Multi-target-oriented unmanned aerial vehicle dynamic allocation and synchronous hunting method

Abstract

The invention discloses a multi-target-oriented unmanned aerial vehicle dynamic allocation and synchronous hunting method, and the method comprises the steps: carrying out the prediction of the target position in the hunting of an unmanned aerial vehicle based on an AP98 wind-induced drift model in combination with the power of a target, achieving the precise target segmentation and reasonable resource allocation through an improved algorithm, and carrying out the task allocation optimization through a conflict solving mechanism and a task allocation optimization link. Tasks can be dynamically adjusted in real time, complex and changeable conditions are effectively dealt with, and the flexibility and effectiveness of task allocation are greatly enhanced. In order to solve the problem of multi-target synchronous hunting, an improved two-stage algorithm for autonomous unmanned aerial vehicle group guidance is adopted, and in the first stage, the initial condition of second-stage guidance is consistent by using a consistency algorithm; in the second stage, an improved proportional guidance algorithm is applied to dynamically adjust the acceleration direction, it is ensured that the unmanned aerial vehicle approaches the target in the optimal path, and the hunting task is completed.

Description

Technical Field

[0001] The invention relates to the field of unmanned aerial vehicle control, and in particular to a method for dynamically allocating and synchronously capturing unmanned aerial vehicles for multiple targets. Background Art

[0002] Drones are widely used in reconnaissance, strike, search and early warning tasks due to their low cost, compactness and flexibility. Drone swarm systems can accomplish more complex and tedious tasks that are difficult for a single drone to accomplish through collaborative cooperation. Among them, the encirclement and capture method of drone swarms is an important research direction in the field of drone swarm technology. It plays an important role in target reconnaissance, encirclement and capture, and has important theoretical research and practical exploration value.

[0003] Most of the current drone capture research focuses on a single target, and the presence of multiple targets will affect the final execution effect of the task. In the drone cluster capture of multiple targets, the environment is complex and dynamically changing, and the interference of wind is particularly prominent. Traditional prediction methods mostly rely on basic motion models and positioning, without considering wind-induced drift, resulting in large prediction deviations and poor capture effects. In drone capture tasks, traditional task allocation methods often lack detailed consideration of target dynamic changes and resource balance. In multi-target scenarios, some targets are easily ignored or resources are unevenly allocated, and it is difficult to flexibly adjust according to the execution of the task. In addition, the position of the drone is constantly changing during the capture process, which can easily be sensed by the target, causing the target to escape. Therefore, how to dynamically allocate capture tasks based on the current real-time positions of the drone and the target, and plan an optimal capture path is a key issue in the current drone multi-target capture research. Summary of the invention

[0004] Purpose of the invention: The present invention aims to provide a method for dynamically allocating and synchronously capturing drones for multiple targets, which can reasonably segment multiple targets and dynamically adjust tasks in real time by utilizing a conflict resolution mechanism and task allocation optimization.

[0005] Technical solution: The multi-target UAV dynamic allocation and synchronous capture method of the present invention comprises the following steps: (1) Construct a multi-UAV kinematic model based on the UAV encirclement target; (2) Determine the target speed and update the target position based on the wind drift model AP98 and the target’s own dynamic velocity component; (3) Based on the UAV position and target position, the K-means clustering algorithm is used to assign tasks with the target position as the initial cluster center; (4) Using the PI performance impact algorithm to determine the RPI removal performance impact matrix and the IPI inclusion performance impact matrix of the roundup mission, we can obtain the UAV task allocation matrix; (5) Perform conflict detection. If there is a conflict, adjust the task allocation. After resolving the conflict, update the UAV task allocation matrix, RPI matrix, and IPI matrix, and proceed to step (6); (6) Considering the task completion time, task execution cost and task completion benefit, construct the objective function, determine the constraints, and optimize the task allocation; (7) Construct a multi-objective information matrix based on the kinematic model of multiple UAVs. The multi-objective information matrix includes the state information matrix and the state information derivative matrix of the UAVs relative to the targets. Auxiliary variables are introduced for each target to coordinate the multi-objective parameters. The virtual control variables are determined based on the derivatives of the auxiliary variables to obtain the actual control signals of the UAVs. (8) Use proportional guidance method to dynamically adjust the acceleration direction of the UAV, optimize the UAV path, and perform the capture mission; (9) Determine the capture effect. If the target escapes or the distance between the drone and the target continues to increase, mark the corresponding task as an unassigned task and return to step (4) to reassign the drone task.

