Optimization method for cooperative detection configuration of multiple ocean unmanned systems based on ring control

Abstract

The present application relates to the technical field of unmanned underwater vehicles, and in particular to a method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on annular containment, comprising: establishing an initial annular multi-UUV containment layout based on the position, detection requirements, and environmental characteristics of underwater targets; calculating the total detection coverage area based on the initial position and detection radius of each UUV, and calculating the detection coverage rate based on the area of the initial annular multi-UUV containment layout; formulating uniformity criteria and detection overlap criteria; calculating the GDOP value of each point and the average GDOP of the target area based on the position of each point in the target area and the initial position of each UUV; establishing an objective function based on the detection coverage rate, uniformity criteria, detection overlap criteria, average GDOP, and communication cost; and optimizing the objective function using a particle swarm optimization algorithm to obtain the optimal annular multi-UUV containment layout. This method effectively improves the accuracy of target positioning.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of unmanned underwater vehicles, and in particular to a method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on annular containment. Background Art

[0002] With the increasing demand for applications such as marine resource development, environmental protection, and target tracking and reconnaissance, underwater unmanned vehicles (UUVs) are playing an increasingly important role in underwater and ocean exploration missions. Traditional single-UUV detection methods, due to their limited coverage and insufficient accuracy, struggle to efficiently perform multi-target detection missions in vast and complex underwater environments. To address this issue, collaborative detection technology using multiple UUVs has been widely researched and applied in recent years. By enabling multiple UUVs to collaborate, detection coverage and accuracy can be improved, helping to achieve more complex mission objectives.

[0003] However, in practical applications, multi-UUV collaborative detection faces a series of challenges. First, due to the complex and changeable underwater environment, the relative positions and detection strategies between UUVs need to be highly dynamic and optimized to ensure efficient detection coverage and accuracy. Secondly, when multiple UUVs work together, problems such as overlapping detection areas, excessive blind spots, and waste of detection resources are prone to occur. This will not only reduce the overall detection efficiency of the system, but may also lead to increased communication overhead and decreased system performance. In addition, in a multi-UUV system, since the communication between each UUV is limited by the underwater environment, how to achieve the best collaborative detection configuration while ensuring the lowest communication cost is also one of the key technical difficulties currently faced.

[0004] To address these issues, research teams have proposed solutions to improve the efficiency of multi-UUV collaborative detection through methods such as geometric layout optimization and multi-target detection algorithms. However, these methods often have the following limitations: First, they cannot fully utilize the UUV layout information to optimize the global detection effect, resulting in blind spots or insufficient detection accuracy; second, in dynamic underwater environments, existing methods are often inefficient when adjusting the UUV detection configuration in real time and have difficulty adapting to rapid environmental changes; third, the system often consumes a large amount of communication resources to maintain the global optimal configuration, which significantly increases energy consumption. Summary of the Invention

[0005] In view of this, the embodiment of the present application proposes a collaborative detection configuration optimization method for multiple ocean unmanned systems based on ring-shaped control. By introducing the particle swarm optimization algorithm and the geometric precision attenuation factor, the reasonable layout and detection path planning of multiple UUVs are realized to optimize the detection coverage, reduce the detection overlap, and improve the target positioning accuracy. Through reasonable geometric layout design, the communication cost is minimized to ensure the detection accuracy and system stability.

[0006] In order to achieve the above-mentioned purpose, an embodiment of the present application proposes a multi-ocean unmanned system collaborative detection configuration optimization method based on annular blockade, which is suitable for detecting underwater targets. The method includes the following steps: according to the position, detection requirements and environmental characteristics of the underwater target, an initial annular multi-UUV blockade layout uniformly distributed along the annular track is established in the target area, and the initial position, initial heading angle, initial detection radius, and initial spacing between two adjacent UUVs of each UUV are determined; wherein the initial detection radius of each UUV is the same, and the initial spacing between two adjacent UUVs is the same; based on the initial position and initial detection radius of each UUV, the total detection coverage area is determined, and based on the total detection coverage area and the area of the target area, the detection coverage rate is calculated; a uniformity criterion for ensuring uniform distribution of multiple UUVs in the layout, and a detection overlap criterion for ensuring cross-detection of multiple UUVs in the same area are formulated; based on the position of each point in the target area and the initial position of each UUV, a pseudorange matrix is calculated, and the GDOP (Geometric Dilution Abstract: In order to improve the detection accuracy of underwater targets, a multi-UUV air defense system with multi-UUV air defense and multi-submarine defense was proposed. The geometric dilution of precision (GDOP) value was calculated and the mean GDOP of the target area was calculated. An objective function based on a weighted reward mechanism was established based on the detection coverage, uniformity criterion, detection overlap criterion, mean GDOP and communication cost. The particle swarm optimization algorithm was used to iteratively optimize the objective function to obtain the optimal annular multi-UUV containment layout, and underwater targets were detected based on the optimal annular multi-UUV containment layout.

