Formation control method and device for realizing multi-target task by drone cluster cooperation

Abstract

The application discloses a formation control method and device for realizing multi-target tasks by cooperation of a UAV cluster. The formation control method comprises the following steps: acquiring initial information of multiple target tasks and basic information of a UAV cluster for executing the multiple target tasks; obtaining cluster grouping data of the UAV cluster when executing the multiple target tasks according to the initial information; obtaining task allocation data of each cluster group according to the cluster grouping data and the initial information; obtaining a reference path of each UAV cluster according to the task allocation data, the cluster grouping data and the initial information; setting a virtual leader based on the reference path, and obtaining expected position and speed data of each UAV for different target tasks according to the reference path and the virtual leader; and obtaining a formation shape of each UAV after reaching a corresponding expected position at a corresponding expected speed according to the basic information of the UAV cluster, the expected position and speed data of each UAV.

Description

Technical Field

[0001] This disclosure generally relates to the field of unmanned aerial vehicle (UAV) swarm technology, and more specifically, to a formation control method and apparatus for UAV swarms to collaboratively achieve multi-target tasks. Background Technology

[0002] In related fields, with the rapid development of artificial intelligence technology, unmanned swarms, represented by drones, are being applied more and more widely in various industries and fields. An unmanned swarm refers to a networked group composed of multiple autonomous or remotely controlled unmanned systems (e.g., drones, unmanned vehicles, unmanned ships, etc.). These unmanned systems, through advanced communication, perception, collaboration, and autonomous decision-making, can achieve efficient information sharing, collaborative operations, and autonomous task execution. Supported by swarm intelligence technology, each swarm intelligence unit can change according to different task requirements and application scenarios. Through ubiquitous networks, it can access anytime, adapt autonomously, flexibly group, and dynamically adjust to form human-machine hybrid and autonomous unmanned swarm systems, thus possessing diverse application capabilities.

[0003] As an important application of unmanned swarm systems, the configuration generation and formation control of unmanned aerial vehicle (UAV) swarms have attracted much attention in various practical applications. Summary of the Invention

[0004] The embodiments of this disclosure provide a formation control method and apparatus for drone swarm collaboration to achieve multi-target tasks, which can realize independent control of multiple drone formations in multi-target, multi-task scenarios.

[0005] In one general aspect, a formation control method for collaborative multi-target task implementation by a drone swarm is provided. The formation control method includes: acquiring initial information of multiple target tasks and basic information of the drone swarm executing the multiple target tasks; the initial information including location information, value attribute information, and area information of multiple target task areas; inputting the initial information into a swarm determination model based on a cuckoo search algorithm to obtain swarm grouping data of the drone swarm when executing the multiple target tasks; inputting the swarm grouping data and the initial information into a task allocation model based on an auction algorithm to obtain task allocation data for each swarm group; and inputting the task allocation data and the swarm grouping data into a task allocation model based on an auction algorithm to obtain task allocation data for each swarm group; and inputting the task allocation data and the swarm grouping data into a task allocation model based on an auction algorithm. The grouping data and the initial information are input into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster. Based on the reference path, a virtual navigator is set for each UAV cluster, and the reference path and the virtual navigator are input into a configuration generation model based on the navigator-follower relationship to obtain the expected position and expected speed data of each UAV for different target tasks. The basic information of the UAV cluster and the expected position and expected speed data of each UAV are input into the formation control model based on the biomimetic swarm algorithm to obtain the formation of each UAV after arriving at the corresponding expected position at the corresponding expected speed, so as to complete the formation control for each target task.

[0006] Optionally, the formation control method may further include: in response to a decrease in the communication quality of a portion of the drones in the drone swarm to below a preset threshold, using the drones in the drone swarm other than the portion of drones as an updated drone swarm for performing the multiple target tasks, and performing the formation control method based on the updated drone swarm to obtain an updated formation, thereby completing the formation update, wherein the preset threshold indicates that the communication quality is defined as the lower limit of the communication quality corresponding to the weak communication situation.

[0007] Optionally, the step of inputting the initial information into a cluster determination model based on the cuckoo search algorithm to obtain cluster grouping data of the UAV cluster when performing the multiple target tasks may include: constructing a task area attribute parameter matrix, an optimization space, and a task area objective function based on the initial information; performing the following processing for each iteration until a preset convergence condition is met: randomly generating an initial search space including multiple initial solutions, wherein the solutions included in the search space represent the number of UAVs to be allocated in each task area; substituting the initial search space, the task area attribute parameter matrix, and the optimization space into the task area objective function, and retaining the solutions corresponding to the higher task area objective function values ​​in the next generation; randomly updating the search space and removing a portion of the solutions using a global Lévy flight strategy, and retaining the solutions that are not removed in the next generation; generating new solutions based on a local random walk strategy; calculating the fitness value of the new solutions based on an adaptive scaling factor parameter, and retaining the solutions corresponding to the higher fitness values ​​in the next generation; in response to meeting the preset convergence condition, taking the current solution as the optimal solution, and obtaining the cluster grouping data of the UAV cluster when performing the multiple target tasks based on the optimal solution.

[0008] Optionally, the step of inputting the cluster group data and the initial information into the task allocation model based on the auction algorithm to obtain the task allocation data for each cluster group may include: constructing a task allocation optimization function based on the cluster group data and the initial information; and obtaining the task allocation data for each cluster group under the condition of maximizing the value of the task allocation optimization function through the auction algorithm based on the task allocation optimization function.

[0009] Optionally, the step of inputting the task allocation data, the cluster grouping data, and the initial information into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster may include: calculating the initial position and target position of each UAV cluster for each target task based on the task allocation data, the cluster grouping data, and the initial information; and calculating the repulsive force of the obstacle area and the attractive force of the target point in the scene corresponding to each target task based on the artificial potential field method to obtain a reference path for each UAV cluster from the initial position to the target position.

[0010] Optionally, the step of inputting the reference path and the virtual navigator into a configuration generation model based on the navigator-follower relationship to obtain the expected position and expected speed data of each UAV for different target tasks may include: obtaining the expected formation topology based on the reference path and the virtual navigator, according to the preset geometric constraint information between the navigator and the follower based on the navigator-follower relationship; and obtaining the expected position and expected speed data of each UAV for different target tasks based on the expected formation topology.

[0011] Optionally, the step of inputting the basic information of the drone swarm, the expected position and expected speed data of each drone into the formation control model based on the biomimetic swarm algorithm, and obtaining the formation of each drone after it arrives at its expected position at the corresponding expected speed, may include: initializing the model parameters of the formation control model based on the biomimetic swarm algorithm based on the basic information of the drone swarm, the expected position and expected speed data of each drone, and determining whether each drone is located at its corresponding expected position; in response to determining that all drones are located at their corresponding expected positions, obtaining the formation of each drone arriving at its corresponding expected position at the corresponding expected speed. The subsequent formation; in response to the determination that there are drones not in their corresponding expected positions, for each drone not in its expected position, the following processes are performed iteratively: calculate the communication connection relationship between drones to obtain the communication connection matrix between drones; based on the communication connection matrix between drones, calculate the attraction of the corresponding expected position to the drones and the interaction force between drones; based on the attraction of the corresponding expected position to the drones and the interaction force between drones, calculate the net force on each drone; based on the net force on each drone, calculate the acceleration of each drone, thereby obtaining the velocity and position of each drone at the next moment.

