Unmanned ship full-coverage connection topological optimization path planning method and system for underwater target search and detection, storage medium and electronic equipment

Abstract

The invention discloses an unmanned ship full-coverage connection topological optimization path planning method and system for underwater target search and detection, a storage medium and electronic equipment, and the method comprises the steps: firstly carrying out the environment modeling and rasterization of a detection region, and obtaining a free grid set and neighborhood state information; the method comprises the following steps: establishing a mixed integer linear programming model containing geometric continuity constraints and topological structure rewards to obtain a global optimal rectangular decomposition scheme under the balance of geometric boundary cost and topological connection rewards; establishing a minimum turning spanning tree to minimize the turning times of the coverage path by judging the connection type and turning cost between the rectangles; and finally, generating a smooth coverage trajectory in combination with the kinematics constraint of the unmanned ship. According to the method, the problem of sonar image distortion caused by too many turning times in the existing coverage path planning method is solved, the global turning times of the coverage path are reduced, the problem of detection precision decline caused by the fact that acoustic imaging conditions are damaged due to frequent turning maneuvering in the traditional path is solved, and the detection path quality is optimized.

Description

Technical Field

[0001] This invention belongs to the field of marine surveying and unmanned system coverage path planning technology, specifically involving a method, system, storage medium and electronic equipment for full coverage connection topology optimization path planning of unmanned surface vessels for underwater target search. Background Technology

[0002] With the development of the marine economy, unmanned surface vessels (USVs) equipped with sonar for seabed topography exploration, search and rescue, and underwater target identification have become the mainstream operational mode. Coverage path planning (CPP) is a key technology for these mission areas. This invention studies a CPP method for underwater target search missions, which have high requirements for coverage path quality.

[0003] The existing technology has the following main problems:

[0004] 1. Sonar imaging is sensitive to motion: Sonar relies on the stable linear motion of the carrier for push-broom imaging. When the USV performs maneuvers such as turning, acceleration, or deceleration that cause significant attitude changes, it can lead to distortion, overlap, or gaps in the sonar imaging, severely affecting target recognition rate and accuracy. Therefore, reducing the number of turns in the path is key to improving detection quality.

[0005] 2. Limitations of traditional domain decomposition methods:

[0006] (1) Boustrophedon Cellular Decomposition (BCD): Although it can divide complex areas into barrier-free sub-regions, it often uses simple "translation" or "U-shaped" connections at the sub-region connections, resulting in a large number of unnecessary auxiliary paths (leading to repeated coverage and increased path length) and turns.

[0007] (2) Minimum Rectangle Decomposition (MRD): The MRD algorithm focuses on how to decompose the region with the fewest rectangles to obtain the minimum spanning tree with the fewest turns, thereby reducing the number of turns in the covered path. This is a purely geometric problem. Figure 3 In (a) and (b), the minimum spanning tree generated by the rectangle decomposition and connection has 20 turns, while (b) only has 14. However, with the same number of rectangles, the global number of turns still differs due to differences in the rectangle topology layout and connection methods. Figure 3Both (b) and (c) consist of four rectangles, but the decomposition and connection in (b) result in 14 turns, while (c) only has 12. Therefore, existing algorithms ignore this "topological quality," and our goal is to integrate this optimization of rectangular topology layout into the MRD.

[0008] In summary, there is an urgent need for an MRD method that can simultaneously consider both "geometric rectangle decomposition" and "topological connectivity" from a global perspective, and generate a minimum turning spanning tree to further optimize the number of turns in the coverage path. Meanwhile, since the unmanned surface vessel travels at a uniform global speed to ensure imaging stability, the Dubins algorithm is used to smooth the coverage path. Summary of the Invention

[0009] The purpose of this invention is to provide a method, system, storage medium, and electronic device for full-coverage connected topology optimization path planning for underwater target search of unmanned surface vessels (USVs), generating a full-coverage detection trajectory for USVs with the fewest turns and smoothest motion.

