A UAV swarm mission planning method for regional coverage reconnaissance

Abstract

The present invention discloses a method for planning unmanned aerial vehicle (UAV) swarm missions for regional coverage reconnaissance, and relates to the technical field of UAV swarm collaborative reconnaissance. The method comprises: determining a target area; performing gridding processing on the target area to obtain multiple grid areas; constructing a global fitness function, using an improved gray wolf optimization algorithm to simulate the hunting strategy of a wolf pack, and evaluating the quality of each gray wolf through the fitness function; constructing a global fitness function as a weighted sum of regional coverage and target point coverage, wherein the regional coverage is determined based on the number of grid areas covered by the sensor, and the target point coverage is calculated by counting the number of times each target point is covered by the sensor and normalizing the result. The method of the present invention can improve the regional coverage rate of UAV swarm regional coverage reconnaissance missions, and has reliability and scalability, thereby promoting the further development and application of UAV swarm regional reconnaissance coverage technology.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV cluster collaborative reconnaissance, and more specifically, to a UAV cluster mission planning method for regional coverage reconnaissance. Background Art

[0002] Drone swarm coordinated operations, which involve the coordinated cooperation of multiple drones to conduct reconnaissance, monitoring, tracking, and fire strikes on key enemy targets, have significant military applications. In the early stages of a war, drone swarms can conduct reconnaissance and search of the combat area, acquiring crucial intelligence information such as the detailed location, defense structure, and combat capability of enemy targets. In the final stages of a war, they can be used to survey the battlefield and eliminate potential threats.

[0003] Efficiently and rapidly conducting reconnaissance of a target area is a key issue in its combat applications. With the advancement of drone technology, collaborative reconnaissance by drone swarms should possess intelligent features such as highly autonomous decision-making and evaluation, task division, trajectory planning, and motion control. Most current research models the mapping relationship between a drone swarm and the area to be reconnaissanced as a task allocation model. By constructing constraints and the performance function to be optimized, centralized or distributed allocation algorithms are designed to optimize overall reconnaissance effectiveness within the constraints of the performance function and spatiotemporal conditions. One of the challenges facing the planning of regional reconnaissance missions for drone swarms is its computational complexity. The problem of collaborative reconnaissance by drone swarms can be modeled as a multi-objective combinatorial optimization problem. The computational complexity of the problem primarily stems from its scale, including the number of reconnaissance targets, the number of drones, and the extent of the area to be reconnaissanced. As the problem scale increases, the search space rapidly expands, making it difficult or even impossible to find an exact optimal solution. Therefore, when solving such problems, it is important to focus on finding near-optimal or satisfactory solutions. Heuristic algorithms are one of the best tools for finding such satisfactory solutions. In addition, the population-based computational nature of heuristic algorithms makes them a powerful tool for solving multi-objective optimization problems. Heuristic multi-objective optimization has become a hot topic in multi-objective optimization research in recent years. The Grey Wolf Optimizer (GWO) is a heuristic algorithm based on the social hierarchy and hunting behavior of gray wolves, used to solve optimization problems. The algorithm simulates the hierarchy and hunting strategies of gray wolf packs to find the global optimal solution. In each iteration, α, β, and δ wolves guide the population update to avoid local optimality. Despite the problems of population diversity and slow convergence in the later stages, GWO has been applied in many fields due to its simplicity and robustness, and some scholars have proposed improvement strategies to enhance its performance.

[0004] The mission scenario considered in this invention involves a reconnaissance swarm consisting of multiple rotary-wing drones carrying optoelectronic payloads entering a target area, knowing only the target area's geographic location and boundary information, as well as the locations of several key targets within the area. The mission objective is to cover as much of the entire area as possible, while maintaining continuous reconnaissance of key targets by at least one drone. Currently, the use of the Gray Wolf Optimization Algorithm to plan regional coverage reconnaissance missions for drone swarms still has some limitations, primarily manifested in the following:

[0005] (1) It is easy to fall into the local optimal solution: Due to the limitations of the gray wolf optimization algorithm, there is a fixed social hierarchy, which limits the diversity and adaptability of the wolf pack during the search process. Once the position updates of α, β, and δ wolves are not enough to guide the group to the global optimal solution, the entire wolf pack may be guided to the local optimal solution, resulting in a decrease in coverage.

[0006] (2) Slow convergence speed: The linearly decreasing convergence factor of the gray wolf optimization algorithm is not applicable to the optimization process of all problems. It may cause the algorithm to concentrate on local areas in the early stage, thus affecting the overall convergence speed.