[0012] In another general aspect, a formation control device for collaborative multi-target task implementation by a drone swarm is provided. The formation control device includes: a data acquisition module configured to acquire initial information of multiple target tasks and basic information of the drone swarm executing the multiple target tasks, the initial information including location information, value attribute information, and area information of multiple target task areas; a swarm grouping module configured to input the initial information into a swarm determination model based on a cuckoo search algorithm to obtain swarm grouping data of the drone swarm when executing the multiple target tasks; a task allocation module configured to input the swarm grouping data and the initial information into a task allocation model based on an auction algorithm to obtain task allocation data for each swarm group; and a path generation module configured to: The task allocation data, the cluster grouping data, and the initial information are input into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster. The configuration generation module is configured to: based on the reference path, set a virtual navigator for each UAV cluster, and input the reference path and the virtual navigator into a configuration generation model based on the navigator-follower relationship to obtain the expected position and expected speed data for each UAV for different target tasks. The formation control module is configured to: input the basic information of the UAV cluster, the expected position and expected speed data of each UAV into the formation control model based on the biomimetic swarm algorithm to obtain the formation of each UAV after arriving at its expected position at the corresponding expected speed, thereby completing the formation control for each target task.

[0013] Optionally, the formation control device may also be configured to: in response to a decrease in the communication quality of a portion of the drones in the drone swarm to below a preset threshold, use the drones in the drone swarm other than the portion of drones as an updated drone swarm for performing the multiple target tasks, and execute the formation control method described above based on the updated drone swarm to obtain an updated formation, thereby completing the update of the formation, wherein the preset threshold indicating communication quality is defined as the lower limit of communication quality corresponding to weak communication conditions.

[0014] Optionally, the operation of the cluster grouping module to input the initial information into a cluster determination model based on the cuckoo search algorithm to obtain the cluster grouping data of the UAV cluster when performing the multiple target tasks may include: constructing a task area attribute parameter matrix, an optimization space, and a task area objective function based on the initial information; performing the following processing for each iteration cycle until a preset convergence condition is met: randomly generating an initial search space including multiple initial solutions, wherein the solutions included in the search space represent the number of UAVs to be allocated in each task area; substituting the initial search space, the task area attribute parameter matrix, and the optimization space into the task area objective function, and retaining the solutions corresponding to the higher task area objective function values ​​in the next generation; randomly updating the search space and removing a portion of the solutions using a global Lévy flight strategy, and retaining the solutions that are not removed in the next generation; generating new solutions based on a local random walk strategy; calculating the fitness value of the new solution based on an adaptive scaling factor parameter, and retaining the solutions corresponding to the higher fitness values ​​in the next generation; in response to meeting the preset convergence condition, taking the current solution as the optimal solution, and obtaining the cluster grouping data of the UAV cluster when performing the multiple target tasks based on the optimal solution.

[0015] Optionally, the task allocation module may input the cluster group data and the initial information into the task allocation model based on the auction algorithm to obtain the task allocation data for each cluster group. This operation may include: constructing a task allocation optimization function based on the cluster group data and the initial information; and obtaining the task allocation data for each cluster group under the condition of maximizing the value of the task allocation optimization function through the auction algorithm based on the task allocation optimization function.

[0016] Optionally, the path generation module inputs the task allocation data, the cluster grouping data, and the initial information into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster. This operation may include: calculating the initial position and target position of each UAV cluster for each target task based on the task allocation data, the cluster grouping data, and the initial information; and calculating the repulsive force of the obstacle area and the attractive force of the target point in the scene corresponding to each target task based on the artificial potential field method to obtain a reference path for each UAV cluster from its initial position to its target position.

[0017] Optionally, the configuration generation module's operation of inputting the reference path and the virtual navigator into a configuration generation model based on the navigator-follower relationship to obtain the expected position and expected speed data of each UAV for different target tasks may include: obtaining the expected formation topology based on the reference path and the virtual navigator, according to the preset geometric constraint information between the navigator and the follower based on the navigator-follower relationship; and obtaining the expected position and expected speed data of each UAV for different target tasks based on the expected formation topology.

[0018] Optionally, the formation control module inputs the basic information of the UAV swarm, the expected position and expected speed data of each UAV, into the formation control model based on the biomimetic swarm algorithm to obtain the formation formation after each UAV arrives at its expected position at the corresponding expected speed. This operation may include: initializing the model parameters of the formation control model based on the basic information of the UAV swarm, the expected position and expected speed data of each UAV, and determining whether each UAV is located at its expected position; in response to determining that all UAVs are located at their expected positions, obtaining the formation formation after each UAV arrives at its expected position at the corresponding expected speed. The formation after the expected position; in response to the determination that there are drones not in the corresponding expected position, for each drone not in the corresponding expected position, the following processes are performed iteratively: calculate the communication connection relationship between drones to obtain the communication connection matrix between drones; based on the communication connection matrix between drones, calculate the attraction of the corresponding expected position to the drones and the interaction force between drones; based on the attraction of the corresponding expected position to the drones and the interaction force between drones, calculate the resultant force on each drone; based on the resultant force on each drone, calculate the acceleration of each drone, thereby obtaining the velocity and position of each drone at the next moment.

[0019] In another general aspect, a computer program product is provided, comprising a computer program / instruction that, when executed by a processor, implements the formation control method described above for collaborative multi-target task implementation in a swarm of unmanned aerial vehicles.

[0020] In another general aspect, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device / server, enables the electronic device / server to perform the formation control method described above for collaborative multi-objective task implementation by a swarm of unmanned aerial vehicles.

[0021] In another general aspect, an electronic device is provided, comprising: at least one processor; and at least one memory storing computer-executable instructions, wherein, when executed by the at least one processor, the computer-executable instructions cause the at least one processor to perform the formation control method described above for collaborative multi-target task implementation by a swarm of unmanned aerial vehicles.

[0022] The formation control method and apparatus for collaborative multi-target missions by unmanned aerial vehicle (UAV) swarms according to embodiments of this disclosure can achieve independent control of multiple UAV swarms in multi-target, multi-task scenarios. Furthermore, by rapidly achieving formation maneuver control of large-scale UAV swarms in different scenarios, it can efficiently complete various tasks such as area reconnaissance and coverage. Attached Figure Description

[0023] The above and other objects and features of the embodiments of this disclosure will become clearer from the following description taken in conjunction with the accompanying drawings illustrating the embodiments, wherein:

[0024] Figure 1 This is a flowchart illustrating a formation control method for collaborative multi-target task implementation by unmanned aerial vehicle (UAV) swarms according to an embodiment of the present disclosure;

[0025] Figure 2 This is a flowchart illustrating an example of a formation control method for collaborative multi-target task implementation by unmanned aerial vehicle (UAV) swarms according to embodiments of the present disclosure;

[0026] Figure 3 This is an example flowchart illustrating a cuckoo search algorithm according to an embodiment of the present disclosure;

[0027] Figure 4 This is an example flowchart illustrating a formation control method based on a biomimetic swarming algorithm according to an embodiment of the present disclosure;

[0028] Figures 5A to 5C This is a diagram illustrating the aggregation-dispersion and communication connection relationships of a drone swarm in a biomimetic swarming algorithm according to an embodiment of the present disclosure;

[0029] Figures 6A to 6E This is a schematic diagram illustrating simulation results of a biomimetic formation according to an embodiment of the present disclosure;

[0030] Figures 7A to 7C This is a schematic diagram illustrating the formation reconstruction process of a biomimetic formation after encountering interference according to an embodiment of the present disclosure;

[0031] Figure 8 This is a block diagram illustrating a formation control device for collaborative multi-target task implementation by a swarm of unmanned aerial vehicles (UAVs) according to an embodiment of the present disclosure;

[0032] Figure 9This is a block diagram illustrating an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0033] The following detailed embodiments are provided to aid the reader in gaining a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be changed as will become clear upon understanding this disclosure, except for operations that must occur in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0034] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings, examples of which are illustrated in the drawings, wherein the same reference numerals always refer to the same parts. The embodiments will now be described with reference to the accompanying drawings in order to explain this disclosure.