[0006] Furthermore, in step (2), the target velocity is determined based on the wind drift model AP98 and the target's own dynamic velocity component. j speed, target j exist x Axis speed for ; Target j exist y Axis speed for ; in, and Respectively represent the target j exist x Axis and y The velocity component of the own power on the shaft, is the downwind drift vector, is the angle between the wind drift direction and the reference coordinate axis, is the vertical wind pressure vector; In step (2), the target j Updated location for ; ; In the formula, For the moment t Target j The location coordinates of .

[0007] Furthermore, step (3) is as follows: Defining the drone location dataset and target location dataset , where N is the total number of drones, M is the total number of targets, and ; The K-means clustering algorithm is used to calculate the distance between the drone and the target, and the target position is used as the initial cluster center for clustering and task allocation. The number of drones corresponding to each task is q ; judge q Is it satisfied? , if the number of drones corresponding to a certain task q Greater than , then calculate the average center of all UAVs in the task, and reallocate the UAV farthest from the average center to the task corresponding to its nearest target.

[0008] Furthermore, in step (5), if there is a conflict, the task allocation adjustment is performed as follows: If the number of drones corresponding to the task q Greater than , then calculate the average center of all drones in the mission and determine the drone farthest from the average center ; Calculate the roundup status indicators for other tasks SI , the drone Assigned to the minimum roundup status indicator minSI in the corresponding task.

[0009] Furthermore, before entering step (7), the objective function is constructed by considering the task completion time, task execution cost and task completion benefit, and constraints are set to solve the optimal task allocation solution.

[0010] Furthermore, the objective function is constructed by considering the task completion time, task execution cost and task completion benefit. J for ; in, , , is the weight coefficient, ; For drones i Complete the task j Time required, For the drone mission list, The cost required for UAV i to complete task j, For drones i Complete the task jThe income obtained.

[0011] Furthermore, in step (7), auxiliary variables are introduced for each target to coordinate multi-target parameters. and as follows: ; ; ; in, For drones i To the target j The distance V is the Euclidean norm of the UAV velocity vector, is the angle between the UAV’s velocity vector and the line of sight LOS, is the deviation angle between the UAV velocity vector and the sighting line in the vertical plane, is the deviation angle between the UAV velocity vector and the aiming line in the horizontal plane.

[0012] Furthermore, in step (7), the dummy control variable is ; ; ; The actual control signal of the drone is ; ; in, and Represent the control signals in the horizontal and vertical directions respectively, and is the first-order derivative of the auxiliary variable.

[0013] Furthermore, in step (8), the proportional guidance method includes a first guidance stage and a second guidance stage. In the first guidance stage, a consistency algorithm is used to adjust the auxiliary variable and Parameter preprocessing is performed to make the distance parameters and angular velocity parameters of the UAV consistent; in the second guidance stage, the acceleration is corrected by introducing the angle position and the acceleration direction of the UAV is dynamically adjusted. The acceleration direction of the UAV is as follows: ; ; in, and Represent the control signals in the horizontal and vertical directions respectively, is the navigation constant for target j, and Represent the line of sight rotation velocity components in the horizontal and vertical directions respectively.

[0014] Furthermore, the consistency algorithm of the first guidance phase in step (8) is as follows: ; ; ; in, and Respectively represent drones i Relative to target j The state information and its derivatives, is a control signal used to achieve consistency of state variables between drones. Indicates i The drone and k The drones can exchange information about the target j information; Indicates i The drone and k The drones cannot exchange information about the target j Information, and is the gain coefficient, .