[0007] (3) Uneven population initialization: The quality and uniformity of population initialization have a significant impact on the global search capability of the algorithm. If the initial population is unevenly distributed, it may cause the algorithm to concentrate in a local area in the early stage, thus affecting the overall convergence accuracy.

[0008] (4) Search capability needs to be improved: The Gray Wolf Optimization Algorithm does not adequately balance global search and local search, resulting in insufficient global exploration capability in the early stages and difficulty in effectively covering the entire search space. In the later stages of local search, there is a lack of effective search strategies, making it easy to fall into local optimality. Summary of the Invention

[0009] In order to solve the above technical problems, the present invention provides a UAV cluster task planning method for regional coverage reconnaissance, aiming to improve the shortcomings of the Gray Wolf optimization algorithm in the field of UAV cluster regional coverage reconnaissance, and achieve more efficient and comprehensive regional coverage reconnaissance effects.

[0010] The present invention provides a method for planning a UAV cluster mission for regional coverage reconnaissance, the method comprising:

[0011] Determine a target area; wherein the target area is an area to be surveyed by the drone cluster;

[0012] Performing rasterization processing on the target area to obtain a plurality of grid areas;

[0013] Constructing a global fitness function, using an improved gray wolf optimization algorithm to simulate the wolf pack's hunting strategy, and evaluating the performance of each gray wolf using the fitness function; wherein the global fitness function is a weighted sum of area coverage and target point coverage, wherein the area coverage is determined based on the number of grid areas covered by the sensor, and the target point coverage is calculated by counting and normalizing the number of times each target point is covered by the sensor, where the target point is the location of the key target in the target area;

[0014] Optimize the initial position of the wolf pack by initializing the gray wolf position using Latin hypercube sampling and amplifying the random sampling data proportionally to obtain the sample position that adapts to the specific search space;

[0015] Introducing a nonlinear convergence factor; the nonlinear convergence factor is used to adjust the search range according to the number of iterations;

[0016] Based on a dynamic alpha wolf selection strategy, a alpha wolf is dynamically selected; wherein the alpha wolf includes alpha wolves, beta wolves, and delta wolves, the alpha wolf is selected from the wolves with the highest fitness value, the beta wolf is randomly selected from the top 10% of the fitness value, and the delta wolf is randomly selected from the top 10% to 20% of the fitness value; the fitness value is calculated according to the global fitness function;

[0017] Based on the wolf pack position update strategy, the positions of all gray wolves are updated, and the fitness value of the gray wolf pack is recalculated based on the global fitness function, and the dynamic leader wolf selection strategy wolf pack position update strategy is executed until the set termination condition is reached. The optimal gray wolf position and corresponding fitness value are output, and the drone cluster area coverage reconnaissance mission planning scheme is obtained.

[0018] Furthermore, based on the dynamic alpha selection strategy, the alpha is dynamically selected, including:

[0019] Randomly generate a group of gray wolves, each gray wolf represents a potential solution x, and calculate its fitness value;

[0020] Define α wolf as the individual with the highest fitness, and select α wolf using the following formula:

[0021] α=argmaxf(x i ) (1)

[0022] Among them, f(x i ) represents the i-th gray wolf x i The fitness value function of

[0023] Beta wolves are selected using the following formula:

[0024] β=RandomSelect({x i |f(x i)∈Top10%\α}) (2)

[0025] Among them, RandomSelect means random selection within the range, TOP10%\α means the wolf pack with the top 10% fitness value after excluding α wolf;

[0026] The δ wolf is selected by the following formula:

[0027] δ=RandomSelect({x i |f(x i )∈Top10%to20%}) (3)

[0028] Among them, Top10% to 20% represents the wolf packs with the top 10% to 20% fitness values.

[0029] Furthermore, the wolf pack position update strategy includes a common wolf pack update strategy, a delta wolf update strategy, a beta wolf update strategy and an alpha wolf update strategy.

[0030] Furthermore, the common wolf pack update strategy includes:

[0031] In the gray wolf optimization algorithm, based on the positions of the three wolves α, β, and δ, the wolf pack disperses and searches, introducing a random value. When the random value is greater than 1, the wolf pack moves away from the prey and conducts a global search. The modeling formula for this process is as follows:

[0032] D=|C·X P (t)-X i (t)| (4)

[0033] X i (t+1)=X P (t)-A·D (5)

[0034] Among them, t represents the current iteration number, A and C are coefficients, X P is the location of the prey, X i (t) is the position of the gray wolf individual in the tth iteration; X i (t+1) is the position of the gray wolf individual at the t+1th iteration; X p (t) represents the location of the prey; D represents the distance between the leader wolf and other individuals;

