UAV swarm reconnaissance mission planning method and equipment based on hierarchical model

Abstract

The present invention provides a method and device for planning reconnaissance missions for drone swarms based on a hierarchical model. The method comprises the following steps: pre-collecting battlefield conditions, performing pre-task planning based on the pre-collected battlefield conditions, and each drone in the drone swarm executing the mission according to the pre-task plan; during mission execution, if a new battlefield situation arises that renders the pre-task plan no longer applicable, dynamic mission planning is triggered; after dynamic mission planning is triggered, alliance formation is performed, and the drone swarm forms a new formation model according to the hierarchical model, collects the new battlefield situation, and re-plans the mission for the drone swarm; in the new formation model, the drone swarm is divided into reconnaissance drones and mission drones, the new battlefield situation is collected, and tasks are re-assigned to the drone swarm. This method considers the problem of dynamic and real-time planning under combat conditions, and achieves higher efficiency in completing missions.

Description

Technical Field

[0001] The present invention belongs to the field of UAV mission planning, and in particular relates to a UAV cluster reconnaissance mission planning method and equipment based on a hierarchical model. Background Art

[0002] With the advancement and development of science and technology, intelligent, unmanned weaponry is increasingly being deployed on modern battlefields, and the application of unmanned aerial vehicle (UAV) swarms has become a hot research area. UAV swarms combine the characteristics of both drones and swarms, offering advantages such as high self-organization, strong adaptability, and robust group stability. In modern warfare, UAV swarm reconnaissance is a popular application of unmanned equipment. However, the complex battlefield environment places higher demands on the timeliness and mobility of UAV swarm reconnaissance.

[0003] Considering the limited resources and the diversity of combat objectives, the problem is generally abstracted as a multi-objective constraint problem. Traditional solution methods include enumeration method, dynamic programming method, etc. However, in large-scale drone swarm combat, there are many parameters that need to be coordinated, and the dimension of the solution space increases geometrically with the number of drones and the number of mission objectives. The low efficiency of traditional solution methods makes it difficult to meet the timeliness requirements under battlefield conditions.

[0004] During reconnaissance missions, drone swarms may encounter unexpected situations, such as drone damage or the emergence of new mission targets, necessitating updates to pre-planned plans. The challenge of quickly integrating resources, processing information, and replanning and issuing tasks requires not only the ability to plan tasks but also consistent situational awareness and stable communication across the swarm. These situations are collectively referred to as the dynamic real-time planning problem. The mainstream solution is to abstract the swarm's situational awareness and planning processes into a coalition formation process. However, existing research on coalition formation strategies for scenarios involving sudden failures of some drones remains centered around the faulty drone. Due to communication limitations, this approach is neither practical nor fully leverages the collaborative nature of the swarm. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and device for planning UAV cluster reconnaissance missions based on a hierarchical model.

[0006] The present invention includes: a method for planning a UAV cluster reconnaissance mission based on a hierarchical model, which includes the following steps:

[0007] Collect battlefield situation in advance, carry out pre-mission planning based on the pre-collected battlefield situation, and each drone in the drone cluster executes the mission according to the pre-mission plan;

[0008] During the mission execution, if a new battlefield situation arises that makes the pre-planned mission plan no longer applicable, dynamic mission planning will be triggered;

[0009] After dynamic mission planning is triggered, an alliance is formed, and the drone cluster forms a new formation model according to the hierarchical model, collects new battlefield situations, and re-plans tasks for the drone cluster. In the new formation model, the drone cluster is divided into two types of drones, including reconnaissance drones and mission drones. The reconnaissance drone is responsible for monitoring the battlefield environment, conducting situation integration and mission planning, and serving as a communication hub to establish a communication network with the mission drones, collecting and processing information uploaded by the mission drones. The mission drone is responsible for enemy reconnaissance, performing tasks according to the pre-planned tasks or the tasks and tracks planned by the reconnaissance drone, and at the same time providing real-time feedback on the completion of the tasks to the reconnaissance drone.

[0010] According to the above technical solution of the present invention, the following improvements can also be made:

[0011] Optionally, pre-mission planning includes the following steps:

[0012] A combat environment parameter model is established based on the pre-collected battlefield situation. The combat environment parameter model has several restrictions, including at least no-fly zones and weapon threats. The combat parameter model avoids these restrictions to plan the trajectory of the UAV swarm.

[0013] Then, a multi-objective constraint model is established based on the constraints existing during the task execution process. Tasks are assigned based on this multi-objective constraint model. The constraints include the UAV’s own constraints and the task constraints. The UAV’s own constraint is that the total fuel consumption of a single UAV must be less than the maximum fuel capacity when the UAV cluster performs the task. The task constraint is that when the UAV cluster performs the task, each task point is completed by a UAV.

[0014] The pre-task planning is formed by coupling trajectory planning and task allocation. During the process, if there is no restriction between the two task targets, the straight line between the two task targets is taken as the trajectory. If there is a restriction between the two task targets, the trajectory is corrected and coupled with the task allocation. When correcting the trajectory, for the case where the straight track passes through the no-fly zone, a point is selected outside the no-fly zone so that the lines connecting this point with the starting point and the end point are tangent to the no-fly zone. The sum of the two corrected trajectories is taken as the trajectory between the two task targets.

[0015] Optionally, the multi-objective constraint model is as follows:

[0016] P1:

[0017] P2:

[0018]

[0019] Among them, P1 and P2 are the no-fly zone sets, n=cu{0,n+1}, Q is the full fuel load of the drone, C ij is the distance between tasks i and j, x ij Indicates whether the arc from task i to j is selected, is the track correction operator, is the vertical distance from the center of the no-fly zone n to the line connecting the two mission points i and j, r P is the radius of the no-fly zone P; is the UAV’s own constraint; k is the number of UAVs, r n is the radius of the no-fly zone n.

