Unmanned aerial vehicle cluster intelligent cooperative control method, system, device and medium

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle cluster intelligent cooperative control method, system and device and a medium. The method comprises the following steps: establishing a detection model of an unmanned aerial vehicle cluster detection task, and establishing an aggregation model of an unmanned aerial vehicle cluster aggregation task; taking the relative distance and the relative angle between the unmanned aerial vehicles in the unmanned aerial vehicle cluster as optimization parameters, and performing iterative solution on the detection model and the aggregation model to obtain the optimal relative distance and the optimal relative angle under the detection task and the aggregation task; through the optimal relative distance and the optimal relative angle, respectively determining the optimal formation under the detection task and the aggregation task; the formation controller of the unmanned aerial vehicle cluster is constructed by taking the minimum energy consumption and the minimum path length during switching of the optimal formation under the detection task and the aggregation task as targets, and the unmanned aerial vehicle cluster is controlled by the formation controller to execute the detection task and the aggregation task in the corresponding optimal formation, so that the working efficiency of the unmanned aerial vehicle cluster is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) control, and particularly to an intelligent cooperative control method, system, device and medium for a UAV swarm. Background Art

[0002] UAV swarm cooperative control refers to forming a swarm of multiple UAVs through means such as sensing technology, communication technology, and cooperative algorithms, and completing common goals and tasks through mutual communication and cooperation. This control method can significantly improve the task execution efficiency of UAVs.

[0003] With the rapid development of technology, UAV swarms have broad application prospects in fields such as environmental monitoring and protection, agricultural management and plant protection, emergency rescue and search. In the initial stage of operation, the UAV swarm first conducts cooperative detection on the target area; once the target is detected, the swarm members will quickly assemble to the target area in a new formation to complete the cooperative operation, which can significantly improve the information processing ability, task completion autonomy and flexibility.

[0004] However, most of the existing research on UAV swarm cooperative control is based on existing desired formations for control, and these studies do not deeply explore how to optimize the formation and flexibly change the formation according to the needs of tasks in different stages, which affects the overall work efficiency. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent cooperative control method, system, device and medium for a UAV swarm, which can solve the technical problem that the formation cannot be optimized and flexibly changed according to the needs of tasks in different stages, resulting in low overall work efficiency.

[0006] To solve the above technical problem, an embodiment of the present invention provides an intelligent cooperative control method for a UAV swarm, including the following steps: Establish a detection model for the detection task according to the horizontal detection range and vertical detection range of the UAV swarm in the detection task, and establish an assembly model for the assembly task according to the assembly range and assembly density of the UAV swarm in the assembly task; Taking the relative distance and relative angle between each UAV in the UAV swarm as optimization parameters, and taking the maximum horizontal detection range and maximum vertical detection range as optimization objectives, iteratively solve the detection model, and taking the maximum assembly range and maximum assembly density as optimization objectives, iteratively solve the assembly model, so as to respectively obtain the optimal relative distance and optimal relative angle in the detection task and the assembly task; Determine the optimal formation in the detection task and the assembly task respectively through the optimal relative distance and optimal relative angle in the detection task and the assembly task; With the goal of minimizing energy consumption and path length during the optimal formation switching between the detection task and the aggregation task, a formation controller for the UAV swarm is constructed, and the UAV swarm is controlled by the formation controller to execute the detection task and the aggregation task in the corresponding optimal formation.

[0007] Optionally, the detection model is: ; In the formula, is the lateral detection range, is the longitudinal detection range, is the weight of the lateral detection range, is the weight of the longitudinal detection range; ; In the formula, represents the maximum lateral distance between the leader and any follower in the lateral plane of the UAV swarm L the maximum value of, represents the lateral detection radius of the leader and the lateral detection radius of the follower the sum; ; In the formula, represents the maximum detection distance of the leader, represents the maximum detection distance of the follower, represents the maximum detection angle of the leader, represents the maximum detection angle of the follower; ; In the formula, represents the maximum longitudinal distance between the leader and any follower in the longitudinal plane H the maximum value of, is the longitudinal detection radius of the leader and the longitudinal detection radius of the follower the sum, .

[0008] Optionally, the aggregation model is: ; In the formula, is the aggregation range, is the aggregation density, is the weight of the aggregation range, is the weight of the aggregation density; ; In the formula, represents the area actually covered by the formation, is the maximum area that can be covered by the formation of the same shape; ; In the formula, D represents the distance between the leader and the last follower gathered at the same target, T represents the expected time difference between the leader and the last follower gathered at the same target, represents the maximum flight speed of the UAV.