[0007] In order to achieve the above-mentioned purpose, the embodiment of the present application also proposes a multi-ocean unmanned system collaborative detection configuration optimization system based on annular blockade, the system including: an initial layout construction module, a detection coverage calculation module, a criterion formulation module, a GDOP calculation module, an objective function construction module and an optimization execution module; the initial layout construction module is used to establish an initial annular multi-UUV blockade layout uniformly distributed along a circular track in the target area according to the position, detection requirements and environmental characteristics of the underwater target, determine the initial position, initial heading angle, initial detection radius and initial spacing between each UUV, the initial detection radius of each UUV is the same, and the initial spacing between two adjacent UUVs is the same; the detection coverage calculation module is used to determine the total detection coverage area based on the initial position and initial detection radius of each UUV, and based on the total detection coverage area The detection coverage is calculated by summing the product of the two parameters and the area of the target area; the criterion formulation module is used to formulate a uniformity criterion for ensuring the uniform distribution of multiple UUVs in the layout, and a detection overlap criterion for ensuring cross-detection of multiple UUVs in the same area; the GDOP calculation module is used to calculate the pseudorange matrix based on the position of each point in the target area and the initial position of each UUV, calculate the GDOP value of each point based on the pseudorange matrix, and calculate the GDOP mean of the target area; the objective function construction module is used to establish an objective function based on the weighted reward mechanism based on the detection coverage, uniformity criterion, detection overlap criterion, GDOP mean and communication cost; the optimization execution module is used to iteratively optimize the objective function using the particle swarm optimization algorithm to obtain the optimal annular multi-UUV containment layout, and detect underwater targets based on the optimal annular multi-UUV containment layout.

[0008] In order to achieve the above-mentioned purpose, an embodiment of the present application also proposes an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the multi-ocean unmanned system collaborative detection configuration optimization method based on ring closure as described above.

[0009] In order to achieve the above-mentioned purpose, an embodiment of the present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the above-mentioned method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on ring closure.

[0010] This application proposes a method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on annular control. First, according to the position, detection requirements and environmental characteristics of the underwater target, an initial annular multi-UUV control layout uniformly distributed along the annular track is established in the target area. The initial annular multi-UUV control layout fully considers the actual situation of the underwater target and lays a good foundation for the subsequent optimization of the collaborative detection configuration of multiple ocean unmanned systems. Next, the detection coverage is calculated, a uniformity criterion is formulated to ensure that multiple UUVs are evenly distributed in the layout, and a detection overlap criterion is formulated to ensure that multiple UUVs conduct cross-detection in the same area, and then the GDOP mean of the target area is calculated. The GDOP value is an indicator used to evaluate the impact of the annular multi-UUV control layout on the target positioning accuracy. The lower the GDOP value of a point, the smaller the positioning error of the annular multi-UUV control layout for the point. The GDOP mean of the target area reflects the positioning accuracy of the annular multi-UUV control layout for the entire target area. Based on detection coverage, uniformity criteria, detection overlap criteria, mean GDOP, and communication cost, an objective function based on a weighted reward mechanism was established. Finally, a particle swarm optimization algorithm was used to iteratively optimize the objective function to obtain the optimal annular multi-UUV containment layout. The optimal annular multi-UUV containment layout corresponds to a higher detection coverage and a lower mean GDOP, while also meeting the requirements for uniform distribution and detection overlap. Detection of underwater targets based on the optimal annular multi-UUV containment layout effectively improves detection accuracy and positioning capabilities. This method not only effectively reduces detection errors but also enables efficient planning within a limited target area. It can adapt to various complex underwater detection mission scenarios and has strong universality.

[0011] Optionally, environmental characteristics include but are not limited to seabed topography, water flow velocity, and sound wave propagation velocity. When determining the initial position, each UUV is set at a location with flat seabed topography, slow water flow velocity, and fast sound wave propagation velocity. When determining the initial detection radius, the more complex the overall seabed topography of the target area, the faster the average water flow velocity, and the slower the average sound wave propagation velocity, the smaller the initial detection radius is set;

[0012] When determining the initial heading angle, ensure that each UUV is heading towards the underwater target;

[0013] The initial spacing between two adjacent UUVs is determined by the following formula:

[0014] ;

[0015] in, represents the initial detection radius, represents the total number of UUVs, is an integer greater than 2, Indicates the initial distance between two adjacent UUVs, It means initialization and has no practical meaning.

[0016] Optionally, the initial position of each UUV is within the target area. The method of determining the total detection coverage area based on the initial position and initial detection radius of each UUV, and calculating the detection coverage rate based on the total detection coverage area and the area of the initial annular multi-UUV containment layout includes:

[0017] The initial detection range of each UUV is calculated based on the initial position and initial detection radius of each UUV. The initial detection range of each UUV is also within the target area.

