Multi-AUV cooperative hunting control method based on virtual multi-target estimation center

Abstract

The invention discloses a multi-AUV (Autonomous Underwater Vehicle) cooperative hunting control method based on a virtual multi-target estimation center. The method comprises the following steps: S1, constructing an AUV kinematic model and initializing a system state; s2, constructing a distributed cooperative multi-target virtual center estimator by using the relative position information between the AUV and the target and the relative position information between the neighbor AUV and the target; s3, using the relative position information between the AUV and the neighbor and the relative position information between the AUV and the estimated multi-target center to construct a multi-target hunting kinematics controller; and S4, performing cooperative control on the AUV by using the kinematics controller constructed in the step S3, and realizing a hunting task of the multi-target virtual center. According to the invention, through cooperative estimation and control of multiple AUVs, distributed hunting control of multiple targets is realized by using information of the AUVs and the targets under the local coordinate system and target information detected by the neighbor AUVs.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-AUV distributed collaborative control, and in particular relates to a multi-AUV collaborative capture control method based on virtual multi-target estimation centers. Background Art

[0002] With the continuous advancement of intelligent agent technology, multi-agent systems have been widely used in many fields, including security monitoring, satellite formation flying, environmental monitoring, and robot formations. In these fields, multi-agent systems must complete complex tasks such as target positioning, target tracking, and area coverage. The problem of multi-target orbiting control is of great practical significance. For example, in security monitoring tasks, multiple agents need to orbit around a target area to achieve continuous monitoring.

[0003] For underwater missions, the underwater environment is complex and changeable, with many uncertain factors such as ocean currents, obstacles, and communication interference, which seriously affect the efficiency and reliability of mission execution. The detection range and mission execution capabilities of a single AUV (autonomous underwater vehicle) are limited. Once a failure occurs, the mission may fail directly. Multi-AUV systems can significantly expand the detection range and improve the reliability and efficiency of mission execution through collaborative operation. However, existing multi-AUV systems have problems such as insufficient underwater positioning accuracy, limited measurement and communication distances, and easy failure of target detection information. In addition, the formation state of multiple targets surrounding is dynamically changing. The targets may suddenly accelerate, turn, or even disappear and reappear, resulting in formation changes. This requires the AUV system to have the ability to respond quickly and adjust in real time to ensure effective tracking and encirclement of the targets. Summary of the Invention

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the existing technology and propose a multi-AUV collaborative capture and control method based on a virtual multi-target estimation center. Through multi-AUV collaborative estimation and control, the information between the AUV and the target in the local coordinate system and the target information detected by the neighboring AUVs are utilized to realize distributed capture and control of multiple targets. The method is suitable for practical application scenarios such as underwater environment development, underwater environment confrontation, and disaster relief, and can significantly improve the mission execution efficiency and reliability of the multi-AUV system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The multi-AUV collaborative capture control method based on virtual multi-target estimation center includes the following steps:

[0007] S1, build the AUV kinematic model and initialize the system state;

[0008] S2, using the relative position information between the AUV and the target and the relative position information between the neighboring AUVs and the target, a distributed collaborative multi-target virtual center estimator is constructed;

[0009] S3, using the relative position information between the AUV and its neighbors and the relative position information between the AUV and the estimated multi-target centers to construct a multi-target capture kinematic controller;

[0010] S4. Use the kinematic controller constructed in step S3 to collaboratively control the AUV to achieve the capture mission of the multi-target virtual center.

[0011] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0012] 1. The present invention adopts a distributed design, and all control and estimation processes rely only on local information. There is no need for global target information or centralized calculation, which reduces the dependence on communication and computing resources. In response to the dynamic changes in target information, such as the dispersion of a target group into several small target subgroups, the present invention uses a distributed multi-target virtual center estimator to adjust the AUV's estimate of the target center in real time, ensuring that the system can still complete the capture mission in a low-information environment. Taking into account various influencing factors such as inaccurate underwater GPS positioning, limited detection range, and restricted communication, the present invention designs a kinematic controller suitable for underwater work, which improves robustness and scalability.

