A method for coordinated multi-target uniform encirclement and control of multiple unmanned surface vessels

Abstract

A method for cooperative multi-target uniform encirclement control of multiple unmanned surface vessels (USVs) is disclosed, relating to the field of cooperative multi-target encirclement technology. To address the shortcomings of existing cooperative multi-target encirclement technologies, such as low efficiency, insufficient encirclement accuracy and stability, and complex gain design that easily leads to excessive gain, this invention provides the following solution: It includes: constructing kinematic models of the multi-USV system and the multi-target system, defining the target state of the encirclement task; establishing a distributed target center estimator based on the kinematic model and target state; constructing an encirclement controller based on the target geometric center information provided by the distributed target center estimator; adjusting the angular velocity of the USVs to achieve consistency in phase interval angles based on the control strategy of the encirclement controller; dynamically adjusting the parameters of the encirclement controller and outputting the results to the multi-USV system. This method is suitable for application in cooperative multi-target uniform encirclement control of multiple USVs.

Description

Technical Field

[0001] This invention relates to the field of multi-unmanned surface vessel (USV) collaborative multi-target encirclement and capture technology, specifically to a method for controlling the uniform encirclement and capture of multiple targets using multi-USV collaborative multi-target encirclement and capture. Background Technology

[0002] In recent years, unmanned surface vessels (USVs) have been widely used in marine transportation, resource exploration, and environmental monitoring, and significant progress has been made in their technological research. Currently, research focuses primarily on path tracking, trajectory planning, and target acquisition for single USVs. For example, some studies have proposed target acquisition methods based on predetermined trajectory planning, which optimize the motion trajectory of a single USV to achieve accurate tracking of static or slowly moving targets. Furthermore, some research has explored single-target acquisition strategies combined with sensor networks, enabling USVs to efficiently capture targets using global information. However, the application of single USVs is limited by the complexity of environmental changes and the high risk of single-point failures, making it difficult to meet the dynamic requirements of multi-target scenarios in complex missions.

[0003] To improve mission efficiency, recent research has increasingly shifted towards multi-unmanned surface vessel (USV) cooperative systems. In multi-target encirclement, two main research approaches exist. One approach involves grouping multiple USVs into groups, each encircling its own target, thus simplifying the problem into multiple single-target encirclement tasks. However, this method lacks global coordination capabilities and is prone to task conflicts or resource waste at group boundaries. The other approach pre-plans encirclement trajectories, forming a circular formation of USVs to confine the target within the encirclement area. While this method can achieve overall target encirclement, it requires frequent trajectory updates when target movement is unpredictable, leading to a sharp increase in computational complexity and potentially even failure.

[0004] Despite significant progress in collaborative multi-unmanned surface vessel (USV) capture operations, several technical challenges remain: First, existing central estimators typically rely on global variable information or specific initialization conditions. When a node fails or a new node is added, the system requires reinitialization, resulting in low efficiency. Second, existing capture control methods lack adaptability to dynamic targets and cannot effectively handle free-moving targets, significantly limiting capture accuracy and stability. Third, traditional methods are highly dependent on global consistency, leading to complex gain design and a tendency for excessive gain, which negatively impacts the robustness of the control system.

[0005] The aforementioned issues limit the application of multi-unmanned surface vessel (USV) cooperative encirclement technology in dynamic and complex environments. Therefore, a novel method is urgently needed to overcome global dependencies, improve estimation accuracy, and adapt to dynamic targets in order to solve these technical problems. Summary of the Invention

[0006] To address the shortcomings of existing multi-unmanned surface vessel (USV) cooperative encirclement technologies, such as low efficiency, insufficient encirclement accuracy and stability, and complex gain design that easily leads to excessive gain, the present invention provides the following technical solution:

[0007] A method for coordinated multi-target uniform encirclement and control of multiple unmanned surface vessels includes:

[0008] Construct kinematic models of multi-unmanned surface vessel systems and multi-target systems, and define the steps for the target state of the encirclement mission;

[0009] The steps for establishing a distributed target center estimator based on the kinematic model and target state;

[0010] The steps for constructing a containment controller based on the target geometric center information provided by the distributed target center estimator;

[0011] Based on the control strategy of the capture controller, the steps of adjusting the angular velocity of the unmanned surface vessel to achieve consistency of the phase interval angle are as follows:

[0012] The steps for dynamically adjusting the parameters of the capture controller and outputting them to the multi-unmanned surface vessel system.

