Multi-ocean unmanned system cooperative detection configuration optimization method based on annular sealing control

Abstract

The invention relates to the technical field of unmanned underwater vehicles, in particular to a multi-ocean unmanned system cooperative detection configuration optimization method based on annular sealing control, and the method comprises the steps: building an initial annular multi-UUV sealing control layout according to the position of an underwater target, detection requirements and environment characteristics; calculating the total detection coverage area based on the initial position and the detection radius of each UUV, and calculating the detection coverage rate in combination with the area of the initial annular multi-UUV sealing control layout; formulating a uniformity criterion and a detection overlapping degree criterion; calculating the GDOP value of each point based on the position of each point in the target area and the initial position of each UUV, and calculating the GDOP mean value of the target area; establishing an objective function based on the detection coverage rate, the uniformity criterion, the detection overlapping degree criterion, the GDOP mean value and the communication cost; and optimizing the objective function by adopting a particle swarm optimization algorithm to obtain an optimal annular multi-UUV sealing control layout. According to the method, the target positioning precision is effectively improved.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of unmanned underwater vehicles, and particularly to an optimization method for collaborative detection configuration of a multi-ocean unmanned system based on annular sealing control. Background Art

[0002] With the continuous increase in the demands of applications such as ocean resource development, environmental protection, and target tracking and reconnaissance, underwater unmanned vehicles (UUVs) play an increasingly important role in underwater / ocean exploration tasks. The traditional detection mode of a single UUV is difficult to efficiently perform multi-target detection tasks in a vast and complex underwater environment due to its limited detection coverage and insufficient accuracy. To solve this problem, in recent years, multi-UUV collaborative detection technologies have been widely studied and applied. By cooperating with each other among multiple UUVs, the detection coverage and accuracy can be improved, and it helps to achieve more complex task objectives.

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

[0004] To solve the above problems, some research teams have proposed solutions to improve the efficiency of multi-UUV collaborative detection by means of geometric layout optimization, multi-target detection algorithms, etc. However, these methods usually have the following limitations: First, they cannot make full use of the layout information of UUVs to optimize the global detection effect, resulting in insufficient blind spots or accuracy in detection; second, in a dynamic underwater environment, the existing methods are often inefficient in real-time adjusting the detection configuration of UUVs and are difficult to adapt to the rapid changes in the environment; third, when the system maintains the global optimal configuration, it often needs to consume a large amount of communication resources, thus significantly increasing the energy consumption. Summary of the Invention

[0005] In view of this, an embodiment of the present application proposes a collaborative detection configuration optimization method for a multi-ocean unmanned system based on circular sealing control. By introducing the particle swarm optimization algorithm and the geometric accuracy attenuation factor, it realizes the reasonable layout of multiple UUVs and the detection path planning, so as to optimize the detection coverage rate, reduce the detection overlap degree, and improve the accuracy of target positioning. And through reasonable geometric layout design, the communication cost is minimized to ensure the detection accuracy and the stability of the system.

[0006] To achieve the above object, an embodiment of the present application proposes a collaborative detection configuration optimization method for a multi-ocean unmanned system based on circular sealing control, which is applicable to detecting underwater targets. The method includes the following steps: According to the position, detection requirements and environmental characteristics of the underwater target, establish an initial circular multi-UUV sealing layout evenly distributed along a circular orbit in the target area, and determine the initial positions, initial heading angles, initial detection radii, and initial distances between adjacent two UUVs of each UUV; wherein, the initial detection radii of each UUV are the same, and the initial distances between adjacent two UUVs are the same; Based on the initial positions and initial detection radii of each UUV, determine the total detection coverage area, and calculate the detection coverage rate based on the total detection coverage area and the area of the target area; formulate a uniformity criterion for ensuring the uniform distribution of multiple UUVs in the layout, and a detection overlap degree criterion for ensuring the cross-detection of multiple UUVs in the same area; Based on the positions of each point in the target area and the initial positions of each UUV, calculate the pseudorange matrix, calculate the GDOP (Geometric Dilution Of Precision) value of each point based on the pseudorange matrix, and calculate the average GDOP value of the target area; Based on the detection coverage rate, uniformity criterion, detection overlap degree criterion, GDOP average value and communication cost, establish an objective function based on a weighted reward mechanism; Use the particle swarm optimization algorithm to iteratively optimize the objective function to obtain the optimal circular multi-UUV sealing layout, and detect the underwater target based on the optimal circular multi-UUV sealing layout.