[0013] 2. The present invention designs a distributed multi-target virtual center estimator based on local information, which uses the estimated values of the multi-target centers of neighboring AUVs and the target information detected by its own AUV to collaboratively estimate the virtual centers of multiple targets; the estimator is based on graph theory and consensus protocol, is suitable for directed balanced graph ring topology structures, and can realize distributed estimation of multi-target centers in a communication-restricted environment.

[0014] 3. Combining the relative position information between the AUV and its neighbors and the relative position information between the AUV and the estimated center of multiple targets, the present invention designs a kinematic controller. This controller drives the AUV to perform collaborative capture around the virtual center of multiple targets by adjusting the forward speed and yaw angular velocity of the AUV, while meeting the stability and convergence requirements of the capture mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the method of the present invention;

[0016] Figure 2 Schematic diagram of multiple AUVs capturing multiple targets with a movement time of 0-200s in Example 1;

[0017] Figure 3Schematic diagram of the estimation error of the virtual center positions of multiple targets during the movement of multiple AUVs in Example 1;

[0018] Figure 4 Schematic diagram of multiple AUVs capturing multiple targets with a movement time of 200s-400s in Example 1;

[0019] Figure 5 Schematic diagram of the capture of a target group by multiple AUVs into two target subgroups in Example 2;

[0020] Figure 6 Schematic diagram of the estimation error of the center positions of two target subgroups during the multi-AUV motion process in Example 2;

[0021] Figure 7 Schematic diagram of capturing a newly formed target subgroup by multiple AUVs when the target in the subgroup moves in Example 2. DETAILED DESCRIPTION

[0022] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0023] This invention focuses on solving the problem of surround control of multiple AUVs over multiple targets. It designs a distributed multi-target virtual center estimation algorithm and surround controller using only the measurement information between the AUVs and their neighbors and detectable targets, i.e., local information. It also proposes an innovative framework that enables each AUV to estimate the target center while measuring the relative position of the target and to surround the virtual center of multiple targets. This invention takes into account the impact of dynamic changes in target detection information in weak underwater communication environments, and uses its own relative position measurement information of the target and the information of its neighbors to collaboratively estimate the virtual centers of multiple targets. This achieves the capture and control of multiple targets by multiple AUVs based on local information, reducing reliance on global information and making it more suitable for underwater information-deficient environments.

[0024] like Figure 1 As shown, the present invention provides a multi-AUV collaborative capture control method based on virtual multi-target estimation centers, comprising the following steps:

[0025] S1. Build the AUV kinematic model and initialize the system state; specifically:

[0026] The kinematic model of AUV is expressed as:

[0027]

[0028] Where, the subscript i represents the i-th AUV, i = 1, 2, ..., n, and n is the number of AUVs; [x i y iis the coordinate of the i-th AUV in the inertial coordinate system, where x i is the abscissa, and y i is the ordinate; θ i is the heading angle of the i-th AUV in the inertial coordinate system; [u i ω i T is the input of the kinematic controller, where u i , ω i respectively represent the forward speed and yaw angular velocity designed for the i-th AUV.

[0029] S2. Use the relative position information between the AUV and the target and the relative position information between the neighboring AUV and the target to construct a distributed cooperative multi-target virtual center estimator; Step S2 includes:

[0030] S21. Define the cooperative pursuit task of the pursuit target and multiple targets; specifically:

[0031] For a team composed of n AUVs, the cooperative pursuit task of multiple targets is to cooperatively pursue the virtual center of m targets T j with an expected radius r i , j = 1, 2,..., m, i = 1, 2,..., n, m ≤ n; if m < n, then some AUVs perform distributed pursuit for the same target;

[0032] Among them, one AUV can detect at most the relative position information j with one target T This relative position information is obtained in the local coordinate system of the AUV.

[0033] S22. Based on the neighbor relationship between multiple AUVs, construct a multi-AUV neighbor relationship topology graph; specifically:

[0034] Efficient data transmission is crucial for improving the exploration efficiency of multi-robots. However, common communication methods often carry a large amount of redundant information, resulting in a high communication load. To address this problem, a previous neighbor is specified for each AUV, where the previous neighbor of the n-th AUV is the 1st AUV, thereby constructing a one-way circular neighbor relationship graph. The information interaction is a directed circular topology structure, so the amount of information transmitted can be effectively reduced. In this topology, each AUV (the (i + 1)-th) only needs to obtain the relative position estimation information about the multi-target virtual center from its neighbor AUV (the i-th); this way of information interaction has the fewest interaction channels in the multi-AUV system and is very suitable for scenarios with poor underwater communication environments. Only n interaction channels are required to meet the communication needs between n AUVs.