[0009] Optionally, the formation controller is designed based on the linear quadratic optimal control theory; The formation controller is: ; In the formula, represents the control gain matrix, represents t the switching topology signal of the communication between UAVs at time represents t the neighbor set of the i th UAV at time represents t the adjacency matrix at time , represents the error state vector of the actual formation state of the i th follower deviating from the expected formation state, represents the error state vector of the actual formation state of the j th follower deviating from the expected formation state, represents the state vector of the i th UAV, represents the spatial coordinate of the i th UAV, represents the velocity vector of the i th UAV, m is the preset spatial dimension, , respectively represent the position vector and velocity vector of the i th follower relative to the leader in the expected formation state, represents the expected control input of the i th follower in the expected formation state, and represents the derivative with respect to time; The control gain matrix of the formation controller is: ; In the formula, represents the minimum eigenvalue of the system Laplacian matrix at time represents a preset constant, and , represents a preset positive definite real symmetric matrix; is the positive solution of the following algebraic Riccati equation: ; In the formula, represents a preset positive semi - definite real symmetric matrix, .

[0010] Optionally, the weights of the lateral detection range, the longitudinal detection range, the aggregation range, and the aggregation density in the detection model and the aggregation model are all determined by the triangular fuzzy number analytic hierarchy process.

[0011] Optionally, the optimal formation of the UAV cluster is a plane - symmetric triangular formation or a plane - linear formation.

[0012] An embodiment of the present invention also provides an intelligent cooperative control system for a UAV cluster, including: A model construction module, configured to establish a detection model for the detection task according to the lateral detection range and the longitudinal detection range of the UAV cluster under the detection task, and establish an aggregation model for the aggregation task according to the aggregation range and the aggregation density of the UAV cluster under the aggregation task; A parameter optimization module, configured to use the relative distance and relative angle between each UAV in the UAV cluster as optimization parameters, take the maximum lateral detection range and the maximum longitudinal detection range as optimization objectives, perform iterative solution on the detection model, and take the maximum aggregation range and the maximum aggregation density as optimization objectives, perform iterative solution on the aggregation model, so as to respectively obtain the optimal relative distance and the optimal relative angle under the detection task and the aggregation task; A formation determination module, configured to respectively determine the optimal formation of the UAV cluster under the detection task and the aggregation task through the optimal relative distance and the optimal relative angle under the detection task and the aggregation task; A cooperative control module, configured to construct a formation controller for the UAV cluster with the goal of minimizing energy consumption and minimizing path length when switching between the optimal formations of the detection task and the aggregation task, and control the UAV cluster to perform the detection task and the aggregation task respectively in the corresponding optimal formations through the formation controller.

[0013] An embodiment of the present invention also provides a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, instructions executable by the at least one processor are stored in the memory, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the above - mentioned intelligent cooperative control method for the UAV cluster.

[0014] An embodiment of the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned intelligent cooperative control method for an unmanned aerial vehicle (UAV) cluster.

[0015] The intelligent cooperative control method for an UAV cluster provided by the present invention has at least the following beneficial effects: By separating the detection task and the assembly task of the UAV cluster, establishing models corresponding to these two tasks respectively, and optimizing the relative distance and relative angle between each UAV in the UAV cluster, the optimal relative distance and the optimal relative angle of the UAV cluster under the detection task and the assembly task can be obtained. Based on the relative distance and relative angle, the formation shape of the UAV cluster can be determined, thereby obtaining the optimal formation shape under the detection task and the assembly task. This optimal formation shape can maximize the horizontal detection range and the vertical detection range of the detection task, and maximize the assembly range and the assembly density of the assembly task, thereby improving the working efficiency of the detection task and the assembly task of the UAV cluster. At the same time, when controlling the UAV cluster to execute the detection task and the assembly task respectively in the corresponding optimal formation shape, with the goal of minimizing the energy consumption and the path length of the transformation between the two optimal formation shapes, comprehensively considering the actual situation when the UAV cluster switches tasks, the working efficiency of the UAV cluster is further optimized, and the resources of the UAVs are saved.

[0016] Therefore, by controlling the UAV cluster to work based on this method, the UAV cluster can form an expected detection formation for cooperative detection. When a target is detected and the work task is converted to formation assembly, the cluster can be controlled to complete the formation reconstruction from the expected detection formation to the expected assembly formation, so as to execute more refined tasks in the target area, realize the smooth transformation of the formation shape, and ensure the maximization of the cooperative working efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] One or more embodiments are illustrated by way of example in the accompanying drawings, and these exemplary illustrations do not limit the embodiments.