[0007] To achieve the above object, an embodiment of the present application further provides a collaborative detection configuration optimization system for a multi-ocean unmanned system based on circular sealing control. The system includes: an initial layout construction module, a detection coverage rate 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 circular multi-UUV sealing control layout evenly distributed along a circular orbit within a target area according to the positions of underwater targets, detection requirements, and environmental characteristics, and determine the initial positions, initial heading angles, initial detection radii of each UUV, and the initial spacing between each UUV. The initial detection radii of each UUV are the same, and the initial spacing between adjacent two UUVs is the same; The detection coverage rate calculation module is used to determine the total detection coverage area based on the initial positions and initial detection radii of each UUV, and calculate the detection coverage rate based on the total detection coverage area and the area of the target area; The criterion formulation module is used to formulate a uniformity criterion for ensuring the uniform distribution of multi-UUVs in the layout, and a detection overlap criterion for ensuring the cross-detection of multi-UUVs in the same area; The GDOP calculation module is used to calculate a pseudorange matrix based on the positions of each point within the target area and the initial positions of each UUV, calculate the GDOP value of each point based on the pseudorange matrix, and calculate the GDOP mean value of the target area; The objective function construction module is used to establish an objective function based on a weighted reward mechanism based on the detection coverage rate, uniformity criterion, detection overlap criterion, GDOP mean value, and communication cost; The optimization execution module is used to iteratively optimize the objective function using a particle swarm optimization algorithm to obtain an optimal circular multi-UUV sealing control layout, and detect underwater targets based on the optimal circular multi-UUV sealing control layout.

[0008] To achieve the above object, an embodiment of the present application further provides an electronic device. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular sealing control as described above.

[0009] To achieve the above object, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it can implement a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular sealing control as described above.

[0010] An optimization method for collaborative detection configuration of multiple marine unmanned systems based on circular containment. First, according to the position, detection requirements, and environmental characteristics of underwater targets, an initial circular multi-UUV containment layout evenly distributed along a circular orbit is established within the target area. This initial circular multi-UUV containment layout fully considers the actual situation of underwater targets and lays a foundation for the subsequent optimization of collaborative detection configuration of multiple marine unmanned systems. Next, calculate the detection coverage rate, formulate a uniformity criterion for ensuring the uniform distribution of multiple UUVs in the layout, formulate a detection overlap criterion for ensuring the cross-detection of multiple UUVs in the same area, and then calculate the average GDOP value of the target area. The GDOP value is an index 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, and the average GDOP value of the target area reflects the positioning accuracy of the circular multi-UUV containment layout for the entire target area. Based on the detection coverage rate, uniformity criterion, detection overlap criterion, GDOP average value, and communication cost, establish an objective function based on a weighted reward mechanism. Finally, use the particle swarm optimization algorithm to iteratively optimize the objective function, and the optimal circular multi-UUV containment layout can be obtained. The optimal circular multi-UUV containment layout corresponds to a larger detection coverage rate, a smaller average GDOP value, and at the same time meets the requirements of uniform distribution and detection overlap. Detecting underwater targets based on the optimal circular multi-UUV containment layout effectively improves the detection accuracy and positioning ability. This method not only effectively reduces the detection error but also can perform efficient planning within the limited target area range, can adapt to various complex underwater detection task scenarios, and has strong universality.

[0011] Optionally, the environmental characteristics include but are not limited to seabed topography, water flow velocity, and acoustic wave propagation velocity. When determining the initial position, each UUV is set at a position with a flat seabed topography, a slow water flow velocity, and a fast acoustic 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 acoustic wave propagation velocity, the smaller the set initial detection radius; When determining the initial heading angle, ensure that each UUV is facing the underwater target; Determine the initial spacing between two adjacent UUVs through the following formula: ; where represents the initial detection radius, represents the total number of UUVs, is an integer greater than 2, represents the initial spacing between two adjacent UUVs, represents the initial, without practical significance.