[0035] ​S23. Based on the neighbor relationships between multiple AUVs and the multi-AUV neighbor relationship topology, calculate the Laplace matrix of the communication graph Specifically:

[0036] Suppose a directed graph G(V,E,A) is represented by (V,E,A), where V is the set of AUV nodes, E is the set of edges, and A=[a ij ] is a weighted adjacency matrix; the in-degree and out-degree of a node in a directed graph are defined as and If the in-degree of each node in the graph is equal to the out-degree, then the directed graph G is balanced; an undirected graph is a special balanced graph with a ji =a ij The property of holds for all i, j;

[0037] A directed graph G is said to be strongly connected if there is a continuous sequence of edges from any node i to another node j in the graph, i≠j;

[0038] Degree matrix Δ=[δ ij ] is a diagonal matrix, where δ ij = 0 for all i≠j, and δ ii =deg out (v i ) holds for all i;

[0039] Based on the neighbor relationship between multiple AUVs, the Laplace matrix of the communication graph G between multiple AUVs is calculated. definition For a strongly connected graph, the rank of its Laplacian matrix is equal to n-1, where n is the Laplacian matrix dimension.

[0040] S24. Define the relative position between the i-th AUV and the j-th target in the local coordinate system The expression is:

[0041]

[0042] in, is the rotation matrix; [x j y j ] T is the coordinate of the jth target in the inertial coordinate system, x j is the horizontal axis, y j is the vertical coordinate;

[0043] S25. Define the relative position of the i-th AUV and the multi-target center in the local coordinate system The expression is:

[0044]

[0045] in, is the rotation matrix; is the coordinate of the multi-target virtual center in the inertial coordinate system, is the horizontal axis, is the vertical coordinate;

[0046] S26. Construct a distributed collaborative multi-target center estimator; specifically:

[0047] Distributed cooperative multi-target center estimator, expressed as:

[0048]

[0049] in, is the multi-target virtual center estimate calculated by the i-th AUV, that is The estimated value of λ i is the internal state variable of the center estimator of the i-th AUV, γ1>0, γ2>0, γ3>0 are the design parameters of the estimator; and λ i+1 The update only requires information from neighboring AUVs and adopts a distributed protocol;

[0050] S27, setting estimator parameters so that the distributed collaborative multi-target center estimator tracks the relative positions of the multi-target centers; specifically:

[0051] Set the estimator parameters γ1, γ2, γ3 so that the input is time-varying The output is The distributed cooperative multi-target center estimator can track And the tracking error is bounded, so the estimator parameters satisfy the following conditions:

[0052] γ3>0 (6)

[0053]

[0054] Among them, a i and b i They represent the Laplace matrices respectively The real and imaginary parts of the i-th eigenvalue of

[0055] In order to ensure the convergence of the protocol, according to the above gain conditions, the following steps are used to select the parameters γ1, γ2, and γ3 in the consensus protocol:

[0056] First assign a positive number to γ3;

[0057] Calculate the lower bound of γ2 max{max(4γ3(b i 2 -a i 2 )),0}, and assign a value greater than the lower bound to γ2;

[0058] Finally, calculate the lower bound of γ1 And assign γ1 a value greater than the lower bound;

[0059] Through parameter selection, the estimation of the virtual centers of multiple targets eventually converges to the bounded region.

[0060] S3. Using the relative position information between the AUV and its neighbors and the relative position information between the AUV and the estimated multi-target centers, a multi-target capture kinematic controller is constructed; including:

[0061] S31. Define the conditions that must be met for the target capture mission; specifically:

[0062]

[0063] Where i = 1, 2, ..., n, is a constant, r i is the radius of the cyclic orbit in the i-th AUV target capture mission, r1=r2=...=r n , t is the movement time; definition And Ψ i Satisfaction i ∈[0,2π), where atan2 is the inverse tangent function;

[0064] S32. Define the relative position between the i-th AUV and its neighbor i+1-th AUV in the local coordinate system The expression of ; specifically:

[0065]

[0066] in, is the rotation matrix; [x i+1 y i+1 ] T is the coordinate of the neighboring AUV (i+1) in the inertial coordinate system, x i+1 is the horizontal axis, y i+1 Is the vertical axis.