[0020]

[0021]

[0022]

[0023] is the task constraint, where K represents the maximum number of arcs from the starting point, and x ik ,x kj Respectively indicate whether the outgoing arc and incoming arc of task point k are selected, x oj Indicates whether the arc from the starting point o to other task points is selected;

[0024] The total path distance is expressed as:

[0025]

[0026] x ij ∈{0,1},

[0027] Where C ij Indicates the distance between two target points;

[0028] The total path risk is expressed as:

[0029]

[0030]

[0031] Where f ij Represents the quantitative risk index between two target points, x ij Indicates whether the route from mission i to j is selected, M is the air defense weapon set, r P is the radius of the no-fly zone p, r m is the radius of the mth air defense zone, is the vertical distance from the air defense zone m to the line connecting the two mission points i and j, and o is the unit fuel consumption.

[0032] Optionally, in the multi-objective constraint model, all drones in the drone cluster are divided into different levels. There is no domination between drones in the same level, and drones in higher levels dominate drones in lower levels. Each drone i is set to have parameters n(i) and s(i), where n(i) is the number of solutions that dominate drone i in the drone cluster, and s(i) is the set of solutions dominated by drone i. Then, the drones are sorted non-dominated according to the following steps:

[0033] S001. Store all drones with n(i)=0 in the drone cluster into set F1, and assign the same non-dominated order i(rank) to the drones in the set.

[0034] S002, the set of statistically dominant drones S = {S i |i∈F1};

[0035] S003. Subtract 1 from n(i) of all drones in the set S;

[0036] S004. Repeat steps S001 to S003 until all drones are classified.

[0037] Optionally, when all drones are classified, the hierarchical relationship within the drone cluster is obtained. For the drones in each level, the drones in this level and the drones in the previous level are composed of a drone sub-population. Each drone in the drone cluster represents an individual in the population. The two drones with the closest distance are selected to establish an intra-layer or inter-layer relationship. The above operation is repeated until all drones complete the establishment of the intra-layer or inter-layer relationship, and the drone cluster super network model is obtained.

[0038] Optionally, congestion calculation is performed based on the obtained drone cluster hypernetwork model. The congestion is calculated using the following method:

[0039]

[0040] Among them, f m is the objective function of the mth dimension, rank is the level corresponding to the drone, and the weights of the distance calculation between different levels are assigned by the level difference. i-1 and i+1 correspond to the two nodes closest to drone i in the drone sub-population composed of the current level and the previous level, respectively.

[0041] Optionally, the genetic operator of the drone swarm hypernetwork model is as follows:

[0042] Selection operator: Perform non-dominated sorting on the population after the merger of the father and son generations, add individuals from each layer in turn, and select several individuals with the lowest crowding degree from the critical layer to be retained in the next generation;

[0043] Crossover operator: Perform a two-point crossover operation on the individual, and then modify the genes outside the crossover region according to the mapping relationship of each gene in the crossover region to eliminate conflicts;

[0044] Mutation operator: A random exchange operator is used to perform mutation operations, randomly selecting two genes from an individual to exchange and obtain the mutated individual.

[0045] Optionally, when dynamic mission planning is triggered, a new drone formation is established. The new drone formation is a three-dimensional drone formation, and then an alliance is formed. The alliance formation includes the following steps:

[0046] Tender Release: When UAV U i When the drone is destroyed, the drone cluster enters the bidding release state. After the reconnaissance drone obtains the information, it becomes the leader of the alliance and establishes a local communication network with the UAVs in the neighborhood to release the bidding information P i :

[0047] Pi={U i ,T};

[0048] Where U i Represents the basic information of the UAV, and T represents the remaining mission objectives of the UAV;

[0049] Bid application: After the tender is released, all drones in the local communication network receive the tender information. The drones first judge the tender information. If a drone meets the conditions in the multi-objective constraint model, it becomes a bidding drone and then sends a bid to the alliance leader.

[0050] Bi={U i ,T i};

[0051] Where Ti represents the target to be auctioned;

[0052] Alliance construction: After the bidding application, the alliance leader receives bids from various bidding drones in the communication network. The alliance leader needs to sort the bids, select the drone with the highest efficiency, and publish the winning bid information. The sorting of each bid takes into account risk and distance cost. The alliance leader constructs an effective set based on the Pareto criterion, randomly selects a bid from the effective set and publishes the winning bid information F i Feedback to each bidding drone, F i Can be represented by a tuple;

[0053] F i={S, TR};

[0054] Where S represents the updated task sequence, and TR represents the updated flight track;

[0055] Execution phase: The UAVs that have won the bid will update their task sequences and flight tracks to complete dynamic task planning.

[0056] Optionally, during the coalition formation process, the flight track planning method for reconnaissance UAVs is as follows:

[0057] Collect relevant information: The flight track of the reconnaissance UAV is planned in segments, changing the heading at fixed time intervals and maintaining a fixed heading and speed within the interval. When a heading change is required, the reconnaissance UAV requests relevant information from each UAV within its communication range;

[0058] Calculate the heading: The artificial potential field method is used to plan the flight track of the reconnaissance UAV. The specific steps are as follows:

[0059] Establish the gravitational field function:

[0060]

[0061] Where d(p, p i ) represents the distance between the mission UAV i and the reconnaissance aircraft, and r i represents the risk index of the mission UAV i. When d < d*, the magnitude of the gravitational potential energy is proportional to the square of the distance from the current position to the target position; when d > d*, the value of the gravitational calculation function is reduced to avoid the problem of excessive gravity when far from the target position. d* represents the communication radius of the reconnaissance UAV.

[0062] Establish the repulsive field function:

[0063]

[0064] Where d(p, p i ) represents the distance between the reconnaissance UAV itself and other reconnaissance UAVs i, α is the repulsive gain constant; q* is the action threshold range of the repulsive force, and the repulsive force will only be generated within this threshold range;

[0065] Calculate the heading based on the artificial force field:

[0066] Superimpose the gravitational field and the repulsive field to obtain the artificial force field U:

[0067] U(p) = U att (q) + U rep (q);

[0068] Take the gradient of the artificial potential field to obtain the potential field force F:

[0069]

[0070] Decompose the potential field force into two directions, X and Y, and normalize them to get the heading;

[0071] Execution of the voyage:

[0072] According to the obtained heading, keep flying at a certain speed.