[0015] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The present invention uses the wind drift model AP98 for the dynamic allocation and synchronous capture of multi-target UAVs, calculates the drift speed in detail by combining the target's own power, wind speed, wind direction and correction coefficient, and then accurately predicts the target position through the discrete time step formula, which can effectively cope with the dynamic changes of the target under complex meteorological environment, greatly improves the accuracy and reliability of target position prediction, and lays a good foundation for subsequent capture; 2. The present invention adopts the improved K - Means algorithm is used to segment the target, realize the reasonable deployment of the encirclement and capture drones, ensure the balance of resources, comprehensively consider multiple key factors to calculate the rewards and indicators when allocating tasks, improve the scientific nature of the allocation, and dynamically adjust the tasks in real time through the conflict resolution mechanism and the task allocation optimization link, effectively respond to complex and changeable conditions, and greatly enhance the flexibility and effectiveness of task allocation; 3. The present invention adopts an improved two-stage algorithm for autonomous drone swarm guidance. In the first stage, a consistency algorithm is used to reach a consensus on the initial conditions of the second stage guidance; in the second stage, an improved proportional guidance algorithm is applied to dynamically adjust the acceleration direction through the proportional guidance method to ensure that the drone approaches the target with the optimal path and completes the encirclement and capture mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the present invention; Figure 2 The wind-induced drift vector diagram of wind-induced drift model AP98; Figure 3 This is a schematic diagram of the capture of drones and the initial position distribution of the target; Figure 4 It is a comparison chart of the convergence curve of the objective function; Figure 5 A graph showing the change in the distance from the capture drone to the target. DETAILED DESCRIPTION

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] The multi-target UAV dynamic allocation and synchronous capture method of the present invention comprises the following steps: (1) Based on the UAV encirclement target, a multi-UAV kinematic model is constructed.

[0019] For the mathematical description of the drone capture problem, for each target j, j=1,2,…,M, where M is the number of targets, the present invention adopts a fifth-order kinematic model, and the distance change rate formula is: ; Where V represents the Euclidean norm of the UAV velocity vector; represents the distance from UAV i to target j; LOS is the line of sight, i.e., the straight line from UAV to target j; is the deviation angle between the UAV velocity vector and the sighting line in the vertical plane. is the deviation angle between the UAV velocity vector and the aiming line in the horizontal plane. It reflects the dynamic change of the distance when the UAV moves towards different targets; The formula for the rotation velocity component of the line of sight is: ; ; In the formula, It indicates the rotation speed of the aiming line in the horizontal direction. Indicates the rotation speed of the aiming line in the vertical direction.

[0020] The formula for the rate of change of angle deviation is: ; It reflects the complex relationship between the angle deviation change of the UAV relative to the target j and the acceleration and other parameters in the multi-target scene. and are the inclination angles of the line of sight (LOS) from the UAV to the target j in the vertical and horizontal planes, respectively.

[0021] ;

[0022] In the formula, is the acceleration component of the UAV in the horizontal direction, The acceleration component of the drone acceleration in the vertical direction is used as the control signal. By adjusting these two acceleration components, the drone can flexibly track the target in a complex environment and meet the needs of multi-target capture tasks. When the target is moving, the model adjusts the LOS direction and the drone speed vector in real time to ensure that the drone can efficiently track the target in a complex environment. By controlling the acceleration signal and , realizing the dynamic allocation and round-up actions of drones.

[0023] (2) Based on the wind drift model AP98 and the target’s own dynamic velocity component, the target velocity is determined and the target position is updated.

[0024] The present invention improves the traditional AP98 model, focuses on the influence of wind drift on the target position, considers the target's own dynamic factors, and proposes a multi-target position prediction method.

[0025] Calculate wind drift parameters: Assume that the velocity component of the target j's own power is and .