[0067] S33. Construct a kinematic controller for the multi-AUV system and set the kinematic controller input. The kinematic controller input for the multi-AUV system is specifically:

[0068]

[0069] Among them, k u >0,k w >0 is the kinematic controller parameter; c is a constant, and its value range is c∈(0,1);

[0070] S34. Define the state variable χ of the i-th AUV i ; Specifically:

[0071] χ i =[y i ,ω i ,β i ] T (12)

[0072] Among them, β i =θ i+1 -θ i ; The first-order differential of formula (12) is:

[0073]

[0074] when When , formula (13) is expressed as:

[0075]

[0076] The state variables of the entire AUV system are: In terms of When , the unique equilibrium state of χ is:

[0077]

[0078] in,

[0079] S35, design control parameter k u ,k w , specifically:

[0080] Design control parameter k u ,k w , such that:

[0081]

[0082] Therefore, k u ,k w The expression that needs to be satisfied is

[0083]

[0084] S4. Use the kinematic controller constructed in step S3 to collaboratively control the AUV to achieve the capture mission of the multi-target virtual center.

[0085] In addition, when the target group begins to disperse and form multiple target small groups, the distributed multi-target center estimator constructed in step S2 will re-estimate the virtual centers of the multiple target small groups, and then use the kinematic controller constructed in step S3 to drive multiple AUVs to surround the dispersed target small groups.

[0086] In order to verify the effectiveness of the method of the present invention, the distributed cooperative multi-target virtual center estimator and multi-target capture kinematic controller obtained in steps S2 and S3 are applied to the capture control of multiple AUVs on multiple targets when the detection target information remains unchanged and the detection targets are dispersed into multiple subgroups (taking 8 AUVs as an example).

[0087] In order to more effectively illustrate the effectiveness of the method of the present invention, all parameters are set to the same. The initialization of the estimator parameters, controller parameters, and state parameters is shown in Table 1 below, where the initial positions of the 8 AUVs are set to random positions.

[0088]

[0089] Table 1

[0090] Example 1

[0091] This embodiment is based on the distributed multi-target capture control of multiple AUVs when detecting multiple targets that are not dispersed.

[0092] In this embodiment, it is designed that each AUV can detect at most one target information, and the expected orbiting radius r of each AUV is i is 15m, and the target position is

[0093] like Figure 2 As shown in the figure, it is a schematic diagram of multiple AUVs encircling multiple targets. The hollow triangle represents the initial position of the multiple AUVs, the filled triangle represents the final position of the multiple AUVs, the black solid line represents the motion path, and the solid origin represents the position of the target. Figure 2 It can be seen that multiple AUVs can achieve the task of capturing and controlling multiple targets from any initial position. Figure 3 As shown in the figure, the estimation error change curve of the multi-target virtual center estimator during the AUV movement process is shown in the figure. Figure 3 It can be seen that multiple AUVs finally reach a consensus on the position estimation of the virtual centers of multiple targets, and the estimation error is within a bounded range. Figure 2 and Figure 3 The motion time shown is 0-200s. In order to more intuitively show the effective implementation of the target capture task, a schematic diagram of the multi-AUV motion trajectory of 200s-400s is given, as shown in Figure 4As shown in the figure, it can be seen that multiple AUVs can realize the distributed estimation of the virtual centers of multiple targets and simultaneously complete the surround of multiple targets, and the estimation error of the virtual centers of multiple targets eventually converges to the bounded area.

[0094] Example 2

[0095] This embodiment is based on the capture control of multiple AUVs on multiple targets when the detected targets are dispersed into two subgroups.

[0096] In order to verify the effectiveness of the present invention, without changing the control parameters, the target is divided into two subgroups, and the coordinate position of the target is At this time, set the desired orbit radius r of each AUV i is 8m.