[0035] As mentioned above, in related technologies, UAV swarm configuration generation and formation control are important application areas of UAV swarm systems, with great potential in various practical applications. This disclosure proposes a biomimetic swarm configuration generation and formation control method for multi-UAV coverage reconnaissance, combining biomimetic features with UAV formation to achieve stable control of large-scale UAV formations, and to realize formation change and formation motion control under weak communication conditions.

[0036] The following reference Figures 1 to 9 This disclosure provides a detailed description of a formation control method and apparatus for collaborative multi-target task implementation by unmanned aerial vehicle (UAV) swarms, based on embodiments of the present disclosure.

[0037] Figure 1 This is a flowchart illustrating a formation control method 100 for collaborative multi-target task implementation by unmanned aerial vehicle (UAV) swarms according to an embodiment of the present disclosure.

[0038] Reference Figure 1 According to an embodiment of this disclosure, in step S101, initial information of multiple target tasks and basic information of the UAV swarm used to execute the multiple target tasks are obtained. Here, the initial information includes location information, value attribute information, and area information of the multiple target task areas, etc.

[0039] According to an embodiment of this disclosure, in step S102, initial information is input into a cluster determination model based on the cuckoo search algorithm to obtain cluster grouping data of the UAV cluster when performing multiple target tasks.

[0040] As an example, step S102 may further include steps S1021 to S1023:

[0041] In step S1021, based on the initial information, the task area attribute parameter matrix, optimization space, and task area objective function are constructed.

[0042] In step S1022, for each iteration cycle, the following processing is performed until the preset convergence condition is met:

[0043] In processing S21, an initial search space is randomly generated, which includes multiple initial solutions. The solutions in the search space represent the number of drones that need to be allocated to each task area.

[0044] In processing S22, the initial search space, the task region attribute parameter matrix, and the optimization space are substituted into the task region objective function, and the solution corresponding to the higher task region objective function value is retained in the next generation.

[0045] Here, the optimization variables in the objective function of the task area include the number of drones required for each task area, and also consider factors such as the value attributes of the task area, so as to optimize the number of drones required for each task area by using the Cuckoo Search algorithm.

[0046] When dealing with S23, the global Lévy flight strategy is used to randomly update the search space.

[0047] In processing S24, a new solution is generated based on a local random walk strategy.

[0048] In processing S25, based on the adaptive scaling factor parameter, the fitness value of the new solution is calculated, and the solution corresponding to the higher fitness value is retained for the next generation.

[0049] In step S1023, in response to satisfying the preset convergence condition, the current solution is taken as the optimal solution, and the cluster grouping data of the UAV cluster when performing multiple target tasks is obtained based on the optimal solution.

[0050] In other words, in the cuckoo search algorithm disclosed herein, through iterative optimization, the number of drones required for each task area is obtained when the preset number of iterations or the preset convergence condition is reached, under the condition of maximizing the objective function (e.g., the search benefit function) of the task area. This completes the large-scale drone swarm grouping and drone swarm formation (also known as grouping).

[0051] The following section will use a reconnaissance mission targeting multiple mission areas, which is required for the operation of a UAV, as an example to illustrate the formation control method disclosed herein.

[0052] Specifically, firstly, a task area attribute parameter matrix can be constructed, as shown in equation (1) below:

[0053]

[0054] Where, λ i For the value attribute of the task area, σ i For prior intelligence information of the mission area, ε i is the area parameter of the task area, and n is the number of task areas.

[0055] Then, an optimization space can be constructed, S = [T, N, C, R], that is, the optimization space includes the mission execution time T, the number of UAVs required for the mission N, the total reconnaissance mission path C, and the mission reward R. However, this disclosure is not limited to this and may also include other parameters.

[0056] In addition, a mission area reconnaissance mission benefit function can be constructed, as shown in equations (2) and (3) below:

[0057]

[0058] n i =t o / ∑t i *∑n i (3)

[0059] Wherein, the execution time of each task area is T = [t1t2t] i …t n ], v i For the drone's flight speed, C i =σ i / ∑σ i n i The number of drones required for each reconnaissance zone.

[0060] Based on the aforementioned task area attribute parameter matrix, optimization space, and task area reconnaissance mission benefit function, this disclosure improves the traditional cuckoo search algorithm for cluster multi-factor optimization problems. The improved cuckoo search algorithm is described as follows: (a) to (c)

[0061] (a) Each cuckoo lays only a few eggs at a time.

[0062] (b) Bird nests of high quality (explanation) will be preserved for the next generation.

[0063] (c) The number of available master birds, m, is fixed, and the probability that a master bird discovers an outsider's egg is P. a , where P a ∈[0,1]; When the owner bird discovers an intruder's egg, it can choose to discard the egg or abandon the nest altogether and build a new nest in a new location. In other words, this assumption can be understood as P being the number of nests in m nests. a ×m bird nests will be replaced by new bird nests.

[0064] In addition, you can refer to Figure 3 To understand the cuckoo search algorithm according to embodiments of this disclosure, Figure 3 This is an example flowchart illustrating a cuckoo search algorithm according to an embodiment of the present disclosure. The specific steps are shown in blocks S301 to S308, and will not be repeated here.

[0065] Therefore, the Cuckoo Search algorithm comprises three elements: selecting the optimal solution, employing local random movement, and performing random selection through global Lévy flight. In other words, this disclosure improves the Cuckoo Search algorithm for combinatorial optimization problems involving multiple coupled variables, thereby optimizing the resource allocation of large-scale UAVs.

[0066] The improved cuckoo search algorithm will be described in detail below from these three elements.

[0067] First, m solutions are randomly generated, each containing m×n variables. The search strategy space for these solutions is shown in equation (4) below:

[0068]

[0069] The range of the optimization variable can be set, for example, t = [t min ,t max In addition, the algorithm supports mixed optimization of continuous and discrete variables.

[0070] Secondly, each row in the search policy space is substituted into the task reward function R to calculate the policy reward value, and solutions with higher quality will be retained in the next generation.

[0071] Then, the global Lévy flight strategy is used to randomly update the search solution space. Here, some solutions may be discarded with probability Pa. Furthermore, new solutions are generated based on a local random walk strategy. Here, solutions that are not discarded are retained for the next generation.

[0072] Finally, the fitness values ​​of the new solutions are calculated, and based on the elite retention strategy, solutions with higher fitness values ​​are selected and retained for the next generation.