[0013] Furthermore, a preferred implementation is provided in which the distributed target center estimator eliminates the consistency error of intermediate state variables through linear driving terms, and combined with the maximum consistency algorithm, allows each unmanned surface vessel to independently calculate its gain based on local information.

[0014] Furthermore, a preferred embodiment is provided in which the construction of the kinematic model of the multi-target system includes the geometric center of the target and its relative position and phase angle relationship with the unmanned surface vessel.

[0015] Furthermore, a preferred implementation is provided in which the linear driving term of the distributed target center estimator achieves consistency based on iterative calculation of local adjacency relationships, and is independently calculated based on the maximum consistency algorithm, and is dynamically adjusted to adapt to different initial distribution conditions.

[0016] Furthermore, a preferred embodiment is provided in which the linear velocity in the capture controller is designed using a proportional gain parameter, the range of which is dynamically adjusted according to the relative distance between the unmanned surface vessel and the target center.

[0017] Furthermore, a preferred embodiment is provided in which the kinematic model of the multi-target system includes the nonlinear trajectory of the target, and the target's velocity and angular velocity are set according to a dynamic function.

[0018] Based on the same inventive concept, the present invention also provides a multi-unmanned surface vessel cooperative multi-target uniform encirclement and control device, comprising:

[0019] Construct kinematic models for multiple unmanned surface vessel systems and multiple target systems, and define modules for the target states of the encirclement mission;

[0020] Based on the kinematic model and target state, a module for establishing a distributed target center estimator is established;

[0021] Based on the target geometric center information provided by the distributed target center estimator, a module for the encirclement controller is constructed.

[0022] Based on the control strategy of the capture controller, a module adjusts the angular velocity of the unmanned surface vessel to achieve consistency in the phase interval angle;

[0023] The parameters of the capture controller are dynamically adjusted and output to the modules of the multi-unmanned surface vessel system.

[0024] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computing program, wherein when the computer program is read by a computer, the computer executes the method described thereon.

[0025] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium, wherein when the processor reads a computer program stored in the storage medium, the computer executes the method described thereon.

[0026] Based on the same inventive concept, the present invention also provides a computer program product, which, when executed, implements the method described.

[0027] Compared with the prior art, the advantages of the technical solution provided by the present invention are as follows:

[0028] The design of the distributed target center estimator significantly improves the system's robustness to initialization errors by introducing a linear term to drive the intermediate state variables to zero consistency. This design effectively avoids the efficiency reduction caused by the need for specific initialization in traditional methods. Compared with existing research, it achieves stable estimation without re-initializing the network, making it particularly suitable for scenarios involving node failures or the addition of new nodes.

[0029] The application of the maximum consensus algorithm in gain design enables each unmanned surface vessel (USV) to independently obtain gain from local information, eliminating dependence on global variable information. This approach avoids the instability caused by excessive gain in traditional methods, significantly enhances the robustness of the estimator in practical applications, ensures that the estimation error converges quickly within a finite time, and is more flexible and reliable compared to methods that rely on global control.

[0030] A multi-target uniform encirclement controller designed based on repulsion theory enables multiple unmanned surface vessel (USV) systems to encircle multiple moving targets, ensuring the stability and uniformity of the encirclement process even with different initial state distributions. Compared with previous single-target strategies, this design not only achieves uniform distribution among targets but also solves the dynamic encirclement problem of freely moving targets, enhancing adaptability in complex environments.

[0031] The control scheme utilizes a distributed target center estimator combined with a uniform encirclement controller, enabling efficient encirclement of multi-target systems without requiring global variables. Compared to existing group encirclement or predetermined trajectory methods, this scheme significantly reduces computational complexity while improving the success rate and efficiency of the encirclement task, validating its superiority in dynamic and unpredictable environments.

