clusters can be established as follows: When At that time, if for the target If the following formula is satisfied, then the objective is... That is, the target at time t+1 Quilt formation choose:

[0068]

[0069] in, Characterizing the target selection of sub-group k at time t+1. The maximum value of the payoff function after selecting the target is the gain relative to the maximum payoff of the aircraft cluster before selecting the target. Hengcheng was established; Characterization target Relative to time t, sub-formation k target center j 0,k The distance dominance of (t), Target center j 0,k The coordinates of (t) are the target coordinates chosen by the aircraft cluster k at the initial moment; λ1, λ2∈[0,1] represent the weights of the sub-formation's maximum gain and range advantage, respectively, for different t∈N * The values ​​of λ1 and λ2 are also different.

[0070] Step 3.2, Aircraft Cluster Target Transfer Rules.

[0071] According to the target selection rules of aircraft clusters, if That is, the maximum benefit of all sub-formations is 0, or So, the target at time t+1 It will not be selected by any sub-formation. At this point, the target that was selected by a sub-formation at time t needs to be reassigned to other aircraft clusters. Therefore, the target transfer rule for aircraft clusters can be established as follows: If for target... Satisfy the following formula

[0072]

[0073] So, the goal That is, the target at time t+1 Transfer from subgroup k' to subgroup At this time, the target center j 0,k The coordinates of (t) are calculated as follows: the average of the horizontal and vertical coordinates of all selected targets in the spacecraft cluster k at time t is taken to obtain the reference coordinate point. The target point closest to this reference coordinate point among all selected targets in the spacecraft cluster k at time t is selected as the target center j at time t. 0,k (t).

[0074] Step 3.3, Distributed Clustering Negotiation Model.

[0075] Based on the aforementioned target selection and transfer rules for aircraft clusters, a distributed clustering negotiation model can be established. Correspondingly, the target allocation problem can be transformed into a target clustering problem, that is, through negotiation and communication between aircraft clusters, each target is dynamically assigned to different sub-formations according to the target selection and transfer rules.

[0076] definition For time t, sub-group k is related to the target. The clustering confidence level and the inter-cluster negotiation mechanism are as follows: At each time step, each aircraft cluster independently pre-selects a new target or pre-transfers a selected target to another sub-formation, and independently calculates the expected change in the sub-formation's gain. Other sub-formations receive the target information pre-selected or transferred by the aircraft cluster, independently calculate the expected gain and clustering confidence level, and send the above information to the cluster's central node. The aircraft cluster assigns the target's affiliation at that moment based on the target selection and transfer rules. Simultaneously, since each sub-formation independently calculates the gain of allocating selected targets within the cluster using a locally centralized approach, the entire cluster completes dynamic target allocation in a locally centralized, globally decentralized manner. Therefore, the above target allocation model is called a distributed clustering negotiation model.

[0077] In the above dynamic process, if the states of the aircraft, the target, and the no-fly zone remain unchanged, the sum of the maximum gains of all sub-formations will gradually increase until the gain is 0, which is consistent with the optimization goal of the cluster.

[0078] Step 4: Design a multi-cluster distributed target allocation fault-tolerant decision algorithm.

[0079] To enable swarm aircraft to partition targets through local information exchange between aircraft clusters within a communication topology, and to allow the swarm to spontaneously adjust target allocation results when the states of some aircraft change, the following approach is adopted: Figure 2 The distributed target allocation fault-tolerant decision-making approach shown in the figure uses the following method to calculate the benefit of each aircraft cluster: Figure 3 The heuristic local optimization algorithm shown.

[0080] Step 4.1: Calculate the revenue of the aircraft cluster using a heuristic local optimization algorithm.

[0081] The goal of this step is to maximize the set of targets N that all aircraft in aircraft cluster k have selected for that cluster at a given time. k The return J when distributing k (X k N k ).

[0082] Based on the particle swarm optimization algorithm, a solution to this optimization problem, namely an X, is obtained. k Let X be considered as a particle. There are a particles, and each particle's attributes include velocity, position, and fitness. Fitness is the property of particle X. k Corresponding return J k (X k N k The fitness of the α-th particle in the s-th iteration is denoted as ). Position using vectors Indicates that χ (i) ∈N k (i = 1, ..., m) k The target number selected by spacecraft i in sub-formation k is represented by α. The velocity of the α-th particle at the s-th iteration is... Location The updated formula is as follows:

[0083]

[0084] in, It is an individual extreme value, G s It is the global extremum, c1 and c2 are learning factors, and r1 s and ω is a random number in (0,1). s This is the inertia factor.