[0073] The following sections will elaborate on the three processing steps involved in the above algorithm (i.e., the process of selecting the optimal solution, the process of global Lévy flight random selection, and the process of generating a new solution through local random movement).

[0074] (I) On the process of selecting the optimal option

[0075] The optimal solution is selected by preserving better solutions. For example, an elite retention strategy ensures that the search movement is always within the range of local optima, so that the optimal solution is retained to the next generation and is not removed from the population.

[0076] (II) The process of random selection for global Lévy flights

[0077] The purpose of global Levy flight, a unique feature of the Cuckoo Search algorithm, is to allow for significant changes to variables during the optimization process, effectively preventing the optimization from getting trapped in local optima. The global random search process is executed according to the Levy flight process, that is, following:

[0078]

[0079] in, This indicates the position of population i in the current iteration (i.e., generation t). Let L(β) denote the position of the next update (i.e., the (t+1)th generation), and let L(β) denote the stochastic optimization direction of the Lévy distribution, whose step size α follows a Lévy distribution, i.e., Lévy = t 1-β (0 < β ≤ 2).

[0080] Furthermore, the cuckoos' continuous hopping here creates a random walk process, namely:

[0081]

[0082] Where u follows a normal distribution σ v =1,

[0083] In this disclosure, in order to fully preserve high-quality solutions and enable the algorithm to converge faster, this application proposes to improve the parameter β in the algorithm. That is, in this disclosure, β can be adaptively adjusted according to the optimization process, so that the obtained solutions have strong diversity in the early iteration process, and the diversity of solutions is gradually reduced in the later iteration process to better perform local search. Based on the above analysis, the adaptive scaling factor can be defined as shown in the following equation (7):

[0084]

[0085] Where β0 is the initial parameter, To determine the task reward for population i in the initial stage, The task reward for population i in generation t-1. This represents the initial total reward for the population participating in the optimization. This represents the total reward for the (t-1)th generation of the population participating in the optimization.

[0086] (III) The process of generating new solutions through local random shifts

[0087] In cases where solutions are discarded after the random selection of Lévy flight, this disclosure employs local random shifts to generate new solutions. The process of generating new solutions through local random shifts can be described by the following equation (8):

[0088]

[0089] in, For the (t+1)th generation, the i-th solution Let be the i-th solution in the t-th generation, and α be the step size (e.g., usually 1). Let H be two random number sequences, H be the Heaviside function, ε be a random number, and Pa be the probability of being discovered in the Cuckoo Search algorithm.

[0090] Through the above two random selection processes, the optimal solution is obtained through iterative cycles, thereby obtaining the optimal number of drones required for each task area and realizing the grouping of large-scale drone swarms.

[0091] In the cluster determination method based on the cuckoo search algorithm disclosed herein, to more closely approximate real-world applications, a resource allocation method based on the cuckoo search optimization algorithm is proposed, taking into account the attributes of the task areas for multiple target tasks (also known as task scenarios) and the biomimetic characteristics of UAVs. A biomimetic adaptive adjustment mechanism for optimization parameters is employed to achieve rapid convergence of the optimization curve, thereby enabling rapid resource allocation for large-scale UAVs while maximizing allocation benefits. Furthermore, the cluster determination method based on the cuckoo search algorithm disclosed herein effectively avoids the problem of wasted UAV resources due to different task area attributes.

[0092] According to an embodiment of this disclosure, in step S103, cluster group data and initial information are input into a task allocation model based on an auction algorithm to obtain task allocation data for each cluster group.

[0093] As an example, step S103 may further include steps S1031 to S1032:

[0094] In step S1031, a task allocation optimization function is constructed based on cluster grouping data and initial information.

[0095] In step S1032, based on the task allocation optimization function, the task allocation data for each cluster group is obtained through an auction algorithm while satisfying the maximization of the task allocation optimization function value.

[0096] Regarding task allocation, based on the number of drones required for each task area and the grouping of large-scale drones, it is determined that the number of task areas is greater than the number of drone groups. Therefore, a drone group needs to perform reconnaissance tasks in multiple task areas. Thus, this disclosure primarily addresses the "many-to-many" task allocation problem. By constructing a task allocation optimization function, the optimization objective is to minimize the total path of drone movement, i.e., to minimize the path of the drone swarm from one task area to the next. Furthermore, based on an auction algorithm, a task allocation method is implemented that maximizes task allocation benefits while ensuring conflict-free drone swarm grouping.

[0097] Based on an auction algorithm, task allocation is performed for UAV formations. The main objective of this task allocation is to minimize the movement path of the UAV formations between task areas.

[0098] Specifically, first, a task allocation optimization function is constructed, as shown in equation (9) below:

[0099]

[0100] Among them, t ij Let λ1 represent the travel time of the UAV formation from mission area i to mission area j, λ2 represent the weight corresponding to the travel time, and λ1+λ2=1. V represents the value allocation parameter for the area j to be covered. j This represents the task benefit index parameter for task area j.

[0101] Based on the task allocation optimization function, the task sequence for each UAV group is obtained through an auction algorithm, as follows:

[0102]

[0103] Each row contains the drone group number, n id The task area number for the drone group.

[0104] According to embodiments of this disclosure, a task allocation method based on an auction algorithm is used to achieve large-scale task allocation for unmanned aerial vehicles based on kinematic constraints and biological characteristics.

[0105] According to an embodiment of this disclosure, in step S104, task allocation data, cluster grouping data, and initial information are input into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster.

[0106] As an example, step S104 may further include steps S1041 to S1042:

[0107] In step S1041, based on task allocation data, cluster grouping data and initial information, the initial position and target position of each UAV cluster for each target task are calculated.

[0108] In step S1042, based on the artificial potential field method, the repulsive force of obstacles in the scene corresponding to each target task and the attractive force of the target point are calculated to obtain the reference path of each UAV cluster from the initial position to the target position.

[0109] Specifically, for example, based on the navigator-follower theory, a virtual navigator is set up for each drone swarm. Based on the drone task allocation results, initial position, and target position information, and using the artificial potential field method, the repulsive force of obstacles in the task scenario and the attractive force of the target point are calculated. This yields the reference path for the navigator drone swarm (hereinafter referred to as the navigator drone) from its initial position to its target position. The specific calculation process is as follows:

[0110] First, the attractive potential field of the target point to the navigator drone is calculated, as shown in equation (11) below:

[0111]

[0112] Where, ρ 2 (q,q goal ξ is the distance between the target location and the current location of the navigator drone, and ξ is a preset parameter. The attraction force is the derivative of the attraction potential field with respect to distance, which is expressed as shown in equation (12) below:

[0113]

[0114] Then, the repulsive force field of the obstacle zone edge on the navigator UAV is calculated, as shown in equation (13) below:

[0115]

[0116] Where ρ(q,q) obs ) represents the distance between the obstacle zone and the current position of the navigator drone, and η is a preset parameter. The repulsive force is the derivative of the repulsive potential field with respect to distance, and it is expressed as shown in equation (14) below:

[0117]

[0118] Therefore, the net force acting on the Navigator UAV can be expressed as shown in equation (15) below:

[0119] F(q)=F att (q)+F rep (q) (15)

[0120] In summary, by calculating the net force acting on the drone through attractive and repulsive forces, the drone's acceleration is obtained, and thus its position is determined. This process continues until the navigator drone reaches the target location, completing the path planning for that location.