[0012] Optionally, the initial positions of each UUV are all within the target area. Based on the initial positions and initial detection radii of each UUV, the total detection coverage area is determined, and based on the total detection coverage area and the area of the initial circular multi-UUV containment layout, the detection coverage rate is calculated, including: Based on the initial positions and initial detection radii of each UUV, the initial detection ranges of each UUV are calculated, and the initial detection ranges of each UUV are also all within the target area; Calculate the sum of the areas of the initial detection ranges of each UUV, and subtract the overlapping area between the initial detection ranges of each UUV from the sum of the areas to calculate the total detection coverage area; Calculate the ratio of the total detection coverage area to the area of the target area, and use the ratio as the detection coverage rate.

[0013] Optionally, the uniformity criterion is expressed by the formula: ; Wherein, represents the distance between the th UUV and the center point of the target area, represents the uniformity criterion; The detection overlap criterion is expressed by the formula: ; Wherein, represents the distance between the th UUV and the th UUV, represents the detection overlap criterion.

[0014] Optionally, based on the positions of each point within the target area and the initial positions of each UUV, a pseudorange matrix is calculated, the GDOP value of each point is calculated based on the pseudorange matrix, and the GDOP mean value of the target area is calculated, including: The target area is divided into grids according to a preset size. Based on the position of each grid point in the grid and the initial positions of each UUV, the pseudorange matrix is calculated respectively. For the th grid point, the th row in its pseudorange matrix is the unit direction vector between the grid point and the th UUV; Based on the pseudorange matrix, the GDOP value of each point is calculated through the following formula: ; Wherein, represents taking the trace of the matrix, and the superscript represents taking the transpose of the matrix, and the superscript denotes taking the inverse matrix of the matrix, denotes the GDOP value of the th grid point; The average GDOP of the target area is calculated through the following formula: ; where, denotes the total number of grid points, denotes the average GDOP of the target area.

[0015] Optionally, based on the detection coverage rate, uniformity criterion, detection overlap criterion, GDOP average value, and communication cost, an objective function based on a weighted reward mechanism is established and implemented through the following formula: ; where, denotes the objective function, denotes the weight coefficient of the detection coverage rate, denotes the weight coefficient of the uniformity criterion, denotes the weight coefficient of the detection overlap criterion, denotes the weight coefficient of the GDOP average value, denotes the weight coefficient of the communication cost, denotes the detection coverage rate, denotes the uniformity criterion, denotes the detection overlap criterion, denotes the GDOP average value, denotes the communication cost.

[0016] Optionally, in the process of using the particle swarm optimization algorithm to iteratively optimize the objective function to obtain the optimal circular multi-UUV containment layout, the velocity and position of the particles are updated through the following formula: ; ; where, denotes the inertia weight, denotes the th iteration of the th particle's velocity, and are both preset learning factors, and are both random numbers, denotes the th iteration of the th particle's position, denotes the individual optimal position in the th iteration, denotes the The global optimal position in the next iteration Indicates the velocity of the updated particle Description of the Drawings

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the following will briefly introduce the drawings required for the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 is provided in an embodiment of the present application, and is a flowchart of a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular sealing and control; Figure 2 is provided in an embodiment of the present application, and is a schematic diagram of the initial circular multi-UUV sealing and control layout; Figure 3 is provided in an embodiment of the present application, and is a schematic diagram of the initial GDOP value distribution; Figure 4 is provided in an embodiment of the present application, and is a schematic diagram of the optimized GDOP value distribution; Figure 5 is provided in an embodiment of the present application, and is a schematic diagram of the change of the average GDOP value of the target area with the increase of the number of iterations of the particle swarm optimization algorithm; Figure 6 is provided in an embodiment of the present application, and is a schematic diagram of the optimal circular multi-UUV sealing and control layout; Figure 7 is provided in another embodiment of the present application, and is a schematic diagram of the structure of a system for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular sealing and control; Figure 8 is provided in another embodiment of the present application, and is a schematic diagram of the structure of an electronic device. Detailed Embodiments

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will elaborate on each embodiment of this application in conjunction with the accompanying drawings. Those of ordinary skill in the art can understand that in each embodiment of this application, many technical details are provided to help readers better understand this application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in this application can still be implemented. The following division of embodiments is for convenience of description and should not constitute any limitation on the specific implementation of this application. The embodiments can be combined and cross-referenced with each other on the premise of not being contradictory.

[0020] An embodiment of this application proposes a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular containment, which is applicable to detecting underwater targets and is applied to electronic devices. Among them, the electronic device can be a terminal or a server. In this embodiment and the following embodiments, the server is taken as an example for illustration. The following will specifically describe the implementation details of a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular containment. The following content is only implementation details provided for convenience of understanding and is not necessary for implementing this solution.

