Ocean information collection method and device based on marine heterogeneous unmanned boat system

Abstract

This application provides a method and device for collecting ocean information based on a marine heterogeneous unmanned vehicle system. The system method includes: determining the number of autonomous underwater vehicles (AUVs) and the placement coordinates of buoys in a set sea area, and dividing the distribution of buoys into regions; determining the sequence of buoy coordinates for the surface unmanned vehicle (USV) to traverse all divided regions, and the locations where the AUVs are deployed; after the buoys dive, determining the optimal heave and sink path of the AUV considering kinematic constraints under different target depths; determining the path planning for each region based on the buoy positions and the relevant data collected from each buoy; and after the AUVs surface, the USVs recover the AUVs and return them. This application implements the shortest path collection task, effectively avoiding the path redundancy problem caused by turning restrictions in traditional path planning.

Description

Technical Field

[0001] The embodiments of the present application relate to ocean information collection technology, and in particular to a heterogeneous ocean robot system that utilizes an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV) to work together to efficiently collect ocean environment information. Background Art

[0002] With the rapid development of marine science and technology, the real-time collection and transmission of ocean information has become a crucial research topic. Traditional ocean data collection methods typically rely on single devices or manual operations, which can be inefficient, have limited coverage, and lack real-time performance. Furthermore, the complex and ever-changing ocean environment, limited communications, and obstacles that hinder path planning pose numerous challenges to the efficient and widespread collection of ocean data using existing technologies.

[0003] In recent years, the development of unmanned systems, such as unmanned surface vehicles (USVs) and autonomous underwater vehicles (AUVs), has provided new solutions for ocean data collection. However, technical challenges remain in addressing multi-device collaboration, including task allocation, path optimization, improving data collection efficiency, and reducing communication constraints. Unfortunately, no relevant technical solutions are currently available. Summary of the Invention

[0004] The present application provides a method and device for collecting ocean information based on a marine heterogeneous unmanned boat system to at least solve the above technical problems existing in the prior art.

[0005] According to a first aspect of the present application, a method for collecting ocean information based on a marine heterogeneous unmanned vehicle system is provided, the method comprising:

[0006] Determine the number of autonomous underwater vehicles (AUVs) and the placement coordinates of buoys in the set sea area, and divide the distribution of buoys into regions;

[0007] Determine the sequence of the buoy coordinates for the USV to traverse all the divided areas, and the location of the AUV deployment;

[0008] After the buoy dives, determine the optimal heave and sink path of the AUV considering kinematic constraints at different target diving depths;

[0009] Determine the path planning for each area based on the location of the buoy and the relevant data collected by each buoy;

[0010] After the AUV floats to the surface, the USV recovers the AUV and returns.

[0011] In some optional embodiments, determining the sequence of buoy coordinates of all divided areas traversed by the unmanned surface vehicle (USV) and the position of deploying the AUV includes:

[0012] Divide the area according to the horizontal coordinates of the buoy;

[0013] A set of coordinates is formed by randomly selecting the horizontal coordinates of a buoy and the USV shore base from each divided area. A non-closed-loop sequence starting from the shore base to reach each divided area is determined. The optimal sequence of this set of coordinates is obtained by solving the traveling salesman problem. A series of optimal sequences are obtained by traversing all points in all divided areas. The path lengths of each sequence are compared to obtain the optimal sequence for deploying the AUV.

[0014] Based on the optimal sequence, the USV is controlled to deploy the AUV when the horizontal distance between the USV and the buoy reaches the set distance range while driving to the next buoy.

[0015] In some optional embodiments, determining the optimal heave path of the AUV considering kinematic constraints at different target diving depths includes:

[0016] The AUV is controlled to dive in a spiral trajectory at a fixed pitch angle. After the AUV is close to the target depth, the AUV is controlled to dive in a parabolic trajectory. After reaching the target depth, the pitch angle of the AUV is adjusted to 0. If the AUV does not reach the information collection range of the buoy, the AUV is controlled to navigate in a straight trajectory. Based on the trajectory homotopy algorithm, a path set of the AUV pitch angle and the target depth is constructed; wherein, the pitch angle of the AUV during the dive does not exceed a first set threshold;

[0017] The path is evaluated based on the path length and the maximum acceleration of the AUV's pitch angle, and the optimal diving path of the AUV considering kinematic constraints at different target diving depths is obtained.

[0018] The diving path parameters at different depths are stored. After the AUV completes the information collection task, it is controlled to surface and wait for recovery.

[0019] In some optional embodiments, determining the number of autonomous underwater vehicles (AUVs) and the placement coordinates of buoys in a set sea area and dividing the distribution of the buoys into regions includes:

[0020] According to the set communication distance between the AUV and the buoy, the underwater acoustic channel capacity at this distance is obtained;

[0021] Calculate the minimum information collection time for each buoy based on the amount of information on each buoy;

[0022] According to the AUV's working depth, the placement depth of the buoy, and the set communication distance, the AUV's movement information around each buoy at the working depth is calculated, and the circumference radius of the circle is collected;

[0023] Path planning is performed based on the Dubins path principle. The planned path consists of a straight path and a circular path. The straight path is the tangent direction of the AUV to the next buoy information collection circle; the circular path is the AUV information collection path. When the AUV's circular path navigation time is greater than the minimum information collection time, the AUV searches for the tangent between the current information collection circle and the next buoy information collection circle on the circular path and uses the tangent as the straight path.

[0024] According to a second aspect of the present application, there is provided an ocean information collection device based on an ocean heterogeneous unmanned boat system, comprising:

[0025] The first determination unit is used to determine the number of autonomous underwater vehicles (AUVs) and the placement coordinates of buoys in a set sea area, and to divide the distribution of buoys into regions;

[0026] The second determining unit is used to determine the sequence of the buoy coordinates of all the divided areas that the unmanned surface vehicle USV traverses, and the position of the deployed AUV;

[0027] The third determination unit is used to determine the optimal heave and sink path of the AUV considering kinematic constraints under different target diving depths after the buoy dives;

[0028] The fourth determining unit is used to determine the path planning of each area according to the location of the buoy and the relevant data collected by each buoy;

[0029] The recovery unit is used to trigger the USV to recover the AUV and return home when the AUV floats to the surface.