[0121] Using the path generation model based on the artificial potential field method, the repulsive force of the obstacle area and the attractive force of the target point in the task scenario are calculated based on the UAV task allocation results, initial position and target position, thereby obtaining the reference path from the initial position to the target position for each UAV cluster.

[0122] According to an embodiment of this disclosure, in step S105, a virtual navigator is set for each drone cluster based on a reference path, and the reference path and the virtual navigator are input into a configuration generation model based on the navigator-follower relationship to obtain the expected position and expected speed data of each drone for different target tasks.

[0123] As an example, step S105 may further include steps S1051 to S1052:

[0124] In step S1051, based on the reference path and the virtual navigator, the expected formation topology is obtained according to the preset geometric constraint information between the navigator and the follower based on the navigator-follower relationship.

[0125] In step S1052, based on the expected formation topology, the expected position and expected speed data of each UAV for different target tasks are obtained.

[0126] Using the configuration generation model based on the leader-follower relationship described above, the desired position of the drone swarm formation is constructed according to the reference path of each drone swarm. Based on the geometric constraint information between the leader and followers, multiple topologies for the entire formation can be formed, thereby obtaining the desired position of each drone in the formation.

[0127] According to an embodiment of this disclosure, in step S106, the basic information of the drone swarm, the expected position and expected speed data of each drone are input into the formation control model based on the biomimetic swarm algorithm to obtain the formation of each drone after it arrives at the corresponding expected position at the corresponding expected speed, so as to complete the formation control for each target task.

[0128] As an example, step S106 may further include steps S1061 to S1063:

[0129] In step S1061, based on the basic information of the drone swarm, the expected position and expected speed data of each drone, the formation control model based on the biomimetic swarm algorithm is initialized with model parameters, and it is determined whether each drone is located at the corresponding expected position.

[0130] In step S1062, in response to determining that all drones are in their respective expected positions, the formation of each drone after arriving at its respective expected position at its respective expected speed is obtained.

[0131] In step S1063, in response to determining that there are drones not located at the corresponding expected locations, for each drone not located at the corresponding expected location, the following processes S61 to S64 are performed cyclically:

[0132] In processing S61, the communication connection relationship between UAVs is calculated to obtain the communication connection matrix between UAVs.

[0133] In processing S62, based on the communication connection matrix between UAVs, the attractive force of the corresponding expected location on the UAVs and the interaction force between the UAVs are calculated.

[0134] In processing S63, the net force on each drone is calculated based on the attractive force of the corresponding expected location on the drone and the interaction force between the drones.

[0135] In processing S64, based on the net force acting on each drone, the acceleration of each drone is calculated, thereby obtaining the velocity and position of each drone at the next moment.

[0136] Furthermore, the formation control method disclosed herein is used to perform biomimetic formation control on each UAV group, achieving large-scale UAV formation control based on the desired positions of the UAV swarm formation. The specific implementation process is as follows:

[0137] In general, the cluster behavior in this disclosure is constructed through the following three rule constraints:

[0138] Rule Constraint 1 - Avoid Collisions: Drones will maintain a certain distance from each other. When the distance is too close, adjacent drones will generate a corresponding repulsive force; when the distance is too far, adjacent drones will generate an attractive force.

[0139] Rule Constraint 2 - Convergence to the Center: Drones will move closer to the surrounding group.

[0140] Rule Constraint 3 - Speed ​​Consistency: The speed of each drone is the same as the average speed of its neighboring drones.

[0141] Specifically, refer to Figure 4 , Figure 4 This is an example flowchart illustrating a formation control method based on a biomimetic swarm algorithm according to an embodiment of the present disclosure.

[0142] First, the parameters of the biomimetic swarm algorithm (S401 and S402) are obtained and initialized. The distance between UAVs in the formation is calculated. The communication connection relationship of the UAVs is obtained based on the communication distance of the UAVs, and the communication connection matrix of the UAV swarm (S404 and S405) is calculated. Here, the method for calculating the communication connection matrix is ​​shown in equations (16) to (21) below:

[0143] a ij (p)=Φ z (z)*nrb∈[0,1] (16)

[0144] Where, Φ z (z) represents the functional function, with the following specific limitations:

[0145] Φ z (z)=ρ h (z / r α (17)

[0146] Where z = ||p j -p i || σ Represents the distance between drones (where p) j p i (These are the positions of the two drones, respectively), r α nrb is a constant, and nrb is the communication relationship between the two UAVs (its expression is shown in equation (20)).

[0147] The σ norm is defined as shown in equation (18) below:

[0148]

[0149] In addition, ρ h (z) is a uniformly changing bump function, specifically defined as shown in equation (19) below:

[0150]

[0151] Then, determine the distance between the drones; if they are within communication range, the two drones are considered adjacent.

[0152]

[0153] Where r_c is the communication distance of the UAV.

[0154] Therefore, the communication connection matrix a of the UAV swarm is obtained. ij (p) is defined as shown in equation (21) below:

[0155] a ij (p)=ρ h (∥pj -p i ∥ σ / r α )*nrb∈[0,1] (21)

[0156] The control inputs of the biomimetic formation control algorithm disclosed herein include the attractive force of the target position on the UAV (S406) and the interaction force between the UAVs (S408). Based on the communication connection matrix of the UAV swarm, if there is a communication relationship between two UAVs, there is an interaction force, and simultaneously, the target position also has an attractive force on the UAV. The specific control input variables can be expressed as shown in equation (22) below:

[0157]

[0158] in, For the interaction forces between drones, The attractiveness of the target location to the drone.

[0159] Here, the interaction force between UAVs consists of two parts: a gradient-based part and a uniform part, as shown in equation (23) below:

[0160]

[0161] Where, p j p i These represent the positions of two drones in the drone swarm, n. ij Let represent the number of neighbors (i.e., adjacent drones) of the i-th drone, as shown in equation (24) below:

[0162]

[0163] In addition, Φ α (z) is the function defined by the following equation (25):

[0164] Φ α (z)=a ij (p)Φ(zd α (25)

[0165]

[0166] Among them, a ij (p) represents the communication connection matrix between the drone swarm, and z represents the distance between the drones. d α a, b, and c are all preset constants.

[0167] In this disclosure, the attraction control input of the target point to the UAV is as shown in equation (27) below:

[0168]

[0169] Where c1 and c2 are preset parameters, v i v t These represent the speed of the drone and the speed of the target point, respectively.

[0170] By calculating the net force acting on the UAVs (S409), the acceleration of the UAVs is obtained, leading to their velocity and position at the next moment (S412), thus updating the formation position. Furthermore, based on the reference path and the proposed leader-follower theory-based configuration generation method, the position information of the virtual leader is updated. The UAV formation position is iteratively updated, and based on a biomimetic swarm algorithm, the aggregation-dispersion process of the UAVs in the formation is completed, driving the UAVs to reach the target position sequentially without collisions, achieving formation operation. This completes the construction of the biomimetic swarm configuration generation and formation control method.

[0171] Based on the above-mentioned formation control model based on the biomimetic swarm algorithm, and using the expected positions of the UAV swarm formation obtained above, large-scale UAV formation control can be achieved.