[0018] Calculating the sum of the areas of the initial detection ranges of the UUVs, and subtracting the overlapping areas between the initial detection ranges of the UUVs from the sum of the areas to calculate the total detection coverage area;

[0019] The ratio of the total detection coverage area to the area of the target region is calculated, and the ratio is taken as the detection coverage rate.

[0020] Alternatively, the uniformity criterion is expressed by the formula:

[0021] ;

[0022] in, Indicates the UUV and the center point of the target area The distance between represents the uniformity criterion;

[0023] The detection overlap criterion is expressed by the formula:

[0024] ;

[0025] in, Indicates the The first UUV and the The distance between UUVs, Represents the detection overlap criterion.

[0026] Optionally, based on the position of each point in the target area and the initial position of each UUV, a pseudorange matrix is calculated, a GDOP value of each point is calculated based on the pseudorange matrix, and a mean GDOP value of the target area is calculated, including:

[0027] The target area is divided into grids according to the preset size. Based on the position of each grid point in the grid and the initial position of each UUV, the pseudo-range matrix is calculated. For a grid point, its pseudorange matrix The Behavior Grid Points With the Unit direction vector between UUVs;

[0028] The GDOP value of each point is calculated based on the pseudorange matrix using the following formula:

[0029] ;

[0030] in, Indicates taking the trace of the matrix, with the upper right corner marked Indicates taking the transpose of the matrix, with the upper right corner subscript Indicates taking the inverse of a matrix, Indicates the GDOP value of each grid point;

[0031] The mean GDOP of the target area is calculated using the following formula:

[0032] ;

[0033] in, represents the total number of grid points, Indicates the mean GDOP of the target area.

[0034] Optionally, based on the detection coverage, uniformity criterion, detection overlap criterion, GDOP mean and communication cost, an objective function based on a weighted reward mechanism is established, which is implemented by the following formula:

[0035] ;

[0036] in, represents the objective function, represents the weight coefficient of detection coverage, represents the weight coefficient of the uniformity criterion, represents the weight coefficient of the detection overlap criterion, Represents the weight coefficient of the GDOP mean, represents the weight coefficient of communication cost, represents the detection coverage, represents the uniformity criterion, represents the detection overlap criterion, represents the GDOP mean, Indicates the communication cost.

[0037] Optionally, in the process of iteratively optimizing the objective function using the particle swarm optimization algorithm to obtain the optimal annular multi-UUV containment layout, the velocity and position of the particles are updated using the following formula:

[0038] ;

[0039] ;

[0040] in, represents the inertia weight, Indicates the In the iteration The speed of the particle, and are all preset learning factors. and are all random numbers, Indicates the In the iteration The position of the particle, Indicates the The optimal position of an individual in the iteration, Indicates the The global optimal position in the iteration, Indicates the updated The speed of the particle, Indicates the updated The position of a particle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related technologies, the following briefly introduces the drawings required for use in the various embodiments of the present application or the description of the related technologies. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of a method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on ring-shaped control, provided in one embodiment of the present application;

[0043] Figure 2 This is a schematic diagram of an initial annular multi-UUV containment layout provided in one embodiment of the present application;

[0044] Figure 3 1 is a schematic diagram of the initial GDOP value distribution provided in one embodiment of the present application;

[0045] Figure 4 1 is a schematic diagram of an optimized GDOP value distribution provided in one embodiment of the present application;

[0046] Figure 5 This is a schematic diagram of a change in the mean GDOP of a target area as the number of iterations of a particle swarm optimization algorithm increases, provided in one embodiment of the present application;

[0047] Figure 6 This is a schematic diagram of an optimal annular multi-UUV containment layout provided in one embodiment of the present application;

[0048] Figure 7 This is a structural diagram of a multi-ocean unmanned system collaborative detection configuration optimization system based on ring-shaped control, provided in another embodiment of the present application;

[0049] Figure 8 It is a structural diagram of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It will be understood by those skilled in the art that in the embodiments of the present application, many technical details are proposed to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present application. The embodiments can be combined with each other and referenced to each other under the premise of no contradiction.

[0051] An embodiment of the present application proposes a method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on annular closure, which is suitable for detecting underwater targets and is applied to electronic devices, wherein the electronic device can be a terminal or a server. This embodiment and the following embodiments are all described using the server as an example. The following is a detailed description of the implementation details of the method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on annular closure proposed in this embodiment. The following content is only the implementation details provided for the convenience of understanding and is not necessary for the implementation of this solution.