[0035] The coefficients A and C are calculated using the following formula:

[0036] A=2a·r1-a (6)

[0037] C=2·r2 (7)

[0038] Where r1 and r2 are random values ​​in [0, 1], a is the convergence factor, and A is a random value in the interval [-a, a], where a decreases from 2 to 0 during the iteration process;

[0039] The hunting is carried out by α, β, and δ wolves. The mathematical model of the gray wolf tracking the location of the prey is described as follows:

[0040] D n =|C1·X n -X i | (8)

[0041] Among them, D n represents the distance between the nth wolf leader and other wolves; X n Represents the current position of the nth wolf leader; C1 is a random number, X i is the current location of the individual gray wolf;

[0042] X1 is calculated using the following formula:

[0043] X1=X α -A1·D α (9)

[0044] X2=X β -A2·D β (10)

[0045] X3=X δ -A3·D δ (11)

[0046] Among them, X1, X2, and X3 represent the positions of the wolves after being adjusted by α wolf, β wolf, and δ wolf, respectively; D α 、D β 、D δ Respectively represent the distances between α wolf, β wolf, δ wolf and other wolf packs; A1, A2, A3 represent the control factors of the adjustment amplitude of the wolf pack to α wolf, β wolf, δ wolf position respectively;

[0047] Update the position of the gray wolf individual using the following formula:

[0048]

[0049] Among them, W α represents the influence weight of wolf α, W β represents the influence weight of β wolf, W δ represents the influence weight of δwolves, f α represents the fitness value of wolf α, f β represents the fitness value of β wolf, f δ represents the fitness value of δ wolf, X i(t+1) is the position of the gray wolf at the t+1th iteration; X1, X2 and X3 are the adjusted positions of the wolf pack under the influence of α wolf, β wolf and δ wolf respectively.

[0050] Furthermore, the δwolf update strategy includes: updating the position of δwolf by the following formula:

[0051]

[0052] Among them, X δ (t+1) is the updated position of wolf δ; X1 and X2 are the adjusted positions of the wolves under the influence of wolf α and wolf β respectively, and W α represents the influence weight of wolf α, W β represents the influence weight of β wolf, f α represents the fitness value of wolf α, f β Represents the fitness value of β wolf.

[0053] Furthermore, the β wolf update strategy includes: updating the position of the β wolf by the following formula:

[0054] X β (t+1)=X1(1+Ν(0,1)cos(2πC1)) (17)

[0055] Among them, X β (t+1) is the updated position of wolf β, Ν(0,1) is a random value drawn from the standard normal function, C1 is a random number, and X1 represents the adjusted position of the wolves under the influence of wolf α;

[0056] The α wolf update strategy includes: updating the position of the α wolf by the following formula:

[0057]

[0058] u=σΝ(0,1) (19)

[0059] v=Ν(0,1) (20)

[0060]

[0061] Where Γ represents the gamma function, N(0,1) represents a random value drawn from the standard normal distribution, γ is the Mantegna index, σ is the standard deviation of the Lévy distribution, u and v are random variables from the standard normal distribution, and X α (t+1) and X α (t) represents the old and new positions of wolf α, respectively.

[0062] Furthermore, the nonlinear convergence factor is expressed as:

[0063]

[0064] Where a represents the convergence factor, t max Represents the maximum number of iterations, e represents a natural constant, and t represents the current number of iterations.

[0065] The present invention has at least the following beneficial effects:

[0066] (1) Improving target coverage: This paper optimizes the deployment of drone clusters using an improved gray wolf optimization algorithm, significantly improving coverage of target points within a specified area. By using a nonlinear convergence factor, the global search range in the early stages of the algorithm is increased, while the local convergence speed in the later stages of the algorithm is increased, allowing the algorithm to approach the optimal solution more quickly and improve target coverage.

[0067] (2) Increased reliability: In order to overcome the problem that the gray wolf optimization algorithm is prone to falling into local optimal solutions, the present invention innovatively uses a dynamic alpha wolf selection strategy. This strategy aims to ensure that the selection of alpha wolves is as diverse as possible in each iteration, thereby enhancing the adaptability and exploration ability of the population, thereby reducing the probability of the algorithm falling into local optimal solutions and improving the reliability of target reconnaissance.

[0068] (3) Improved scalability: The global function fitness value in this invention can adapt to a variety of different reconnaissance needs. Whether it is coverage of the target area or monitoring of key points of the target, different reconnaissance needs can be met by changing the different fitness value calculation modes, and the reconnaissance mode can be adjusted in real time according to environmental changes and battlefield requirements.