[0172] According to the formation control method disclosed herein, the shortcomings of the prior art in lacking an overall design for optimizing task allocation and biomimetic control of large-scale UAV swarms can be overcome, thereby realizing the optimization of task allocation for large-scale UAVs, biomimetic swarm formation control, and formation reorganization and control under weak communication conditions.

[0173] Furthermore, according to embodiments of this disclosure, the formation control method may further include the following processing: in response to a decrease in the communication quality of a portion of the drones in the drone swarm to below a preset threshold, the drones in the drone swarm other than a portion of the drones are treated as an updated drone swarm for performing multiple target tasks; based on the updated drone swarm, the above-described steps of the formation control method are performed to obtain an updated formation, thereby completing the formation update. Here, the preset threshold indicating communication quality is defined as the lower limit of communication quality corresponding to a weak communication situation.

[0174] In other words, when UAV communication is interfered with, the method disclosed herein can achieve formation reorganization of UAV swarms and formation maneuver control under weak communication conditions. Specifically, when UAV swarm communication encounters interference, based on the formation control method, the UAV swarm can adjust its formation in a timely manner to complete formation reorganization under weak communication conditions. Furthermore, the UAV swarm can change to different formations at any time to achieve stable formation control.

[0175] For example, when drones pass through an interference zone, the communication network of the drone swarm is disrupted, and some drones lose communication (the communication quality is so poor that communication is impossible), thus breaking the original formation. In this situation, given the existing number of drones (including those capable of normal communication and those capable of weak communication), a configuration generation method based on the leader-follower relationship can obtain the desired position of the new formation. This allows the original drone formation to change its formation structure through reorganization, thereby achieving self-healing of the damaged drone swarm formation.

[0176] The following is for reference Figure 2 An example of a formation control method for collaborative multi-target missions using unmanned aerial vehicle (UAV) swarms, according to this disclosure, is described. Figure 2 This is a flowchart illustrating an example of a formation control method for collaborative multi-target missions using unmanned aerial vehicle (UAV) swarms, according to embodiments of the present disclosure.

[0177] Reference Figure 2 In step S201, based on the initial position and task attribute value, the drone cluster is divided into groups according to the cuckoo search method, thus completing the drone cluster formation grouping.

[0178] In step S202, based on the grouping situation, and under the condition of maximizing task allocation benefits, task allocation for drone swarm grouping is performed using an auction algorithm.

[0179] In step S203, after completing the drone swarm grouping and task allocation, a reference path is generated for each drone swarm based on the artificial potential field method.

[0180] In step S204, based on the reference path, a virtual navigator is set for each UAV cluster group, and the desired position and desired speed of each UAV in the group are obtained based on the navigator-follower configuration generation method.

[0181] In step S205, based on the biomimetic swarm algorithm, the aggregation-dispersion process of the UAVs in the formation is completed, and the UAVs are driven to reach the target position in sequence without collision, forming a formation to achieve formation maneuver control.

[0182] In step S206, based on the formation control algorithm, when the communication of the UAV swarm encounters interference, the UAV swarm can adjust its formation in a timely manner to complete the formation reorganization under weak communication. At the same time, the UAV swarm can change different formations at any time to achieve stable formation control.

[0183] Here, it should be noted that the above regarding Figure 2 The descriptions of the various steps are merely exemplary, and the steps in the method according to this disclosure are not limited thereto.

[0184] The following section describes the effectiveness of the formation control method disclosed herein based on simulation results. Figures 5A to 5C This is a diagram illustrating the aggregation-dispersion and communication connection relationships of a drone swarm in a biomimetic swarming algorithm according to an embodiment of the present disclosure. Figures 6A to 6E This is a schematic diagram illustrating simulation results of a biomimetic formation according to an embodiment of the present disclosure, and Figures 7A to 7C This is a schematic diagram illustrating the formation reconstruction process of a biomimetic formation after encountering interference, according to an embodiment of the present disclosure.

[0185] Reference Figures 5A to 5C , Figure 5A The aggregation status of the drone swarm is shown. Figure 5B This shows the state where the cluster begins to separate. Figure 5C This shows the status of the drone swarm having separated.

[0186] Reference Figures 6A to 6E , Figure 6A A schematic diagram illustrating the construction of communication relationships within a drone swarm is shown. Figure 6B A schematic diagram illustrating the construction of a drone swarm target formation is shown. Figure 6C A schematic diagram showing the movement of a drone swarm target formation is provided. Figure 6D This diagram illustrates an example of constructing and maintaining motion in a "V" formation of a drone swarm. Figure 6E This diagram illustrates another example of how a drone swarm can be constructed in a V-formation and maintained in motion.

[0187] Reference Figures 7A to 7C , Figure 7A The diagram illustrates how a drone swarm's "V" formation can be disrupted by communication interference. Figure 7B This diagram illustrates the process of a drone swarm gradually reconnecting after communication is disrupted. Figure 7C A schematic diagram illustrating the formation changes and maintenance of a drone swarm is shown.

[0188] In the simulation verification of this disclosure, it is assumed that all electromagnetic reconnaissance and communication equipment carried by the UAVs are functioning normally. The simulation results show that the formation control method proposed in this disclosure can achieve independent control of multiple UAV formations in multi-target, multi-mission scenarios. By employing various optimized control algorithms as described above, an effective framework for biomimetic swarm configuration generation and formation control for multi-UAV coverage reconnaissance is proposed.

[0189] Figure 8 This is a block diagram illustrating a formation control device 800 for collaborative multi-target missions of unmanned aerial vehicle swarms according to an embodiment of the present disclosure.

[0190] Reference Figure 8According to embodiments of the present disclosure, a formation control device 800 for unmanned aerial vehicle (UAV) swarm collaboration to achieve multi-target tasks may include a data acquisition module 810, a swarm grouping module 820, a task allocation module 830, a path generation module 840, a configuration generation module 850, and a formation control module 860.

[0191] According to embodiments of this disclosure, the data acquisition module 810 can perform the following operations: acquire initial information of multiple target tasks and basic information of a drone swarm used to perform the multiple target tasks. Here, the initial information includes location information, value attribute information, and area information of the multiple target task areas.

[0192] According to an embodiment of this disclosure, the cluster grouping module 820 can perform the following: input initial information into a cluster determination model based on the cuckoo search algorithm to obtain cluster grouping data of the UAV cluster when performing multiple target tasks.

[0193] As an example, the cluster grouping module 820 inputs initial information into a cluster determination model based on the cuckoo search algorithm to obtain cluster grouping data of the UAV cluster when performing multiple target tasks. This operation may include operations 821) to 823).

[0194] In operation 821), based on the above initial information, the task area attribute parameter matrix, optimization space, and task area objective function are constructed.

[0195] In operation 822), for each iteration cycle, perform the following processes 1) to 5) until the preset convergence condition is met: 1) Randomly generate an initial search space including multiple initial solutions, where the solutions in the search space represent the number of UAVs to be allocated in each task area; 2) Substitute the initial search space, the task area attribute parameter matrix, and the optimization space into the task area objective function, and retain the solutions corresponding to the higher task area objective function values ​​in the next generation; 3) Using the global Lévy flight strategy, randomly update the search space and remove a portion of the solutions, and retain the solutions that are not removed in the next generation; 4) Generate new solutions based on the local random walk strategy; 5) Calculate the fitness value of the new solutions based on the adaptive scaling factor parameter, and retain the solutions corresponding to the higher fitness values ​​in the next generation.