[0026] Based on the AP98 model principle, the actual wind pressure drift vector (Leeway, L) can be decomposed into two parts: downwind drift vector (Downwind Leeway, DWL) and vertical wind pressure vector (Crosswind Leeway, CWL). For each target j, according to the wind speed at 10 meters Calculate the wind drift vector.

[0027] Downwind Drift Vector : ; Vertical wind pressure vector Right vertical direction and left vertical direction : ; ; in, and is the fitting parameter of target j in the downwind direction, is the regression deviation, and the other parameters are similar.

[0028] Calculate the total velocity of target j: ; ; in, is the angle between the wind drift direction and the reference coordinate axis.

[0029] Elapsed time Elapsed time After that, the position coordinates of target j are updated to After that, the position coordinates of target j are updated to ; ; In the formula, For the moment t Target j The location coordinates of .

[0030] Through the above steps, the AP98 model is used to predict and update the positions of multiple targets.

[0031] (3) Based on the UAV position and target position, the K-means clustering algorithm is used to assign tasks with the target position as the initial cluster center.

[0032] Consider a multi-agent system consisting of N capture drones and M targets. In order to ensure that all targets are successfully captured, . Ensure that each target corresponds to a subsystem, and the number of capture drones q contained in each subsystem must meet the prerequisite: In order to reasonably utilize the capture capability of the multi-agent system, the number of capture drones corresponding to each target should be evenly distributed. Therefore, the number q should also satisfy: .

[0033] (31) Define a data set containing N captured drone locations as The target position is set to the initial M cluster center, defined as , and The distance between It is calculated by the following formula.

[0034] ; According to the calculation results, Plan to the data cluster represented by the nearest target vector.

[0035] (32) Through step (31), M subsystems are initially formed. Then, the number of captured drones in the subsystem is calculated, denoted as q. Determine whether q satisfies .

[0036] (33) For The subsystem h for capturing drones is calculated. The mean center of subsystem h is calculated, and the capturing drone farthest from the mean center is found, and the capturing drone is divided into the other subsystems closest to it. In order to prevent the same capturing drone from moving back and forth between the two closest subsystems and causing an algorithm loop, it is necessary to ensure that the mobilized capturing drone cannot be divided into the previous subsystem.

[0037] (34) Repeat steps (33) and (34) until all elements in the subsystem meet the requirements.

[0038] (4) The PI performance impact algorithm is used to determine the RPI removal performance impact matrix and the IPI inclusion performance impact matrix of the roundup mission to obtain the UAV task allocation matrix.

[0039] According to the target segmentation result of the improved K-means algorithm, each subsystem containing the target is regarded as a whole task.

[0040] Treat each subsystem's roundup task as an independent task . Calculate task execution rewards ; in, is the weight of target j, which is used to measure the importance or priority of target j. is the Euclidean distance from drone i to target j, is the maximum distance constant, is the remaining energy of drone i, is the total energy of UAV i.

[0041] In the PI algorithm, both the benefits of the multi-UAV system and the impact of the task on other tasks are considered. , which can explain the task The impact on the i-th UAV in the task allocation process. Task The impact on the i-th UAV can be expressed as ; in, It is the task list of drone i, which records the tasks assigned to the drone. Indicates the task From Removed. l is the task exist Possible insertion positions in . is The execution reward of the task inserted at index w of . Indicates that the task Included in The reward for the i-th drone. yes There are no tasks in The reward for the i-th drone.

[0042] For measurement tasks The local inclusion performance of , using the inclusion performance affects IPI. In addition, the mission of the i-th UAV The IPI can be defined as the task Rewards plus mission execution The maximum difference in reward is the next task. Calculation task IPI value: ; in, Indicates the task is added to at position l . And determine the insertion position l, and put the task Add to appropriate drone mission list At the same time, the number of drones in the subsystem after adding q satisfies In the PI algorithm, each drone stores an RPI table. and a list of drones , defines a single matrix representing the RPI list matrix , to store the assignments between drones and tasks. and Combined into one matrix, The size is .in addition, Elements in Denotes the estimated mission of UAV i The RPI value of the update Matrix to reflect the task Association with drone i.