[0052] The specific process of the multi-ocean unmanned system collaborative detection configuration optimization method based on ring control proposed in this embodiment can be as follows: Figure 1 Shown, including:

[0053] Step 11: Based on the location, detection requirements and environmental characteristics of the underwater target, an initial circular multi-UUV containment layout uniformly distributed along a circular track is established in the target area, and the initial position, initial heading angle, initial detection radius and initial spacing between two adjacent UUVs are determined. The initial detection radius of each UUV is the same, and the initial spacing between two adjacent UUVs is the same.

[0054] In the specific implementation, the server first needs to determine the approximate location of the underwater target to be detected, determine the detection requirements of this detection mission, and also need to obtain the environmental characteristics of the target waters (target area). Next, the server establishes an initial circular multi-UUV containment layout evenly distributed along a circular track in the target area based on the location, detection requirements and environmental characteristics of the underwater target. At the same time, the server also needs to determine the initial position, initial heading angle, initial detection radius, and initial spacing between two adjacent UUVs of each UUV. It should be noted that the initial detection radius of each UUV is the same, and the initial spacing between two adjacent UUVs is also the same.

[0055] In one example, the target area is a two-dimensional area of 100m×100m. The initial position of each UUV is within the target area. Even in subsequent optimizations, each UUV always moves within the target area. The initial circular multi-UUV containment layout evenly distributed along the circular track established by the server can be as follows: Figure 2 shown.

[0056] In one example, the environmental characteristics of the target area include but are not limited to seabed topography, water flow velocity, and sound wave propagation speed. When determining the initial position of each UUV, the server needs to set the initial position of each UUV at a location where the seabed topography is relatively flat, the water flow velocity is relatively slow, and the sound wave propagation speed is relatively fast. When determining the initial detection radius of each UUV, it is necessary to use the environmental characteristics of the target area as a reference. The more complex the overall seabed topography of the target area, the faster the average water flow velocity, and the slower the average sound wave propagation speed, the smaller the initial detection radius will be. In the subsequent iterative optimization process, it is necessary to continuously adjust the position and detection radius of each UUV to ensure that the best detection effect can be obtained in a complex environment.

[0057] In one example, when the server determines the initial heading angle of each UUV, it needs to ensure that each UUV is facing the underwater target. The initial heading angle of the UUV is .

[0058] It is understandable that in the subsequent iterative optimization process, the heading angle will be updated in real time according to the motion trajectory of the underwater target to ensure that each UUV is always facing the underwater target and maximize the detection accuracy.

[0059] In one example, the server determines the initial distance between two adjacent UUVs using the following formula:

[0060] ;

[0061] in, represents the initial detection radius, represents the total number of UUVs, An integer greater than 2 (usually 5 UUVs are set), Indicates the initial distance between two adjacent UUVs, It means initialization and has no practical meaning.

[0062] Step 12: Determine the total detection coverage area based on the initial position and initial detection radius of each UUV, and calculate the detection coverage rate based on the total detection coverage area and the area of the target area.

[0063] In practice, after establishing the initial circular multi-UUV containment layout, the server calculates various metrics and constraints. The first target for subsequent iterative optimization is detection coverage. A higher detection coverage indicates a stronger detection capability. The server determines the total detection coverage area based on the initial position and initial detection radius of each UUV. The server then calculates the detection coverage rate based on the total detection coverage area and the area of the target area.

[0064] In one example, the server first needs to calculate the initial detection range of each UUV based on the initial position and initial detection radius of each UUV. The initial detection range of each UUV is also within the target area. Next, the server calculates the sum of the initial detection ranges of each UUV and subtracts the overlapped area between the initial detection ranges of each UUV from the calculated sum (if there is no overlap, the overlapped area is recorded as 0) to calculate the total detection coverage area. Finally, the server calculates the ratio of the total detection coverage area to the area of the target area and uses this ratio as the detection coverage rate, which is recorded as .

[0065] Step 13: formulate a uniformity criterion for ensuring that multiple UUVs are evenly distributed in the layout, and a detection overlap criterion for ensuring that multiple UUVs perform cross-detection in the same area.

[0066] In practice, a ring-shaped multi-UUV containment layout must meet two criteria. The first is uniformity, which ensures uniform distribution of multiple UUVs. This uniformity avoids detection blind spots. The second is detection overlap, which ensures cross-detection between multiple UUVs within the same area. This overlap improves the accuracy of detection results.

[0067] In one example, the uniformity criterion is formulated as:

[0068] ;

[0069] in, Indicates the UUV and the center point of the target area The distance between represents the uniformity criterion;

[0070] The detection overlap criterion is expressed by the formula:

[0071] ;

[0072] in, Indicates the The first UUV and the The distance between UUVs, Represents the detection overlap criterion.

[0073] Step 14: Calculate a pseudorange matrix based on the position of each point in the target area and the initial position of each UUV, calculate the GDOP value of each point based on the pseudorange matrix, and calculate the average GDOP value of the target area.