[0030] In some optional embodiments, the second determining unit is further configured to:

[0031] Divide the area according to the horizontal coordinates of the buoy;

[0032] A set of coordinates is formed by randomly selecting the horizontal coordinates of a buoy and the USV shore base from each divided area. A non-closed-loop sequence starting from the shore base to reach each divided area is determined. The optimal sequence of this set of coordinates is obtained by solving the traveling salesman problem. A series of optimal sequences are obtained by traversing all points in all divided areas. The path lengths of each sequence are compared to obtain the optimal sequence for deploying the AUV.

[0033] Based on the optimal sequence, the USV is controlled to deploy the AUV when the horizontal distance between the USV and the buoy reaches the set distance range during the process of driving to the next buoy.

[0034] In some optional embodiments, the third determining unit is further configured to:

[0035] The AUV is controlled to dive in a spiral trajectory at a fixed pitch angle. After the AUV is close to the target depth, the AUV is controlled to dive in a parabolic trajectory. After reaching the target depth, the pitch angle of the AUV is adjusted to 0. If the AUV does not reach the information collection range of the buoy, the AUV is controlled to navigate in a straight trajectory. Based on the trajectory homotopy algorithm, a path set of the AUV pitch angle and the target depth is constructed; wherein, the pitch angle of the AUV during the dive does not exceed a first set threshold;

[0036] The path is evaluated based on the path length and the maximum acceleration of the AUV's pitch angle, and the optimal diving path of the AUV considering kinematic constraints at different target diving depths is obtained.

[0037] The diving path parameters at different depths are stored. After the AUV completes the information collection task, it is controlled to surface and wait for recovery.

[0038] In some optional embodiments, the third determining unit is further configured to:

[0039] According to the set communication distance between the AUV and the buoy, the underwater acoustic channel capacity at this distance is obtained;

[0040] Calculate the minimum information collection time for each buoy based on the amount of information on each buoy;

[0041] According to the AUV's working depth, the placement depth of the buoy, and the set communication distance, the AUV's movement information around each buoy at the working depth is calculated, and the circumference radius of the circle is collected;

[0042] Path planning is performed based on the Dubins path principle. The planned path consists of a straight path and a circular path. The straight path is the tangent direction of the AUV to the next buoy information collection circle; the circular path is the AUV information collection path. When the AUV's circular path navigation time is greater than the minimum information collection time, the AUV searches for the tangent between the current information collection circle and the next buoy information collection circle on the circular path and uses the tangent as the straight path.

[0043] In some optional embodiments, the variation of the longitudinal tilt angle of the AUV parabolic trajectory diving with depth is expressed as:

[0044]

[0045] in, is the set pitch angle, For AUV at depth The pitch angle at Dive deep for your goal, is the depth of the intersection of the spiral trajectory and the parabola trajectory.

[0046] In some optional embodiments, the third determining unit is further configured to:

[0047]

[0048] in, represents the pseudo-trajectory homotopy function, 、 、 is the subfunction of the pseudo-trajectory homology, Indicates the starting point of the path, represents the intersection of the spiral trajectory and the parabola trajectory, Indicates the end point of the path;

[0049] Judge the rationality of the path and introduce , When it is 1, the path is discarded, and when it is 0, the path is saved. The AUV has exceeded the end point after completing the parabolic dive. ;

[0050]

[0051] in, , for The horizontal coordinate of for The horizontal coordinate of is the horizontal coordinate of the end point of the parabola path;

[0052] The optimal diving and ascent paths are selected based on the path length and the rate of change of the pitch angle.

[0053] In some optional embodiments, the second determining unit is further configured to:

[0054] Signal transmission loss relative to transmission distance and frequency for:

[0055]

[0056] in, is the spreading factor; is the absorption coefficient;

[0057] The absorption coefficient is expressed according to the empirical formula:

[0058]

[0059] Taking turbulence, ships, waves and thermal noise as components of the simulated ocean soundscape, according to the empirical formula, represents the power spectral density of the simulated turbulence, represents the power spectral density of the simulated ship, represents the power spectral density of the simulated wave, The power spectral density of the simulated thermal noise is expressed as follows:

[0060]

[0061] in, and represent shipping activity factor and wind speed respectively;

[0062] The power spectral density of the total ocean noise is:

[0063]

[0064] Combining the transmission loss and noise gives the nominal signal-to-noise ratio as follows:

[0065]

[0066] The underwater acoustic channel capacity is:

[0067]

[0068] Indicates bandwidth, Indicates the source level;

[0069] According to the amount of information of each buoy, the minimum information collection time of each buoy is calculated as

[0070]

[0071] in, Indicates the data size, =1500 , is the speed of sound propagation in water.

[0072] In some optional embodiments, the third determining unit is further configured to:

[0073] Assuming there is an obstacle on the process path, the obstacle is enclosed as a cylinder with a radius of , decompose the obstacle avoidance path of AUV into three arc paths, where is the radius of the first arc, is the radian angle of the first arc, It is half of the arc angle formed by the intersection of the original straight path of the AUV and the obstacle cylinder, The coordinates of the AUV starting to perform the obstacle avoidance task and the distance between the obstacle, is the safe distance between the AUV and the obstacle. The constraints between the above parameters are as follows:

[0074]

[0075] By changing the safety distance , to obtain different obstacle avoidance paths, it is necessary to ensure The following conditions are met:

[0076] ,but .

[0077] According to a third aspect of the present application, an electronic device is provided, including:

[0078] at least one processor; and

[0079] a memory communicatively connected to the at least one processor; wherein,

[0080] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the ocean information collection method based on the ocean heterogeneous unmanned boat system described in this application.

[0081] According to a fourth aspect of the present application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the steps of the ocean information collection method based on the ocean heterogeneous unmanned boat system described in the present application.