[0010] The objective of this invention is achieved through the following technical solution:

[0011] A method for full-coverage connection topology optimization path planning for underwater target search using unmanned surface vessels includes the following steps:

[0012] Step 1: Obtain a raster map of the task area and construct a free grid set based on the raster map;

[0013] Step 2: Construct a rectangular decomposition model for connectivity optimization. By introducing decision variables, constraint variables, and reward variables, the free mesh set is divided into rectangles, while optimizing the geometry and topology to minimize the turning cost of the coverage path.

[0014] Step 3: Solve the rectangular decomposition model to obtain the optimal solution that minimizes the global weighted cost, and determine the rectangular decomposition result based on the decision variables in the optimal solution to form a set of rectangles;

[0015] Step 4: Based on the set of rectangles, calculate the connection cost between adjacent rectangles, and generate a minimum turning spanning tree based on the connection cost, so that all rectangles are connected and the total connection cost is minimized;

[0016] Step 5: Generate a coverage path based on the minimum turning generation tree, smooth the coverage path, and output the final detection trajectory that satisfies the kinematic constraints of the unmanned surface vessel.

[0017] Furthermore, the objective function of the connection optimization rectangle decomposition model constructed in step 2 is:

[0018]

[0019] in, The geometric boundary cost term is used to represent the number of rectangles generated by the decomposition and the degree of fragmentation. Its value is determined based on the consistency of the extension direction between each grid in the free mesh set and its adjacent grids. When the extension directions of adjacent grids are inconsistent, the corresponding position is marked as a rectangular boundary. The topology reward item is used to represent a rectangular topology that is conducive to reducing turns. Its value is determined according to the preset topology connection pattern formed between each grid and its adjacent grids in the free grid set. The preset topology connection pattern includes the pattern of extending horizontally and then turning vertically, and the pattern of extending vertically and then turning horizontally. and These are the corresponding weighting coefficients.

[0020] Furthermore, the geometric boundary cost term and topology reward items Specifically:

[0021]

[0022]

[0023] in, Represents a free mesh set The first in One grid; and These represent the horizontal and vertical directions, respectively. Represents a grid The right neighbor grid and the bottom neighbor grid; For boundary indicator variables; For grid Boundary indicator variable between the grid cells to the left or above; For grid Boundary indicator variable between the grid cells to the right or below; For topological reward variables; For grid The "horizontal to vertical" structural reward variable between the grid cells to the right or below; For grid The "vertical to horizontal" reward variable between the grid cells to the right or below; Each grid in the system has a binary geometric continuity property, which is used to identify whether the extension direction of the grid is horizontal or vertical.

[0024] Furthermore, in step 4, the connection cost calculation between adjacent rectangles is based on the fact that each of the two endpoints of an independent rectangle has two turns. The impact value of connecting adjacent grids in adjacent rectangles on the global number of turns is determined. The impact value includes: when the ends of two adjacent orthogonal rectangles form an L-shaped connection, the number of turns decreases by 2 compared to the inherent number of turns of the two independent rectangles; when the ends of two adjacent parallel rectangles form a U-shaped connection, the number of turns remains unchanged; when the ends and non-ends of two adjacent orthogonal rectangles form a T-shaped connection, no additional maneuvering turns are added; when the ends and non-ends of two adjacent parallel rectangles form an h-shaped connection, an additional transition path along the edge is added, increasing the number of turns by 2; when the non-ends of two adjacent parallel rectangles form an H-shaped connection, the coverage continuity is severely disrupted, increasing the number of turns by 4.

[0025] Furthermore, the method of generating the minimum turning spanning tree using a greedy strategy specifically includes:

[0026] Initialize the edge set of the generator tree, and treat each independent rectangle as an independent edge and put it into the edge set;

[0027] Iterate through all adjacent rectangle pairs and select the edge with the minimum connection cost to connect the two rectangles. If the edge does not form a cycle when added to the edge set, then add the edge to the spanning tree.

[0028] Repeat the above process until all rectangles are connected, generating the minimum turning spanning tree.