[0032] It is suitable for use in the coordinated and uniform encirclement and control of multiple unmanned surface vessels. Attached Figure Description

[0033] Figure 1 Diagram illustrating the encirclement and capture of the target;

[0034] Figure 2 Schematic diagram of the trajectory for multi-unmanned surface vessel coordinated target encirclement;

[0035] Figure 3 A schematic diagram illustrating the consistency error of a distributed target center estimator;

[0036] Figure 4 A schematic diagram illustrating the estimation error of a distributed target center estimator;

[0037] Figure 5 Schematic diagram of distance error in multi-unmanned surface vessel coordinated encirclement and capture;

[0038] Figure 6 Schematic diagram of phase error in multi-unmanned surface vessel coordinated encirclement and capture;

[0039] Figure 7 A schematic diagram of the angle tracking error in the coordinated encirclement and capture of multiple unmanned surface vessels. Detailed Implementation

[0040] To make the advantages and benefits of the technical solution provided by the present invention clearer, the technical solution provided by the present invention will now be described in further detail with reference to the accompanying drawings:

[0041] Implementation Method 1: This implementation method provides a multi-unmanned surface vessel (USV) cooperative multi-target uniform encirclement and control method, including:

[0042] Construct kinematic models of multi-unmanned surface vessel systems and multi-target systems, and define the steps for the target state of the encirclement mission;

[0043] The steps for establishing a distributed target center estimator based on the kinematic model and target state;

[0044] The steps for constructing a containment controller based on the target geometric center information provided by the distributed target center estimator;

[0045] Based on the control strategy of the capture controller, the steps of adjusting the angular velocity of the unmanned surface vessel to achieve consistency of the phase interval angle are as follows:

[0046] The steps for dynamically adjusting the parameters of the capture controller and outputting them to the multi-unmanned surface vessel system.

[0047] The distributed target center estimator eliminates the consistency error of intermediate state variables through linear driving terms, and combined with the maximum consistency algorithm, allows each unmanned surface vessel to independently calculate its gain based on local information.

[0048] The construction of the kinematic model of the multi-target system includes the geometric center of the target and its relative position and phase angle relationship with the unmanned surface vessel.

[0049] The linear driving term of the distributed target center estimator achieves consistency based on iterative calculation of local adjacency relationships and is independently calculated based on the maximum consistency algorithm, and is dynamically adjusted to adapt to different initial distribution conditions.

[0050] The linear velocity in the capture controller is designed using a proportional gain parameter, the range of which is dynamically adjusted based on the relative distance between the unmanned surface vessel and the target center.

[0051] The kinematic model of the multi-objective system includes the nonlinear trajectory of the target, and the target's velocity and angular velocity are set according to a dynamic function.

[0052] Implementation Method Two: This implementation method is a detailed description of the technical solution provided in Implementation Method One, specifically:

[0053] Step 1: Model Building and Problem Statement

[0054] Determine the kinematic models for multi-unmanned surface vessel systems and multi-objective systems:

[0055] For unmanned surface vessels, a nonlinear kinematic model is defined to describe their position, yaw angle, forward speed, drift speed, and yaw rate.

[0056] For a target, define its kinematic model to describe its position, yaw angle, and motion parameters.

[0057] Define the geometric center of the target and the target distribution characteristics of the multi-target system.

[0058] Describe the problem of uniform encirclement of multiple targets:

[0059] The multi-target encirclement task can be abstracted into the problem of controlling the relative position and angle between the multi-unmanned surface vessel system and the geometric center of the target.

[0060] Define the safe radius and uniform distribution condition for the encirclement, and formalize the encirclement problem into a trajectory planning problem that satisfies a set of control constraints.

[0061] Output: The dynamic relationship model between the unmanned surface vessel and the target, as well as the mathematical description of the encirclement mission, are obtained, providing a basis for the subsequent design of the estimator and controller.

[0062] Step 2: Design a distributed target center estimator

[0063] Introducing intermediate state variables and linear driving terms:

[0064] A distributed target center estimator is designed for each unmanned surface vessel to drive the consistency error of intermediate state variables to approach zero.

[0065] The estimator is guaranteed to be robust to initialization errors and can achieve target center estimation without specific initialization.