[0069] (4) Enhanced collaborative search capabilities of wolves: By optimizing the wolf pack's position update strategy, this invention strengthens information sharing and synergy within the wolf pack. In particular, by giving the alpha wolf greater influence and introducing an independent exploration strategy for the alpha wolf, the algorithm can more effectively integrate the group's intelligence, improving the collaborative nature and overall performance of the search. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of an application scenario according to the prior art is shown.

[0071] Figure 2 A flowchart of a method for planning a UAV cluster mission for regional coverage reconnaissance according to an embodiment of the present invention is shown.

[0072] Figure 3 A flow chart of a dynamic alpha wolf selection strategy according to an embodiment of the present invention is shown.

[0073] Figure 4The diagram shows the layout of drones after the traditional gray wolf optimization algorithm and the method proposed in the present invention according to an embodiment of the present invention; wherein, (a) is the traditional gray wolf optimization algorithm; (b) is the method proposed in the present invention.

[0074] Figure 5 A schematic diagram comparing the convergence speeds of two convergence factors according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0075] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention are further described in detail below with reference to the accompanying drawings and specific embodiments, but are not intended to limit the present invention. For the various steps described herein, if there is no necessity for a contextual relationship between each other, the order in which they are described as examples herein should not be regarded as limiting, and those skilled in the art should know that they can be adjusted in order as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0076] The embodiment of the present invention provides a method for planning a UAV cluster mission for regional coverage reconnaissance. Figure 2 As shown, the method includes steps S1 to S7, which are described in detail as follows.

[0077] S1, determining a target area; wherein the target area is an area to be surveyed by the drone cluster.

[0078] For example, this embodiment provides a task scenario, such as Figure 1 As shown in the figure, the mission scenario involves a reconnaissance swarm consisting of multiple rotary-wing drones carrying electro-optical payloads entering a target area. Knowing only the target area's geographic location and boundaries, as well as the locations of several key targets within the area, the mission's mission is to cover as much of the area as possible, while maintaining continuous reconnaissance of key targets by at least one drone.

[0079] Based on the above mission scenarios, a task allocation model for drone swarms can be constructed, taking into account factors such as geographic information, drone sensors, and target point locations for systematic modeling, thereby determining the scope of the target area and the key targets within the target area. It should be noted that the construction of the task allocation model for drone swarms and the method of systematic modeling to obtain the target area can adopt existing technologies. At the same time, this embodiment only illustrates a method for determining the target area. In actual implementation, the target area does not necessarily need to be selected in the manner exemplified above.

[0080] S2, performing a gridding process on the target area to obtain a plurality of grid areas.

[0081] In this embodiment, the target area is gridded, simplifying the complex environment into manageable units, achieving effective regional coverage and key target monitoring, providing clear spatial division for subsequent task allocation, and optimizing resource allocation.

[0082] S3, constructing a global fitness function, using an improved gray wolf optimization algorithm to simulate the hunting strategy of the wolf pack, and evaluating the pros and cons of each gray wolf through the fitness function; wherein, the constructed global fitness function is a weighted sum of the area coverage rate and the target point coverage rate, the area coverage rate is determined according to the number of grid areas covered by the sensor, the target point coverage rate is calculated by counting the number of times each target point is covered by the sensor and normalizing it, and the target point is the location point of the key target in the target area.

[0083] In this embodiment, the global fitness function combines area coverage and target point coverage, using adjustable weights to meet different requirements. Area coverage is determined by dividing the area into small grids and counting the number of grids covered by sensors, while target point coverage is calculated by counting the number of times each target point is covered by sensors and normalizing the result. Ultimately, the weighted sum of these two coverage rates constitutes the global fitness function, which guides the drone swarm's mission planning, ensuring that key targets are continuously monitored while maximizing area coverage and improving mission execution efficiency.

[0084] S4, optimize the initial position of the wolf pack, initialize the gray wolf position by using Latin hypercube sampling, and amplify the random sampling data proportionally to obtain the sample position that adapts to the specific search space.

[0085] In this embodiment, by using Latin Hypercube Sampling (LHS) to initialize the gray wolf position and proportionally amplifying the random sampling data, the sample position adapted to the specific search space can be obtained, thereby improving the global search capability of the algorithm.

[0086] S5, introducing a nonlinear convergence factor; the nonlinear convergence factor is used to adjust the search range according to the number of iterations.