[0018] Figure 1 is a flowchart of an intelligent cooperative control method for an UAV cluster according to an embodiment of the present invention; Figure 2 is a schematic diagram of an index system for formation cooperative working efficiency according to an embodiment of the present invention; Figure 3 is a schematic diagram of an UAV cluster formation shape according to an embodiment of the present invention; Figure 4 is a schematic diagram of a simulation result of a detection formation according to an embodiment of the present invention; Figure 5 It is a schematic diagram of the simulation result of the assembly formation according to an embodiment of the present invention; Figure 6 It is a schematic diagram of a system switching signal according to an embodiment of the present invention; Figure 7 It is an interactive topology diagram of a system according to an embodiment of the present invention; Figure 8 It is a schematic diagram of the simulation result of the detection formation control according to an embodiment of the present invention; Figure 9 It is a schematic diagram of the simulation result of the assembly formation control according to an embodiment of the present invention. Detailed implementation manners

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that in the embodiments of the present invention, many technical details are proposed to help readers better understand the present invention. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions required to be protected by the present invention can still be implemented. The following division of each embodiment is for convenience of description and should not constitute any limitation to the specific implementation manner of the present invention. Each embodiment can be combined and cross-referenced with each other without conflict.

[0020] An embodiment of the present invention relates to a method for intelligent cooperative control of an unmanned aerial vehicle (UAV) cluster. The specific process of the method for intelligent cooperative control of the UAV cluster in this embodiment can be as Figure 1 shown and includes: Step 101: Establish a detection model for the detection task according to the lateral detection range and the longitudinal detection range of the UAV cluster in the detection task, and establish an assembly model for the assembly task according to the assembly range and the assembly density of the UAV cluster in the assembly task.

[0021] Step 102: Use the relative distance and relative angle between each UAV in the UAV cluster as optimization parameters, and use the maximum lateral detection range and the maximum longitudinal detection range as optimization objectives to perform iterative solution on the detection model, and use the maximum assembly range and the maximum assembly density as optimization objectives to perform iterative solution on the assembly model, so as to respectively obtain the optimal relative distance and the optimal relative angle under the detection task and the assembly task.

[0022] Step 103: Determine the optimal formation of the UAVs under the detection task and the assembly task respectively through the optimal relative distance and the optimal relative angle under the detection task and the assembly task.

[0023] In step 104, aiming at minimizing the energy consumption and the path length when switching between the optimal formation shapes for the detection task and the assembly task, a formation controller for the UAV cluster is constructed, and the UAV cluster is controlled by the formation controller to execute the detection task and the assembly task respectively in the corresponding optimal formation shapes.

[0024] In this embodiment, the detection task and the assembly task of the UAV cluster are separated, models corresponding to these two tasks are established respectively, and the relative distances and relative angles between the UAVs in the UAV cluster are optimized. The optimal relative distances and optimal relative angles of the UAV cluster under the detection task and the assembly task can be obtained. Based on the relative distances and relative angles, the formation shapes of the UAV cluster can be determined, and thus the optimal formation shapes under the detection task and the assembly task are obtained. The optimal formation shapes can maximize the lateral detection range and the longitudinal detection range of the detection task, and maximize the assembly range and the assembly density of the assembly task, thereby improving the working efficiency of the UAV cluster in the detection task and the assembly task. At the same time, when controlling the UAV cluster to execute the detection task and the assembly task respectively in the corresponding optimal formation shapes, aiming at minimizing the energy consumption and the path length of the transformation between the two optimal formation shapes, the actual situation during the task switching of the UAV cluster is comprehensively considered, the working efficiency of the UAV cluster is further optimized, and the resources of the UAVs are saved.

[0025] Therefore, by controlling the UAV cluster to work based on this method, the UAV cluster can form the desired detection formation for collaborative detection. When a target is detected and the working task is converted to formation assembly, the cluster can be controlled to complete the formation reconstruction from the desired detection formation to the desired assembly formation, so as to execute more refined tasks in the target area, realize the smooth transformation of the formation shape, and ensure the maximization of the collaborative working efficiency.

[0026] The implementation details of the intelligent collaborative control method for the UAV cluster in this embodiment are specifically described below. The following content is only the implementation details provided for convenient understanding and is not necessary for implementing this solution.

[0027] In step 101, as Figure 2 shown, a formation collaborative working efficiency index system is established, and a detection model based on the detection task and an assembly model based on the assembly task are constructed respectively as follows: ; In the formula, , , , are the four indexes of the lateral detection range, the longitudinal detection range, the assembly range, and the assembly density respectively, , ,​ , are the weights of these four indicators.

[0028] Among them, for the detection model based on the detection task, in this embodiment, the detection range of the unmanned aerial vehicle (UAV) is taken as an example of a cone, and the generatrix length of the cone is the maximum detection distance of the UAV , and the included angle between the generatrix and the height of the cone is the maximum detection angle of the UAV .

[0029] First, it is stipulated that in the detection formation r the value range is as follows: ; In the formula, the subscript L represents the leader in the UAV cluster, F represents the follower in the UAV cluster.