[0066] Gain design using the maximum consensus algorithm:

[0067] The gain is calculated independently based on local information, avoiding the problem of excessive gain caused by relying on global variables in traditional methods.

[0068] Using Lyapunov functions, it is proven that the estimation error can quickly converge to a small region within a finite time.

[0069] Output: The unmanned surface vessel can obtain the position information of the geometric center of the target through a distributed target center estimator, providing input for controller design.

[0070] Step 3: Design a multi-target uniform encirclement controller

[0071] Develop control strategies based on distributed estimation results:

[0072] An algorithm for the encirclement and control of unmanned surface vessels (USVs) is constructed using repulsion theory to ensure that targets are evenly distributed within the encirclement area.

[0073] The control signals of the unmanned surface vessel are divided into encirclement signals and pursuit signals, which are composed of linear velocity and angular velocity respectively, in order to achieve stable encirclement and capture of the target.

[0074] Ensure the stability and phase uniformity of the encirclement:

[0075] Define the tracking error of the capture radius and the phase interval angle, and design the corresponding Lyapunov function to analyze the system stability.

[0076] Verify the effectiveness of the containment controller under conditions of free target movement, ensuring that the phase difference converges to a uniform state of target distribution.

[0077] Output: The multi-unmanned surface vessel system achieves stable encirclement and capture of multiple targets based on the results of the target center estimator.

[0078] Step 4 Simulation Verification

[0079] Set simulation parameters and initial conditions:

[0080] Select the initial position and speed of the unmanned surface vessel and the target, as well as the safe radius for the encirclement mission.

[0081] Set the design parameters for the distributed estimator and control algorithm, including gain value, velocity angle, etc.

[0082] Perform simulation experiments and verify the results:

[0083] Record key indicators such as the unmanned surface vessel's encirclement trajectory, target center estimation error, distance error, and phase error.

[0084] The simulation results were analyzed to verify the stability and accuracy of the multi-target system being successfully surrounded and captured by the unmanned surface vessel system.

[0085] Output: Simulation results show that the multi-unmanned surface vessel cooperative control algorithm can effectively achieve uniform encirclement and capture of multiple targets, verifying the feasibility and stability of the method.

[0086] Implementation Method 3: Combination Figure 1-7 This embodiment describes the technical solution provided above in further detail through specific examples. Specifically:

[0087] include:

[0088] Step (1): Consider a multi-unmanned surface vessel (USV) system consisting of n USVs. The nonlinear kinematic model of the i-th USV is expressed as:

[0089]

[0090] In the formula (x i ,y i ) represents the position of the i-th unmanned surface vessel. Indicates the yaw angle, u i v i r i Let η represent the forward velocity, drift velocity, and yaw rate, respectively. i =[x i ,y i ] T υ i =[u i ,vi ] T .set up Let be the rotation matrix, defined as

[0091]

[0092] Then there is

[0093] Consider a multi-objective system consisting of l targets. The kinematic equation of the i-th target is as follows:

[0094]

[0095] In the formula This represents the position of the i-th target. Indicates the yaw angle. r i t Let these represent forward speed, drift speed, and bow roll rate, respectively.

[0096] Define the target center as There exists a positive constant. satisfy Then the relative distance ρ between the i-th unmanned surface vessel and the target center is... i and phase angle φ i Represented as:

[0097]

[0098] Multi-target uniform encirclement: Consider the aforementioned multi-unmanned surface vessel system and multi-target system. For example... Figure 1 As shown, ρ is defined d The pre-designed safe encirclement radius. Successful multi-target encirclement is achieved if the following relationship is met:

[0099]

[0100] Step (2): First, design the distributed target center estimator as follows:

[0101]

[0102] In the formula, i∈V l θ1 is a positive constant. i It is an intermediate state function, c i and Let the center position and velocity of the target of the i-th unmanned surface vessel be represented, and then define... The following results were obtained:

[0103] Theorem 1: Consider the distributed target centroid estimator designed above. Each unmanned surface vessel can estimate the target centroid, and the estimation error converges to a small region in a finite time.