[0085] Meanwhile, based on the simulated annealing algorithm, local optimization is performed on each particle, and the position of the particle is updated with a certain acceptance probability in each iteration.

[0086] The algorithm steps are as follows:

[0087] Step 1. Randomly generate an initial population of 'a' particles, initialize the velocity and position of each particle, and assign an initial inertia factor ω. 0 Learning factors c1 and c2, initial acceptance probability p r ∈(0,1) and the temperature decay coefficient ξ∈(0,1);

[0088] Step 2. Calculate the fitness of each particle α to obtain the initial individual extreme value. Global Extremum G 0 and initialize the annealing temperature to

[0089] Step 3. If the algorithm reaches the termination condition, output the result; otherwise, perform the following loop from 0 to S, where S is the maximum number of iterations.

[0090] Step 4. Calculate the fitness of each particle. Update individual and global extrema;

[0091] Step 5. According to the formula ω s =ω 0 -(ω 0 -ω S The inertia factor is updated by s / S, and based on the chaotic optimization formula r ι s+1 =ur ι s (1-r ι s ),ι=1,2 to get r1 s and Thus, the velocity and position of the new particle can be calculated;

[0092] Step 6. Calculate the fitness of each new particle. make like or If the new position is accepted, the original position will be retained.

[0093] Step 7. Annealing temperature T s+1 =ξT s , s = s + 1, go to Step 4.

[0094] When performing the above steps, the central node within the aircraft cluster outputs the global extremum and its corresponding target allocation solution after the algorithm reaches the termination condition, based on the state information of the aircraft within the cluster, the target to be matched, and the no-fly zone information, thereby realizing heuristic local optimization within the cluster.

[0095] Step 4.2, Distributed dynamic target allocation fault-tolerant decision-making approach.

[0096] Based on the heuristic local optimization algorithm, and according to the target selection rules and target transfer rules of the aircraft cluster, the following distributed dynamic target allocation fault-tolerant decision algorithm is designed:

[0097] Step 1. The central node of each aircraft cluster initializes the status information of the aircraft within the cluster, the status information of all targets to be assigned, and the no-fly zone information. For an aircraft cluster k∈{1,…,p}, the following steps are performed in parallel;

[0098] Step 2. Initialize the set of unassigned targets for aircraft cluster k. The cluster has selected the target set. Assuming an initial distance dominance of 0.5, an initial target is randomly selected as the target center j for each cluster. 0,k (0), and initialize the maximum gain based on the heuristic local optimization algorithm. And initialize the clustering confidence parameters λ1, λ2;

[0099] Step 3. If If the maximum benefit gain for all sub-formations is 0, then the target selection phase is complete, and the target center j of each aircraft cluster is updated. 0,k (t) and go to Step 8, otherwise proceed as follows: t from 0 to t half The loop part, where t half This is the maximum number of iterations during the target selection phase;

[0100] Step 4. The aircraft cluster k randomly selects targets in advance. And based on the heuristic local optimization algorithm, the maximum intra-cluster allocation benefit after selecting this objective is calculated.

[0101] Step 5. Dynamically adjust λ1 and λ2, and update the spacecraft cluster k at time t for the target. Clustering confidence And according to the cluster communication topology, the central node The target number and cluster confidence information of the aircraft cluster k at that moment are sent to the center nodes of other sub-formations.

[0102] Step 6. The central node of the aircraft cluster k receives information about the target from other sub-formations. Based on the clustering confidence and maximum intra-cluster gain information, and according to the target selection rule, the sub-group with the highest confidence that satisfies the condition that the maximum gain is greater than 0 is designated as the target selection target.

[0103] Step 7. Update the set of unassigned targets The target set has been selected by the aircraft cluster k. t = t + 1, go to Step 4;

[0104] Step 8. When t = t fault When the state of the aircraft in aircraft cluster k changes, such as some aircraft failing and being unable to continue their mission, the maximum allocation benefit of the current aircraft cluster k is calculated based on a heuristic local optimization algorithm. Obtain the change in revenue At this point, the total revenue of the cluster decreases;

[0105] Step 9. If the algorithm meets the termination condition, output the result; otherwise, proceed as follows: from t to t fault to t max The loop part, where t max It is the maximum number of iterations;

[0106] Step 10. Let k' = k, and pre-assign a target to the aircraft cluster k'. To be transferred, calculate the change in intra-cluster revenue before and after the target is transferred. The central node will assign the target number. Information on the change in revenue before and after the transfer is sent to the central nodes of other sub-squadrons.