[0030] The expression of the lateral detection range is as follows: ; In the formula, represents the maximum value of the lateral distance L between the leader and any follower in the UAV cluster in the lateral plane, represents the lateral detection radius of the leader and the sum of the lateral detection radius of the follower.

[0031] ; In the formula, represents the maximum detection distance of the leader, represents the maximum detection distance of the follower, represents the maximum detection angle of the leader, represents the maximum detection angle of the follower.

[0032] When is always less than or equal to , there is no blind area in detection. The larger is, the larger the lateral detection range is, the higher the formation cooperation efficiency is, and according to the optimization principle, the smaller the fitness is. When , there may be a blind area in detection. When , the targets within the range of the area

[0033] Longitudinal detection range The expression is as follows: ; In the formula, represents the maximum value of the longitudinal distance between the leader and any follower in the lateral plane H , is the longitudinal detection radius of the leader and the longitudinal detection radius of the follower sum, .

[0034] Regarding the aggregation model based on the aggregation task, first stipulate the value range of r in the aggregation formation: ; In the formula, represents the minimum maneuvering radius of a single UAV, represents the maximum flight speed of the UAV, represents the maximum available overload that the UAV can withstand.

[0035] Aggregation range The expression is as follows: ; In the formula, represents the area actually covered by the formation, is the maximum area that a formation of the same shape can cover.

[0036] In a triangular formation , ; In a single-file formation , where represents the width of the UAV, represents the length of the UAV, then when r takes the maximum value, S reaches the maximum value . Therefore The larger

[0037] Aggregation density The expression is as follows: ; In the formula, D represents the distance between the leader and the last follower gathered at the same target, T represents the expected time difference between the leader and the last follower gathered at the same target, represents the maximum flight speed of the UAV.

[0038] In a triangular formation, D = r, and in a linear formation, D = 2r. The smaller D is, the greater the concentration density, the higher the formation's collaborative working efficiency, and the lower the fitness.

[0039] In one example, 、 、 、 are the weights of these four indicators, all solved through the triangular fuzzy number analytic hierarchy process.

[0040] The specific calculation steps of the triangular fuzzy number analytic hierarchy process are as follows: (1) The number of indicators to be evaluated by experts is ones, and the number of invited experts is ones. The professional prestige of the nd expert is , and there is .

[0041] (2) Evaluation of expert importance relationships: Each expert evaluates the importance relationship between the i st indicator and the j rd indicator, and then obtains the extended triangular fuzzy number (where , is the lower bound of the value of the fuzzy number , is 's upper bound of the value, and is the value corresponding to the highest possibility of the fuzzy number).

[0042] Use the following formula to solve the importance relationship between the j nd and the i rd indicators.

[0043] .

[0044] Table 1 Triangular Fuzzy Number Evaluation Table (3) Calculate the triangular fuzzy number: The total triangular fuzzy number : ; (4) Calculate the fuzzy probability: ; In the formula, the possibility that the judgment is is 's times, and the possibility that is 's times. According to the empirical value, it is considered that , 。

[0045] (5) Calculate non-fuzzy numbers : ; (6) Perform reciprocity adjustment on non-fuzzy numbers: ; In the formula, is the non-fuzzy number of the i-th index relative to the j-th index after reciprocity adjustment, is the non-fuzzy number of the j-th index relative to the i-th index after reciprocity adjustment 7) Solve for weights: The weight i of the k-th index has the following expression: 。

[0046] In step 102, taking the relative distance and relative angle between each UAV in the UAV cluster as the formation parameters to be optimized, the particle swarm optimization algorithm is used to iteratively solve the above model (both the detection model and the aggregation model can be regarded as fitness functions). Before that, the following preprocessing operations need to be performed on the data:

[0047] ; In the formula, is the normalized value, is r the lower limit of the value of, is r the upper limit of the value of; represents the value of the k-th performance index of the UAV, i is the processed value, represents the power of 10.

[0048] The specific steps of the particle swarm optimization algorithm are as follows: Step 1 Initialization: Set the scale of the particle swarm, the speed and position limits of each particle, the number of iterations, the individual learning factor , the swarm learning factor , the inertia weight , and randomly initialize the speed and position of the particles. Set the spatial position limit and speed limit of the particles according to the variation range of the formation parameters.

[0049] Step 2 Calculate fitness: Call the fitness expression (i.e., the first fitness function and the second fitness function) to calculate the fitness value of each particle, where each parameter needs to be preprocessed according to the above formula.

[0050] Step 3 Update the individual optimal fitness: Compare the current fitness of each particle with its historical optimal fitness. If the current value is lower, immediately update the individual optimal value to ensure that the individual continuously approaches the optimal solution.