[0104] Proof: First, define the Lyapunov function as follows:

[0105]

[0106] For V e Differentiating, we have:

[0107]

[0108] Substituting the above estimator into V1, we get:

[0109]

[0110] In the formula, κ = 0.2785, combined with The definitions are:

[0111]

[0112] Furthermore, it can be deduced that:

[0113]

[0114]

[0115] It can be written as:

[0116]

[0117] Choose a Lyapunov function Differentiation yields:

[0118]

[0119] Choose a Lyapunov candidate function as V0 = V e +V θ The derivative of V0 can be written as:

[0120]

[0121] This means It converges to a small region within a finite time. They converge to θ simultaneously within a finite time. * .

[0122] The second step, considering the distributed target center estimator described above, is as follows:

[0123]

[0124] because So there are Furthermore, c can be derived from the first step. i =c j Then we have:

[0125]

[0126] Proof complete.

[0127] Next, a uniform encirclement controller based on a distributed target center estimator is designed. The control schemes for each unmanned surface vessel are designed as follows:

[0128]

[0129] in k is an integer. It is an ideal velocity angle. and These are the encirclement signal and the pursuit signal, designed as follows:

[0130]

[0131] i = 1, 2, ..., n, σ i where i = 1, 2, ..., n is the designed linear velocity. and μ i Let i = 1, 2, ..., n be the design parameters. The following results are obtained:

[0132] Theorem 2: Considering the above-mentioned multi-unmanned surface vessel system and multi-objective system, if the control scheme is designed as described above, the control objective of this paper will be achieved.

[0133] Proof: Define the radius tracking error ρ e_i =ρ i -ρ d , ρ e_i The derivative with respect to time is:

[0134]

[0135] Therefore, it can be deduced that:

[0136]

[0137] in It can be further written as:

[0138]

[0139] in have:

[0140]

[0141] in Choose Lyapunov functions as V ρ The derivative is:

[0142]

[0143] in Since it is a positive constant, we have:

[0144]

[0145] This means The phase interval angle is defined as:

[0146] γ 12 =φ1-φ2

[0147] γ 23 =φ2-φ3

[0148]

[0149] γ n1 =φ n -φ1+2π

[0150] The angle error can then be expressed as:

[0151]

[0152] φ e_i The derivative is:

[0153]

[0154] Further, there are:

[0155]

[0156] The following Lyapunov function is given:

[0157]

[0158] It can be obtained if and only if φ e_1 =φ e_2 =...=φ e_n hour Using V e and φ e_i The boundedness of can be obtained Therefore, the conclusion is

[0159] The proof is complete.

[0160] Step (3) involves numerical simulation to demonstrate the effectiveness of the proposed control algorithm. Consider a multi-unmanned surface vessel (USV) system consisting of three USVs. l = {1, 2, 3} and a multi-objective system V t The adjacency matrix between each unmanned surface vessel (USV) is composed of {1, 2, 3}. The adjacency matrix between an unmanned surface vessel and a target is defined as follows: The initial positions of each unmanned surface vessel are represented as follows: The initial velocity is [u i (0),v i (0),r i [0)] = [0.01, 0, 0] T Let i = 1, 2, 3, and the target's trajectory be defined as...

[0161]

[0162] The data associated with the distributed target center estimator is defined as: θ1 = 1. The parameter design of the multi-unmanned surface vessel cooperative multi-target uniform encirclement control algorithm is as follows: ρ d =10, σ i =0.5, μ i =0.6, i=1,2,3. The simulation duration was set to 300 seconds. Using the above parameter values, a simulation experiment was designed to verify the effectiveness of the proposed method. The simulation results are shown in the attached figure.

[0163] The simulation results of the proposed control algorithm are as follows: Figure 2-7 As shown. The pursuit trajectories of the unmanned surface vessel and the moving target are as follows. Figure 2 As shown, it can be observed that the multi-target system was successfully surrounded by the multi-unmanned surface vessel system. The time response of the consensus error of the distributed target center estimator is as follows. Figure 3 As shown. The time response of the estimation error of the distributed target center estimator is as follows. Figure 4 As shown, the mean square error of the target center estimation is 0.22. This indicates that the proposed algorithm effectively estimates the target center. The time response of the distance error is shown in the figure. Figure 5 As shown, the relative distance error converges to a small region. To demonstrate that the phase difference between each pair of unmanned surface vessels converges to 2π / n, the phase difference curve is shown below. Figure 6 As shown, the feasibility of phase control is verified. The time response of the angle tracking error is as follows. Figure 7 As shown. In summary, the experimental results demonstrate the effectiveness of the present invention.