[0043] (5) Perform conflict detection. If there is a conflict, adjust the task allocation. After resolving the conflict, update the UAV task allocation matrix, RPI matrix, and IPI matrix and proceed to step (6).

[0044] Regular exchange matrix between drones , Timestamp , Target position changes and roundup status (Such as distance to the target, completeness of encirclement, etc.).

[0045] Check the number of drones in each subsystem If there are UAVs in subsystem h , calculate the average center of the drones in this subsystem , find the capture drone farthest from the mean center .

[0046] For other subsystems k, calculate their current roundup status indicators :

[0047] ; in, and is the weight coefficient, is the distance from UAV i to the target in subsystem k, is the variance of the distance.

[0048] Will Divide into The smallest (i.e. the worst roundup state) subsystem k, but it is necessary to ensure To prevent looping.

[0049] Adjust and update according to drone Matrix and Task Execution Order For the adjusted drone , recalculate its RPI value estimate for the new subsystem task and update ; Update the task list of subsystem k of the receiving drone and correlation matrix elements.

[0050] (6) Considering the task completion time, task execution cost and task completion benefit, the objective function is constructed, the constraints are determined, and task allocation is optimized.

[0051] Considering the task completion time, task execution cost, and task completion benefit, we construct the objective function. The time required for drone i to complete task j, is the UAV task list, then the partial objective function of the task completion time is ; set up is the cost required for UAV i to complete task j, then the partial objective function of the task execution cost is ; set up The profit obtained by the drone when completing the mission, then the partial objective function of the mission completion profit is ; Set the weight factor , , To balance the importance of the three factors of task completion time, task execution cost and task completion benefit. Then the comprehensive objective function J is ; The aim is to minimize the total time required for all drones to complete their respective tasks, thereby improving mission execution efficiency, effectively reducing resource consumption during mission execution, and improving resource utilization.

[0052] Identify constraints and define binary decision variables , when the i-th capture drone is assigned to the j-th target, The value of is 1; when the i-th capture drone is not assigned to the j-th target, The value of is 0.

[0053] Target quantity constraint: , indicating that all targets must be fully assigned to ensure that each target has a corresponding drone to perform the mission and avoid the situation where the target is not assigned.

[0054] Multi-UAV collaboration constraints: , which means that a UAV can only be assigned to one target at most, ensuring the rational use of UAV resources and avoiding resource dispersion and task conflicts caused by a UAV performing multiple target tasks at the same time.

[0055] Task time constraints: , which is the time for the drone to perform the mission At its maximum flight time within the range.

[0056] After determining the objective function and constraints constructed by minimizing the task completion time, minimizing the task execution cost, and maximizing the task completion benefit, the existing improved multi-objective particle swarm algorithm is used to optimize the task allocation results. The optimal or better task allocation scheme that achieves a balance between multiple objectives and satisfies the constraints is found to achieve efficient and reasonable task allocation.

[0057] (7) A multi-objective information matrix is ​​constructed based on the kinematic model of multiple UAVs. The multi-objective information matrix includes the state information matrix and the state information derivative matrix of the UAV relative to the target. Auxiliary variables are introduced for each target to coordinate the multi-objective parameters. The virtual control variables are determined based on the derivatives of the auxiliary variables to obtain the actual control signal of the UAV.

[0058] After the optimization of the capture task allocation is completed in step (6), each task drone group performs the task of capturing the target. The consistency algorithm can coordinate the movement of the drones and adjust the flight parameters of each drone. Ensure that the flight parameters of each drone are consistent, preparing for the subsequent guidance stage.

[0059] Constructing a multi-objective information matrix and ,in and Respectively represent the state information and derivatives of UAV i relative to target j, i = 1, 2, ..., N, N is the number of UAVs; It can be information about the distance from drone i to target j. The rate of change information.