[0051] Step 4 Update the global optimal fitness: Take the minimum value of all individual optimal values as the global optimal value.

[0052] Step 5 Update velocity and position: The update mechanism follows the following mathematical formula: ; In the formula, the subscript represents the th particle, the subscript g represents the population, and the sub-subscript represents the th iteration. , are two random numbers generated by the computer. , , represent the inertia weight, individual learning factor, and social learning factor at the d th iteration. is the velocity and position of the th iteration of the th particle. represents the optimal position corresponding to the individual optimal fitness. represents the optimal position corresponding to the global optimal fitness.

[0053] is a non-linear decreasing weight, and its formula is as follows: ; In the formula, iter is the total number of iterations. The initial value of the inertia weight is relatively large, and the termination value is relatively small.

[0054] , are asymmetric learning factors, and their formula is as follows: ; In the formula, the initial value c of the individual learning factor is relatively large, and the termination value is relatively small. The initial value c of the social learning factor Smaller, termination value Larger.

[0055] Step 6 Iteration: Repeat Steps 2 to 5 until the number of iterations is reached. Finally, output a sub-optimal solution under the random number given by the current computer. At this time, the sub-optimal solution is the current sub-optimal formation parameter.

[0056] Step 7 Repetition: Repeat multiple groups of experiments. By comparing the results of multiple runs, select the solution corresponding to the lowest fitness value as the optimal solution, which is the optimal formation parameter.

[0057] Based on this, the optimal relative distance and optimal relative angle corresponding to the detection task and the assembly task can be obtained respectively.

[0058] In Step 103, through the optimal relative distance and optimal relative angle corresponding to the detection task and the assembly task respectively, the optimal formation of the UAV cluster under the detection task and the assembly task can be determined. Among them, the formation of the UAV cluster includes, for example Figure 3 The plane symmetric triangle formation and the plane one-line formation as shown. In the figure, the UAV cluster includes a leader UAV and two follower UAVs with exactly the same performance.

[0059] In Step 104, first describe the functions of each UAV in the UAV cluster and the communication connection relationship between the UAVs: Establish a formation model, the leader-follower model, and make the following regulations: Denote the leader number as 1, and the follower number as , and it is considered that the leader can transmit information to at least one follower. The interaction topology between the followers is undirected. In addition, it is considered that the leader has no external control input, and the followers have external control inputs.

[0060] Establish a communication topology model. Given the communication connection relationship between each UAV, the communication topology model of the system can be obtained, that is, the Laplacian matrix of the system .

[0061] In the formula, Represents the signal under the switching topology, Represents t The index of the graph at time , and it is considered that the signal switches at the switching time Represents t The interaction topology graph at time Represents t The adjacency matrix at time Represents t The degree matrix at time Represents t The Laplacian matrix at time denote t the i neighbor set of the

[0062] Establish a state equation model. The dynamic model of a single UAV in the system is: ; wherein, represents the spatial coordinates of the UAV, represents the velocity vector of the UAV, m is the spatial dimension; represents the control vector of the UAV, and .

[0063] Then the state equation of a single UAV in the system is as follows: ; wherein, , , , represents the i th state vector of the UAV.

[0064] Finally, in order to reduce the energy consumption and path length of the UAV swarm formation transformation, the following uses the optimal control principle under the quadratic performance index to design the formation controller as follows: The formation controller for any follower is: ; wherein, represents the error state vector of the actual state of the i th UAV deviating from the expected formation state; , respectively represent the position vector and velocity vector of the i th follower relative to the leader in the expected formation, where the expected formation is specified by the expected detection formation and the expected aggregation formation optimized by the particle swarm optimization algorithm; represents the expected control input of the i th follower in the expected formation, and .

[0065] The control gain matrix is: ; wherein, is the th smallest eigenvalue of the system Laplacian matrix at time is an arbitrarily given constant, is an arbitrarily given positive definite real symmetric matrix, is the positive solution of the following algebraic Riccati equation: ; wherein, is an arbitrarily given positive semi - definite real symmetric matrix.

[0066] The state equation of the linear system under the optimal control principle with quadratic performance index is The quadratic performance index is ; wherein, is a positive semi - definite real symmetric matrix, is a positive definite real symmetric matrix, then the optimal control is wherein, is the solution of the algebraic Riccati equation .

[0067] In this embodiment, the state equation of the linear system is , the first term of the performance index reflects the magnitude of the deviation of the formation error state vector from the equilibrium value. Since is a positive semi - definite matrix, this term is non - negative, and its integral with respect to time can reflect the length of the path during the process of the UAVs forming a formation; the second term of the performance index reflects the consumption of control power. Since is a positive definite matrix, this term is positive, and its integral with respect to time can reflect the energy consumption generated by the control action during the flight of the UAVs. Using Theorem 1 to design the formation controller can make the performance index reach the minimum value, thereby effectively reducing the energy consumption and path length of the UAV swarm formation transformation.