[0087] In some embodiments, a convergence factor based on a sigmoid function is used as a nonlinear convergence factor. By adopting the nonlinear convergence factor of the sigmoid function, the range of the factor is adjusted according to the number of iterations: when the absolute value of the convergence factor is greater than 1, the wolf pack will expand the search range for a global search; as the iteration proceeds, when the absolute value of the convergence factor is less than 1, the wolf pack will narrow the search range and focus on local search.

[0088] Specifically, based on the classic gray wolf optimization algorithm, it can be seen that when |A|>1, the gray wolf group will expand the search range to find prey, that is, global search, and the convergence speed is fast; when |A|<1, the gray wolf group will shrink the search range to attack the prey, that is, local search, and the convergence speed is slow. Therefore, the size of A is closely related to the global search and local search capabilities of the gray wolf optimization algorithm. A changes with the change of the nonlinear convergence factor a. The nonlinear convergence factor a decreases linearly from 2 to 0 with the number of iterations, but the algorithm is not linear in the process of continuous convergence, so the nonlinear convergence factor a cannot fully reflect the actual optimization search process. Therefore, this embodiment adopts a convergence factor based on the sigmoid function, which is expressed as follows:

[0089]

[0090] By adjusting the function (22) to conform to the usage scenario of this patent, the updated convergence factor is as follows:

[0091]

[0092] Where a represents the convergence factor, t max Represents the maximum number of iterations, e represents a natural constant, and t represents the current number of iterations.

[0093] Figure 5 This is a comparison chart of the convergence speed of the original linear convergence factor and the convergence factor of the improved grey wolf optimization algorithm. Figure 5 It can be seen that the nonlinear convergence factor adopted in this application can increase the global search ability of the wolf pack in the early stage and the local convergence speed in the later stage compared with the traditional linear convergence factor.

[0094] S6, based on the dynamic alpha wolf selection strategy, dynamically select the alpha wolf; wherein, the alpha wolf includes α wolf, β wolf and δ wolf, the α wolf selects the one with the highest fitness value, the β wolf is randomly selected from the top 10% of wolves in fitness value, and the δ wolf is randomly selected from the top 10% to 20% of wolves; the fitness value is calculated according to the global fitness function.

[0095] In this embodiment, the dynamic alpha wolf selection strategy can solve the problem of fixed wolf pack levels causing the algorithm to fall into a local optimal solution, and can also increase the adaptability and exploration ability of the population, effectively improving the performance of the algorithm in complex optimization problems.

[0096] In some embodiments, as Figure 3 As shown in Figure 2, the specific steps of the dynamic wolf pack selection strategy are as follows:

[0097] S61, first initialize and randomly generate a group of gray wolves, each gray wolf represents a potential solution x, and then calculate its fitness value.

[0098] S62, selection of α wolf, where α wolf is defined as the individual with the highest fitness.

[0099] α=argmaxf(x i ) (1)

[0100] Among them, f(x i ) represents the i-th gray wolf x i The fitness value function of .

[0101] S63, while β wolves are randomly selected from the top 10% of fitness ranking (excluding the first one)

[0102] β=RandomSelect({x i |f(x i )∈Top10%\α}) (2)

[0103] Among them, RandomSelect means random selection within the range, and TOP10% means the wolf packs with the top 10% fitness values ​​excluding wolf α.

[0104] For S64, the δ wolves are randomly selected from the top 10% to 20% of the fitness ranking.

[0105] δ=RandomSelect({x i |f(x i )∈Top10%to20%}) (3)

[0106] Among them, Top10% to 20% represents the wolf packs with the top 20% fitness values.

[0107] The dynamic wolf selection strategy proposed in this example dynamically adjusts the selection of alpha wolves, ensuring that the selection of β and δ wolves varies with each iteration, maximizing diversity. This dynamic selection mechanism enhances the adaptability and exploration capabilities of the population, allowing each iteration to explore the solution space from different angles and scopes, significantly improving the algorithm's performance and search efficiency in complex optimization problems. By continuously updating the alpha sequence, the algorithm can continuously introduce new search directions and information, avoiding the limitations of a fixed search pattern and increasing the probability of finding the global optimal solution.

[0108] S7, based on the wolf pack position update strategy, updates the positions of all gray wolves, and recalculates the fitness value of the gray wolf pack based on the global fitness function and executes the dynamic leader wolf selection strategy wolf pack position update strategy until the set termination condition is reached, outputs the optimal gray wolf position and corresponding fitness value, and obtains the drone cluster area coverage reconnaissance mission planning plan.