[0073] According to another aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the above methods when executing the program.

[0074] The present invention provides a method and device for planning UAV swarm reconnaissance missions based on a hierarchical model. Focusing on the task of UAV swarms performing target strikes in battlefield environments, the present invention proposes a new solution to the problem that traditional models cannot meet the latency requirements for alliance formation. The technical effects include:

[0075] (1) For the UAV swarm mission planning problem, the proposed method is superior to the traditional UAV swarm mission planning method in terms of timeliness and solution space.

[0076] (2) Hypernetwork modeling was performed to introduce a new measurement perspective for determining the crowding degree of genetic individuals and evaluating their priority.

[0077] (3) Considering the dynamic planning problem under the condition of UAV damage, a new UAV formation model is proposed. By moving the planning and communication center to the command UAV, a multi-level formation of command and task UAVs is established to solve the problem of cluster mission replanning when UAVs are damaged. The new formation model can effectively enhance the UAVs' collaborative capabilities and improve the mission completion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 Schematic diagram of the UAV cluster collaborative task execution of the present invention.

[0079] Figure 2 This is a schematic diagram of the no-fly zone correction of the present invention.

[0080] Figure 3 This is a coding diagram of the present invention.

[0081] Figure 4 This is a schematic diagram of the hypernetwork modeling of the present invention.

[0082] Figure 5 The following is a schematic diagram of the offspring selection of the operator of the present invention.

[0083] Figure 6 Schematic diagram of the task planning method of the genetic method of the present invention.

[0084] Figure 7 This is a schematic diagram of the UAV cluster communication network of the present invention.

[0085] Figure 8 Schematic diagram of the traditional formation model of the present invention.

[0086] Figure 9 Schematic diagram of the new formation model of the present invention.

[0087] Figure 10 Schematic diagram of simulation scenario three of the present invention.

[0088] Figure 11 Schematic diagram of simulation scenario 1 of the present invention.

[0089] Figure 12 Schematic diagram of simulation scenario 2 of the present invention.

[0090] Figure 13 Schematic diagram of the results of different methods for simulation scenario 1 of the present invention.

[0091] Figure 14 Schematic diagram of the results of different methods for simulation scenario 2 of the present invention.

[0092] Figure 15 Schematic diagram of the results of different methods for simulation scenario three of the present invention.

[0093] Figure 16 Schematic diagram of the execution time of the method of the present invention.

[0094] Figure 17 Schematic diagram of the formation track of the present invention.

[0095] Figure 18 Schematic diagram of the impact of the formation model of the present invention on the task completion rate.

[0096] Figure 19 Schematic diagram of the impact of the formation model of the present invention on the total sailing distance. DETAILED DESCRIPTION

[0097] like Figures 1-19 As shown, the present invention provides a method and device for planning a UAV cluster reconnaissance mission based on a hierarchical model, the method comprising:

[0098] Collect battlefield situation in advance, carry out pre-mission planning based on the pre-collected battlefield situation, and each drone in the drone cluster executes the mission according to the pre-mission plan;

[0099] During the mission execution, if a new battlefield situation arises that makes the pre-planned mission plan no longer applicable, dynamic mission planning will be triggered;

[0100] After dynamic mission planning is triggered, an alliance is formed, new battlefield situations are collected, and tasks are reassigned to the drone cluster.

[0101] In this embodiment, the mission objective can be described as dispatching m drones to explore n target points. Once the collected target information returns to base, the mission is considered complete. First, based on pre-collected battlefield information, including enemy and friendly positions, battlefield environment, and other factors, pre-mission planning is performed. Each drone then proceeds to execute the mission according to the planned results. During mission execution, if a new battlefield situation, such as drone damage, arises, making the pre-planned mission no longer applicable, dynamic mission planning is triggered. Through alliance formation, the new battlefield situation is collected, and tasks are reassigned to the drone cluster.

[0102] Figure 1 An example of a mission scenario with 4 mission points and 2 drones is shown.

[0103] Optionally, the drone swarm is divided into two types of drones, including reconnaissance drones and mission drones, where

[0104] The reconnaissance drone is responsible for monitoring the battlefield environment, conducting situation integration and mission planning, and serving as a communication hub to establish a communication network with the mission drones, collect and process information uploaded by the mission drones, and play the role of a communication center and planning center.

[0105] The function of the mission drone is to conduct reconnaissance against the enemy. It does not have autonomy and executes according to the mission and trajectory planned in advance or by the reconnaissance drone. At the same time, it provides real-time feedback on the completion of the mission to the reconnaissance drone.

[0106] In this embodiment, the drone formation consists of two types of drones: reconnaissance drones and mission drones. The reconnaissance drones are responsible for monitoring the battlefield environment, conducting situational awareness and mission planning, and serve as a communications hub, establishing a communication network with the mission drones, collecting and processing information uploaded by the mission drones, and acting as a communications and planning center. The mission drones, however, are responsible for enemy reconnaissance and lack autonomy. They execute missions and routes pre-planned or planned by the reconnaissance drones, providing real-time feedback on mission completion to the reconnaissance drones. Swarm mission execution must consider constraints such as flight performance, communication range, enemy fire threat, and battlefield conditions, aiming for low-risk, low-cost mission planning. The operation concludes when the drone swarm completes all n tasks or is unable to complete the remaining tasks due to damage or other factors.

[0107] Optionally, based on the pre-collected battlefield situation, a battlefield environment model is established as follows: E = {Z, W}; where E is the battlefield environment, Z = {Z i |i=1,2,…n2} is the terrain set, W={W i |i=1,2,…nW} for the enemy's air defense weapons collection;

[0108] Pre-mission planning includes the following steps:

[0109] A combat environment parameter model is established based on the pre-collected battlefield situation. The combat environment parameter model has several restrictions, including at least no-fly zones and weapon threats. The combat parameter model avoids these restrictions to plan the trajectory of the UAV swarm.