[0021] The specific process of a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular containment proposed in this embodiment can be as Figure 1 shown, including: Step 11: According to the position, detection requirements, and environmental characteristics of the underwater target, establish an initial circular multi-UUV containment layout evenly distributed along a circular orbit in the target area, and determine the initial positions, initial heading angles, initial detection radii, and initial spacings between adjacent two UUVs. The initial detection radii of all UUVs are the same, and the initial spacings between adjacent two UUVs are the same.

[0022] In specific implementation, the server first needs to determine the approximate position of the underwater target to be detected, determine the detection requirements of this detection task, and also needs to obtain the environmental characteristics of the target water area (target area). Next, the server establishes an initial circular multi-UUV containment layout evenly distributed along a circular orbit in the target area according to the position, detection requirements, and environmental characteristics of the underwater target. At the same time, the server also needs to determine the initial positions, initial heading angles, initial detection radii, and initial spacings between adjacent two UUVs. It should be noted that the initial detection radii of all UUVs are the same, and the initial spacings between adjacent two UUVs are also the same.

[0023] In one example, the target area is a two-dimensional area with a size of 100m × 100m. The initial positions of each UUV are all within the target area, and even during subsequent optimization, each UUV always moves within the target area. The initial circular multi-UUV containment layout established by the server and evenly distributed along the circular orbit can be as Figure 2 shown.

[0024] In one example, the environmental characteristics of the target area include but are not limited to seabed topography, water flow velocity, and acoustic wave propagation velocity, etc. When the server determines the initial positions of each UUV, it is necessary to set the initial positions of each UUV at positions where the seabed topography is relatively flat, the water flow velocity is relatively slow, and the acoustic wave propagation velocity 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 acoustic wave propagation velocity, the smaller the set initial detection radius. During the subsequent iterative optimization process, it is necessary to continuously adjust the positions and detection radii of each UUV to ensure the best detection effect in a complex environment.

[0025] In one example, when the server determines the initial heading angles of each UUV, it is necessary to ensure that each UUV heads towards the underwater target. Denote the initial heading angle of the th UUV as .

[0026] It can be understood that during the subsequent iterative optimization process, the heading angles will be updated in real-time according to the movement trajectory of the underwater target to ensure that each UUV always heads towards the underwater target and maximizes the detection accuracy.

[0027] In one example, the server determines the initial spacing between two adjacent UUVs through the following formula: ; where represents the initial detection radius, represents the total number of UUVs, is an integer greater than 2 (generally set to 5 UUVs), represents the initial spacing between two adjacent UUVs, represents the initial, without practical significance.

[0028] Step 12: Based on the initial positions and initial detection radii of each UUV, determine the total detection coverage area, and calculate the detection coverage rate based on the total detection coverage area and the area of the target area.

[0029] In specific implementation, after the server completes the establishment of the initial circular multi-UUV containment layout, it needs to calculate various indicators and constraints as the goals for subsequent iterative optimization. The first one is the detection coverage rate. The higher the detection coverage rate, the stronger the detection ability of the circular multi-UUV containment layout. The server needs to determine the total detection coverage area based on the initial positions and initial detection radii of each UUV. Next, based on the total detection coverage area and the area of the target region, the detection coverage rate can be calculated.

[0030] In an example, the server first needs to calculate the initial detection ranges of each UUV based on the initial positions and initial detection radii of each UUV, and the initial detection ranges of each UUV are also within the target region. Next, calculate the sum of the areas of the initial detection ranges of each UUV, and subtract the overlapping area between the initial detection ranges of each UUV (if there is no overlap, record the overlapping area as 0) from the calculated sum of the areas to obtain the total detection coverage area. Finally, calculate the ratio of the total detection coverage area to the area of the target region, and use this ratio as the detection coverage rate, denoted as 。

[0031] Step 13, formulate a uniformity criterion for ensuring the uniform distribution of multi-UUVs in the layout, and a detection overlap criterion for ensuring cross-detection of multi-UUVs in the same region.

[0032] In specific implementation, the circular multi-UUV containment layout must meet two criteria. One is the uniformity criterion for ensuring the uniform distribution of multi-UUVs in the layout. The formulation of the uniformity criterion can avoid detection blind spots. The other is the detection overlap criterion for ensuring cross-detection of multi-UUVs in the same region. The formulation of the detection overlap criterion can improve the accuracy of detection results.