[0082] The ocean information collection method and device based on the marine heterogeneous unmanned boat system of this application is aimed at the information collection needs in complex marine environments. Through the efficient collaboration of heterogeneous marine robot systems, it comprehensively considers the solutions of task allocation, path optimization, data collection and communication constraints, thereby achieving a significant improvement in information collection efficiency and the optimal execution of the overall task.

[0083] This application improves the system's task execution efficiency through region division and task allocation algorithms. A k-means clustering algorithm can be used to evenly divide sensors (buoys) within the ocean area into several sub-areas, ensuring balanced task distribution within each sub-area. Furthermore, each AUV focuses on its assigned area, reducing task overlap between devices and improving overall task execution efficiency. The precise region division reduces ineffective navigation between sub-areas by USVs, significantly reducing overall mission energy consumption. This application utilizes an ant colony optimization algorithm and a pseudo-trajectory homotopy method, demonstrating superior performance in USV and AUV task execution. The ant colony optimization algorithm significantly optimizes the USV's cruising path within the ocean area, ensuring coverage of all sub-areas with the shortest possible distance, significantly reducing task execution time. The pseudo-trajectory homotopy method addresses path smoothness and kinematic constraints during AUV descent and ascent, enabling the AUV to complete depth transition missions using the optimal path, ensuring smoothness and reducing energy consumption. Furthermore, the AUV uses Dubins path planning to complete the shortest path acquisition task within the sub-area, effectively avoiding the path redundancy caused by turning restrictions in traditional path planning.

[0084] In terms of obstacle avoidance, this application utilizes a Dubins path and obstacle avoidance strategy to enhance the system's adaptability. Common terrain obstacles and floating objects in marine environments pose significant challenges to information collection tasks. This invention utilizes Dubins path planning to ensure the AUV can flexibly navigate around obstacles while maintaining the overall superiority of shortest path planning.

[0085] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present application are shown in an illustrative and non-limiting manner, in which:

[0087] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts.

[0088] Figure 1 A schematic diagram showing a flow chart of a method for collecting ocean information based on a marine heterogeneous unmanned vehicle system according to an embodiment of the present application is shown;

[0089] Figure 2 This is a schematic diagram of the structure of the marine heterogeneous unmanned boat system according to an embodiment of the present application;

[0090] Figure 3A schematic diagram of the implementation process of the ocean information collection method based on the ocean heterogeneous unmanned vehicle system according to an embodiment of the present application is shown;

[0091] Figure 4 A schematic diagram showing the task allocation result and the path of the USV deploying the AUV according to an embodiment of the present application is shown;

[0092] Figure 5 A schematic diagram of solving the AUV diving path by pseudo-trajectory homotopy in an embodiment of the present application is shown;

[0093] Figure 6 A schematic diagram of a buoy access sequence for AUV information collection according to an embodiment of the present application is shown;

[0094] Figure 7 A schematic diagram showing the principle of the Dubins obstacle avoidance strategy of the AUV in an embodiment of the present application is shown;

[0095] Figure 8 A schematic diagram showing the effect of an embodiment of the present application is shown;

[0096] Figure 9 A structural schematic diagram of an ocean information collection device based on an ocean heterogeneous unmanned boat system according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0097] In order to make the purpose, features, and advantages of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0098] Figure 1 The flow chart of the ocean information collection method based on the marine heterogeneous unmanned vehicle system of the embodiment of the present application is shown. The heterogeneous unmanned vehicle system consists of a USV, an AUV and a submarine buoy. First, the sea area to be processed is divided into regions based on the number of AUVs based on the K-means algorithm; a point is randomly selected in each divided area to form a set of coordinates with the shore base of the USV. The ant colony algorithm is used to solve each set with the shore base as the starting point to reach the USV deployment sequence and the deployment position of the AUV in each sub-region; according to the improved pseudo-trajectory homotopy method, the deep trajectory parameters of the AUV at different target depths are calculated; combined with the Dubins path, the path of the AUV in each sub-region is planned; after completing the information collection task, the AUV floats to the surface and waits for the USV to recover it. The USV recovers and returns according to the order in which the AUVs return to the surface. The following is a substantive introduction to the technical solution of the embodiment of the present application in conjunction with the accompanying drawings.

[0099] like Figure 1 As shown, the ocean information collection method based on the marine heterogeneous unmanned vehicle system of the embodiment of the present application includes the following steps:

[0100] Step 101: determine the number of autonomous underwater vehicles (AUVs) and the placement coordinates of buoys in a set sea area, and divide the distribution of the buoys into regions.

[0101] In an embodiment of the present application, the underwater acoustic channel capacity at a set communication distance between the AUV and the buoy is obtained; the minimum information collection time of each buoy is calculated based on the amount of information of each buoy; the information of the AUV's movement around each buoy at the working depth is calculated based on the working depth of the AUV, the placement depth of the buoy, and the set communication distance, and the circumferential radius of the collection circle is collected; path planning is performed based on the Dubins path principle, and the planned path consists of a straight path and a circular path; the straight path is the tangent direction of the AUV to the next buoy information collection circle; the circular path is the AUV information collection path, and when the AUV's circular path navigation time is greater than the minimum information collection time, the AUV searches for the tangent between the current information collection circle and the information collection circle of the next buoy on the circular path, and uses the tangent as the straight navigation path.

[0102] As an implementation method, the regions are divided as follows:

[0103] AUV position collection P ={ p 1, p 2,..., p n},in p i =( xi , yi ) indicates the i The longitude and latitude coordinates of each AUV, and the preset number of clusters k , that is, the number of areas to be divided.

[0104] Random selection k AUV positions as initial cluster centers:

[0105]

[0106] is the coordinate of the center of the jth region,

[0107] Calculate the Euclidean distance from each AUV to each cluster center and assign it to the nearest cluster:

[0108]

[0109] Recalculate the cluster center, that is, take the mean within the cluster, as follows:

[0110]

[0111] For the j AUV collection in a region;

[0112] Iteration stops when any of the following conditions are met:

[0113] The center point change is less than the threshold ,Right now: , t Index of the number of iterations; or the maximum number of iterations is reached T max .