[0110] Then, a multi-objective constraint model is established based on the constraints existing during the task execution process. Tasks are assigned based on this multi-objective constraint model. The constraints include the UAV’s own constraints and the task constraints. The UAV’s own constraint is that the total fuel consumption of a single UAV must be less than the maximum fuel capacity when the UAV cluster performs the task. The task constraint is that when the UAV cluster performs the task, each task point is completed by a UAV.

[0111] The pre-task planning is formed by coupling trajectory planning and task allocation. During the process, if there is no restriction between the two task targets, the straight line between the two task targets is taken as the trajectory. If there is a restriction between the two task targets, the trajectory is corrected and coupled with the task allocation. When correcting the trajectory, for the case where the straight track passes through the no-fly zone, a point is selected outside the no-fly zone so that the lines connecting this point with the starting point and the end point are tangent to the no-fly zone. The sum of the two corrected trajectories is taken as the trajectory between the two task targets.

[0112] Optionally, the multi-objective constraint model is as follows:

[0113] P1:

[0114] P2:

[0115]

[0116] Among them, P1 and P2 are the no-fly zone sets, n=cu{0,n+1}, Q is the full fuel load of the drone, C ij is the distance between tasks i and j, x ij Indicates whether the arc from task i to j is selected, is the track correction operator, is the vertical distance from the center of the no-fly zone n to the line connecting the two mission points i and j, r P is the radius of the no-fly zone P; is the UAV’s own constraint; k is the number of UAVs, r n is the radius of the no-fly zone n.

[0117]

[0118]

[0119]

[0120] is the task constraint, where K represents the maximum number of arcs from the starting point, and x ik ,x kj Respectively indicate whether the outgoing arc and incoming arc of task point k are selected, x oj Indicates whether the arc from the starting point o to other task points is selected;

[0121] The total path distance is expressed as:

[0122]

[0123] x ij ∈{0,1},

[0124] Where C ij Indicates the distance between two target points;

[0125] The total path risk is expressed as:

[0126]

[0127]

[0128] Where f ij Represents the quantitative risk index between two target points, x ij Indicates whether the route from mission i to j is selected, M is the air defense weapon set, r P is the radius of the no-fly zone p, r m is the radius of the mth air defense zone, is the vertical distance from the air defense zone m to the line connecting the two mission points i and j, and o is the unit fuel consumption.

[0129] In this embodiment, mission planning includes two parts: task allocation and trajectory planning. In this embodiment, the two parts are coupled. If there is no obstacle between the two targets, a straight line is taken as the trajectory. If there is an obstacle between the two targets, correction is performed according to the following method:

[0130] like Figure 2 , introducing the distance correction operator For a straight line trajectory passing through a no-fly zone, select a point outside the zone such that the lines connecting that point and both the starting and ending points of the trajectory are tangent to the no-fly zone. The sum of the two corrected trajectory segments is taken as the distance between the two points. The constraints on the drone itself primarily consider the fuel constraints when the drone performs a mission, that is, the total fuel consumption of a single drone must be less than the maximum fuel capacity. Mission constraints describe the limitations on the execution of missions by a drone swarm. The drone swarm departs from a starting point, and each mission point is completed by a specific drone. The first two formulas in the mission constraint formula indicate that each target point must be reached once and only once, and the latter formula indicates that the starting point can have at most K outgoing arcs.

[0131] In this embodiment, based on NSGA-Ⅱ and combined with the idea of ​​super network modeling, the N2SGA-Ⅱ method is proposed, and the N2SGA-Ⅱ method is defined as a genetic method based on super network modeling.

[0132] Variable coding design, the solution code W consists of two parts: W = {M, S}; where M = {m1, m2, ..., m n} represents the number of tasks assigned to each drone, and S represents the execution sequence of the tasks. Taking two drones performing four tasks as an example, the solution is encoded as Figure 3 As shown, Figure 3 It means that UAV 1 performs two tasks, and the task sequence is to perform task 2 first and then task 3. UAV 2 performs two tasks, and the task sequence is to perform task 1 first and then task 4.

[0133] The standard NSGA-II method only considers the solution set at the same level in the congestion calculation, lacking a global consideration of the solution set. In addition, due to the discrete nature of the solution space, clustered duplicate solutions are prone to occur in the context of this example, affecting the efficiency of iterative improvement. To address the above situation, a genetic method based on hypernetwork modeling is proposed, which is called N2SGA-II in this example:

[0134] In the multi-objective constraint model, all drones in the drone cluster are divided into different levels. There is no domination between drones in the same level. High-level drones dominate low-level drones. Each drone i is set with parameters n(i) and s(i), where n(i) is the number of solutions that dominate drone i in the drone cluster, and s(i) is the set of solutions dominated by drone i. Then, the non-dominated sorting of drones is performed according to the following steps:

[0135] S001. Store all drones with n(i)=0 in the drone cluster into set F1, and assign the same non-dominated order i(rank) to the drones in the set.

[0136] S002, the set of statistically dominant drones S = {S i |i∈F1};

[0137] S003. Subtract 1 from n(i) of all drones in the set S;

[0138] S004. Repeat steps S001 to S003 until all drones are classified.

[0139] Optionally, when all drones are classified, the hierarchical relationship within the drone cluster is obtained. For the drones in each level, the drones in this level and the drones in the previous level are composed of a drone sub-population. Each drone in the drone cluster represents an individual in the population. The two drones with the closest distance are selected to establish an intra-layer or inter-layer relationship. The above operation is repeated until all drones complete the establishment of the intra-layer or inter-layer relationship, and the drone cluster super network model is obtained.