[0029] Furthermore, step 5 involves smoothing the coverage path using a trajectory smoothing method that considers the kinematic constraints of the unmanned surface vessel, specifically including:

[0030] The turning trajectory of the path is defined as a continuous curve composed of circular arc segments and straight line segments, wherein the radius of the circular arc segment is not less than the minimum turning radius of the unmanned surface vessel.

[0031] Obtain the starting state vector and ending state vector of the current path segment, and construct a candidate set containing multiple combinations of motion primitives; the combinations of motion primitives include at least "left turn-straight line-left turn", "left turn-straight line-right turn", "right turn-straight line-left turn", "right turn-straight line-right turn", "right turn-left turn-right turn" and "left turn-right turn-left turn".

[0032] By calculating the normalized distance and displacement vector angle, the trajectory length generated by each candidate combination is calculated, and the combination type with the smallest length is selected as the optimal smooth trajectory.

[0033] A full-coverage path planning system for underwater target search unmanned surface vessels (USVs) based on connectivity topology optimization includes modules for processing instructions at each step of the full-coverage connectivity topology optimization path planning method for underwater target search.

[0034] Map processing module: Determines grid size based on sonar parameters and environmental data, and generates a binary raster map;

[0035] Model building module: Used to generate mixed-integer linear programming models that include geometric continuity constraints and rectangular topology rewards;

[0036] Optimization Solver Module: Used to call the solver to solve the model and output the mesh direction matrix;

[0037] Topology Analysis and Spanning Tree Module: Used to analyze the adjacency relationships between rectangles and generate a minimum turning spanning tree based on a greedy strategy;

[0038] Path generation and smoothing module: Used to generate covering paths around the spanning tree and use a trajectory smoothing algorithm to transform discrete paths into continuous trajectories that conform to the kinematic constraints of the unmanned surface vessel.

[0039] A computer system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a method for full-coverage connection topology optimization path planning for underwater target search of unmanned surface vessels.

[0040] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of steps in a method for full-coverage connected topology optimization path planning for underwater target search by unmanned surface vessels.

[0041] An electronic device, comprising:

[0042] Memory, used to store computer programs;

[0043] A processor for executing the computer program to implement a method for instruction tracking of unmanned surface vessels with full-coverage connection topology optimization path planning for underwater target search.

[0044] The beneficial effects of this invention are as follows:

[0045] This invention addresses the critical requirement for platform stability in sonar imaging during underwater target search missions. By incorporating the kinematic constraints of unmanned surface vessels (USVs), it constructs a Junctional Optimized Rectangle Decomposition (JORD) model based on Mixed Integer Linear Programming (MILP). By coupling geometric shape optimization and topology optimization at the mathematical model level, it overcomes the limitations of traditional algorithms that focus solely on local optima or geometric quantity. Utilizing the explicit reward of the JORD model helps reduce the topology of turns, resulting in a fully covered path that significantly reduces the number of global turns while ensuring complete coverage. Furthermore, the Dubins smoothing strategy ensures trajectory smoothness, contributing to improved sonar imaging quality and enhancing the operational efficiency and detection effectiveness of large-scale underwater target search missions. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the principle of the present invention;

[0047] Figure 2 This is a schematic diagram illustrating the influence of different rectangular topologies and connection methods on the number of turns in this invention;

[0048] Figure 3 This is a schematic diagram illustrating the impact of different rectangle division methods on the number of turns in this invention;

[0049] Figure 4 This is a simulation result diagram of the rectangular division of an embodiment of the present invention;

[0050] Figure 5 This is a simulation result diagram of the minimum turning spanning tree of an embodiment of the present invention;

[0051] Figure 6 This is a flowchart illustrating the principle of the core concept of this invention, the "connection optimization rectangle decomposition model". Detailed Implementation

[0052] The present invention will now be further described with reference to the accompanying drawings.