[0068] Next, the intelligent cooperative control method of the UAV swarm in this embodiment is verified by simulation: (1) Formation shape optimization simulation: It is known that , and the performance indexes of the UAVs are given as shown in Table 2: Table 2 Performance indexes of formation members wherein, represents the width of the UAV, represents the length of the UAV.

[0069] (2) Detection shape optimization simulation: According to the triangular fuzzy number analytic hierarchy process, the detection index weights are calculated as , .

[0070] In the triangular formation, the dimensionality of the particle motion space of the particle swarm optimization algorithm , let the population size the number of iterations ; The particle spatial position limit is , and the particle velocity limit is ; Set the initial value of the inertia weight w to 0.9 and the termination value to 0.4; the individual learning factor has an initial value of 2 and a termination value of 1, and the social learning factor has an initial value of 1 and a termination value of 2.

[0071] In the single-file formation, the particle motion space dimension of the particle swarm optimization algorithm , let the population size , and the number of iterations ; The particle spatial position limit is , and the particle velocity limit is ; The setting values of the inertia weight and learning factor are the same as those of the above triangular formation.

[0072] Using the particle swarm optimization algorithm for formation optimization simulation, the optimal fitness of the triangular formation is 0.0212, and the optimal fitness of the single-file formation is 0.1818. By comparison, it can be seen that the fitness of the triangular formation is lower and the formation coordination working efficiency is greater. Therefore, the triangular formation is selected as the optimal formation. The simulation results are as Figure 4 shown.

[0073] (3) Rendezvous formation optimization simulation: According to the triangular fuzzy number analytic hierarchy process, the rendezvous index weight is calculated as , .

[0074] The particle motion space dimension of the triangular formation , let the population size , and the number of iterations ; The particle spatial position limit is , and the particle velocity limit is ; Set the initial value of the inertia weight w to 0.9 and the termination value to 0.4; the individual learning factor has an initial value of 2 and a termination value of 1, and the social learning factor has an initial value of 1 and a termination value of 2.

[0075] The particle motion space dimension of the single-file formation , let the population size , and the number of iterations ; The particle spatial position limit is , and the particle velocity limit is ; The setting values of the inertia weight and learning factor are the same as above.

[0076] Using the particle swarm optimization algorithm for formation optimization simulation, the optimal fitness of the triangular formation is 0.1631, and the optimal fitness of the linear formation is 0.3778. By comparison, it can be seen that the fitness of the triangular formation is lower, so the triangular formation is selected as the optimal formation. The simulation results are as Figure 5 shown.

[0077] The optimal formation parameters and fitness design results are shown in Table 3: Table 3 Optimal formation design results (4)Formation control simulation: Communication environment description: Facing During the simulation of three unmanned aerial vehicles, the communication connection relationships among the three unmanned aerial vehicles are as follows: During: The leader unmanned aerial vehicle can transmit information to both followers normally, and the two follower unmanned aerial vehicles can also transmit information to each other; During: The communication channel between the leader and the follower numbered 3 is interfered, and it can only transmit information to the follower numbered 2. In addition, it is considered that the two followers can transmit information to each other normally; During, the communication channel between the leader and the follower numbered 3 is restored and can transmit information to the follower numbered 3. During this period, the communication sensing device of the follower numbered 2 fails and cannot receive the information from the leader. In addition, it is considered that the two followers can transmit information to each other normally.

[0078] Switching interaction topology representation: According to the description of the communication environment, the switching signal can be used Figure 6 to represent.

[0079] Therefore, During, the interaction topology graph of the system will switch as shown in Figure 7 shown.

[0080] From this, it can be obtained that the Laplacian matrix during is as follows: Set , , , from this, it can be obtained that .

[0081] Optimal formation control simulation for detection formation: In the ground coordinate system, the initial coordinates of the three unmanned aerial vehicles are all , in meters, and the initial speeds are all 0; according to the optimized simulation results of the detection formation, the expected position of the leader is set to , and the expected positions of the followers are respectively , , in meters, and the expected speeds are all 0.

[0082] The simulation results of the detection formation formation control are as Figure 8 shown.

[0083] Optimal formation control simulation of the assembly formation: The initial position of the leader is , and the initial positions of the followers are respectively , , in meters, and the initial speeds are all 0; according to the optimized simulation results of the assembly formation, the expected positions of the three are set to be respectively , , , in meters, and the expected speeds are all 0.

[0084] The simulation results of the assembly formation formation control are as Figure 9 shown.

[0085] It can be seen that the three drones start from the same initial position, first transform to the optimal formation based on the detection task, and then transform to form the optimal formation based on the assembly task. The formation transformation process is stable and easy to physically implement.