[0074] In the specific implementation, in addition to the detection coverage, uniformity criterion, and detection overlap criterion, the most important indicator is the GDOP average of the target area. The GDOP value is an indicator used to evaluate the impact of the circular multi-UUV containment layout on the target positioning accuracy. The lower the GDOP value of a point, the smaller the positioning error of the circular multi-UUV containment layout for that point. The GDOP average of the target area effectively reflects the positioning accuracy of the circular multi-UUV containment layout for the entire target area. The lower the GDOP average of the target area, the smaller the positioning error of the circular multi-UUV containment layout for the entire target area. The server needs to calculate a pseudorange matrix based on the position of each point in the target area and the initial position of each UUV, and then calculate the GDOP value of each point based on the pseudorange matrix, and then calculate the GDOP average of the target area.

[0075] It is understandable that it is unrealistic to calculate the GDOP of every point in the target area. Therefore, the server divides the target area into grids according to the preset size. The grids are staggered to form a number of grid points. The server calculates the pseudorange matrix based on the position of each grid point in the grid and the initial position of each UUV. For a grid point, its pseudorange matrix The Behavior Grid Points With the The unit direction vector between the UUVs.

[0076] Next, the server calculates the GDOP value of each point based on the pseudorange matrix using the following formula:

[0077] ;

[0078] in, Indicates taking the trace of the matrix, with the upper right corner marked Indicates taking the transpose of the matrix, with the upper right corner subscript Indicates taking the inverse of a matrix, Indicates the GDOP value of each grid point;

[0079] The mean GDOP of the target area is calculated using the following formula:

[0080] ;

[0081] in, represents the total number of grid points, Indicates the mean GDOP of the target area.

[0082] In one example, the server can also draw the GDOP value distribution to visualize the optimization. The GDOP value distribution under the initial ring multi-UUV sealing layout (initial GDOP value distribution) is as follows: Figure 3 As shown in the figure, the GDOP values at different locations are represented by the depth of color and the height of the image, which intuitively shows the spatial distribution of detection accuracy.

[0083] Step 15: Based on the detection coverage, uniformity criterion, detection overlap criterion, GDOP mean and communication cost, an objective function based on a weighted reward mechanism is established.

[0084] In the specific implementation, after the server calculates the detection coverage rate, the GDOP average of the target area, and formulates the uniformity criterion and the detection overlap criterion, it can establish an objective function based on the weighted reward mechanism based on the detection coverage rate, uniformity criterion, detection overlap criterion, GDOP average and communication cost (that is, the communication cost required for each UUV to transmit the signal to the central server).

[0085] In one example, the server establishes an objective function based on a weighted reward mechanism based on detection coverage, uniformity criterion, detection overlap criterion, GDOP mean, and communication cost, which can be implemented by the following formula:

[0086] ;

[0087] in, represents the objective function, represents the weight coefficient of detection coverage, represents the weight coefficient of the uniformity criterion, represents the weight coefficient of the detection overlap criterion, Represents the weight coefficient of the GDOP mean, represents the weight coefficient of communication cost, represents the detection coverage, represents the uniformity criterion, represents the detection overlap criterion, represents the GDOP mean, represents the communication cost, 、 、 、 and It can be adjusted according to actual needs.

[0088] Step 16: Use a particle swarm optimization algorithm to iteratively optimize the objective function to obtain an optimal annular multi-UUV sealing and control layout, and detect underwater targets based on the optimal annular multi-UUV sealing and control layout.

[0089] In the specific implementation, the server uses the particle swarm optimization algorithm (PSO) to iteratively optimize the objective function. After reaching the maximum number of iterations, it obtains the optimal circular multi-UUV containment layout. Finally, it instructs each UUV to detect underwater targets according to the optimal circular multi-UUV containment layout.

[0090] In one example, the PSO algorithm simulates the behavior of particles in the search space, continuously adjusting the positions of each UUV to minimize the average GDOP of the entire target area. In each iteration, the PSO algorithm updates the particle's velocity and position based on a weighted combination of the current optimal solution and the individual historical optimal solutions, thereby driving each UUV toward the global optimal position. After each position update, the particle's coordinates are constrained to within the target area, ensuring that each UUV does not explore beyond the target area. Through multiple iterations, the PSO algorithm gradually optimizes the layout of each UUV, resulting in a gradual decrease in the average GDOP of the target area.

[0091] In one example, the server uses the particle swarm optimization algorithm to iteratively optimize the objective function to obtain the optimal circular multi-UUV containment layout. The server updates the particle speed and position using the following formula:

[0092] ;

[0093] ;

[0094] in, represents the inertia weight, Indicates the In the iteration The speed of the particle, and are all preset learning factors. and are all random numbers, Indicates the In the iteration The position of the particle, Indicates the The optimal position of an individual in the iteration, Indicates the The global optimal position in the iteration, Indicates the updated The speed of the particle, Indicates the updated The position of a particle.