[0060] Defining the comprehensive adjacency matrix , the element determination rule is as follows: If UAV i and UAV k can exchange information about target j, then , otherwise it is 0. This matrix is ​​used to describe the connection relationship between UAVs in multi-target information sharing.

[0061] The consistency algorithm formula is as follows: ; ; in, , and is the gain coefficient, It is used to adjust the consistency between drones, realize collaborative calculation between drones under multi-target information through comprehensive adjacency matrix, make the state variables of drones on different targets asymptotically reach consistency, and promote the coordinated action of the capture drone group in multi-target tasks.

[0062] To coordinate multi-objective parameters, for each objective j, an auxiliary variable is introduced and ,in represents the distance from UAV i to target j. is the angle between the velocity vector of the drone and the LOS, which is determined as follows: ; Considering the fifth-order kinematic model, the above two formulas are differentiated: ; ; Virtual Control according to The expression of is determined, and the virtual control is introduced as ; Convert the fifth-order kinematic model into an equivalent linear model. Linearize the complex nonlinear control problem. Consider the auxiliary variables and virtual control , the above two equations can be rewritten as ; ; Actual control signals of the drone and can be The calculation result is: ; ; Through these control signals, the UAV can adjust the flight parameters in the multi-target scenario, realize the parameter coordination of each UAV on different targets, and make the initial guidance conditions of the multi-target proportional guidance algorithm consistent.

[0063] (8) Use the proportional guidance method to dynamically adjust the acceleration direction of the UAV, optimize the UAV path, and execute the capture mission.

[0064] The improved multi-target proportional guidance algorithm is used to aim at the target of each UAV, ensuring that the UAV can approach the target in the optimal path and finally complete the round-up mission. It generates a UAV acceleration control signal proportional to the LOS angular velocity. However, before the algorithm is used directly to round up the target, it is necessary to coordinate the specified parameters, namely the relative distance to the target and the angle between the UAV velocity vector and the target line of sight. Therefore, the guidance process can be divided into two stages. In the first stage, the consistency algorithm of step 7 is used to make the initial guidance conditions consistent. In the second stage, the improved multi-target proportional guidance algorithm is used to aim at the target of each UAV.

[0065] (81) First guidance stage The goal of the first guidance phase is to achieve a close condition for each UAV at the beginning of the next phase. To achieve this, the result obtained in step 7 is used, that is, the initial guidance conditions are made consistent through the consensus algorithm. Using the control values ​​obtained in step 7, ensure that each UAV has the same parameters at the end of this phase. and is close: ; ; in, It is the drone number. and The accuracy is allowed to be within the range. Ensure that the distance parameters and angular velocity parameters of each drone gradually become consistent.

[0066] For a single UAV, entering the next stage of guidance within a limited time interval means that the difference between its alignment parameters and similar parameters of all other UAVs is already less than a small value that represents the coordination accuracy.

[0067] When the parameters of each drone are adjusted to the point where the difference between all drones is less than the set threshold, the first stage is completed and the next stage can be entered for more precise guidance.

[0068] (82) Second guidance stage An improved multi-target proportional guidance algorithm is adopted, and angle position information is introduced to correct the acceleration, so that the UAV can capture the target more accurately.

[0069] The control signal formula is: ; ; in, The navigation constant for target task j is dynamically adjusted according to the target characteristics and the real-time relative position between the UAV and the target. The acceleration direction is dynamically adjusted through the proportional guidance method to ensure that the UAV approaches the target in the optimal path. The improved multi-target proportional guidance algorithm is used to guide each UAV to the capture point around the target to complete the capture task.

[0070] (9) Determine the capture effect. If the target escapes or the distance between the drone and the target continues to increase, mark the corresponding task as an unassigned task and return to step (4) to reassign the drone task.