[0097] like Figure 5 As shown in Fig. 1, it is the motion trajectory of multiple AUVs surrounding the two target subgroups when the target group disperses to form two target subgroups, as shown in Fig. Figure 6 As shown in Figure 1, the estimation error of the virtual center of the two target subgroups by the multi-AUV system is shown. It can be seen that the multi-target virtual center estimator can still estimate the center of the subgroup, and the estimation error eventually converges to a bounded range, meeting the design requirements. Figure 7 As shown in the figure, when targets T5 and T8 occupy the same position, the multi-AUV system can still estimate the subgroup center through the multi-target virtual center estimator and drive the AUV through the controller to achieve the multi-target capture task.

[0098] This paper provides a multi-AUV collaborative capture control method based on virtual multi-target estimation centers. The information used by the multi-target virtual center estimator and controller depends only on the relative positions of the AUVs to their neighbors and to the target in their local coordinate system. By dynamically estimating the multi-target virtual centers and simultaneously driving the AUVs to achieve distributed target capture, the stability of the system is guaranteed. This provides an effective solution for multi-AUV systems to achieve multi-target capture control in weak underwater communication environments.

[0099] It should also be noted that, in this specification, terms such as "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or apparatus comprising the element.

[0100] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-AUV collaborative capture control method based on virtual multi-target estimation center, characterized by: The following steps are involved: S1, build the AUV kinematic model and initialize the system state; S2, using the relative position information between the AUV and the target and the relative position information between the neighboring AUVs and the target, a distributed collaborative multi-target virtual center estimator is constructed; S3, using the relative position information between the AUV and its neighbors and the relative position information between the AUV and the estimated multi-target centers to construct a multi-target capture kinematic controller; S4. Use the kinematic controller constructed in step S3 to collaboratively control the AUV to achieve the capture mission of the multi-target virtual center.

2. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 1 is characterized in that: Step S1 is specifically as follows: Construct the kinematic model of AUV, which is expressed as: Where, the subscript i represents the i-th AUV, i = 1, 2, ..., n, and n is the number of AUVs; [x i y i ] is the coordinate of the i-th AUV in the inertial coordinate system, x i is the horizontal axis, y i is the vertical coordinate; θ i is the heading angle of the i-th AUV in the inertial coordinate system; [u i ω i ] T is the kinematic controller input, u i ,ω i Represent the forward speed and yaw angular velocity of the i-th AUV design respectively.

3. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 1 is characterized in that: Step S2 includes: S21. Define the capture target and the collaborative capture task of multiple targets; S22. Based on the neighbor relationships among multiple AUVs, a multi-AUV neighbor relationship topology map is constructed; S23. Based on the neighbor relationships between multiple AUVs and the multi-AUV neighbor relationship topology, calculate the Laplace matrix of the communication graph S24. Define the relative position between the i-th AUV and the j-th target in the local coordinate system The expression is: in, is the rotation matrix; [x j y j ] T is the coordinate of the jth target in the inertial coordinate system, x j is the horizontal axis, y j is the vertical coordinate; S25. Define the relative position of the i-th AUV and the multi-target center in the local coordinate system The expression is: in, is the rotation matrix; is the coordinate of the multi-target virtual center in the inertial coordinate system, is the horizontal axis, is the vertical coordinate; S26. Construct a distributed collaborative multi-target center estimator; S27. Set estimator parameters to enable the distributed cooperative multi-target center estimator to track the relative positions of the multi-target centers.

4. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 3 is characterized in that: Step S21 is specifically as follows: For a team consisting of n AUVs, the multi-target cooperative enclosing task is to enclose the virtual center of m targets T j with an expected radius r i cooperatively, where j = 1, 2,..., m and i = 1, 2,..., n, and m ≤ n; if m < n, then some AUVs perform distributed enclosing on the same target; Among them, an AUV can detect at most one target T j Relative position information The relative position information is obtained in the local coordinate system of the AUV.

5. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 4 is characterized in that: Step S22 is specifically as follows: Each AUV is assigned a previous neighbor, where the previous neighbor of the nth AUV is the first AUV, thus constructing a one-way cyclic neighbor relationship graph. The information exchange is a directed ring topology. In this topology, each AUV only needs to obtain the relative position estimation information about the virtual center of multiple targets from its neighbor AUVs. Step S23 is specifically as follows: Suppose a directed graph G(V,E,A) is represented by (V,E,A), where V is the set of AUV nodes, E is the set of edges, and A=[a ij ] is a weighted adjacency matrix; the in-degree and out-degree of a node in a directed graph are defined as and If the in-degree of each node in the graph is equal to the out-degree, then the directed graph G is balanced; an undirected graph is a special balanced graph with a ji =a ij The property of holds for all i, j; A directed graph G is said to be strongly connected if there is a continuous sequence of edges from any node i to another node j in the graph, i≠j; Degree matrix Δ=[δ ij ] is a diagonal matrix, where δ ij = 0 for all i≠j, and δ ii =deg out (v i ) holds for all i; Based on the neighbor relationship between multiple AUVs, the Laplace matrix of the communication graph G between multiple AUVs is calculated. definition For a strongly connected graph, the rank of its Laplacian matrix is equal to n-1, where n is the Laplacian matrix dimension.

6. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 3 is characterized in that: Step S26 is specifically as follows: Distributed cooperative multi-target center estimator, expressed as: in, is the multi-target virtual center estimate calculated by the i-th AUV, that is The estimated value of λ i is the internal state variable of the center estimator of the i-th AUV, γ1>0, γ2>0, γ3>0 are the design parameters of the estimator; and λ i+1 The update only requires information from neighboring AUVs and adopts a distributed protocol; Step S27 is specifically as follows: Set the estimator parameters γ1, γ2, γ3 so that the input is time-varying The output is The distributed cooperative multi-target center estimator can track And the tracking error is bounded, so the estimator parameters satisfy the following conditions: γ3>0 (6) Among them, a i and b i They represent the Laplace matrices respectively The real and imaginary parts of the i-th eigenvalue of In order to ensure the convergence of the protocol, according to the above gain conditions, the following steps are used to select the parameters γ1, γ2, and γ3 in the consensus protocol: First assign a positive number to γ3; Calculate the lower bound of γ2 max{max(4γ3(b i 2 -a i 2 )),0}, and assign a value greater than the lower bound to γ2; Finally, calculate the lower bound of γ1 And assign γ1 a value greater than the lower bound; Through parameter selection, the estimation of multi-target virtual centers eventually converges to the bounded region.

7. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 1 is characterized in that: Step S3 includes: S31. Define the conditions that must be met for the target capture mission; S32. Define the relative position between the i-th AUV and its neighbor i+1-th AUV in the local coordinate system Expressions of S33, constructing a kinematic controller for the multi-AUV system and setting the kinematic controller input; S34. Define the state variable χ of the i-th AUV i ; S35, design control parameter k u ,k w .

8. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 7 is characterized in that: In step S31, the target capture task needs to meet the following conditions: Where i = 1, 2, ..., n, is a constant, r i is the radius of the cyclic orbit in the i-th AUV target capture mission, r1=r2=...=r n , t is the movement time; definition And Ψ i Satisfaction i ∈[0,2π), where atan2 is the inverse tangent function; In step S32, the relative position between the i-th AUV and its neighbor i+1-th AUV in the local coordinate system is The specific expression is: in, is the rotation matrix; [x i+1 y i+1 ] T is the coordinate of the neighboring AUV (i+1) in the inertial coordinate system, x i+1 is the horizontal axis, y i+1 Is the vertical axis.

9. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 7 is characterized in that: In step S33, the kinematic controller input of the multi-AUV system is specifically: Among them, k u >0,k w >0 is the kinematic controller parameter; c is a constant, and its value range is c∈(0,1); In step S34, the state variable x of the i-th AUV is i Specifically: x i =[u i ,oh i ,b i ] T (12) Among them, β i =θ i+1 -θ i ; The first-order differential of formula (12) is: when When , formula (13) is expressed as: The state variables of the entire AUV system are: In terms of When , the only equilibrium state of χ is: in, Step S35 is specifically as follows: Design control parameter k u ,k w , such that: Therefore, k u ,k w The expression that needs to be satisfied is 10. The multi-AUV collaborative capture control method based on virtual multi-target estimation center according to claim 1 is characterized in that: When the target group begins to disperse and form multiple target small groups, the distributed multi-target center estimator constructed in step S2 re-estimates the virtual centers of the multiple target small groups, and then uses the kinematic controller constructed in step S3 to drive multiple AUVs to surround the dispersed target small groups.

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