[0140] Optionally, congestion calculation is performed based on the obtained drone cluster hypernetwork model. The congestion is calculated using the following method:

[0141]

[0142] Among them, f m is the objective function of the mth dimension, rank is the level corresponding to the drone, and the weights of the distance calculation between different levels are assigned by the level difference. i-1 and i+1 correspond to the two nodes closest to drone i in the drone sub-population composed of the current level and the previous level, respectively.

[0143] like Figure 4 As shown, F1, F2, and F3 represent the first, second, and third levels obtained by non-dominated sorting, respectively, and the connecting lines represent the relationships established according to the above conditions.

[0144] When calculating crowding, the standard NSGA method only considers the distance between individuals on the same layer. Considering the iterative improvement of the solution, a solution close to the Pareto solution set surface can often be converted into a Pareto solution in the next iteration. Therefore, the crowding is calculated based on the population super network model and the crowding is defined as follows:

[0145]

[0146] Among them, f m is the objective function of the mth dimension, rank is the level corresponding to the individual, and the weights of the distance calculation between different levels are assigned by the level difference. i-1 and i+1 correspond to the two nodes closest to individual i in the sub-population composed of the current level and the previous level, respectively.

[0147] exist Figure 4 In the figure, the congestion degree of solution i is the weighted sum of the perimeters of the rectangles enclosed by the dotted lines.

[0148] The specific operation operators of the individual are defined as follows (the specific process is as follows Figure 6 shown):

[0149] like Figure 5 As shown in the figure, the standard NSGA-Ⅱ method based on non-dominated sorting and crowding selection is retained. The population after the merger of the father and son generations is stratified by non-dominated sorting, and individuals from each layer are added in turn. Several individuals with the lowest crowding are selected from the critical layer and retained in the next generation.

[0150] The standard NSGA-II method uses a simulated binary crossover operator, which selects a gene from each parent and performs a weighted sum according to a specific formula. However, in this problem, since the genes are integer-encoded, the solution obtained by performing a simulated binary crossover operation does not meet the encoding specifications and is therefore not applicable. This embodiment uses a partially implicit crossover operator (PMX). The crossover operation is completed in two steps. The first step is to perform a conventional two-point crossover operation on the individuals; the second step is to modify the genes outside the crossover region based on the mapping relationship between the genes in the crossover region to eliminate conflicts.

[0151] The standard NSGA-II method uses a polynomial mutation operator, which adds or subtracts a random number between 0 and 1 from a gene in the code through a formula. This example uses a random swap operator to perform the mutation operation, randomly selecting two genes from an individual and swapping them to obtain the mutated individual.

[0152] UAVs can achieve two-way communication through communication ad hoc networks, and can transmit tasks, their own status and other information through the network. The UAV network is limited by the maximum communication distance h max and the maximum number of communication nodes a max Considering the limitations of information transmission delay and transmission loss during actual missions, information is not allowed to be chained through adjacent drones. The adjacent drone group is called the domain of the central drone, and its information can be shared with drones in the neighborhood.

[0153] like Figure 7 As shown, Figure 7 There are six UAVs in China. The dotted circle represents U1's communication range, and the dotted lines represent the network connections between UAVs. U1 forms a communication ad hoc network Ls = {U2, U3, U4}. When U1 discovers new situations or is damaged, it can send information to the UAVs in Ls.

[0154] The drones formed by the alliance form a three-dimensional drone formation, such as Figure 9 The new formation shown divides drones into mission drones and reconnaissance drones. Mission drones only perform tasks and upload basic information such as their own position and status to reconnaissance drones. Reconnaissance drones are responsible for mission planning, alliance formation, and other tasks, and transmit the planning results to each mission drone. Figure 8This is a traditional formation model. Alliance formation specifically includes the following steps:

[0155] Tender Release: When UAV U i When the swarm system enters the bidding release state, the reconnaissance UAV, after observing this phenomenon, becomes the leader of the alliance and establishes a local communication network with the UAVs in the neighborhood to release the bidding information. i :

[0156] Pi={U i ,T};

[0157] Where U i Represents the basic information of the UAV, and T represents the remaining mission objectives of the UAV;

[0158] Bid application: After the above steps, all drones in the local communication network have received the bidding information. First, they judge the bidding information. If the fuel and other constraints are met, the drone becomes the bidding drone and sends the bid Bi to the leader:

[0159] Bi={U i ,T i};

[0160] Where Ti represents the target to be auctioned;

[0161] Alliance construction: After the bidding application in the above steps, the alliance leader receives the bids from various bidding drones in the communication network. The leader needs to sort the bids, select the drone with the highest efficiency, and publish the winning bid information; the sorting of each bid takes into account the risk and distance cost. The leader constructs an effective set according to the Pareto criterion, randomly selects a bid from the effective set and publishes the winning bid information F i Feedback to each bidding drone, F i Can be represented by a tuple;

[0162] F i ={S,TR};

[0163] Where S represents the updated task sequence, TR represents the updated trajectory;

[0164] Execution phase: The successful UAV will update its mission sequence and trajectory to complete dynamic mission planning.

[0165] Reconnaissance drones serve as communication relays and computing nodes in combat missions. In order to fully utilize their effectiveness and ensure efficient completion of missions, the following two requirements are met.

[0166] (1) The reconnaissance drone should cover as many mission drones as possible within its communication range so that more resources can be mobilized to deal with emergencies.

[0167] (2) The reconnaissance UAV should give priority to ensuring that the UAVs at risk are within the communication range.