[0086] The advantages of the UAV swarm intelligent cooperative control method of the present invention are mainly reflected in the following four aspects: (1) High efficiency: The improved particle swarm optimization algorithm is used to optimize the formation parameters, enabling the swarm to perform detection and assembly tasks in the optimal formation, resulting in higher detection accuracy and assembly efficiency, thus ensuring the maximization of work efficiency.

[0087] (2) Low energy consumption: Based on the traditional distributed consensus algorithm, the optimal control algorithm is used to improve the formation controller, which can effectively reduce the energy consumption and path length of the UAV swarm formation transformation.

[0088] (3) Strong adaptability: By studying the formation control strategy under the switched topology, the ability of the UAV formation to cope with harsh communication environments can be enhanced, and the cooperative work efficiency and survival ability can be improved.

[0089] (4) High flexibility: Flexibly adjusting the formation according to the task requirements and completing the formation reconstruction can improve the ability to cope with emergencies and adapt to complex tasks.

[0090] The step divisions of the above various methods are only for clear description. When implemented, they can be combined into one step or some steps can be split into multiple steps. As long as the same logical relationships are included, they are all within the protection scope of the present invention; adding insignificant modifications to the algorithm or process or introducing insignificant designs, but not changing the core designs of the algorithm and process are all within the protection scope of the invention.

[0091] Another embodiment of the present invention relates to an intelligent cooperative control system for an unmanned aerial vehicle (UAV) cluster. The implementation details of the intelligent cooperative control system for the UAV cluster in this embodiment will be specifically described below. The following content is only the implementation details provided for convenient understanding and is not necessary for implementing this solution. The intelligent cooperative control system for the UAV cluster in this embodiment includes: A model construction module, configured to establish a detection model for a detection task according to the lateral detection range and longitudinal detection range of the UAV cluster under the detection task, and establish an aggregation model for the aggregation task according to the aggregation range and aggregation density of the UAV cluster under the aggregation task; A parameter optimization module, configured to use the relative distance and relative angle between each UAV in the UAV cluster as optimization parameters, and use the maximum lateral detection range and maximum longitudinal detection range as optimization objectives to iteratively solve the detection model, and use the maximum aggregation range and maximum aggregation density as optimization objectives to iteratively solve the aggregation model, so as to respectively obtain the optimal relative distance and optimal relative angle under the detection task and the aggregation task; A formation determination module, configured to respectively determine the optimal formation of the UAV cluster under the detection task and the aggregation task through the optimal relative distance and optimal relative angle under the detection task and the aggregation task; A cooperative control module, configured to construct a formation controller for the UAV cluster with the goal of minimizing the energy consumption and path length when switching between the optimal formations of the detection task and the aggregation task, and control the UAV cluster to execute the detection task and the aggregation task respectively in the corresponding optimal formations through the formation controller.

[0092] It is not difficult to find that this embodiment is a system embodiment corresponding to the above method embodiment, and this embodiment can be implemented in cooperation with the above method embodiment. The relevant technical details and technical effects mentioned in the above embodiments are still valid in this embodiment. To avoid repetition, they will not be elaborated here. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiments.

[0093] It is worth mentioning that each module involved in this embodiment is a logical module. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or implemented as a combination of multiple physical units. In addition, to highlight the innovative part of the present invention, units that are not closely related to solving the technical problems proposed by the present invention are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0094] Another embodiment of the present invention relates to a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the drone swarm intelligent collaborative control method in the above embodiments.

[0095] Among them, the memory and the processor are connected in a bus manner. The bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and the memory together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art, so they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one component or multiple components, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna. Further, the antenna also receives data and transmits the data to the processor.

[0096] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory can be used to store data used by the processor when executing operations.

[0097] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the above method embodiments are implemented.

[0098] That is, those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0099] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present invention. In actual applications, various changes can be made to them in form and details without departing from the spirit and scope of the present invention.

Claims

1. An intelligent cooperative control method for an unmanned aerial vehicle cluster, characterized in that The method includes: Establish a detection model for the detection task according to the lateral detection range and longitudinal detection range of the UAV cluster under the detection task, and establish an assembly model for the assembly task according to the assembly range and assembly density of the UAV cluster under the assembly task; Taking the relative distance and relative angle between each UAV in the UAV cluster as optimization parameters, and taking the maximum lateral detection range and maximum longitudinal detection range as optimization objectives, iteratively solve the detection model, and taking the maximum assembly range and maximum assembly density as optimization objectives, iteratively solve the assembly model to respectively obtain the optimal relative distance and optimal relative angle under the detection task and the assembly task; Determine the optimal formation of the UAV cluster under the detection task and the assembly task respectively through the optimal relative distance and optimal relative angle under the detection task and the assembly task; Taking the minimum energy consumption and minimum path length when switching between the optimal formations of the UAV cluster under the detection task and the assembly task as the objectives, construct a formation controller for the UAV cluster, and control the UAV cluster to execute the detection task and the assembly task respectively with the corresponding optimal formations through the formation controller.