[0109] In this embodiment, the position update strategy of the wolf pack is optimized. Except for the alpha wolf, the position updates of ordinary gray wolves are only influenced by the alpha, β, and δ wolves. A dynamic weighting strategy is introduced to increase the influence of the alpha wolf, which has a higher fitness value, on the pack update. The updates of the δ wolf are only influenced by the β and α wolves. The updates of the β wolf are only influenced by the α wolf. Since the α wolf has the highest rank and status, it should not be influenced by any other wolf pack. Therefore, a Levy flight-based independent exploration strategy is considered for the α wolf. This approach allows the α wolf to explore the search space more flexibly, improving the performance of the algorithm for complex optimization problems.

[0110] In some embodiments, the overall wolf pack update strategy is as follows:

[0111] Common wolf pack update strategy: In the gray wolf optimization algorithm, the common wolf pack's update strategy depends on the positions of the three wolves α, β, and δ. The pack disperses its search and then converges to attack its prey. To enhance exploration, the algorithm introduces random values, which force the pack to deviate from its current prey location in search of a better solution. This divergence model is implemented by random values. When the random value is greater than 1, the pack conducts a global search away from its prey. The modeling formula for this process is as follows:

[0112] D=|C·X P (t)-X i (t)| (4)

[0113] X i (t+1)=X P (t)-A·D (5)

[0114] Among them, t represents the current iteration number, A and C are coefficients, X P is the location of the prey, X i (t) is the position of the gray wolf individual in the tth iteration; X i (t+1) is the position of the gray wolf individual at the t+1th iteration; X p (t) represents the location of the prey; D represents the distance between the leader wolf and other individuals;

[0115] The calculation methods of A and C are as follows:

[0116] A=2a·r1-a (6)

[0117] C=2·r2 (7)

[0118] where r1 and r2 are random values ​​in the range [0, 1], and a is a convergence factor. To simulate approaching prey, A is a random value in the interval [-a, a], where a decreases from 2 to 0 during the iterations.

[0119] Gray wolves are able to identify the location of prey and hunt them in groups. This grouping involves alpha, beta, and delta wolves. The mathematical model for how individual gray wolves track their prey is described below:

[0120] D n =|C1·X i -X| (8)

[0121] Among them, D n represents the distance between the nth wolf leader and other wolves; X n Represents the current position of the nth wolf leader; C1 is a random number, X i is the current location of the individual gray wolf.

[0122] X1=X α -A1·D α (9)

[0123] X2=X β -A2·D β (10)

[0124] X3=X δ -A3·D δ (11)

[0125] Among them, X1, X2, and X3 represent the positions of the wolves adjusted by the influence of α wolf, β wolf, and δ wolf respectively. α 、D β 、D δ They represent the distances between α wolf, β wolf, δ wolf and other wolf packs respectively; A1, A2, A3 represent the control factors of the adjustment amplitude of the wolf pack to the position of α wolf, β wolf, δ wolf respectively.

[0126] X1, X2 and X3 are the positions of the wolf pack ω after being affected by α wolf, β wolf and δ wolf respectively. The dynamic weight formula is as follows:

[0127]

[0128]

[0129] Among them, W α represents the influence weight of wolf α, W β represents the influence weight of β wolf, W δ represents the influence weight of δwolves, f α represents the fitness value of wolf α, f β represents the fitness value of β wolf, f δ represents the fitness value of δ wolf, X i (t+1) is the position of the gray wolf at the t+1th iteration; X1, X2 and X3 are the adjusted positions of the wolf pack under the influence of α wolf, β wolf and δ wolf respectively.

[0130] The weights of the three wolves are calculated and assigned according to their fitness values, so that wolf α has a greater weight in the hunting process, followed by wolf β and wolf δ.

[0131] δ wolf update strategy: The update of this wolf is only affected by β and α wolves, and its update method is as follows:

[0132]

[0133] Among them, X δ (t+1) is the updated position of wolf δ; X1 and X2 are the adjusted positions of the wolves under the influence of wolf α and wolf β respectively, and W α represents the influence weight of wolf α, W β represents the influence weight of β wolf, f α represents the fitness value of wolf α, f β Represents the fitness value of β wolf.

[0134] β wolf update strategy: Similar to δ, this wolf is more influenced by α wolf, and its update method is as follows:

[0135] X β (t+1)=X1(1+Ν(0,1)cos(2πC1)) (17)

[0136] Among them, X β (t+1) is the updated position of β wolf, Ν(0,1) is a random value drawn from the standard normal function, C1 is a random number, and X1 represents the adjusted position of the wolves under the influence of α wolf. The introduction of random numbers is used to increase the exploration ability of β wolf.