[0033] In an example, the uniformity criterion is expressed by the formula: ; where represents the distance between the th UUV and the center point of the target region, represents the uniformity criterion; The detection overlap criterion is expressed by the formula: ; where represents the distance between the th UUV and the th UUV, represents the detection overlap criterion.

[0034] Step 14: Based on the positions of the points in the target area and the initial positions of each UUV, calculate the pseudorange matrix, calculate the GDOP value of each point based on the pseudorange matrix, and calculate the average GDOP value of the target area.

[0035] In specific implementation, in addition to the detection coverage rate, uniformity criterion, and detection overlap criterion, the most important indicator is the average GDOP value 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 average GDOP value of the target area effectively reflects the positioning accuracy of the circular multi-UUV containment layout for the entire target area. The lower the average GDOP value 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 positions of the points in the target area and the initial positions of each UUV, then calculate the GDOP value of each point based on the pseudorange matrix, and further calculate the average GDOP value of the target area.

[0036] It can be understood that it is unrealistic to calculate the GDOP of each point in the target area. Therefore, the server divides the target area into grids according to a preset size, and the grids are staggered to form several grid points. The server calculates the pseudorange matrix based on the positions of each grid point in the grid and the initial positions of each UUV. For the th grid point, its pseudorange matrix The th row is the unit direction vector between the grid point and the

[0037] th UUV. ; where, represents taking the trace of the matrix, the superscript represents taking the transpose of the matrix, the superscript represents taking the inverse matrix of the matrix, represents the th GDOP value of the grid point; Calculate the average GDOP value of the target area through the following formula: ; where, represents the total number of grid points, represents the average GDOP value of the target area.

[0038] In one example, the server can also draw the GDOP value distribution for optimized visualization. The GDOP value distribution under the initial circular multi-UUV containment layout (initial GDOP value distribution) is as Figure 3 shown. The GDOP values at different positions are represented by the depth of color and the height, intuitively showing the spatial distribution of the detection accuracy.

[0039] Step 15: Based on the detection coverage rate, uniformity criterion, detection overlap criterion, GDOP mean value, and communication cost, establish an objective function based on a weighted reward mechanism.

[0040] In a specific implementation, after the server calculates the detection coverage rate and the GDOP mean value of the target area, and formulates the uniformity criterion and the detection overlap criterion, it can establish an objective function based on a weighted reward mechanism based on the detection coverage rate, uniformity criterion, detection overlap criterion, GDOP mean value, and communication cost (i.e., the communication cost that each UUV needs to pay to transmit the signal to the central server).

[0041] In one example, the server establishes an objective function based on a weighted reward mechanism based on the detection coverage rate, uniformity criterion, detection overlap criterion, GDOP mean value, and communication cost, which can be achieved through the following formula: ; where represents the objective function, represents the weight coefficient of the detection coverage rate, 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 value, represents the weight coefficient of the communication cost, represents the detection coverage rate, represents the uniformity criterion, represents the detection overlap criterion, represents the GDOP mean value, represents the communication cost, 、 、 、 and can be adjusted according to actual needs.

[0042] Step 16: Use the particle swarm optimization algorithm to iteratively optimize the objective function to obtain the optimal circular multi-UUV containment layout, and detect underwater targets based on the optimal circular multi-UUV containment layout.

[0043] In a specific implementation, the server uses the Particle Swarm Optimization (PSO) algorithm to iteratively optimize the objective function. After reaching the maximum number of iterations, the optimal circular multi-UUV containment layout is obtained, and finally, each UUV is instructed to detect underwater targets according to the optimal circular multi-UUV containment layout.

[0044] In one example, the PSO algorithm simulates the behavior of particles in the search space and continuously adjusts the positions of each UUV to minimize the mean GDOP of the entire target area. In each iteration, the PSO algorithm updates the velocity and position of the particles according to the weighted combination of the current optimal solution and the individual historical optimal solution, thereby driving each UUV closer to the global optimal position. After each position update, the coordinates of the particles are constrained within the target area to ensure that each UUV does not detect outside the target area. Through multiple iterations, the PSO algorithm gradually optimizes the layout of each UUV, making the mean GDOP of the target area gradually decrease.