[0114] Among them, path planning based on Dubins path principle includes:

[0115] Assuming there is an obstacle on the process path, the obstacle is enclosed as a cylinder with a radius of , decompose the obstacle avoidance path of AUV into three arc paths, where is the radius of the first arc, is the radian angle of the first arc, It is half of the arc angle formed by the intersection of the original straight path of the AUV and the obstacle cylinder, The coordinates of the AUV starting to perform the obstacle avoidance task and the distance between the obstacle, is the safe distance between the AUV and the obstacle. The constraints between the above parameters are as follows:

[0116]

[0117] By changing the safety distance , get different obstacle avoidance paths, and ensure The following conditions are met:

[0118] ,but .

[0119] In an embodiment of the present application, an AUV that meets the minimum turning radius constraint can be used to plan the shortest path in a two-dimensional plane.

[0120] The core constraints are as follows:

[0121] Kinematic model: x ˙= v cos θ , y ˙= v sin θ , θ ˙= u , minimum turning radius: , Path type: Six possible sequences of straight line segments (S) and circular arc segments (L / R): P={ LSL , RSR , LSR , RSL , LRL , RLR}. The pose of AUV is expressed as q =( x , y , θ ),in, ( x , y ) are plane coordinates, θ is the heading angle (radians), and its steering angular velocity is limited to: , for any starting point q s and end point q g ,The Dubins path can be represented as a combination of three basic ,motions, namely, left-turning circle, right-turning circle and straight line segment. ,The length of all path types is calculated and the shortest path that satisfies the constraints is ,selected.

[0122] Step 102: determine the sequence of buoy coordinates of all divided areas that the unmanned surface vehicle (USV) traverses, and the location where the AUV is deployed.

[0123] In an embodiment of the present application, regions are divided according to the horizontal plane coordinates of a buoy; the horizontal plane coordinates of a buoy are randomly selected from each divided region and a USV shore base is used to form a coordinate set, and a non-closed-loop sequence starting from the shore base to reach each divided region is determined; the optimal sequence of the coordinate set is obtained by solving the traveling salesman problem; a series of optimal sequences are obtained by traversing all points in all divided regions, and the path length of each sequence is compared to obtain the optimal sequence for deploying the AUV; based on the optimal sequence, the USV is controlled to deploy the AUV when the horizontal distance between the USV and the buoy reaches a set distance range during the process of sailing to the next buoy.

[0124] Specifically, the signal transmission loss relative to the transmission distance and frequency for:

[0125]

[0126] in, is the spreading factor; is the absorption coefficient;

[0127] The absorption coefficient is expressed according to the empirical formula:

[0128]

[0129] Taking turbulence, ships, waves and thermal noise as components of the simulated ocean soundscape, according to the empirical formula, represents the power spectral density of the simulated turbulence, represents the power spectral density of the simulated ship, represents the power spectral density of the simulated wave, The power spectral density of the simulated thermal noise is expressed as follows:

[0130]

[0131] in, and represent shipping activity factor and wind speed respectively;

[0132] The power spectral density of the total ocean noise is:

[0133]

[0134] Combining the transmission loss and noise gives the nominal signal-to-noise ratio as follows:

[0135]

[0136] The underwater acoustic channel capacity is:

[0137]

[0138] Indicates bandwidth, Indicates the source level;

[0139] According to the amount of information of each buoy, the minimum information collection time of each buoy is calculated as

[0140]

[0141] in, Indicates the data size, =1500 , is the speed of sound propagation in water.

[0142] Step 103: After the buoy dives, the optimal heave and sink paths of the AUV are determined under different target diving depths, taking into account kinematic constraints.

[0143] In an embodiment of the present application, the AUV is controlled to dive on a spiral trajectory at a fixed pitch angle. After determining that the AUV is close to the target depth of the AUV, the AUV is controlled to dive on a parabolic trajectory. After reaching the target depth, the pitch angle of the AUV is adjusted to 0. If the AUV does not reach the information collection range of the buoy, the AUV is controlled to navigate on a straight trajectory. Based on the trajectory homotopy algorithm, a path set of the AUV pitch angle and the target depth is constructed; wherein the pitch angle during the AUV dive does not exceed a first set threshold; the path is evaluated based on the path length and the maximum acceleration of the AUV pitch angle, and the optimal diving path of the AUV considering kinematic constraints at different target diving depths is obtained; the diving path parameters at different depths are stored, and after the AUV completes the information collection task, the AUV is controlled to surface and wait for recovery.

[0144] Among them, the change of the longitudinal inclination angle of the AUV parabolic trajectory diving with depth is expressed as:

[0145]

[0146] in, is the set pitch angle, For AUV at depth The pitch angle at Dive deep for your goal, is the depth of the intersection of the spiral trajectory and the parabola trajectory.

[0147] Based on the trajectory homotopy algorithm, a path set about the AUV pitch angle and target depth is constructed, including:

[0148]

[0149] in, represents the pseudo-trajectory homotopy function, 、 、 is the subfunction of the pseudo-trajectory homology, Indicates the starting point of the path, represents the intersection of the spiral trajectory and the parabola trajectory, Indicates the end point of the path;

[0150] Judge the rationality of the path and introduce , When it is 1, the path is discarded, and when it is 0, the path is saved. The AUV has exceeded the end point after completing the parabolic dive. ;

[0151]

[0152] in, , for The horizontal coordinate of for The horizontal coordinate of is the horizontal coordinate of the end point of the parabola path;

[0153] The optimal diving and ascent paths are selected based on the path length and the rate of change of the pitch angle.

[0154] Step 104: Determine the path planning for each area based on the location of the buoy and the relevant data collected by each buoy.

[0155] In the embodiment of the present application, the paths of each area are sorted according to the area weight to determine the best path. For the specific planning method, please refer to the relevant records of the above steps.

[0156] Step 105: After the AUV floats to the surface, the USV recovers the AUV and returns.