[0107] Step 11. The central node of the aircraft cluster k' receives information from other sub-formations regarding the target. Based on the clustering confidence and maximum intra-cluster gain information, and according to the target transfer rule, if the maximum gain of all sub-formations except for aircraft cluster k' is not greater than the target... Changes in revenue within the aircraft cluster k' before and after the transfer If the target does not meet the target transfer rules, the aircraft cluster k' will reassign a target to be transferred and proceed to Step 10; otherwise, a new target will be assigned based on the target transfer rules. Attribution, update the target center j of spacecraft cluster k' 0,k' (t) and proceed to Step 10.

[0108] Step 5: Simulation verification.

[0109] Step 5.1, simulation of a fault-free scenario.

[0110] The simulation, based on the Matlab platform, considers a target allocation scenario consisting of 30 aircraft, 24 targets, and 4 no-fly zones. The 30 aircraft are divided into 5 sub-formations, each consisting of 6 aircraft. The maneuverability of each sub-formation, the importance coefficient of each target, and the center coordinates, radius, and risk level of each no-fly zone are shown in Tables 1, 2, and 3. Initially, none of the aircraft in the sub-formations have selected a target. like Figure 4 As shown.

[0111] Table 1

[0112]

[0113] Table 2

[0114]

[0115] Table 3

[0116] 1 1643 881 500 0.4627 2 1786 1331 500 0.4150 3 1506 1817 500 0.4744 4 1802 2250 500 0.3523

[0117] In the absence of faults, based on the target selection rule, the cluster confidence parameters λ1 and λ2 are dynamically changed, λ1 = 1 - exp(-0.08 × t) and λ2 = 1 - λ1. In the early stage of iteration, the distance advantage index is emphasized, while in the later stage of iteration, the sub-formation gain index is emphasized. Figure 5 The target allocation results for each sub-formation aircraft are given under fault-free conditions, where the lines connecting each aircraft and each target represent the matching relationship between the aircraft and the target.

[0118] Figure 6 The curves showing the change of the total cluster revenue with the number of iterations are presented. It can be seen that under the designed distributed target allocation algorithm, the total cluster revenue continuously increases and finally converges at the 19th iteration.

[0119] Step 5.2, Fault scenario simulation.

[0120] The distributed task allocation algorithm proposed in this invention can adapt to failures and faults. Figure 2 It can be seen that for any aircraft i∈{1,…,m}, after removing the failed aircraft, the currently selected objective may not be to optimize the performance index. To maximize the optimal objective, it is necessary to make local adjustments to the target allocation of aircraft based on the target transfer rules. The cluster will continue to iterate until the total cluster gain converges.

[0121] To compare with the fault-free scenario, the coordinates of each sub-formation aircraft and the target are the same as in the fault-free scenario, such as... Figure 4 As shown, the maneuverability of aircraft in each sub-formation, the importance coefficient of each target, the center coordinates, radius, and risk level of each no-fly zone are the same as in Tables 1, 2, and 3. The cluster adjusts its target allocation based on the target allocation results under the fault-free scenario. Considering that all aircraft experiencing complete failure belong to sub-formation 1, after the failure, assuming the clustering confidence parameters λ1 = λ2 = 0.5, the target allocation results of the cluster are as follows: Figure 7 As shown, the lines connecting each aircraft to each target represent the matching relationship between the aircraft and the target.

[0122] Figure 8The curves showing the change of the total cluster revenue with the number of iterations are presented. It can be seen that the total cluster revenue decreases in the 25th iteration due to the occurrence of a complete failure. Subsequently, under the designed distributed target allocation fault-tolerant decision-making algorithm, the total cluster revenue continues to increase and finally converges in the 30th iteration.