[0045] In one example, during the process of the server using the Particle Swarm Optimization algorithm to iteratively optimize the objective function and obtaining the optimal circular multi-UUV containment layout, the velocity and position of the particles are updated through the following formula: ; ; where, represents the inertia weight, represents the th particle's velocity in the and are both preset learning factors, and are both random numbers, represents the th particle's position in the represents the individual optimal position in the th iteration, represents the global optimal position in the th iteration, represents the updated th particle's velocity, represents the updated th particle's position.

[0046] In one example, the distribution of the GDOP values of the optimized target area corresponding to the optimal circular multi-UUV containment layout is as Figure 4 shown, and the variation of the mean GDOP of the target area with the increase in the number of iterations of the PSO algorithm is asFigure 5 As shown, compared with the initial GDOP value distribution layout, the optimized GDOP value distribution layout is more reasonable, the average GDOP value in the target area is lower, and the overall detection accuracy has been significantly improved.

[0047] In one example, the optimal circular multi-UUV containment layout can be as Figure 6 shown.

[0048] A method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular containment proposed in this embodiment first establishes an initial circular multi-UUV containment layout evenly distributed along a circular orbit within the target area according to the position, detection requirements, and environmental characteristics of the underwater target. This initial circular multi-UUV containment layout fully considers the actual situation of the underwater target and lays a foundation for the subsequent optimization of the collaborative detection configuration of the multi-ocean unmanned system. Next, calculate the detection coverage rate, formulate a uniformity criterion for ensuring the uniform distribution of multi-UUVs in the layout, formulate a detection overlap criterion for ensuring cross-detection of multi-UUVs in the same area, and then calculate the average GDOP value of the target area. The GDOP value is an index 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, and the average GDOP value of the target area reflects the positioning accuracy of the circular multi-UUV containment layout for the entire target area. Based on the detection coverage rate, uniformity criterion, detection overlap criterion, average GDOP value, and communication cost, establish an objective function based on a weighted reward mechanism, and finally use the particle swarm optimization algorithm to iteratively optimize the objective function to obtain the optimal circular multi-UUV containment layout. The optimal circular multi-UUV containment layout corresponds to a larger detection coverage rate, a smaller average GDOP value, and at the same time meets the requirements of uniform distribution and detection overlap. Detecting the underwater target based on the optimal circular multi-UUV containment layout effectively improves the detection accuracy and positioning ability. This method not only effectively reduces the detection error but also can perform efficient planning within the limited target area range, can adapt to various complex underwater detection task scenarios, and has strong universality.

[0049] The step division of the above various methods is only for clear description. When implemented, they can be combined into one step, or some steps can be split into multiple steps as long as the same logical relationship is included, and they are all within the protection scope of this application. Insignificant modifications added to the algorithm or process, or insignificant designs introduced, but without changing the core design of the algorithm and process, are all within the protection scope of this application.

[0050] Another embodiment of the present application proposes a collaborative detection configuration optimization system for multiple marine unmanned systems based on circular containment, which is applicable to the detection of underwater targets. Hereinafter, the details of a collaborative detection configuration optimization system for multiple marine unmanned systems based on circular containment proposed in this embodiment will be specifically described. The following content is only implementation details provided for convenience of understanding and is not necessary for implementing this example.

[0051] Figure 7 FIG. 4 is a schematic structural diagram of a collaborative detection configuration optimization system for multiple marine unmanned systems based on circular containment proposed in this embodiment. The system specifically includes: an initial layout construction module 21, a detection coverage rate 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.

[0052] The initial layout construction module 21 is configured to establish an initial circular multi-UUV containment layout evenly distributed along a circular orbit in the target area according to the position of the underwater target, the detection requirements, and the environmental characteristics, and determine the initial positions, initial heading angles, initial detection radii of each UUV, and the initial spacing between each UUV. The initial detection radii of each UUV are the same, and the initial spacing between adjacent two UUVs is the same.

[0053] The detection coverage rate calculation module 22 is configured to determine the total detection coverage area based on the initial positions and initial detection radii of each UUV, and calculate the detection coverage rate based on the total detection coverage area and the area of the target area.

[0054] The criterion formulation module 23 is configured to formulate a uniformity criterion for ensuring the uniform distribution of multiple UUVs in the layout, and a detection overlap criterion for ensuring the cross-detection of multiple UUVs in the same area.