[0196] In operation 823), in response to the satisfaction of the preset convergence condition, the current solution is taken as the optimal solution, and the cluster grouping data of the UAV cluster when performing multiple target tasks is obtained based on the optimal solution.

[0197] According to an embodiment of this disclosure, the task allocation module 830 can perform the following: inputting cluster group data and initial information into a task allocation model based on an auction algorithm to obtain task allocation data for each cluster group.

[0198] As an example, the task allocation module 830 inputs cluster group data and initial information into the task allocation model based on the auction algorithm to obtain task allocation data for each cluster group. The operation may include: constructing a task allocation optimization function based on the cluster group data and initial information; and obtaining task allocation data for each cluster group under the condition of maximizing the value of the task allocation optimization function through the auction algorithm based on the task allocation optimization function.

[0199] According to an embodiment of this disclosure, the path generation module 840 can perform the following: input task allocation data, cluster grouping data and initial information into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster.

[0200] As an example, the path generation module 840 inputs task allocation data, cluster grouping data, and initial information into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster. This operation may include: calculating the initial position and target position of each UAV cluster for each target task based on the task allocation data, cluster grouping data, and initial information; and calculating the repulsive force of the obstacle area and the attractive force of the target point in the scene corresponding to each target task based on the artificial potential field method to obtain a reference path for each UAV cluster from the initial position to the target position.

[0201] According to an embodiment of this disclosure, the configuration generation module 850 can perform the following: based on a reference path, set a virtual navigator for each UAV cluster, and input the reference path and the virtual navigator into a configuration generation model based on a navigator-follower relationship to obtain the expected position and expected speed data of each UAV for different target tasks.

[0202] As an example, the configuration generation module 850 inputs the reference path and virtual navigator into the configuration generation model based on the navigator-follower relationship to obtain the expected position and expected velocity data of each UAV for different target tasks. This operation may include: obtaining the expected formation topology based on the reference path and virtual navigator, according to the preset geometric constraint information between the navigator and follower based on the navigator-follower relationship; and obtaining the expected position and expected velocity data of each UAV for different target tasks based on the expected formation topology.

[0203] According to embodiments of this disclosure, the formation control module 860 can perform the following: inputting basic information of the drone swarm, the expected position and expected speed data of each drone into a formation control model based on a biomimetic swarming algorithm, to obtain the formation of each drone after it arrives at its expected position at the corresponding expected speed, so as to complete the formation control for each target task.

[0204] As an example, the formation control module 860 inputs the basic information of the drone swarm, the expected position and expected speed data of each drone into the formation control model based on the biomimetic swarm algorithm, and the operation to obtain the formation of each drone after it arrives at the corresponding expected position at the corresponding expected speed may include operations 861) to 863):

[0205] In Operation 861), based on the basic information of the drone swarm, the expected position and expected speed data of each drone, the model parameters of the formation control model based on the biomimetic swarm algorithm are initialized, and it is determined whether each drone is located at the corresponding expected position.

[0206] In operation 862), in response to determining that all drones are in their respective expected positions, the formation of each drone after arriving at its respective expected position at its respective expected speed is obtained.

[0207] In operation 863), in response to the determination that there are drones not located in the corresponding expected positions, for each drone not located in the corresponding expected positions, the following processes 1) to 4) are performed cyclically: 1) Calculate the communication connection relationship between drones to obtain the communication connection matrix between drones; 2) Based on the communication connection matrix between drones, calculate the attraction of the corresponding expected position to the drones and the interaction force between drones; 3) Based on the attraction of the corresponding expected position to the drones and the interaction force between drones, calculate the net force on each drone; 4) Based on the net force on each drone, calculate the acceleration of each drone, thereby obtaining the velocity and position of each drone at the next moment.

[0208] Furthermore, according to embodiments of this disclosure, the formation control device may also be configured to: respond to a decrease in the communication quality of a portion of the drones in the drone swarm to below a preset threshold, treat the drones in the drone swarm other than a portion of the drones as an updated drone swarm for performing multiple target tasks, and execute the formation control method described above based on the updated drone swarm to obtain an updated formation, thereby completing the update of the formation.

[0209] Here, the preset threshold indicating communication quality is defined as the lower limit of communication quality corresponding to weak communication scenarios.

[0210] It should be noted that the operations performed on the above structural frames can be compared with those in the reference section. Figure 1 The related content is similar, so I will not repeat it here.

[0211] Figure 9 This is a block diagram illustrating an electronic device 900 according to an embodiment of the present disclosure.

[0212] Reference Figure 9An electronic device 900 according to embodiments of the present disclosure may include a processor 910 and a memory 920. The processor 910 may include (but is not limited to) a central processing unit (CPU), a digital signal processor (DSP), a microcomputer, a field-programmable gate array (FPGA), a system-on-a-chip (SoC), a microprocessor, an application-specific integrated circuit (ASIC), etc. The memory 920 may store computer-executable instructions to be executed by the processor 910. The memory 920 includes high-speed random access memory and / or a non-volatile computer-readable storage medium. When the processor 910 executes the computer-executable instructions stored in the memory 920, the formation control method for collaborative multi-target mission implementation by a swarm of unmanned aerial vehicles (UAVs) as described above can be implemented.

[0213] The formation control method for collaborative multi-target task implementation by unmanned aerial vehicle (UAV) swarms according to embodiments of this disclosure can be written as a computer program / instructions to form a computer program product and stored on a computer-readable storage medium. When the computer program / instructions are executed by a processor, the formation control method for collaborative multi-target task implementation by UAV swarms as described above can be implemented. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device / server, the electronic device / server is enabled to execute the formation control method for collaborative multi-target task implementation by UAV swarms as described above. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-RLTH, B D-RE, Blu-ray or optical disc storage, hard disk drive (HDD), solid-state drive (SSD), card storage (such as multimedia cards, secure digital (SD) cards, or ultra-fast digital (XD) cards), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, and any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and to provide the computer program and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the computer program. In one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system, such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner through one or more processors or computers.

[0214] The formation control method for collaborative multi-target task implementation of UAV swarms according to embodiments of the present disclosure can realize independent control of multiple UAV swarms in multi-target, multi-task scenarios.

[0215] On the other hand, the formation control method and apparatus for collaborative multi-target missions of UAV swarms according to the embodiments of this disclosure can efficiently complete various tasks such as regional reconnaissance and coverage by rapidly realizing the formation maneuver control of large-scale UAV swarms in different scenarios.

[0216] On the other hand, the formation control method and apparatus for drone swarm collaboration to achieve multi-target tasks according to the embodiments of this disclosure can realize drone swarm formation resource optimization, task allocation, path planning and independent formation control in multi-target and multi-task scenarios.

[0217] On the other hand, the formation control method and apparatus for collaborative multi-target missions by unmanned aerial vehicle (UAV) swarms according to embodiments of this disclosure can generate biomimetic configurations for different scenarios and maintain formation motion. Furthermore, in situations with weak communication, it can quickly complete formation reorganization and configuration transformation, achieving the effect of intelligent emergence of individual weak UAV swarm behavior, thus achieving a breakthrough in the technology of intelligent emergence and evolution of unmanned aerial vehicle (UAV) swarm behavior in multi-domain operations.