Claims

1. A fault-tolerant decision-making method for multi-cluster aircraft target allocation based on cluster negotiation, characterized in that, The decision-making method includes the following: (1) Define the decision variable matrix to characterize the matching relationship between the aircraft and the target, determine the aircraft-target conflict constraints and target matching constraints, and establish a multi-cluster aircraft communication topology; Specifically, (1) includes: set up aircraft and One objective, defining the decision variable matrix. ,in, Characterizing the aircraft and target The matching relationship treats each aircraft cluster as a sub-formation, given... Sub-formations, Sub-formations Depend on The formation consists of a group of aircraft, defining the set of aircraft within a sub-formation. and sub-formations The set of targets selected by the internal aircraft ; Establish goal conflict constraints: ； Establish target matching constraints: ； Construct an undirected graph To characterize the communication topology of a multi-cluster aircraft swarm, where the set of nodes... The set representing all aircraft, the set of edges Characterizes the set of communication links between aircraft; sub-formations The communication topology is represented as a graph. subgraph ,in, This represents the collection of aircraft within the sub-formation. Characterization The central node, This represents the set of communication links within the sub-formation that are connected to the central node; get: ； ； (2) Based on the aircraft's penetration performance in no-fly zones, construct an aircraft performance function for the target mission; design a revenue function for the aircraft cluster based on the performance function; and establish an optimized performance index for the aircraft cluster; specifically, (2) includes: Based on the coordinates of the i-th aircraft No-fly zone center coordinates and coverage radius , build aircraft No-fly zone The penetration performance function is as follows: ； in, Less than positive numbers, It is a characterization of aircraft The parameters of its mobility, It represents a no-fly zone Parameters of risk level, It is an aircraft Straight flight path and no-fly zone The distance of overlap; According to the aircraft No-fly zone Penetration capabilities and targets coordinates , build aircraft For the target The task execution performance function is as follows: ； in, It is an aircraft With the goal Connections and No-Fly Zones Overlapping distance, It is a very small positive number. and They are aircraft ,Target to the no-fly zone The straight-line distance between the centers It is a no-fly zone Center to spacecraft With the goal The perpendicular distance between the lines; Define an aircraft cluster, i.e., a sub-formation. Decision matrix of each aircraft , yes Sub-arrays, sub-units The payoff functions for each aircraft in selecting targets are as follows: ； in, It is a representation of the target Parameters indicating importance; The optimization objectives for establishing the entire aircraft cluster are as follows: ； in, Characterizing the aircraft For the target Task execution efficiency Characterization target Importance coefficient; (3) Based on the revenue function of the aircraft cluster and the optimization performance index of the aircraft cluster, design the target selection rule and target transfer rule of the aircraft cluster, and establish a distributed clustering negotiation model. (4) Based on the target selection rules and target transfer rules of the aircraft cluster, and combined with the heuristic local optimization algorithm, a multi-cluster cluster distributed target allocation fault-tolerant decision algorithm is designed to realize the cluster dynamic fault-tolerant target allocation under the multi-cluster aircraft communication topology.

2. The method according to claim 1, characterized in that: Specifically, (3) includes: definition Characterization Time Formation The target set selected by the internal aircraft, the target Characterization At that moment, the center of the target set of the sub-formation, Characterization The set of targets that are not selected by any sub-group at any given time. yes Time Formation The decision matrix, Characterization Time Formation The benefits; The target selection rules for aircraft clusters are as follows: If for target If the following formula is satisfied, then the objective is... ,Right now Momentary Goal Quilt formation choose: ； in, Characterization target Compared to Time Formation Target Center Distance advantage The weights representing the maximum gain and distance advantage of the sub-formation are respectively used. The target transfer rules for aircraft clusters are designed as follows: If for the target... If the following formula is satisfied, then the objective is... ,Right now Momentary Goal Sub-formation Transfer to sub-formation : ； A distributed clustering negotiation model is established based on the target selection rules and target transfer rules of the aforementioned aircraft cluster.

3. The method according to claim 2, characterized in that: Specifically, (4) includes: Design a multi-cluster distributed target allocation fault-tolerant decision algorithm, which realizes target partitioning through local information interaction between aircraft clusters and automatically adjusts target allocation when the aircraft state changes; During the dynamic target allocation process in the cluster, the aircraft cluster employs a heuristic local optimization algorithm to calculate the cluster's allocation benefit. It utilizes particle swarm optimization and simulated annealing strategies for local optimization, thereby maximizing the allocation benefit of all aircraft in the cluster against the selected target set at a given moment. Profits during distribution ; Based on the target selection rules and target transfer rules of the aircraft cluster, define for Time Formation For the goal Clustering confidence; Output the cluster target allocation results to realize dynamic target allocation and fault-tolerant decision-making for cluster aircraft.

4. The method according to claim 3, characterized in that: Based on the heuristic local optimization algorithm, in one iteration, the aircraft cluster pre-selects a target, recalculates the cluster's allocation of benefits to the target, and communicates and negotiates with the central nodes of other aircraft clusters based on the above clustering confidence to determine the ownership of the target.

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