[0055] The GDOP calculation module 24 is configured to calculate a pseudorange matrix based on the positions of each point in the target area and the initial positions 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.

[0056] The objective function construction module 25 is configured to establish an objective function based on a weighted reward mechanism based on the detection coverage rate, the uniformity criterion, the detection overlap criterion, the average GDOP value, and the communication cost.

[0057] The optimization execution module 26 is configured to iteratively optimize the objective function by using a particle swarm optimization algorithm to obtain an optimal circular multi-UUV containment layout, and detect the underwater target based on the optimal circular multi-UUV containment layout.

[0058] It is not difficult to find that this embodiment is a system embodiment corresponding to the above method embodiment, and this embodiment can be implemented in cooperation with the above method embodiment. The relevant technical details and technical effects mentioned in the above method embodiment are still valid in this embodiment. To avoid repetition, they will not be elaborated here. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiment.

[0059] It is worth mentioning that each module involved in this embodiment is a logical module. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovative part of this application, units that are not closely related to solving the technical problems proposed in this application are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0060] Another embodiment of this application proposes an electronic device, and its specific structure can be as Figure 8 shown, including: at least one processor 31; and a memory 32 communicatively connected to the at least one processor 31; wherein, the memory 32 stores instructions executable by the at least one processor 31, and the instructions are executed by the at least one processor 31 to enable the at least one processor 31 to execute a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on ring control as described in the above method embodiment.

[0061] Among them, the memory and the processor can be connected in a bus manner. The bus can include any number of interconnected lines and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art, so they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be an element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted over the wireless medium through the antenna. Further, the antenna also receives data and transmits the data to the processor.

[0062] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory can be used to store the data used by the processor when executing operations.

[0063] Another embodiment of the present application proposes a computer-readable storage medium storing a computer program, which when executed by a processor, can implement a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on ring control as described in the above method embodiment.

[0064] That is, those skilled in the art can understand that all or part of the steps in the above method embodiment can be completed by instructing relevant hardware through a program. The program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on ring control as described in the method embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0065] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present application. In actual applications, various changes can be made in form and details without departing from the spirit and scope of the present application.

Claims

1. A collaborative detection configuration optimization method for multiple marine unmanned systems based on circular sealing control, applicable to the detection of underwater targets, characterized in that The method includes: According to the position, detection requirements, and environmental characteristics of the underwater target, an initial circular multi-UUV containment layout evenly distributed along a circular orbit is established within the target area, and the initial positions, initial heading angles, initial detection radii, and initial spacings between adjacent UUVs are determined; among them, the initial detection radii of each UUV are the same, and the initial spacings between adjacent UUVs are the same; Based on the initial positions and initial detection radii 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 the uniform distribution of multi-UUVs in the layout and a detection overlap criterion for ensuring the cross-detection of multi-UUVs in the same area are formulated; Based on the positions of each point in the target area and the initial positions of each UUV, a pseudorange matrix is calculated, the GDOP value of each point is calculated based on the pseudorange matrix, and the average GDOP value of the target area is calculated; Based on the detection coverage rate, uniformity criterion, detection overlap criterion, average GDOP value, 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 circular multi-UUV containment layout, and the underwater target is detected based on the optimal circular multi-UUV containment layout.

2. The collaborative detection configuration optimization method for a multi-ocean unmanned system based on circular sealing control according to claim 1, wherein The environmental characteristics include but are not limited to the seabed topography, water flow velocity, and acoustic wave propagation velocity. When determining the initial positions, each UUV is set at a position with a flat seabed topography, a slow water flow velocity, and a fast acoustic 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 acoustic wave propagation velocity, the smaller the set initial detection radius; When determining the initial heading angle, ensure that each UUV is directed towards the underwater target; The initial spacing between adjacent UUVs is determined by the following formula: ； Among them, represents the initial detection radius, represents the total number of UUVs, is an integer greater than 2, represents the initial spacing between two adjacent UUVs, represents the initial stage and has no practical significance.

3. An optimization method for collaborative detection configuration of a multi-ocean unmanned system based on annular sealing control according to claim 2, characterized in that, The initial positions of each UUV are all within the target area. Based on the initial positions and initial detection radii of each UUV, the total detection coverage area is determined, and based on the total detection coverage area and the area of the initial circular multi-UUV containment layout, the detection coverage rate is calculated, including: Based on the initial positions and initial detection radii of each UUV, the initial detection ranges of each UUV are calculated, and the initial detection ranges of each UUV are also all within the target area; Calculate the sum of the areas of the initial detection ranges of each UUV, and subtract the overlapping area between the initial detection ranges of each UUV from the sum of the areas to calculate the total detection coverage area; Calculate the ratio of the total detection coverage area to the area of the target area, and use the ratio as the detection coverage rate.