[0071] When the subsystem capture effect is not good (such as the target escapes, the distance between the drone and the target continues to increase, etc.), the capture task of this subsystem is Mark the task as unassigned. , return to step 4 and recalculate the RPI and IPI values ​​for each drone , try to include other suitable drone mission lists , and the number of drones in the subsystem is limited.

[0072] In order to verify the effectiveness and rationality of the method proposed in the present invention, the following examples are given: In the MATLAB environment, a simulation experiment is conducted to verify the proposed multi-target UAV dynamic allocation and synchronous capture method. In order to simplify the calculation, the capture UAVs and targets are regarded as particle models in the MATLAB platform to verify the capture effectiveness and convergence of the proposed method.

[0073] Set 9 capture drones to capture 3 moving targets, where the positions of the targets and capture drones are randomly generated and distributed in a square area of ​​800 m × 800 m. The simulation scene settings are shown in Table 1.

[0074] Table 1 Simulation scenario settings ; A scene at a certain moment is selected to verify the effectiveness of the algorithm. The relative distribution positions of multiple capture drones and targets are as follows: Figure 3 As shown, a circle with the target as the center is formed around the target, and the captured drones are evenly distributed on the circle. The multi-target UAV dynamic allocation and synchronous capture method proposed in the present invention is used to carry out multi-UAV collaborative capture.

[0075] Set the weight factor , then the comprehensive objective function J is ; In order to verify the performance of the algorithm, the multi-target hunting method in this paper is compared with the multi-target particle swarm algorithm and the multi-target wolf pack algorithm. The objective function convergence curve after 100 iterations is compared. Figure 4 As shown in FIG. 1 , after 100 iterations, the multi-target hunting method proposed in the present invention shows a better convergence effect than the multi-target particle swarm algorithm and the multi-target wolf pack algorithm. The optimal solution can be quickly reached during the iteration process.

[0076] Take a subsystem in a multi-target capture task as an example. Three capture drones are used in the subsystem, namely UAV1, UAV2, and UAV3, to capture the target Target. The simulation step size is set to 0.1, and the capture radius is set to 10m. Figure 5 As shown, the distances of each drone relative to the target during the capture process converged to a consistent distance of 10 meters at 42 seconds, completing the capture of the target, which met the expected results.

Claims

1. A multi-target UAV dynamic allocation and synchronous capture method, characterized in that: The following steps are involved: (1) Construct a multi-UAV kinematic model based on the UAV encirclement target; (2) Determine the target speed and update the target position based on the wind drift model AP98 and the target’s own dynamic velocity component; (3) Based on the UAV position and target position, the K-means clustering algorithm is used to assign tasks with the target position as the initial cluster center; (4) Using the PI performance impact algorithm to determine the RPI removal performance impact matrix and the IPI inclusion performance impact matrix of the roundup mission, we can obtain the UAV task allocation matrix; (5) Perform conflict detection. If there is a conflict, adjust the task allocation. After resolving the conflict, update the UAV task allocation matrix, RPI matrix, and IPI matrix, and proceed to step (6); (6) Considering the task completion time, task execution cost and task completion benefit, construct the objective function, determine the constraints, and optimize the task allocation; (7) Construct a multi-objective information matrix based on the kinematic model of multiple UAVs. The multi-objective information matrix includes the state information matrix and the state information derivative matrix of the UAVs relative to the targets. Auxiliary variables are introduced for each target to coordinate the multi-objective parameters. The virtual control variables are determined based on the derivatives of the auxiliary variables to obtain the actual control signals of the UAVs. (8) Use proportional guidance method to dynamically adjust the acceleration direction of the UAV, optimize the UAV path, and perform the capture mission; (9) Determine the capture effect. If the target escapes or the distance between the drone and the target continues to increase, mark the corresponding task as an unassigned task and return to step (4) to reassign the drone task.