[0157] Control the AUV's heave and sink path parameters, complete the AUV's ascent, and send a recovery instruction to the USV after reaching the water surface. The USV recovers the AUV in sequence and returns according to the received recovery instruction.

[0158] The following is a combination of specific examples to further illustrate the essence of the technical solutions of the embodiments of the present application.

[0159] Figure 2 This is a schematic diagram of the structure of the marine heterogeneous unmanned boat system according to an embodiment of the present application. Figure 2 As shown, the marine heterogeneous unmanned vehicle system of the embodiment of the present application includes at least three AUVs and at least one USV, and the USV can deploy and recover the AUVs. There is also a submarine buoy on the bottom of the ocean.

[0160] Figure 3 The following is a schematic diagram showing the implementation process of the ocean information collection method based on the ocean heterogeneous unmanned boat system according to the embodiment of the present application. Figure 3 As shown, the method of the embodiment of the present application includes the following steps:

[0161] Step 1: Assign tasks based on the buoy location information and the number of AUVs.

[0162] In the embodiment of the present application, the arrangement of the buoy and the AUV and the information collection configuration are carried out according to the sea area to be processed, such as Figure 4 shown.

[0163] Step 2: Calculate the USV deployment path and AUV deployment position.

[0164] According to the number of AUVs equipped with information collection equipment, the distribution of buoys in the sea area is divided into regions; the sequence of buoy coordinates of all sub-regions traversed by the USV and the location of the deployed AUVs are solved.

[0165] In this embodiment, the coordinates of the submarine buoys are known. The K-means algorithm is used to cluster the sea area according to their horizontal coordinates and the number of AUVs, and the sea area is divided into three sub-areas. From each divided sub-area, the horizontal coordinates of a buoy are randomly selected together with the USV shore base to form a set of coordinates. The ant colony algorithm is used to solve the non-closed loop sequence starting from the shore base to reach each sub-area; the traveling salesman problem is used to solve the optimal sequence of the set of coordinates; a series of optimal sequences are obtained by traversing all points in all sub-areas, and the path length of each sequence is compared to obtain the optimal sequence for the USV to deploy the AUV; Figure 4 As shown in the figure, considering that the AUV spiral dives to the specified depth and cannot meet the horizontal coordinates of the buoy, the USV starts to deploy the AUV when the horizontal distance between the USV and the buoy reaches 200 meters when it is sailing to the next buoy. After the deployment is completed, it sails to the next buoy.

[0166] Step 3: Control the AUV to dive in a pseudo-trajectory homotopy manner.

[0167] Step 4: The AUV follows a straight line towards the buoy. During the driving process, it is necessary to determine whether there are obstacles along the way and control the AUV to avoid them according to the Dubins path.

[0168] After being deployed, the AUV begins to dive and the optimal heave and sink path of the AUV considering kinematic constraints at different target diving depths is solved.

[0169] In this embodiment, Figure 5 As shown in the figure, the parameters of the AUV heave path are consistent, and the calculation is based on the AUV dive path. The maximum pitch angle of the AUV is limited to a certain range. When the AUV speed remains unchanged, the AUV first performs a spiral dive with the same projected circle radius; after approaching the target depth, the AUV begins to dive along a parabolic trajectory. After reaching the target depth, the pitch angle is 0, where the AUV pitch angle changes with depth as follows:

[0170] (1)

[0171] in, is the set pitch angle, For AUV at depth The longitudinal tilt angle at . Dive deep for your goal, is the depth of the intersection of the spiral trajectory and the parabolic trajectory. If the AUV does not reach the information collection range of the buoy, it will navigate on a straight trajectory. Combined with the trajectory homotopy method, a path set about the AUV pitch angle and the target depth is constructed. The pseudo-trajectory homotopy algorithm is expressed as:

[0172] (2)

[0173] represents the pseudo-trajectory homotopy function, 、 、 is the subfunction of the pseudo-trajectory homology, Indicates the starting point of the path, represents the intersection of the spiral trajectory and the parabola trajectory, Indicates the end point of the path.

[0174] like Figure 5 As shown, after setting the depth ,and and Under known conditions, a series of pseudo-trajectory homotopic paths can be obtained by changing the value of the pitch angle. On this basis, the rationality of the path is judged, so the , When it is 1, the path is discarded, and when it is 0, the path is saved. Due to the limitations of the AUV's kinematic performance, the rate of change of the pitch angle There is a maximum value , when a path , ; If the AUV has exceeded the end point after completing the parabolic dive, .therefore Expressed as:

[0175] (3)

[0176] in , for The horizontal coordinate of for The horizontal coordinate of is the horizontal coordinate of the end point of the parabola path. Finally, the optimal diving path is selected by combining the path length and the rate of change of the pitch angle. The same is true for the AUV's ascent path, which will not be repeated here.

[0177] The AUV completes the path planning of the sub-area based on the location of the buoy and the amount of data for each buoy.

[0178] In this embodiment, the sequence of AUV visiting the buoy must first be determined, such as Figure 5 、 Figure 6 As shown in the figure, the ant colony algorithm is used to solve the non-closed loop sequence of AUVs visiting all buoys in the sub-region starting from the coordinates in the USV deployment sequence; and the traveling salesman problem is solved to obtain the optimal sequence for AUVs to visit buoys.

[0179] Signal transmission loss relative to transmission distance and frequency for:

[0180] (4)

[0181] in, is the spreading factor, take 1.5; is the absorption coefficient, in units of .

[0182] The absorption coefficient is expressed according to the empirical formula:

[0183] (5)

[0184] Due to the complexity of ocean ambient noise, turbulence, ship, wave, and thermal noise need to be considered as components of the simulated ocean soundscape. According to empirical formulas, they are expressed in turn as follows:

[0185] (6)

[0186] in and represent shipping activity factor and wind speed respectively.

[0187] Therefore, the power spectral density of the total ocean noise is:

[0188] (7)

[0189] Combining transmission loss and noise gives the nominal signal-to-noise ratio (SNR):

[0190] (8)

[0191] The underwater acoustic channel capacity is:

[0192] (9)

[0193] in Indicates bandwidth, Indicates the source level.