2. The method for intelligent collaborative control of an unmanned aerial vehicle cluster according to claim 1, wherein The detection model is: ； Wherein, is the horizontal detection range, is the vertical detection range, is the weight of the horizontal detection range, is the weight of the vertical detection range; ； In the formula, represents the maximum value of the lateral distance between the leader and any follower in the UAV swarm within the lateral plane L , represents the leader's lateral detection radius and the sum of the lateral detection radii of the followers ; ； In the formula, represents the maximum detection distance of the leader, represents the maximum detection distance of the follower, represents the maximum detection angle of the leader, represents the maximum detection angle of the follower; ； In the formula, represents the maximum value of the longitudinal distance between the leader and any follower in the lateral plane H , is the longitudinal detection radius of the leader and the sum of the longitudinal detection radius of the follower . .

3. The method for intelligent cooperative control of an unmanned aerial vehicle cluster according to claim 2, wherein The assembly model is: ； In the formula, is the assembly range, is the assembly density, is the weight of the assembly range, is the weight of the assembly density; ； In the formula, represents the area actually covered by the formation, is the maximum area that can be covered by formations of the same shape; ； In the formula, D represents the distance between the leader and the last follower gathered at the same target, T represents the expected time difference between the leader and the last follower gathered at the same target, represents the maximum flight speed of the UAV.

4. The method for intelligent collaborative control of an unmanned aerial vehicle cluster according to claim 1, wherein The formation controller is designed based on the linear quadratic optimal control theory; The formation controller is: ； wherein, represents the control gain matrix, represents t the switching topology signal of the communication between UAVs at time represents t at time i the neighbor set of the represents t adjacency matrix at time , represents the error state vector of the actual formation state of the i th follower deviating from the desired formation state, represents the error state vector of the actual formation state of the j th follower deviating from the desired formation state, represents the state vector of the i th UAV, represents the spatial coordinate of the i th UAV, represents the velocity vector of the i th UAV, m is the preset spatial dimension, , respectively represent the position vector and velocity vector of the i th follower relative to the leader in the desired formation state, represents the desired control input of the i th follower in the desired formation state, and represents the derivative with respect to time; The control gain matrix of the formation controller is: ； In the formula, represents the minimum eigenvalue of the Laplacian matrix of the time system , represents a preset constant, and , represents a preset positive definite real symmetric matrix; is the positive solution of the following algebraic Riccati equation: ； In the formula, represents a preset positive semi - definite real symmetric matrix, .

5. The method for intelligent cooperative control of an unmanned aerial vehicle cluster according to claim 3, wherein The weights of the lateral detection range, longitudinal detection range, assembly range, and assembly density in the detection model and the assembly model are all determined by the triangular fuzzy number analytic hierarchy process.

6. The method for intelligent cooperative control of an unmanned aerial vehicle cluster according to claim 1, wherein The optimal formation of the UAV cluster is a plane symmetric triangle formation or a plane one-line formation.

7. An intelligent cooperative control system for an unmanned aerial vehicle cluster, characterized in that, The system includes: A model construction module for establishing a detection model for the detection task according to the lateral detection range and longitudinal detection range of the UAV cluster under the detection task, and establishing an assembly model for the assembly task according to the assembly range and assembly density of the UAV cluster under the assembly task; A parameter optimization module for taking the relative distance and relative angle between each UAV in the UAV cluster as optimization parameters, taking the maximum lateral detection range and maximum longitudinal detection range as optimization objectives, iteratively solving the detection model, and taking the maximum assembly range and maximum assembly density as optimization objectives, iteratively solving the assembly model to respectively obtain the optimal relative distance and optimal relative angle under the detection task and the assembly task; A formation determination module for determining the optimal formation of the UAV cluster under the detection task and the assembly task respectively through the optimal relative distance and optimal relative angle under the detection task and the assembly task; A cooperative control module for taking the minimum energy consumption and minimum path length when switching between the optimal formations of the UAV cluster under the detection task and the assembly task as the objectives, constructing a formation controller for the UAV cluster, and controlling the UAV cluster to execute the detection task and the assembly task respectively with the corresponding optimal formations through the formation controller.

8. A computer device, characterized in that, Includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method for intelligent cooperative control of an unmanned aerial vehicle cluster according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the method for intelligent cooperative control of an unmanned aerial vehicle cluster according to any one of claims 1 to 6 is implemented.

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