[0168] Based on this, this embodiment draws on the idea of the artificial potential field method and proposes a track planning method for reconnaissance UAVs based on gravity. The track planning method for reconnaissance UAVs is as follows:

[0169] Collect relevant information: The track of the reconnaissance UAV is planned in segments, changing the heading at fixed time intervals and maintaining a fixed heading and speed within the interval. When triggering a heading change, the reconnaissance aircraft requests relevant information from each UAV within the communication range;

[0170] Calculate the heading: The track of the reconnaissance UAV is planned using the artificial potential field method. The specific steps are as follows:

[0171] Establish the gravitational field function:

[0172]

[0173] In the formula, d(p, p i ) represents the distance between the mission UAV i and the reconnaissance aircraft, and r i represents the risk index of the mission UAV i. When d < d*, the magnitude of the gravitational potential energy is proportional to the square of the distance from the current position to the target position; when d > d*, the value of the gravitational calculation function is reduced to avoid the problem of excessive gravity when far from the target position. d* represents the communication radius of the reconnaissance aircraft.

[0174] Establish the repulsive field function:

[0175]

[0176] In the formula, d(p, p i ) represents the distance between the reconnaissance aircraft itself and other reconnaissance aircraft i, α is the repulsive gain constant; q* is the action threshold range of the repulsion, and the repulsion will only be generated within this threshold range;

[0177] Calculate the heading based on the artificial force field:

[0178] Superimpose the gravitational field and the repulsive field to obtain the artificial force field U:

[0179] U(p) = U att (q) + U rep (q);

[0180] Take the gradient of the artificial potential field to obtain the potential field force F:

[0181]

[0182] Decompose the potential field force into the X and Y directions and normalize it to obtain the heading;

[0183] Execution of the voyage:

[0184] According to the obtained heading, keep flying at a certain speed.

[0185] Optionally, the dynamic mission planning of the drone swarm includes the following steps:

[0186] Mission replanning: After receiving the replanning command, the drone uses its current location as the starting point and its remaining tasks and the set of accepted tasks as the target set, and replans using the above method;

[0187] Calculation of bidding cost: The drone calculates the fitness F1 after accepting the task and the fitness F2 after rejecting the task, and takes F1-F2 as the cost;

[0188] The winning drone is selected based on the bidding price, and the winning drone will carry out drone swarm mission re-planning.

[0189] Simulation and result analysis:

[0190] According to the number of task objectives, the design is as follows Figure 10-12 The three mission scenarios are shown in Figure 1. The regular octagon represents the target, the triangle represents the starting point, the circle represents the location of the enemy weapon, the surrounding black dotted line represents the effective attack range of the weapon, and the gray circle represents the no-fly zone. The number of targets in scenarios 1, 2, and 3 is 5, 10, and 20, respectively. Target points are randomly distributed across the mission scenarios to analyze the performance of the method under different mission requirements. The experimental environment and parameters are shown in the following table:

[0191]

[0192] The performance of the method is tested from the solution quality and running time. The parameter settings are shown in the following table:

[0193]

[0194]

[0195] Calculate the Pareto solution surface of the above three methods in each scenario, and the results are as follows Figure 13-15As shown, in this embodiment, the solution space is the allocation and ordering of tasks, so the solution space is discrete and grows geometrically with the task load. For multi-objective problems, the comparison of solution sets first compares the optimal fitness of each objective, which in this embodiment minimizes the total distance and risk value. Furthermore, the breadth and diversity of the solution sets are compared. The results show that the improved NSGA-II method performs well in all scenarios. The particle swarm optimization method performs poorly in scenarios with relatively large task loads, that is, scenarios with complex solution spaces. The MOEAD method is not as robust as the improved NSGA-II method, performing worst in scenarios with medium task loads. In scenarios with high task loads, the solution set is inferior to the improved NSGA-II, but superior to the particle swarm optimization method. The improved NSGA-II method performs close to that of the traditional NSGA-II in scenarios one and two, and has greater solution diversity in scenario three.

[0196] Statistics of the running time of each method in different scenarios, the results are as follows Figure 16 As shown in the figure, the running time of the improved NSGA-Ⅱ method is significantly better than that of the other three methods. The improved NSGA-Ⅱ method improves the mutation operator, making the offspring generated by mutation more effective, so the running time of the method is improved to a certain extent. The more complex the task scenario, the more variable dimensions, and the larger the solution space, the better the improvement effect.

[0197] The simulation results of the UAV swarm dynamic planning for damage are shown as follows Figure 17 As shown, Figure 17 The full trajectory of two drones and a reconnaissance aircraft during a mission is shown. Routes 1 and 2 are the drones' initial planned paths, while Route 3 is the reconnaissance aircraft's trajectory. The drones initially execute their combat mission according to pre-planned results. When Drone 1 reaches coordinates (-0.85, -1.8), it is destroyed by enemy weapons. Upon receiving notification of Drone 1's destruction, the reconnaissance drone integrates the remaining tasks uploaded by Drone 1 and the available resource, Drone 2. It immediately replans using the aforementioned method (Route 4) and sends the planned trajectory to Drone 2, which takes over the remaining tasks from Drone 1. The results demonstrate the method's ability to rapidly respond to battlefield changes and coordinate remaining resources, demonstrating the feasibility of the proposed method for solving drone swarm planning problems.

[0198] The new formation model improves the efficiency of drone swarm mission planning. There are many perspectives for evaluating the efficiency of drone swarm reconnaissance missions, primarily considering the degree of mission completion and the cost of mission completion. This example examines these two perspectives and selects a key metric: the improvement in the new formation model's mission completion degree and the total flight distance required for the drone formation to complete the mission, while ensuring mission completion. This new formation model is then compared with the traditional model.

[0199] Improvement of task completion by the new formation model: Assuming that the enemy has an air defense weapon and there are ten target tasks to be completed, the improved NSGA-Ⅱ method is used for planning and solving. Simulation experiments are carried out using the new formation model and the traditional formation model respectively. The specific scenario is shown in the above scenario 2. Different numbers of drones are selected in each round of simulation. The experiment is repeated 10 times and the number of completed tasks is averaged. The results are as follows: Figure 18 As shown, as the number of drones increases, the number of tasks that can be accomplished by drones in both modes increases. However, the new formation model achieves a significantly faster increase in the task completion rate than the traditional formation model. Furthermore, the performance difference between the two models is closely related to the number of drones. The difference is negligible when the number of drones is high or low, while the gain in task completion is most pronounced when the number of drones is moderate. This is because the new formation model can plan for more drones when dealing with drone damage, better ensuring task completion. When the number of drones is low or high, swarm planning faces the challenge of either having too few or too many resources, making it difficult to demonstrate the advantages of the formation model in resource planning. Overall, however, the new formation model effectively improves task completion rates.