[0053] This invention presents a full-coverage connected topology optimization path planning method for underwater target search using unmanned surface vessels (USVs). It proposes a JORD model to simultaneously optimize the geometry and topology of the region decomposition, constructs a minimum-turn spanning tree, and utilizes a kinematic smoothing algorithm to generate the final trajectory. This method optimizes the quality of the detection coverage trajectory, improving the overall data quality and operational efficiency of underwater target search missions.

[0054] like Figure 1As shown, the system involved in this invention includes a map processing module, a JORD model construction and solution module, a topology analysis and spanning tree module, and a path smoothing module. The specific process is as follows: Figure 6 As shown, it includes the following steps:

[0055] Step 1: Map building and initialization.

[0056] Acquire prior map information of the target sea area, and determine the grid size of the raster map based on the effective detection swath of the sonar on the unmanned surface vessel and the preset overlap rate requirements, ensuring that the grid width is less than or equal to the sonar detection swath to meet the full coverage detection requirements.

[0057] Discretize the free region into a grid set. Initialize grid properties and identify each grid. The four neighbor states (up, down, left, right) are marked as Null if the neighbor is an obstacle or boundary.

[0058] Step 2: Establish and solve the Join Optimization Rectangular Decomposition (JORD) model.

[0059] To minimize the number of global turns while decomposing the region, this invention establishes a JORD model based on MILP. This model transforms the physical geometric continuity and topological connectivity requirements into mathematical linear constraints and objective functions.

[0060] Step 2.1: Define decision variables.

[0061] Define binary variables Represents an arbitrary grid Coverage extension direction:

[0062]

[0063] Step 2.2: Construct geometric boundary detection constraints.

[0064] To calculate the number of rectangles after decomposition, it is necessary to count the number of "boundaries" where adjacent grid orientations are inconsistent. Define boundary indicator variables. For any grid and its right neighboring grid The right boundary constraint in the horizontal direction is:

[0065]

[0066] The meaning of this formula is: If In the horizontal direction ( )and Non-horizontal ( ), then the right boundary is forcibly generated ( ).

[0067] Similarly, define the left boundary in the horizontal direction. and the upper boundary in the vertical direction and lower boundary .

[0068]

[0069] Step 2.3: Construct topology connection reward constraints.

[0070] To guide the generation of topologies that reduce turns, a topology reward variable is defined. . Grid Its right neighbor grid For example, construct the following logical constraints to identify two specific topological connection structures:

[0071] (1) Horizontal to vertical connection (H-to-V):

[0072]

[0073] The above set of inequalities forms a logical AND gate, whose physical meaning is: only if the grid... A horizontal termination boundary was generated at that location (i.e.) (The right endpoint of the horizontal rectangle), and its right neighboring grid. A vertical start or end boundary was generated at that point (i.e.) When both the upper and lower endpoints of the vertical rectangle are satisfied, the reward variable... Only when the value is 1 is allowed, it means that the path can smoothly transition from the end of the horizontal rectangle to the side endpoint of the vertical rectangle, forming an L-shaped connection.

[0074] We only consider The reason for determining the topology with the right and lower neighbors is: when Calculate its right neighbor At that time, it had already been calculated and Connectivity between them; and The same applies to connectivity between them. Therefore, only consider... and The model is sufficient to capture the global topology layout.

[0075] (2) Vertical to horizontal connection (V-to-H):

[0076]

[0077] Similarly, for the neighbors below... Construct the corresponding H-to-V and V-to-H reward constraints.

[0078] Step 2.4: Construct the objective function and solve for rectangle aggregation

[0079]

[0080] in, and These are the weighting coefficients. The objective function minimizes the boundary variables. The sum is used to reduce the number of rectangles (geometric optimization), while maximizing the reward. The sum of these factors promotes the generation of L-shaped connections (topology optimization). (By...) Figure 2 (a) shows that each independent rectangular boundary It produces two turns; and by Figure 2 (b) It can be seen that each "L"-shaped topology layout reduces the number of turns by 2. Therefore, we directly set... This allows the aforementioned effects to be directly reflected in the objective function.