4. The collaborative detection configuration optimization method for a multi - ocean unmanned system based on circular sealing control according to claim 2, wherein, The uniformity criterion is expressed by the formula: ； Among them, represents the distance between the center point of the target area and the th UUV, and represents the uniformity criterion; The detection overlap criterion is expressed by the formula: ； Among them, represents the th distance between the th UUV and the represents the detection overlap criterion.

5. A method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on ring-shaped sealing control according to claim 2, characterized in that Based on the positions of each point in the target area and the initial positions of each UUV, a pseudorange matrix is calculated, the GDOP value of each point is calculated based on the pseudorange matrix, and the average GDOP value of the target area is calculated, including: Divide the target area into grids according to a preset size. Based on the positions of each grid point in the grid and the initial positions of each UUV, calculate the pseudorange matrix respectively. For the th grid point, its pseudorange matrix The th row in is the unit direction vector between the grid point and the th UUV; The GDOP value of each point is calculated based on the pseudorange matrix by the following formula: ； Among them, represents taking the trace of a matrix, and the superscript represents taking the transpose of a matrix, and the superscript represents taking the inverse matrix of a matrix, represents the GDOP value of the The average GDOP value of the target area is calculated by the following formula: ； Among them, represents the total number of grid points, represents the average GDOP of the target area.

6. A method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular sealing control, according to any one of claims 1 to 5, characterized in that, Based on the detection coverage rate, uniformity criterion, detection overlap criterion, mean GDOP, and communication cost, an objective function based on a weighted reward mechanism is established and implemented through the following formula: ； Among them, represents the objective function, represents the weight coefficient of the detection coverage rate, represents the weight coefficient of the uniformity criterion, represents the weight coefficient of the detection overlap criterion, represents the weight coefficient of the average GDOP, represents the weight coefficient of the communication cost, represents the detection coverage rate, represents the uniformity criterion, represents the detection overlap criterion, represents the average GDOP, represents the communication cost.

7. A method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on annular sealing control, 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 through the following formula: ； ； Among them, represents the inertia weight, represents the th iteration of the th particle's velocity, and are both preset learning factors, and are both random numbers, represents the th iteration of the th particle's position, represents the individual optimal position in the th iteration, represents the global optimal position in the th iteration, represents the updated th particle's velocity, represents the updated th particle's position.

8. A collaborative detection configuration optimization system for multi-ocean unmanned systems based on ring-shaped sealing control, applicable to detecting underwater targets, characterized in that, The system includes: An initial layout construction module, which is used to establish an initial circular multi-UUV containment layout evenly distributed along a circular orbit within the target area according to the position of the underwater target, detection requirements, and environmental characteristics, and determine the initial positions, initial heading angles, initial detection radii of each UUV, and the initial spacing between each UUV. The initial detection radii of each UUV are the same, and the initial spacing between adjacent two UUVs is the same; A detection coverage rate calculation module, which is used to determine the total detection coverage area based on the initial positions and initial detection radii 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, which is used to formulate a uniformity criterion for ensuring the uniform distribution of multi-UUVs in the layout and a detection overlap criterion for ensuring the cross-detection of multi-UUVs in the same area; A GDOP calculation module, which is used to calculate the pseudorange matrix based on the positions of each point in the target area and the initial positions of each UUV, calculate the GDOP value of each point based on the pseudorange matrix, and calculate the mean GDOP of the target area; An objective function construction module, which is used to establish an objective function based on a weighted reward mechanism based on the detection coverage rate, uniformity criterion, detection overlap criterion, mean GDOP, and communication cost; An optimization execution module, which is used to iteratively optimize the objective function using the particle swarm optimization algorithm to obtain the optimal circular multi-UUV containment layout, and detect the underwater target based on the optimal circular multi-UUV containment layout.

9. An electronic device, characterized in that, Includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular containment as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can implement a method for optimizing the collaborative detection configuration of a multi-ocean unmanned system based on circular containment as described in any one of claims 1 to 7.

Images