[0200] Improvement of the total flight range by the new formation model under the premise of ensuring mission completion: To further test the performance of the new formation model on the key data of total flight range, this embodiment designs mission scenarios with different numbers of missions, randomly changes the positions of the mission target points in the scenarios, controls the number of drones to 10, conducts repeated experiments, and counts the experimental results of the two models to ensure that the mission completion rate is higher than 80%. The average value of the total flight track is calculated. The results are as follows: Figure 19 As shown: Whether it is a scenario with a small number of targets or a large number of targets, the new formation model has a significant improvement in the total flight distance because the leader aircraft has a wider communication range, has more options for dealing with drone damage, and can better mobilize the superior drones in the cluster to take over the mission.

[0201] Conclusion. The embodiment demonstrates the realization of UAV mission planning in a combat environment. First, based on the combat assumptions, the mission planning problem is abstracted into a mathematical problem based on VPR, and the goals and constraints of the problem are sorted out. A multi-objective constraint model is established for solution. In terms of model solution, this embodiment is based on NSGA-Ⅱ and combined with hypernetwork modeling to design a new N2SGA-Ⅱ method. Three scenarios are designed according to the relative task volume. Compared with three other popular modern optimization methods: traditional NSGA-Ⅱ, MOEAD, and particle swarm optimization, the results show that the improved NSGA-Ⅱ method is more efficient and has better robustness, and can obtain a better solution set. The calculation of congestion in the NSGA-Ⅱ method is essentially to measure the quality of a solution and evaluate the priority of the solution in the cluster accordingly. The purpose is to obtain a broad, uniform and high-quality Pareto solution set surface. The introduction of a hypernetwork to model populations provides a new evaluation approach, such as based on node centrality, betweenness, and coreness. It considers the dynamic real-time planning problem under combat conditions, specifically the reallocation of swarm tasks after the loss of a UAV under limited communication conditions. Addressing the shortcomings of traditional UAV formations, a new formation model is proposed. Results show that the new formation model significantly improves the overall effectiveness of UAV swarm combat, raising the upper limit of UAV mission completion to a certain extent, reducing losses caused by formation navigation, and achieving higher mission efficiency.

[0202] According to another aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the above methods when executing the program.

[0203] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0204] The one or more embodiments of this application are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this application should be included in the scope of protection of this application.

Claims

1. A UAV swarm reconnaissance mission planning method based on a hierarchical model is characterized by: The following steps are involved: Collect battlefield situation in advance, carry out pre-mission planning based on the pre-collected battlefield situation, and each drone in the drone cluster executes the mission according to the pre-mission plan; During the mission execution, if a new battlefield situation arises that makes the pre-planned mission plan no longer applicable, dynamic mission planning will be triggered; After dynamic mission planning is triggered, alliance formation is carried out, and the drone cluster forms a new formation model according to the hierarchical model, collects new battlefield situation, and re-plans the mission for the drone cluster; In the new formation model, drone swarms are divided into two types of drones: reconnaissance drones and mission drones. Reconnaissance drones are responsible for monitoring the battlefield environment, conducting situational integration and mission planning, and acting as a communication hub to establish a communication network with mission drones, collecting and processing information uploaded by mission drones. Mission drones are responsible for enemy reconnaissance, executing missions according to pre-planned missions or missions and tracks planned by reconnaissance drones, and providing real-time feedback on mission completion to reconnaissance drones. The new formation model of the drone cluster is formed according to the hierarchical model, which includes dividing all drones in the drone cluster into different levels. There is no domination between drones in the same level, and drones in the higher level dominate drones in the lower level. Each drone i is set to have parameters n(i) and s(i). n(i) is the number of solutions that dominate drone i in the drone cluster, and s(i) is the set of solutions dominated by drone i. Then follow the steps below to perform non-dominated sorting of drones: S001. Store all drones with n(i)=0 in the drone cluster into set F1, and assign the same non-dominated order i(rank) to the drones in the set. S002, the set of statistically dominant drones S = {S i |i∈F1}; S003. Subtract 1 from n(i) of all drones in the set S; S004. Repeat steps S001 to S003 until all drones are classified.

2. The method for planning a UAV swarm reconnaissance mission based on a hierarchical model as claimed in claim 1, wherein: Pre-mission planning includes the following steps: A combat environment parameter model is established based on the pre-collected battlefield situation. The combat environment parameter model has several restrictions, including at least no-fly zones and weapon threats. The combat parameter model avoids these restrictions to plan the trajectory of the UAV swarm. Then, a multi-objective constraint model is established based on the constraints existing during the task execution process. Tasks are assigned based on this multi-objective constraint model. The constraints include the UAV’s own constraints and the task constraints. The UAV’s own constraint is that the total fuel consumption of a single UAV must be less than the maximum fuel capacity when the UAV cluster performs the task. The task constraint is that when the UAV cluster performs the task, each task point is completed by a UAV. The pre-task planning is formed by coupling trajectory planning and task allocation. During the process, if there is no restriction between the two task targets, the straight line between the two task targets is taken as the trajectory. If there is a restriction between the two task targets, the trajectory is corrected and coupled with the task allocation. When correcting the trajectory, for the case where the straight track passes through the no-fly zone, a point is selected outside the no-fly zone so that the lines connecting this point with the starting point and the end point are tangent to the no-fly zone. The sum of the two corrected trajectories is taken as the trajectory between the two task targets.