[0218] While some embodiments of this disclosure have been disclosed and described, those skilled in the art will understand that modifications and variations may be made to these embodiments without departing from the concept and spirit of this disclosure, which is defined by the claims and their equivalents.

Claims

1. A formation control method for collaborative multi-target task implementation by unmanned aerial vehicle (UAV) swarms, characterized in that, The formation control method includes: Acquire initial information for multiple target tasks and basic information for a drone swarm used to execute the multiple target tasks. The initial information includes location information, value attribute information, and area information of the multiple target task areas. The initial information is input into a cluster determination model based on the cuckoo search algorithm to obtain cluster grouping data of the UAV cluster when performing the multiple target tasks; The cluster group data and the initial information are input into the task allocation model based on the auction algorithm to obtain the task allocation data for each cluster group. The task allocation data, the cluster grouping data, and the initial information are input into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster. Based on the reference path, a virtual navigator is set for each drone cluster, and the reference path and the virtual navigator are input into a configuration generation model based on the navigator-follower relationship to obtain the expected position and expected speed data of each drone for different target tasks. The basic information of the drone swarm, the expected position and speed data of each drone are input into the formation control model based on the biomimetic swarm algorithm to obtain the formation of each drone after it arrives at the corresponding expected position at the corresponding expected speed, so as to complete the formation control for each target task.

2. The formation control method according to claim 1, characterized in that, The formation control method also includes: In response to a situation where the communication quality of a portion of the drones in the drone swarm drops below a preset threshold, the remaining drones in the swarm are treated as an updated drone swarm for executing the multiple target tasks. Based on this updated drone swarm, the formation control method is executed to obtain an updated formation, thus completing the formation update. The preset threshold indicating communication quality is defined as the lower limit of communication quality corresponding to weak communication conditions.

3. The formation control method according to claim 1, characterized in that, The step of inputting the initial information into a cluster determination model based on the cuckoo search algorithm to obtain cluster grouping data of the UAV cluster when performing the multiple target tasks includes: Based on the initial information, construct the task area attribute parameter matrix, optimization space, and task area objective function; For each iteration, perform the following processing until the preset convergence condition is met: An initial search space is randomly generated, which includes multiple initial solutions. The solutions in the search space represent the number of drones that need to be allocated to each task area. Substitute the initial search space, the task region attribute parameter matrix, and the optimization space into the task region objective function, and retain the solution corresponding to the higher task region objective function value in the next generation; Using a global Raven flight strategy, the search space is randomly updated and a portion of the solutions are removed. The solutions that are not removed are retained for the next generation. New solutions are generated based on a local random walk strategy; Based on the adaptive scaling factor parameter, the fitness value of the new solution is calculated, and the solution corresponding to the higher fitness value is retained in the next generation; In response to the satisfaction of the preset convergence condition, the current solution is taken as the optimal solution, and the cluster grouping data of the UAV cluster when performing the multiple target tasks is obtained based on the optimal solution.

4. The formation control method according to claim 1, characterized in that, The step of inputting the cluster group data and the initial information into the task allocation model based on the auction algorithm to obtain task allocation data for each cluster group includes: Based on the cluster grouping data and the initial information, a task allocation optimization function is constructed; Based on the task allocation optimization function, the task allocation data for each cluster group is obtained through an auction algorithm while maximizing the value of the task allocation optimization function.

5. The formation control method according to claim 1, characterized in that, The step of inputting the task allocation data, the cluster grouping data, and the initial information into a path generation model based on the artificial potential field method to obtain a reference path for each UAV cluster includes: Based on the task allocation data, the cluster grouping data, and the initial information, calculate the initial position and target position of each UAV cluster for each target task; Based on the artificial potential field method, the repulsive force of the obstacle area and the attractive force of the target point in the scene corresponding to each target task are calculated to obtain the reference path of each drone cluster from the initial position to the target position.

6. The formation control method according to claim 1, characterized in that, The step of inputting the reference path and the virtual navigator into a configuration generation model based on the navigator-follower relationship to obtain the expected position and expected speed data of each UAV for different target tasks includes: Based on the reference path and the virtual navigator, and according to the preset geometric constraint information between the navigator and the follower based on the navigator-follower relationship, the expected formation topology is obtained. Based on the expected formation topology, the expected position and speed data of each UAV for different target missions are obtained.

7. The formation control method according to claim 1, characterized in that, The step of inputting the basic information of the drone swarm, the expected position and expected speed data of each drone into the formation control model based on the biomimetic swarm algorithm, and obtaining the formation of each drone after it arrives at its expected position at the corresponding expected speed includes: Based on the basic information of the drone swarm, the expected position and expected speed data of each drone, the formation control model based on the biomimetic swarm algorithm is initialized with model parameters, and it is determined whether each drone is located at the corresponding expected position. In response to determining that all drones are in their respective expected positions, the formation of each drone after it arrives at its respective expected position at its respective expected speed is obtained. In response to the determination that there are drones not located in the corresponding expected locations, for each drone not located in the corresponding expected location, the following process is performed cyclically: Calculate the communication connections between drones to obtain the communication connection matrix between drones; Based on the communication connection matrix between drones, the attractive force of the corresponding expected location on the drones and the interaction force between the drones are calculated. Based on the attractive force of the corresponding expected location on the drone and the interaction force between the drones, calculate the net force on each drone; Based on the net force acting on each drone, the acceleration of each drone is calculated, thereby obtaining the velocity and position of each drone at the next moment.

8. A formation control device for collaborative multi-target task implementation by a swarm of unmanned aerial vehicles (UAVs), characterized in that, The formation control device includes: The data acquisition module is configured to acquire initial information of multiple target tasks and basic information of the UAV cluster used to execute the multiple target tasks. The initial information includes location information, value attribute information and area information of the multiple target task areas. The cluster grouping module is configured to: input the initial information into a cluster determination model based on the cuckoo search algorithm to obtain cluster grouping data of the UAV cluster when performing the multiple target tasks; The task allocation module is configured to input the cluster group data and the initial information into the task allocation model based on the auction algorithm to obtain task allocation data for each cluster group. The path generation module is configured to input the task allocation data, the cluster grouping data and the initial information into the path generation model based on the artificial potential field method to obtain the reference path for each UAV cluster. The configuration generation module is configured to: set a virtual navigator for each UAV cluster based on the reference path, and input the reference path and the virtual navigator into a configuration generation model based on the navigator-follower relationship to obtain the expected position and expected speed data of each UAV for different target tasks; The formation control module is configured to input the basic information of the UAV cluster, the expected position and expected speed data of each UAV, into the formation control model based on the biomimetic swarm algorithm to obtain the formation of each UAV after arriving at the corresponding expected position at the corresponding expected speed, so as to complete the formation control for each target task.

9. A computer program product, characterized in that, The computer program product includes a computer program / instruction that, when executed by a processor, implements the formation control method for collaborative multi-target task implementation of unmanned aerial vehicle (UAV) swarms as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes: at least one processor; at least one memory storing computer-executable instructions, wherein, when executed by the at least one processor, the computer-executable instructions cause the at least one processor to perform the formation control method for collaborative multi-target task implementation of any one of claims 1 to 7.

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