[0081] Solve the above model using a solver, and based on the output... The value aggregates adjacent grids with the same orientation into a rectangular set. .

[0082] Step 3: Construct the Minimum Turn Spanning Tree (MST).

[0083] Based on the set of rectangles obtained in step 2.4 A greedy strategy is employed to determine the optimal connection order between independent rectangles. This process traverses adjacent rectangles and evaluates their connection costs, which depend on the relative topological positions between the rectangles and the impact of the connection on the number of turns. By iteratively selecting the connection with the minimum cost that does not form a loop, all discrete rectangles are connected into a minimum spanning tree, thereby ensuring that the final generated full-coverage path has the fewest global turns.

[0084] Step 3.1: Define the connection cost function

[0085] To quantify the impact of different geometric connection methods on the global number of turns, a connection cost function is defined. This function represents adjacent rectangles. and Through their respective edge grids The impact of the connection on the global number of turns.

[0086] Based on the geometric relationship between the endpoints of the rectangle and the connection point, the specific mathematical expression and physical meaning are as follows:

[0087]

[0088] Specific physical meaning explanation:

[0089] L-connection ( ): By using the L-shaped connection point of two rectangles for direct transition, the number of turns after connection is 2 compared to the inherent number of turns of two independent rectangles.

[0090] U-connection ( ): The ends of the two rectangles are connected, and the number of turns remains unchanged.

[0091] T-connection ( ): Two rectangles are orthogonally connected to the non-endpoints, without adding extra maneuvering turns.

[0092] h-connection ( ): Connect two rectangles in parallel to non-endpoints, add a transition path along the edge, and increase the number of turns by 2.

[0093] H-connection ( ): The parallel connection at the non-endpoints of the two rectangles severely disrupts the continuity of the coverage, turning +4.

[0094] Step 3.2: Construct the minimum spanning tree using a greedy strategy

[0095] After determining the connection cost of each edge, a minimum turning spanning tree is iteratively constructed using a greedy strategy. The specific execution steps are as follows:

[0096] (1) Initialize the edge set of the generator tree , each independent rectangle Each is considered an independent edge. Add to edge set ;

[0097] (2) For The cost of traversing between two rectangles Minimal connection As , Let be the sides connecting the two rectangles. If join in If no loop is formed, then update. ;

[0098] (3) Repeat the above process until the minimum spanning tree is generated.

[0099] Step 4: Full coverage path generation and path smoothing.

[0100] Based on the minimum spanning tree, a covering path is first generated around the spanning tree. Then, for each corner in the path, the Dubins algorithm is used for smoothing.

[0101] Step 4.1: State Definition and Function Composition

[0102] The trajectory of an unmanned surface vessel (USV) is subject to kinematic constraints and a minimum turning radius. This method uses the Dubins smoothing algorithm to smooth the path. The first unmanned surface vessel Segment trajectory The generation is determined by the composition function. control:

[0103]

[0104] in, These are the starting coordinates and movement angles of the path segment; These are the ending coordinates and motion angle of the path segment; It is the minimum turning radius of the unmanned surface vessel obtained through a turning experiment.

[0105] Step 4.2: Optimal Trajectory Type Decision

[0106] The optimal trajectory type is determined by finding the maneuver that produces the minimum length. The decision-making process can be formalized as follows:

[0107]

[0108] in, It is the set of all possible Dubins maneuvers. , It corresponds The length function.

[0109] Step 4.3: Trajectory stitching

[0110] splicing the first The complete trajectory of an unmanned surface vessel is obtained by analyzing all its sub-trajectories. as follows:

[0111]

[0112] in, This represents the total number of path points.

[0113] Implementation Case:

[0114] This case study uses a real map of the Zhoushan Islands with a size of 40.8km × 40.8km as the implementation object. The map resolution is 255 × 255, the minimum spanning tree level grid size is 160m × 160m, and the coverage path level grid size is 80m. Through simulation turning experiments, the minimum turning radius of the unmanned surface vessel in this implementation case was measured to be 20m, which was used to generate the Dubins trajectory.