[0095] In one example, the distribution of GDOP values in the optimized target area corresponding to the optimal annular multi-UUV containment layout is as follows: Figure 4 As shown in Figure 2, the mean GDOP of the target area changes with the increase in the number of iterations of the PSO algorithm. Figure 5 As shown in the figure, compared with the initial GDOP value distribution layout, the optimized GDOP value distribution layout is more reasonable, the GDOP mean value in the target area is lower, and the overall detection accuracy is significantly improved.

[0096] In one example, the optimal annular multi-UUV containment layout can be as follows: Figure 6 shown.

[0097] This embodiment proposes a method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on annular control. First, based on the position, detection requirements and environmental characteristics of the underwater target, an initial annular multi-UUV control layout uniformly distributed along the annular track is established in the target area. The initial annular multi-UUV control layout fully considers the actual situation of the underwater target and lays a good foundation for the subsequent optimization of the collaborative detection configuration of multiple ocean unmanned systems. Next, the detection coverage is calculated, a uniformity criterion is formulated to ensure that multiple UUVs are evenly distributed in the layout, and a detection overlap criterion is formulated to ensure that multiple UUVs perform cross-detection in the same area, and then the GDOP mean of the target area is calculated. The GDOP value is an indicator used to evaluate the impact of the annular multi-UUV control layout on the target positioning accuracy. The lower the GDOP value of a point, the smaller the positioning error of the annular multi-UUV control layout for the point. The GDOP mean of the target area reflects the positioning accuracy of the annular multi-UUV control layout for the entire target area. Based on detection coverage, uniformity criteria, detection overlap criteria, mean GDOP, and communication cost, an objective function based on a weighted reward mechanism was established. Finally, a particle swarm optimization algorithm was used to iteratively optimize the objective function to obtain the optimal annular multi-UUV containment layout. The optimal annular multi-UUV containment layout corresponds to a higher detection coverage and a lower mean GDOP, while also meeting the requirements for uniform distribution and detection overlap. Detection of underwater targets based on the optimal annular multi-UUV containment layout effectively improves detection accuracy and positioning capabilities. This method not only effectively reduces detection errors but also enables efficient planning within a limited target area. It can adapt to various complex underwater detection mission scenarios and has strong universality.

[0098] The steps of the various methods described above are for clarity of description only. During implementation, they can be combined into a single step, or some steps can be split into multiple steps. As long as they contain the same logical relationships, they are all within the scope of protection of this application. Insignificant modifications to the algorithm or process, or insignificant designs introduced that do not change the core design of the algorithm or process, are also within the scope of protection of this application.

[0099] Another embodiment of the present application proposes a multi-ocean unmanned system collaborative detection configuration optimization system based on ring-shaped blockade, which is suitable for detecting underwater targets. The details of the multi-ocean unmanned system collaborative detection configuration optimization system based on ring-shaped blockade proposed in this embodiment are described in detail below. The following content is only the implementation details provided for easy understanding and is not necessary for the implementation of this example.

[0100] Figure 7This is a structural diagram of a multi-ocean unmanned system collaborative detection configuration optimization system based on ring closure proposed in this embodiment. The system specifically includes: an initial layout construction module 21, a detection coverage calculation module 22, a criterion formulation module 23, a GDOP calculation module 24, an objective function construction module 25 and an optimization execution module 26.

[0101] The initial layout construction module 21 is used to establish an initial circular multi-UUV containment layout evenly distributed along a circular track in the target area according to the position, detection requirements and environmental characteristics of the underwater target, and determine the initial position, initial heading angle, initial detection radius and initial spacing between each UUV. The initial detection radius of each UUV is the same, and the initial spacing between two adjacent UUVs is the same.

[0102] The detection coverage calculation module 22 is used to determine the total detection coverage area based on the initial position and initial detection radius of each UUV, and calculate the detection coverage rate based on the total detection coverage area and the area of the target area.

[0103] The criterion formulation module 23 is used to formulate a uniformity criterion for ensuring that multiple UUVs are evenly distributed in the layout, and a detection overlap criterion for ensuring that multiple UUVs perform cross-detection in the same area.

[0104] The GDOP calculation module 24 is used to calculate a pseudorange matrix based on the position of each point in the target area and the initial position of each UUV, calculate the GDOP value of each point based on the pseudorange matrix, and calculate the average GDOP value of the target area.

[0105] The objective function building module 25 is used to build an objective function based on a weighted reward mechanism based on the detection coverage, the uniformity criterion, the detection overlap criterion, the GDOP mean and the communication cost.

[0106] The optimization execution module 26 is used to iteratively optimize the objective function using a particle swarm optimization algorithm to obtain an optimal annular multi-UUV sealing and control layout, and detect underwater targets based on the optimal annular multi-UUV sealing and control layout.