[0194] The minimum information collection time of each buoy can be calculated based on the amount of information of each buoy:

[0195] (10)

[0196] in Indicates the data size, Indicates the speed of sound propagation in water;

[0197] Based on this, the AUV's information collection path can be planned based on the Dubins path principle. A straight path is where the AUV navigates in the direction of a tangent to the next buoy's information collection circle. A circular path is where the AUV navigates at a certain speed. When the time exceeds the minimum information collection time required for each buoy's information transmission, the AUV searches for the tangent between the current information collection circle and the next buoy's information collection circle on the circular path and switches to straight-line navigation.

[0198] If there are obstacles on the path of the AUV when it is heading towards the buoy, such as Figure 6 、 Figure 7 As shown, the obstacle is enclosed as a cylinder with a radius of According to the Dubins path, the obstacle avoidance path of the AUV is combined into three arc paths. is the radius of the first arc, is the radian angle of the first arc, It is half of the arc angle formed by the intersection of the original straight path of the AUV and the obstacle cylinder, The coordinates of the AUV starting to perform the obstacle avoidance task and the distance between the obstacle, is the safe distance between AUV and obstacles, such as Figure 6 There is a certain relationship between these parameters:

[0199] (11)

[0200] By changing the safety distance , different obstacle avoidance paths can be obtained. Existence requires:

[0201] (12)

[0202] By calculation, we can get Then, the optimal obstacle avoidance path is selected based on the total path length. The final obstacle avoidance path of the AUV is as follows: Figure 6 As shown in (c) in .

[0203] In step 5, if there are no obstacles along the route, the AUV navigates a circular trajectory around the buoy and determines whether the AUV has completed information collection. In other words, it determines whether the AUV has completed the task of collecting buoy information in the subarea. If the task is not completed, the AUV is controlled to navigate to another buoy to confirm the information collection results.

[0204] Step 6: The AUVs surface and wait for the USV to recover them. The USVs recover the AUVs one by one in the order in which they surface and return.

[0205] The AUV completes the ascent according to the heave and sink path parameters, and sends a recovery instruction to the USV after reaching the water surface. The USV recovers and returns in sequence according to the received AUV recovery instruction.

[0206] like Figure 8 As shown, to meet the information collection needs in complex marine environments, a solution that comprehensively considers task allocation, path optimization, data collection, and communication constraints through the efficient collaboration of heterogeneous marine robotic systems achieves significant improvements in information collection efficiency and optimal overall mission execution. The system's task execution efficiency is enhanced through region division and task allocation algorithms. Sensors (buoys) within the ocean area are evenly divided into several sub-areas, ensuring balanced distribution of tasks within each sub-area. Furthermore, each AUV focuses on its assigned area, reducing task overlap between devices and improving overall mission execution efficiency. The precise region division reduces ineffective navigation between sub-areas for USVs and significantly reduces overall mission energy consumption. This application utilizes an ant colony optimization algorithm and a pseudo-trajectory homotopy method, demonstrating superior performance in USV and AUV mission execution. The ant colony optimization algorithm significantly optimizes the USV's cruising path within the ocean area, ensuring coverage of all sub-areas with the shortest possible distance, significantly reducing mission execution time. The AUV is controlled to complete the depth conversion mission along the optimal path, ensuring path stability and reducing energy consumption. Furthermore, the AUV uses Dubins path planning to complete the shortest path collection task within the sub-area, effectively avoiding the path redundancy caused by turning restrictions in traditional path planning. Common terrain obstacles and floating objects in the marine environment pose a severe challenge to information collection tasks. This application uses Dubins path planning to ensure that the AUV can flexibly circumvent obstacles while maintaining the overall superiority of shortest path planning.

[0207] Figure 9 The structure diagram of the ocean information collection device based on the ocean heterogeneous unmanned boat system according to the embodiment of the present application is shown as follows: Figure 9 As shown, the ocean information collection device based on the marine heterogeneous unmanned boat system of the embodiment of the present application includes:

[0208] The first determining unit 90 is used to determine the number of autonomous underwater vehicles (AUVs) and the placement coordinates of the buoys in the set sea area, and to divide the distribution of the buoys into regions;

[0209] The second determining unit 91 is used to determine the sequence of the buoy coordinates of all the divided areas that the unmanned surface vehicle USV traverses, and the position of the deployed AUV;

[0210] The third determining unit 92 is configured to determine the optimal heave path of the AUV considering kinematic constraints at different target diving depths after the buoy dives;

[0211] The fourth determining unit 93 is used to determine the path planning of each area based on the location of the buoy and the relevant data collected by each buoy;

[0212] The recovery unit 94 is used to trigger the USV to recover the AUV and return home when the AUV floats to the surface.

[0213] In some optional embodiments, the second determining unit 91 is further configured to:

[0214] Divide the area according to the horizontal coordinates of the buoy;

[0215] A set of coordinates is formed by randomly selecting the horizontal coordinates of a buoy and the USV shore base from each divided area. A non-closed-loop sequence starting from the shore base to reach each divided area is determined. The optimal sequence of this set of coordinates is obtained by solving the traveling salesman problem. A series of optimal sequences are obtained by traversing all points in all divided areas. The path lengths of each sequence are compared to obtain the optimal sequence for deploying the AUV.

[0216] Based on the optimal sequence, the USV is controlled to deploy the AUV when the horizontal distance between the USV and the buoy reaches the set distance range during the process of driving to the next buoy.

[0217] In some optional embodiments, the third determining unit 92 is further configured to:

[0218] The AUV is controlled to dive in a spiral trajectory at a fixed pitch angle. After the AUV is close to the target depth, the AUV is controlled to dive in a parabolic trajectory. After reaching the target depth, the pitch angle of the AUV is adjusted to 0. If the AUV does not reach the information collection range of the buoy, the AUV is controlled to navigate in a straight trajectory. Based on the trajectory homotopy algorithm, a path set of the AUV pitch angle and the target depth is constructed; wherein, the pitch angle of the AUV during the dive does not exceed a first set threshold;

[0219] The path is evaluated based on the path length and the maximum acceleration of the AUV's pitch angle, and the optimal diving path of the AUV considering kinematic constraints at different target diving depths is obtained.