[0115] Based on steps 1-2 above, we obtain Figure 4 The rectangle partitioning result (545 rectangles) is shown, where red lines represent horizontal rectangles and blue lines represent vertical rectangles. Then, based on step 3, the minimum turning spanning tree is obtained, as shown below. Figure 5 As shown, the green lines represent the connecting edges between rectangles. Finally, according to step 4, the final Dubins coverage trajectory is obtained. The coverage trajectory obtained by this invention contains a total of 2192 turns, which is a significant improvement compared to the 2318 turns of the OARP method in the paper "Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning" and the 2632 turns of the TMC method in the paper "Turn-minimizing multirobot coverage".

[0116] Through the above steps, this invention achieves a closed loop from environmental modeling, topology optimization decomposition, sequential planning to trajectory smoothing. This helps solve the problem of decreased detection accuracy caused by frequent turning maneuvers that significantly alter the attitude of the sonar platform (hull) during large-scale underwater target search missions, leading to compromised acoustic imaging conditions. This invention optimizes the quality of the detection coverage trajectory, contributing to improved overall data quality and operational efficiency in underwater target search missions.

[0117] In particular, in some preferred embodiments of the present invention, a computer device is also provided, including a memory and a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the unmanned surface vessel full-coverage connection topology optimization path planning method for underwater target search described in any of the above embodiments.

[0118] In some other preferred embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program / instruction is stored, wherein when the computer program is executed by a processor, the steps of the unmanned surface vessel full-coverage connection topology optimization path planning method for underwater target search described in any of the above embodiments are implemented.

[0119] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the unmanned surface vessel full-coverage connection topology optimization path planning method for underwater target search, which will not be repeated here.

[0120] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0121] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0122] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0124] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0125] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0126] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0127] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0128] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for full-coverage connected topology optimization path planning for underwater target search using unmanned surface vessels, characterized in that, Includes the following steps: Step 1: Obtain a raster map of the task area and construct a free grid set based on the raster map; Step 2: Construct a rectangular decomposition model for connectivity optimization. By introducing decision variables, constraint variables, and reward variables, the free mesh set is divided into rectangles, while optimizing the geometry and topology to minimize the turning cost of the coverage path. Step 3: Solve the rectangular decomposition model to obtain the optimal solution that minimizes the global weighted cost, and determine the rectangular decomposition result based on the decision variables in the optimal solution to form a set of rectangles; Step 4: Based on the set of rectangles, calculate the connection cost between adjacent rectangles, and generate a minimum turning spanning tree based on the connection cost, so that all rectangles are connected and the total connection cost is minimized; Step 5: Generate a coverage path based on the minimum turning generation tree, smooth the coverage path, and output the final detection trajectory that satisfies the kinematic constraints of the unmanned surface vessel.

2. The method for full-coverage connection topology optimization path planning of unmanned surface vessels for underwater target search according to claim 1, characterized in that, The objective function of the connection optimization rectangular decomposition model constructed in step 2 is: in, The geometric boundary cost term is used to represent the number of rectangles generated by the decomposition and the degree of fragmentation. Its value is determined based on the consistency of the extension direction between each grid in the free mesh set and its adjacent grids. When the extension directions of adjacent grids are inconsistent, the corresponding position is marked as a rectangular boundary. The topology reward item is used to represent a rectangular topology that is conducive to reducing turns. Its value is determined according to the preset topology connection pattern formed between each grid and its adjacent grids in the free grid set. The preset topology connection pattern includes the pattern of extending horizontally and then turning vertically, and the pattern of extending vertically and then turning horizontally. and These are the corresponding weighting coefficients.