[0107] It is not difficult to find that this embodiment is a system embodiment corresponding to the above-mentioned method embodiment, and this embodiment can be implemented in conjunction with the above-mentioned method embodiment. The relevant technical details and technical effects mentioned in the above-mentioned method embodiment are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above-mentioned method embodiment.

[0108] It is worth mentioning that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovation of this application, this embodiment does not include units that are not closely related to solving the technical problem proposed by this application. However, this does not mean that other units do not exist in this embodiment.

[0109] Another embodiment of the present application provides an electronic device, the specific structure of which can be as follows: Figure 8 As shown, it includes: at least one processor 31; and a memory 32 communicatively connected to the at least one processor 31; wherein the memory 32 stores instructions that can be executed by the at least one processor 31, and the instructions are executed by the at least one processor 31 so that the at least one processor 31 can execute a multi-ocean unmanned system collaborative detection configuration optimization method based on ring closure as described in the above method embodiment.

[0110] The memory and processor can be connected using a bus. The bus can include any number of interconnected lines and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and therefore will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor.

[0111] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0112] Another embodiment of the present application involves proposing a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement a multi-ocean unmanned system collaborative detection configuration optimization method based on ring closure as described in the above method embodiment.

[0113] That is, those skilled in the art will understand that all or part of the steps in the above-described method embodiments can be implemented by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (which may be a single-chip microcomputer, chip, etc.) or processor to execute all or part of the steps of the method for optimizing the coordinated detection configuration of multiple unmanned ocean systems based on ring-shaped control, as described in the method embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0114] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on annular control, suitable for detecting underwater targets, characterized in that: The method comprises: Based on the location, detection requirements, and environmental characteristics of the underwater target, an initial circular multi-UUV containment layout is established within the target area, evenly distributed along a circular track. The initial position, initial heading angle, initial detection radius, and initial spacing between adjacent UUVs are determined for each UUV. The initial detection radius of each UUV is the same, and the initial spacing between adjacent UUVs is the same. Based on the initial position and initial detection radius of each UUV, the total detection coverage area is determined, and the detection coverage rate is calculated based on the total detection coverage area and the area of the target area; Develop uniformity criteria to ensure uniform distribution of multiple UUVs in the layout, and detection overlap criteria to ensure cross-detection of multiple UUVs in the same area; Based on the position of each point in the target area and the initial position of each UUV, a pseudorange matrix is calculated, the GDOP value of each point is calculated based on the pseudorange matrix, and the mean GDOP value of the target area is calculated; Based on the detection coverage, uniformity criterion, detection overlap criterion, GDOP mean and communication cost, an objective function based on a weighted reward mechanism is established; The particle swarm optimization algorithm is used to iteratively optimize the objective function to obtain the optimal annular multi-UUV containment layout, and underwater targets are detected based on the optimal annular multi-UUV containment layout. Based on the position of each point in the target area and the initial position of each UUV, the pseudorange matrix is calculated, the GDOP value of each point is calculated based on the pseudorange matrix, and the GDOP mean of the target area is calculated, including: The target area is divided into grids according to the preset size. Based on the position of each grid point in the grid and the initial position of each UUV, the pseudo-range matrix is calculated. For a grid point, its pseudorange matrix The Behavior Grid Points With the Unit direction vector between UUVs; The GDOP value of each point is calculated based on the pseudorange matrix using the following formula: ； in, Indicates taking the trace of the matrix, with the upper right corner marked Indicates taking the transpose of the matrix, with the upper right corner subscript Indicates taking the inverse of a matrix, Indicates the GDOP value of each grid point; The mean GDOP of the target area is calculated using the following formula: ； in, represents the total number of grid points, Indicates the mean GDOP of the target area.

2. The method for optimizing the configuration of collaborative detection of multiple unmanned ocean systems based on annular control according to claim 1 is characterized in that: Environmental characteristics include seabed topography, water flow velocity, and sound wave propagation speed. When determining the initial position, each UUV is set at a location with flat seabed topography, slow water flow velocity, and fast sound wave propagation speed. When determining the initial detection radius, the more complex the overall seabed topography of the target area, the faster the average water flow velocity, and the slower the average sound wave propagation speed, the smaller the initial detection radius is set; When determining the initial heading angle, ensure that each UUV is heading towards the underwater target; The initial distance between two adjacent UUVs is determined by the following formula: ； in, represents the initial detection radius, represents the total number of UUVs, is an integer greater than 2, Indicates the initial distance between two adjacent UUVs, Indicates the initial stage and has no practical meaning.