[0220] The diving path parameters at different depths are stored. After the AUV completes the information collection task, it is controlled to surface and wait for recovery.

[0221] In some optional embodiments, the third determining unit 92 is further configured to:

[0222] According to the set communication distance between the AUV and the buoy, the underwater acoustic channel capacity at this distance is obtained;

[0223] Calculate the minimum information collection time for each buoy based on the amount of information on each buoy;

[0224] According to the AUV's working depth, the placement depth of the buoy, and the set communication distance, the AUV's movement information around each buoy at the working depth is calculated, and the circumference radius of the circle is collected;

[0225] Path planning is performed based on the Dubins path principle. The planned path consists of a straight path and a circular path. The straight path is the tangent direction of the AUV to the next buoy information collection circle; the circular path is the AUV information collection path. When the AUV's circular path navigation time is greater than the minimum information collection time, the AUV searches for the tangent between the current information collection circle and the next buoy information collection circle on the circular path and uses the tangent as the straight path.

[0226] In some optional embodiments, the variation of the longitudinal tilt angle of the AUV parabolic trajectory diving with depth is expressed as:

[0227]

[0228] in, is the set pitch angle, For AUV at depth The pitch angle at Dive deep for your goal, is the depth of the intersection of the spiral trajectory and the parabola trajectory.

[0229] In some optional embodiments, the third determining unit 92 is further configured to:

[0230]

[0231] in, represents the pseudo-trajectory homotopy function, 、 、 is the subfunction of the pseudo-trajectory homology, Indicates the starting point of the path, represents the intersection of the spiral trajectory and the parabola trajectory, Indicates the end point of the path;

[0232] Judge the rationality of the path and introduce , When it is 1, the path is discarded, and when it is 0, the path is saved. The AUV has exceeded the end point after completing the parabolic dive. ;

[0233]

[0234] in, , for The horizontal coordinate of for The horizontal coordinate of is the horizontal coordinate of the end point of the parabola path;

[0235] The optimal diving and ascent paths are selected based on the path length and the rate of change of the pitch angle.

[0236] In some optional embodiments, the second determining unit 91 is further configured to:

[0237] Signal transmission loss relative to transmission distance and frequency for:

[0238]

[0239] in, is the spreading factor; is the absorption coefficient;

[0240] The absorption coefficient is expressed according to the empirical formula:

[0241]

[0242] Taking turbulence, ships, waves and thermal noise as components of the simulated ocean soundscape, according to the empirical formula, represents the power spectral density of the simulated turbulence, represents the power spectral density of the simulated ship, represents the power spectral density of the simulated wave, The power spectral density of the simulated thermal noise is expressed as follows:

[0243]

[0244] in, and represent shipping activity factor and wind speed respectively;

[0245] The power spectral density of the total ocean noise is:

[0246]

[0247] Combining the transmission loss and noise gives the nominal signal-to-noise ratio as follows:

[0248]

[0249] The underwater acoustic channel capacity is:

[0250]

[0251] Indicates bandwidth, Indicates the source level;

[0252] According to the amount of information of each buoy, the minimum information collection time of each buoy is calculated as

[0253]

[0254] in, Indicates the data size, =1500 , is the speed of sound propagation in water.

[0255] In some optional embodiments, the third determining unit 92 is further configured to:

[0256] Assuming there is an obstacle on the process path, the obstacle is enclosed as a cylinder with a radius of , decompose the obstacle avoidance path of AUV into three arc paths, where is the radius of the first arc, is the radian angle of the first arc, It is half of the arc angle formed by the intersection of the original straight path of the AUV and the obstacle cylinder, The coordinates of the AUV starting to perform the obstacle avoidance task and the distance between the obstacle, is the safe distance between the AUV and the obstacle. The constraints between the above parameters are as follows:

[0257]

[0258] By changing the safety distance , get different obstacle avoidance paths, and ensure The following conditions are met:

[0259] ,but .

[0260] In an exemplary embodiment, each processing unit in the ocean information collection device based on the ocean heterogeneous unmanned boat system of the embodiment of the present application can be implemented by one or more central processing units (CPU), graphics processing units (GPU), application-specific integrated circuits (ASIC), DSP, programmable logic devices (PLD), complex programmable logic devices (CPLD), field programmable gate arrays (FPGA), general-purpose processors, controllers, microcontrollers (MCU), microprocessors, or other electronic components.

[0261] Regarding the device in the above embodiment, the specific manner in which each module and unit performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0262] According to an embodiment of the present application, the present application also describes an electronic device and a readable storage medium.

[0263] An embodiment of the present application also records an electronic device, comprising: at least one processor; 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 the steps of the ocean information collection method based on the ocean heterogeneous unmanned boat system described in the present application.

[0264] An embodiment of the present application also records a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the steps of the ocean information collection method based on the ocean heterogeneous unmanned boat system described in the present application.

[0265] The scope of protection of this application shall be based on the scope of protection of the claims.

Claims

1. A method for collecting ocean information based on a marine heterogeneous unmanned vehicle system, characterized in that: The method comprises: Determine the number of autonomous underwater vehicles (AUVs) and the placement coordinates of buoys in the set sea area, and divide the distribution of buoys into regions; Determine the sequence of buoy coordinates for the unmanned surface vehicle (USV) to traverse all divided areas, as well as the location for deploying the AUV. Specifically, the areas are divided according to the horizontal coordinates of the buoys. The horizontal coordinates of a buoy in each divided area are randomly selected together with the USV shore base to form a coordinate set, and a non-closed loop sequence starting from the shore base to reach each divided area is determined. The optimal sequence of the coordinate set is obtained by solving the traveling salesman problem. A series of optimal sequences are obtained by traversing all points in all divided areas, and the path length of each sequence is compared to obtain the optimal sequence for deploying the AUV. Based on the optimal sequence, the USV is controlled to deploy the AUV when the horizontal distance between the USV and the buoy reaches a set distance range while driving to the next buoy. After the buoy dives, determine the optimal heave and sink path of the AUV considering kinematic constraints at different target diving depths; Determine the path planning for each area based on the location of the buoy and the relevant data collected by each buoy; After the AUV floats to the surface, the USV recovers the AUV and returns.