[0164] The above description of several specific embodiments further details the technical solution provided by the present invention in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, combinations of embodiments, and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for multi-unmanned surface vehicle cooperative multi-target uniform encirclement control, characterized in that, The method comprises the following steps: a step of constructing a kinematic model of a multi-unmanned vehicle system and a multi-target system, and defining a target state of a surround task; a step of establishing a distributed target center estimator based on the kinematic model and the target state; a step of constructing a surround controller according to target geometric center information provided by the distributed target center estimator; a step of adjusting an angular velocity of the unmanned vehicle to achieve consistency of a phase interval angle based on a control strategy of the surround controller; a step of dynamically adjusting parameters of the surround controller and outputting to the multi-unmanned vehicle system; the distributed target center estimator eliminates consistency errors of intermediate state variables through a linear driving term, and combines a maximum consistency algorithm to allow each unmanned vehicle to independently calculate a gain based on local information; the distributed target center estimator is designed as follows: where i ∈ V l , θ1is a constant, a i is an intermediate state function, c i and denote the target's center position and velocity of the ith USV, and then define a control scheme of each unmanned vehicle is designed as follows: wherein k is an integer, is an ideal velocity angle; and are a surround signal and a chase signal, respectively, designed as follows: i = 1, 2,..., n, σ i i = 1, 2,..., n is the designed linear velocity, and μ i i = 1, 2,..., n is the design parameter.

2. The method of claim 1, wherein, construction of the kinematic model of the multi-target system comprises a target geometric center and a relative position and a phase angle relationship between the target geometric center and the unmanned vehicle.

3. The method of claim 1, wherein, the linear driving term of the distributed target center estimator achieves consistency based on iterative calculation of local adjacency relationships, and is independently calculated based on the maximum consistency algorithm, and is dynamically adjusted to adapt to different initial distribution conditions.

4. The method of claim 1, wherein, linear velocities in the surround controller are designed through proportional gain parameters, and a range of the proportional gain parameters is dynamically adjusted according to a relative distance between the unmanned vehicle and the target center.

5. The method of claim 1, wherein, the kinematic model of the multi-target system comprises a nonlinear trajectory of the target, and a speed and an angular velocity of the target are set according to a dynamic function.

6. A multi-unmanned vehicle cooperative multi-target uniform encirclement control device, characterized in that, The method comprises the following steps: a module of constructing a kinematic model of a multi-unmanned vehicle system and a multi-target system, and defining a target state of a surround task; a module of establishing a distributed target center estimator based on the kinematic model and the target state; a module of constructing a surround controller according to target geometric center information provided by the distributed target center estimator; a module of adjusting an angular velocity of the unmanned vehicle to achieve consistency of a phase interval angle based on a control strategy of the surround controller; a module of dynamically adjusting parameters of the surround controller and outputting to the multi-unmanned vehicle system; the distributed target center estimator eliminates consistency errors of intermediate state variables through a linear driving term, and combines a maximum consistency algorithm to allow each unmanned vehicle to independently calculate a gain based on local information; the distributed target center estimator is designed as follows: where i ∈ V l , θ1is a constant, a i is an intermediate state function, c i and denote the target's center position and velocity of the ith USV, and then define a control scheme of each unmanned vehicle is designed as follows: wherein k is an integer, is an ideal velocity angle; and are a surround signal and a chase signal, respectively, designed as follows: i = 1, 2,..., n, σ i i = 1, 2,..., n is the designed linear velocity, and μ i i = 1, 2,..., n is the design parameter.

7. Computer storage medium for storing a computing program, characterized in that when the computer program is read by the computer, the computer executes the method of claim 1.

8. A computer comprising a processor and a storage medium, characterized in that when the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1.

9. Computer program product as computer program, characterized in that when the computer program is executed, the method of claim 1 is implemented.

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