3. The UAV swarm reconnaissance mission planning method based on a hierarchical model as claimed in claim 2, characterized in that: The multi-objective constraint model is as follows: Among them, P1 and P2 are the no-fly zone sets, n=cu{0,n+1}, Q is the full fuel load of the drone, C ij is the distance between tasks i and j, x ij Indicates whether the arc from task i to j is selected, is the track correction operator, is the vertical distance from the center of the no-fly zone n to the line connecting the two mission points i and j, r P is the radius of the no-fly zone P; is the UAV’s own constraint; k is the number of UAVs, r n is the radius of the no-fly zone n; The task constraints are expressed as follows: Among them, K represents the maximum number of arcs from the starting point, x ik 、x kj Respectively indicate whether the outgoing arc and incoming arc of task point k are selected, x oj Indicates whether the arc from the starting point o to other task points is selected; The total path distance is expressed as: Where C ij Represents the distance between two target points, x ij Indicates whether the route from task i to j is selected; The total path risk is expressed as: Where f ij represents the quantitative risk index between two target points, M is the air defense weapon set, r P is the radius of the no-fly zone p, r m is the radius of the mth air defense zone, is the vertical distance from the air defense zone m to the line connecting the two mission points i and j, and o is the unit fuel consumption.

4. The method for planning a UAV swarm reconnaissance mission based on a hierarchical model as claimed in claim 3, wherein: When all drones are classified, the hierarchical relationship within the drone cluster is obtained. For drones in each level, the drones in this level and the drones in the previous level are composed of a drone sub-population. Each drone in the drone cluster represents an individual in the population. The two drones with the closest distance are selected to establish an intra-layer or inter-layer relationship. The above operation is repeated until all drones have completed the establishment of intra-layer or inter-layer relationships, and the drone cluster super network model is obtained.

5. The method for planning a UAV swarm reconnaissance mission based on a hierarchical model as claimed in claim 4, wherein: The congestion degree is calculated based on the obtained UAV cluster super network model. The congestion degree is calculated in the following way: Among them, f m is the objective function of the mth dimension, rank is the level corresponding to the drone, and the weights of the distance calculation between different levels are assigned by the level difference. i-1 and i+1 correspond to the two nodes closest to drone i in the drone sub-population composed of the current level and the previous level, respectively.

6. The method for planning a UAV swarm reconnaissance mission based on a hierarchical model as claimed in claim 5, wherein: The genetic operators of the drone swarm hypernetwork model are as follows: Selection operator: Perform non-dominated sorting on the population after the merger of the father and son generations, add individuals from each layer in turn, and select several individuals with the lowest crowding degree from the critical layer to be retained in the next generation; Crossover operator: Perform a two-point crossover operation on the individual, and then modify the genes outside the crossover area according to the mapping relationship of each gene in the crossover area to eliminate conflicts; Mutation operator: A random exchange operator is used to perform mutation operations, randomly selecting two genes from an individual to exchange and obtain the mutated individual.

7. The method for planning a UAV swarm reconnaissance mission based on a hierarchical model as claimed in claim 1, wherein: When dynamic mission planning is triggered, a new drone formation is established. The new drone formation is a three-dimensional drone formation, and then an alliance is formed. Alliance formation includes the following steps: Tender Release: When UAV U i When the drone is destroyed, the drone cluster enters the bidding release state. After the reconnaissance drone obtains the information, it becomes the leader of the alliance and establishes a local communication network with the UAVs in the neighborhood to release the bidding information P i : Pi={U i ,T}; Where U i Represents the basic information of the UAV, and T represents the remaining mission objectives of the UAV; Bid application: After the tender is released, all drones in the local communication network receive the tender information. The drones first judge the tender information. If a drone meets the conditions in the multi-objective constraint model, it becomes a bidding drone and then sends a bid to the alliance leader. Bi={U i ,T i }; Where Ti represents the target to be auctioned; Alliance construction: After the bidding application, the alliance leader receives bids from various bidding drones in the communication network. The alliance leader needs to sort the bids, select the drone with the highest efficiency, and publish the winning bid information. The sorting of each bid takes into account risk and distance cost. The alliance leader constructs an effective set based on the Pareto criterion, randomly selects a bid from the effective set and publishes the winning bid information F i Feedback to each bidding drone, F i Can be represented by a tuple; F i ={S,TR}; Where S represents the updated task sequence, TR represents the updated trajectory; Execution phase: The successful UAV will update its mission sequence and trajectory to complete dynamic mission planning.

8. The method for planning a UAV swarm reconnaissance mission based on a hierarchical model as claimed in claim 7, wherein: During the alliance formation process, the reconnaissance drone trajectory planning method is as follows: Collect relevant information: The reconnaissance drone's trajectory is planned in sections, with the course changed at fixed intervals. The fixed course and speed are maintained during the intervals. If a course change is required, the reconnaissance drone requests relevant information from all drones within the communication range. Calculate heading: Use the artificial potential field method to plan the reconnaissance drone's trajectory. The specific steps are as follows: Establish the gravitational field function: where \(d(p,p i )\) represents the distance between mission UAV \(i\) and the reconnaissance UAV, and \(r i \) represents the risk index of mission UAV \(i\). When \(d < d^*\), the magnitude of the gravitational potential energy is proportional to the square of the distance from the current position to the target position; when \(d>d^*\), the value of the gravitational calculation function is reduced to avoid the problem of excessive gravity when far away from the target position. \(d^*\) represents the communication radius of the reconnaissance UAV. Establish a repulsive field function: Where d(p,p i ) represents the distance between the reconnaissance drone itself and other reconnaissance drones i, α is the repulsive gain constant; q * It is the threshold range of repulsive force, and repulsive force will only be generated within this threshold range; Calculate heading based on artificial force field: Superimposing the gravitational field and the repulsive field, we get the artificial force field U: U(p)=U att (q)+U rep (q); Find the gradient of the artificial potential field and obtain the potential field force F: Decompose the potential field force into two directions, X and Y, and normalize them to get the heading; Execution of the voyage: According to the obtained heading, keep flying at a certain speed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

Images