2. The multi-target UAV dynamic allocation and synchronous capture method according to claim 1 is characterized in that: In step (2), the target is determined based on the wind drift model AP98 and the target's own dynamic velocity component. j speed, target j exist x Axis speed for ； Target j exist y Axis speed for ； in, and Respectively represent the target j exist x Axis and y The velocity component of the own power on the shaft, is the downwind drift vector, is the angle between the wind drift direction and the reference coordinate axis, is the vertical wind pressure vector; In step (2), the target j Updated location for ； ； In the formula, For the moment t Target j The location coordinates of .

3. The multi-target UAV dynamic allocation and synchronous capture method according to claim 2 is characterized in that: Step (3) is as follows: Defining the drone location dataset and target location dataset , where N is the total number of drones, M is the total number of targets, and ; The K-means clustering algorithm is used to calculate the distance between the drone and the target, and the target position is used as the initial cluster center for clustering and task allocation. The number of drones corresponding to each task is q ; judge q Is it satisfied? , if the number of drones corresponding to a certain task q Greater than , then calculate the average center of all drones in the task, and reallocate the drone farthest from the average center to the task corresponding to its nearest target.

4. The multi-target UAV dynamic allocation and synchronous capture method according to claim 3 is characterized in that: In step (5), there is a conflict, and the task allocation adjustment is as follows: If the number of drones corresponding to the task q Greater than , then calculate the average center of all drones in the mission and determine the drone farthest from the average center ; Calculate the roundup status indicators for other tasks SI , the drone Assigned to the minimum roundup status indicator minSI in the corresponding task.

5. The multi-target UAV dynamic allocation and synchronous capture method according to claim 4 is characterized in that: Before entering step (7), consider the task completion time, task execution cost and task completion benefit to construct the objective function, set constraints and solve the optimal task allocation plan.

6. The multi-target UAV dynamic allocation and synchronous capture method according to claim 4 is characterized in that: Construct the objective function by considering the task completion time, task execution cost and task completion benefit J for ； in, , , is the weight coefficient, ; For drones i Complete the task j Time required, For the drone mission list, The cost required for UAV i to complete task j, For drones i Complete the task j The income obtained.

7. The multi-target UAV dynamic allocation and synchronous capture method according to claim 6 is characterized in that: In step (7), auxiliary variables are introduced for each target to coordinate multi-target parameters. and as follows: ； ； ； in, For drones i To the target j The distance V is the Euclidean norm of the UAV velocity vector, is the angle between the UAV’s velocity vector and the line of sight LOS, is the deviation angle between the UAV velocity vector and the sighting line in the vertical plane, is the deviation angle between the UAV velocity vector and the aiming line in the horizontal plane.

8. The multi-target UAV dynamic allocation and synchronous capture method according to claim 7 is characterized in that: In step (7), the dummy control variable is ； ； ； The actual control signal of the drone is ； ； in, and Represent the control signals in the horizontal and vertical directions respectively, and is the first-order derivative of the auxiliary variable.

9. The multi-target UAV dynamic allocation and synchronous capture method according to claim 8 is characterized in that: In step (8), the proportional guidance method includes a first guidance stage and a second guidance stage. In the first guidance stage, the auxiliary variable and Parameter preprocessing is performed to make the distance parameters and angular velocity parameters of the UAV consistent; in the second guidance stage, the acceleration is corrected by introducing the angle position and the acceleration direction of the UAV is dynamically adjusted. The acceleration direction of the UAV is as follows: ； ； in, and Represent the control signals in the horizontal and vertical directions respectively, is the navigation constant for target j, and Represent the line of sight rotation velocity components in the horizontal and vertical directions respectively.

10. The multi-target UAV dynamic allocation and synchronous capture method according to claim 9, characterized in that: The consistency algorithm of the first guidance phase in step (8) is as follows: ； ； ； in, and Respectively represent drones i Relative to target j The state information and its derivatives, is a control signal used to achieve consistency of state variables between drones. Indicates i The drone and k The drones can exchange information about the target j information; Indicates i The drone and k The drones cannot exchange information about the target j Information, and is the gain coefficient, .

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