[0137] α wolf update strategy: α wolf has the highest level and should not be affected by other wolves, so its update strategy is random exploration. Lévy flights have the characteristics of long and short jumps. By introducing Lévy flights into the individual update process, the α wolf pack can explore the search space more flexibly, improving the algorithm's performance in complex optimization problems. Its formula is as follows:

[0138]

[0139] u=σΝ(0,1) (19)

[0140] v=Ν(0,1) (20)

[0141]

[0142] Where Γ represents the gamma function, N(0,1) represents a random value drawn from the standard normal distribution, γ is the Mantegna index (here taken as 1.5), σ is the standard deviation of the Lévy distribution, u and v are random variables from the standard normal distribution, and X α(t+1) and X α (t) represents the old and new positions of wolf α, respectively.

[0143] This embodiment is also based on the same application scenario, that is, Figure 1 In the application scenario shown in the figure, the traditional gray wolf optimization algorithm and the method proposed in the present invention are used to perform task planning at the same time. The performance comparison is shown in Table 1. The UAV layout diagram after the traditional gray wolf optimization algorithm and the method proposed in the present invention is shown in Figure 4 shown.

[0144] Table 1 Performance comparison between the traditional grey wolf optimization algorithm and the method proposed in this invention

[0145]

[0146] From Table 1 and Figure 4 It can be seen that the method proposed in the present invention is based on the improved gray wolf optimization algorithm, which effectively improves the efficiency and reliability of drone swarm mission planning. The introduction of a nonlinear convergence factor optimizes the search process and enhances target detection coverage. The dynamic leader wolf selection mechanism improves the adaptability and exploration ability of the population and reduces the risk of local optimality. The flexibility of the global fitness function allows the algorithm to adjust its strategy in real time according to reconnaissance needs. In addition, by optimizing the wolf pack position update strategy, the utilization of swarm intelligence is enhanced, and the search coordination and overall performance are improved. These improvements enable the present invention to exhibit greater adaptability and robustness in complex environments.

[0147] Furthermore, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention having equivalent elements, modifications, omissions, combinations (e.g., schemes where various embodiments intersect), adaptations, or changes. The elements in the claims are to be interpreted broadly based on the language employed in the claims and are not limited to the examples described in this specification or during the prosecution of this application, which examples are to be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, with the true scope and spirit being indicated by the following claims and the full scope of their equivalents.

[0148] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of their solutions) can be used in combination with each other. For example, those of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present invention. This should not be interpreted as an intention that a feature of an invention that is not claimed for protection is necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of the embodiments of a particular invention. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents to which these claims are entitled.

Claims

1. A UAV swarm mission planning method for regional coverage reconnaissance, characterized by: The method comprises: Determine a target area; wherein the target area is an area to be surveyed by the drone cluster; Performing rasterization processing on the target area to obtain a plurality of grid areas; Constructing a global fitness function, using an improved gray wolf optimization algorithm to simulate the wolf pack's hunting strategy, and evaluating the performance of each gray wolf using the fitness function; wherein the global fitness function is a weighted sum of area coverage and target point coverage, wherein the area coverage is determined based on the number of grid areas covered by the sensor, and the target point coverage is calculated by counting and normalizing the number of times each target point is covered by the sensor, where the target point is the location of the key target in the target area; Optimize the initial position of the wolf pack by initializing the gray wolf position using Latin hypercube sampling and amplifying the random sampling data proportionally to obtain the sample position that adapts to the specific search space; Introducing a nonlinear convergence factor; the nonlinear convergence factor is used to adjust the search range according to the number of iterations; Based on a dynamic alpha wolf selection strategy, a alpha wolf is dynamically selected; wherein the alpha wolf includes alpha wolves, beta wolves, and delta wolves, the alpha wolf is selected from the wolves with the highest fitness value, the beta wolf is randomly selected from the top 10% of the fitness value, and the delta wolf is randomly selected from the top 10% to 20% of the fitness value; the fitness value is calculated according to the global fitness function; Based on the wolf pack position update strategy, the positions of all gray wolves are updated, and the fitness value of the gray wolf pack is recalculated based on the global fitness function and the dynamic leader selection strategy is executed. The wolf pack position update strategy is implemented until the set termination condition is reached, and the optimal gray wolf position and corresponding fitness value are output to obtain the UAV cluster area coverage reconnaissance mission planning scheme; The nonlinear convergence factor is expressed as: Where a represents the convergence factor, t max Represents the maximum number of iterations, e represents a natural constant, and t represents the current number of iterations.