3. The method for optimizing the configuration of multi-ocean unmanned system collaborative detection based on ring-shaped control according to claim 2 is characterized in that: The initial position of each UUV is within the target area. Based on the initial position and initial detection radius of each UUV, the total detection coverage area is determined, and based on the total detection coverage area and the area of the initial annular multi-UUV containment layout, the detection coverage rate is calculated, including: The initial detection range of each UUV is calculated based on the initial position and initial detection radius of each UUV. The initial detection range of each UUV is also within the target area. Calculating the sum of the areas of the initial detection ranges of the UUVs, and subtracting the overlapping areas between the initial detection ranges of the UUVs from the sum of the areas to calculate the total detection coverage area; The ratio of the total detection coverage area to the area of the target region is calculated, and the ratio is taken as the detection coverage rate.

4. The method for optimizing the configuration of multi-ocean unmanned system collaborative detection based on ring-shaped control according to claim 2 is characterized in that: The uniformity criterion is expressed by the formula: ； in, Indicates the UUV and the center point of the target area The distance between represents the uniformity criterion; The detection overlap criterion is expressed by the formula: ； in, Indicates the The first UUV and the The distance between UUVs, Represents the detection overlap criterion.

5. A method for optimizing the collaborative detection configuration of multiple ocean unmanned systems based on annular containment according to any one of claims 1 to 4, characterized in that: Based on the detection coverage, uniformity criterion, detection overlap criterion, GDOP mean and communication cost, an objective function based on a weighted reward mechanism is established, which is implemented by the following formula: ； in, represents the objective function, represents the weight coefficient of detection coverage, represents the weight coefficient of the uniformity criterion, represents the weight coefficient of the detection overlap criterion, Represents the weight coefficient of the GDOP mean, represents the weight coefficient of communication cost, represents the detection coverage, represents the uniformity criterion, represents the detection overlap criterion, represents the GDOP mean, Indicates the communication cost.

6. The method for optimizing the configuration of multi-ocean unmanned system collaborative detection based on ring-shaped control according to claim 5 is characterized in that: In the process of iteratively optimizing the objective function using the particle swarm optimization algorithm to obtain the optimal circular multi-UUV containment layout, the velocity and position of the particles are updated using the following formula: ； ； in, represents the inertia weight, Indicates the In the iteration The speed of the particle, and are all preset learning factors. and are all random numbers, Indicates the In the iteration The position of the particle, Indicates the The optimal position of an individual in the iteration, Indicates the The global optimal position in the iteration, Indicates the updated The speed of the particle, Indicates the updated The position of a particle.

7. A multi-ocean unmanned system collaborative detection configuration optimization system based on ring-shaped control, suitable for detecting underwater targets, characterized by: The system comprises: The initial layout construction module is used to establish an initial circular multi-UUV containment layout evenly distributed along a circular track within the target area based on the location, detection requirements, and environmental characteristics of the underwater target. It also determines the initial position, initial heading angle, initial detection radius, and initial spacing between each UUV. The initial detection radius of each UUV is the same, and the initial spacing between adjacent UUVs is the same. A detection coverage calculation module is used to determine the total detection coverage area based on the initial position and initial detection radius of each UUV, and calculate the detection coverage rate based on the total detection coverage area and the area of the target area; A criterion formulation module, used for formulating a uniformity criterion for ensuring uniform distribution of multiple UUVs in a layout, and a detection overlap criterion for ensuring cross-detection of multiple UUVs in the same area; The GDOP calculation module is used to calculate the pseudo-range matrix based on the position of each point in the target area and the initial position of each UUV, calculate the GDOP value of each point based on the pseudo-range matrix, and calculate the GDOP mean of the target area; The objective function construction module is used to establish an objective function based on a weighted reward mechanism based on detection coverage, uniformity criterion, detection overlap criterion, GDOP mean and communication cost; An optimization execution module is used to iteratively optimize the objective function using a particle swarm optimization algorithm to obtain the optimal annular multi-UUV containment layout, and detect underwater targets based on the optimal annular multi-UUV containment layout; Based on the position of each point in the target area and the initial position of each UUV, the pseudorange matrix is calculated, the GDOP value of each point is calculated based on the pseudorange matrix, and the GDOP mean of the target area is calculated, including: The target area is divided into grids according to the preset size. Based on the position of each grid point in the grid and the initial position of each UUV, the pseudo-range matrix is calculated. For a grid point, its pseudorange matrix The Behavior Grid Points With the Unit direction vector between UUVs; The GDOP value of each point is calculated based on the pseudorange matrix using the following formula: ； in, Indicates taking the trace of the matrix, with the upper right corner marked Indicates taking the transpose of the matrix, with the upper right corner subscript Indicates taking the inverse of a matrix, Indicates the GDOP value of each grid point; The mean GDOP of the target area is calculated using the following formula: ； in, represents the total number of grid points, Indicates the mean GDOP of the target area.

8. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a multi-ocean unmanned system collaborative detection configuration optimization method based on ring closure as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it can implement a multi-ocean unmanned system collaborative detection configuration optimization method based on ring closure as described in any one of claims 1 to 6.

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