3. The method for full-coverage connection topology optimization path planning of unmanned surface vessels for underwater target search according to claim 2, characterized in that, The geometric boundary cost term and topology reward items Specifically: in, Represents a free mesh set The first in One grid; and These represent the horizontal and vertical directions, respectively. Represents a grid The right neighbor grid and the bottom neighbor grid; For boundary indicator variables; For grid Boundary indicator variable between the grid cells to the left or above; For grid Boundary indicator variable between the grid cells to the right or below; For topological reward variables; For grid The "horizontal to vertical" structural reward variable between the grid cells to the right or below; For grid The "vertical to horizontal" reward variable between the grid cells to the right or below; Each grid in the system has a binary geometric continuity property, which is used to identify whether the extension direction of the grid is horizontal or vertical.

4. The method for full-coverage connection topology optimization path planning of unmanned surface vessels for underwater target search according to claim 1, characterized in that, In step 4, the connection cost calculation between adjacent rectangles is based on the fact that each of the two endpoints of an independent rectangle has two turns. The impact value of connecting adjacent grids in adjacent rectangles on the global number of turns is determined. The impact value includes: when the ends of two adjacent orthogonal rectangles form an L-shaped connection, the number of turns decreases by 2 compared to the inherent number of turns of the two independent rectangles; when the ends of two adjacent parallel rectangles form a U-shaped connection, the number of turns remains unchanged; when the ends and non-ends of two adjacent orthogonal rectangles form a T-shaped connection, no additional maneuvering turns are added; when the ends and non-ends of two adjacent parallel rectangles form an H-shaped connection, an additional transition path along the edge is added, increasing the number of turns by 2; when the non-ends of two adjacent parallel rectangles form an H-shaped connection, the coverage continuity is severely disrupted, increasing the number of turns by 4.

5. A method for full-coverage connected topology optimization path planning for underwater target search using unmanned surface vessels according to claim 1 or 4, characterized in that, The method of generating a minimum turning spanning tree using a greedy strategy specifically includes: Initialize the edge set of the generator tree, and treat each independent rectangle as an independent edge and put it into the edge set; Iterate through all adjacent rectangle pairs and select the edge with the minimum connection cost to connect the two rectangles. If the edge does not form a cycle when added to the edge set, then add the edge to the spanning tree. Repeat the above process until all rectangles are connected, generating the minimum turning spanning tree.

6. The method for full-coverage connection topology optimization path planning of unmanned surface vessels for underwater target search according to claim 1, characterized in that, Step 5 involves smoothing the coverage path using a trajectory smoothing method that considers the kinematic constraints of the unmanned surface vessel. Specifically, this includes: The turning trajectory of the path is defined as a continuous curve composed of circular arc segments and straight line segments, wherein the radius of the circular arc segment is not less than the minimum turning radius of the unmanned surface vessel. Obtain the starting state vector and ending state vector of the current path segment, and construct a candidate set containing multiple combinations of motion primitives; the combinations of motion primitives include at least "left turn-straight-left turn", "left turn-straight-right turn", "right turn-straight-left turn", "right turn-straight-right turn", "right turn-left turn-right turn", and "left turn-right turn-left turn". By calculating the normalized distance and displacement vector angle, the trajectory length generated by each candidate combination is calculated, and the combination type with the smallest length is selected as the optimal smooth trajectory.

7. A full-coverage path planning system for underwater target search unmanned surface vessels based on connectivity topology optimization, characterized in that, Includes modules for executing processing instructions for each step of the unmanned surface vessel (USV) full-coverage connected topology optimization path planning method for underwater target search as described in any one of claims 1-6. Map processing module: Determines grid size based on sonar parameters and environmental data, and generates a binary raster map; Model building module: Used to generate mixed-integer linear programming models that include geometric continuity constraints and rectangular topology rewards; Optimization Solver Module: Used to call the solver to solve the model and output the mesh direction matrix; Topology Analysis and Spanning Tree Module: Used to analyze the adjacency relationships between rectangles and generate a minimum turning spanning tree based on a greedy strategy; Path generation and smoothing module: Used to generate covering paths around the spanning tree and use a trajectory smoothing algorithm to transform discrete paths into continuous trajectories that conform to the kinematic constraints of the unmanned surface vessel.

8. A computer system comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the instruction tracing method as described in any one of claims 1 to 6.

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