2. The method according to claim 1, characterized in that Based on the dynamic alpha selection strategy, the alpha is selected dynamically, including: Randomly generate a group of gray wolves, each gray wolf represents a potential solution x, and calculate its fitness value; Define α wolf as the individual with the highest fitness, and select α wolf using the following formula: α=argmaxf(x i ) (1) Among them, f(x i ) represents the i-th gray wolf x i The fitness value function of Beta wolves are selected using the following formula: β=RandomSelect({x i |f(x i )∈Top10%\α}) (2) Among them, RandomSelect means random selection within the range, TOP10%\α means excluding the wolf α from the wolves ranked in the top 10% of fitness values; The δ wolf is selected by the following formula: δ=RandomSelect({x i |f(x i )∈Top10%to20%}) (3) Among them, Top10% to 20% represents the wolf packs with the top 10% to 20% fitness values.

3. The method according to claim 1, characterized in that The wolf pack position update strategy includes a common wolf pack update strategy, a delta wolf update strategy, a beta wolf update strategy and an alpha wolf update strategy.

4. The method according to claim 3, characterized in that The common wolf pack update strategy includes: In the gray wolf optimization algorithm, based on the positions of the three wolves α, β, and δ, the wolf pack disperses and searches, introducing a random value. When the random value is greater than 1, the wolf pack moves away from the prey and conducts a global search. The modeling formula for this process is as follows: D=|C·X P (t)-X i (t)| (4) X i (t+1)=X P (t)-A·D (5) Among them, t represents the current iteration number, A and C are coefficients, X P is the location of the prey, X i (t) is the position of the gray wolf individual in the tth iteration; X i (t+1) is the position of the gray wolf individual at the t+1th iteration; X p (t) represents the location of the prey; D represents the distance between the leader wolf and other individuals; The coefficients A and C are calculated using the following formula: A=2a·r1-a (6) C＝2·r2 (7) Where r1 and r2 are random values ​​in [0, 1], a is the convergence factor, and A is a random value in the interval [-a, a], where a decreases from 2 to 0 during the iteration process; The hunting is carried out by α, β, and δ wolves. The mathematical model of the gray wolf tracking the location of the prey is described as follows: D n =|C1·X n -X i | (8) Among them, D n represents the distance between the nth wolf leader and other wolves; X n Represents the current position of the nth wolf leader; C1 is a random number, X i is the current location of the individual gray wolf; Calculate X1, X2, and X3 using the following formula: X1=X α -A1·D α (9) X2=X β -A2·D β (10) X3=X δ -A3·D δ (11) Among them, X1, X2, and X3 represent the positions of the wolves after being adjusted by the α wolf, β wolf, and δ wolf, respectively; X α 、X β 、X δ Represents the current positions of α wolf, β wolf, and δ wolf respectively; D α 、D β 、D δ Respectively represent the distances between α wolf, β wolf, δ wolf and other wolf packs; A1, A2, A3 represent the control factors of the adjustment amplitude of the wolf pack to α wolf, β wolf, δ wolf position respectively; Update the position of the gray wolf individual using the following formula: Among them, W α represents the influence weight of wolf α, W β represents the influence weight of β wolf, W δ represents the influence weight of δwolves, f α represents the fitness value of wolf α, f β represents the fitness value of β wolf, f δ represents the fitness value of δ wolf, X i (t+1) is the position of the gray wolf individual in the t+1th iteration.

5. The method according to claim 3, characterized in that The δ wolf update strategy includes: updating the position of δ wolf by the following formula: Among them, X δ (t+1) is the updated position of wolf δ; X1 and X2 are the adjusted positions of the wolves under the influence of wolf α and wolf β respectively, and W α represents the influence weight of wolf α, W β represents the influence weight of β wolf, f α represents the fitness value of wolf α, f β Represents the fitness value of β wolf.

6. The method according to claim 3, characterized in that The β wolf update strategy includes: updating the position of the β wolf by the following formula: X β (t+1)=X1(1+Ν(0,1)cos(2πC1)) (17) Among them, X β (t+1) is the updated position of wolf β, Ν(0,1) is a random value drawn from the standard normal function, C1 is a random number, and X1 represents the adjusted position of the wolf pack under the influence of wolf α.

7. The method according to claim 3, characterized in that The α wolf update strategy includes: updating the position of the α wolf by the following formula: u=σΝ(0,1) (19) v=Ν(0,1) (20) Where Γ represents the gamma function, N(0,1) represents a random value drawn from the standard normal distribution, γ is the Mantegna index, σ is the standard deviation of the Lévy distribution, u and v are random variables from the standard normal distribution, and X α (t+1) and X α (t) represents the old and new positions of wolf α, respectively.

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