2. The collection method according to claim 1, characterized in that: Determining the optimal heave path of the AUV considering kinematic constraints at different target diving depths includes: The AUV is controlled to dive in a spiral trajectory at a fixed pitch angle. After the AUV is close to the target depth, the AUV is controlled to dive in a parabolic trajectory. After reaching the target depth, the pitch angle of the AUV is adjusted to 0. If the AUV does not reach the information collection range of the buoy, the AUV is controlled to navigate in a straight trajectory. Based on the trajectory homotopy algorithm, a path set of the AUV pitch angle and the target depth is constructed; wherein, the pitch angle of the AUV during the dive does not exceed a first set threshold; The path is evaluated based on the path length and the maximum acceleration of the AUV's pitch angle, and the optimal diving path of the AUV considering kinematic constraints at different target diving depths is obtained. The diving path parameters at different depths are stored. After the AUV completes the information collection task, it is controlled to surface and wait for recovery.

3. The collection method according to claim 1, characterized in that: The determining of the number of autonomous underwater vehicles (AUVs) and the placement coordinates of buoys in a set sea area and the regional division of the distribution of buoys includes: According to the set communication distance between the AUV and the buoy, the underwater acoustic channel capacity at this distance is obtained; Calculate the minimum information collection time for each buoy based on the amount of information on each buoy; According to the AUV's working depth, the placement depth of the buoy, and the set communication distance, the AUV's movement information around each buoy at the working depth is calculated, and the circumference radius of the circle is collected; Path planning is performed based on the Dubins path principle. The planned path consists of a straight path and a circular path. The straight path is the tangent direction of the AUV to the next buoy information collection circle; the circular path is the AUV information collection path. When the AUV's circular path navigation time is greater than the minimum information collection time, the AUV searches for the tangent between the current information collection circle and the next buoy information collection circle on the circular path and uses the tangent as the straight path.

4. The collection method according to claim 2, characterized in that: The variation of the longitudinal tilt angle of the AUV parabolic trajectory diving with depth is expressed as: in, is the set pitch angle, For AUV at depth The pitch angle at Dive deep for your goal, is the depth of the intersection of the spiral trajectory and the parabola trajectory.

5. The collection method according to claim 4, characterized in that: The trajectory homotopy algorithm is used to construct a path set about the AUV pitch angle and the target depth, including: in, represents the pseudo-trajectory homotopy function, 、 、 is the subfunction of the pseudo-trajectory homology, Indicates the starting point of the path, represents the intersection of the spiral trajectory and the parabola trajectory, Indicates the end point of the path; Judge the rationality of the path and introduce , When it is 1, the path is discarded, and when it is 0, the path is saved. The AUV has exceeded the end point after completing the parabolic dive. ; in, , for The horizontal coordinate of for The horizontal coordinate of is the horizontal coordinate of the end point of the parabola path; The optimal diving and ascent paths are selected based on the path length and the rate of change of the pitch angle.

6. The collection method according to claim 3, characterized in that: The method randomly selects the horizontal coordinates of a buoy from each divided area and forms a set of coordinates with the USV shore base, and determines a non-closed loop sequence starting from the shore base to reach each divided area; and solves the traveling salesman problem to obtain the optimal sequence of the set of coordinates, including: Signal transmission loss relative to transmission distance and frequency for: in, is the spreading factor; is the absorption coefficient; The absorption coefficient is expressed according to the empirical formula: Taking turbulence, ships, waves and thermal noise as components of the simulated ocean soundscape, according to the empirical formula, represents the power spectral density of the simulated turbulence, represents the power spectral density of the simulated ship, represents the power spectral density of the simulated wave, The power spectral density of the simulated thermal noise is expressed as follows: in, and represent shipping activity factor and wind speed respectively; The power spectral density of the total ocean noise is: Combining the transmission loss and noise gives the nominal signal-to-noise ratio as follows: The underwater acoustic channel capacity is: Indicates bandwidth, Indicates the source level; According to the amount of information of each buoy, the minimum information collection time of each buoy is calculated as in, Indicates the data size, =1500 , is the speed of sound propagation in water.

7. The collection method according to claim 6, characterized in that: The path planning based on the Dubins path principle includes: Assuming there is an obstacle on the process path, the obstacle is enclosed as a cylinder with a radius of , decompose the obstacle avoidance path of AUV into three arc paths, where is the radius of the first arc, is the radian angle of the first arc, It is half of the arc angle formed by the intersection of the original straight path of the AUV and the obstacle cylinder, The coordinates of the AUV starting to perform the obstacle avoidance task and the distance between the obstacle, is the safe distance between the AUV and the obstacle. The constraint relationship between the parameters is as follows: By changing the safety distance , to obtain different obstacle avoidance paths, it is necessary to ensure The following conditions are met: ,but .

8. A marine information collection device based on a marine heterogeneous unmanned boat system, characterized in that: The device comprises: The first determination unit is used to determine the number of autonomous underwater vehicles (AUVs) and the placement coordinates of buoys in a set sea area, and to divide the distribution of buoys into regions; The second determination unit is used to determine the sequence of buoy coordinates of all divided areas traversed by the unmanned surface vehicle (USV) and the location of the deployed AUV. Specifically, the area is divided according to the horizontal coordinates of the buoy; the horizontal coordinates of a buoy in each divided area are randomly selected together with the USV shore base to form a coordinate set, and a non-closed loop sequence is determined to reach each divided area starting from the shore base; the optimal sequence of the coordinate set is obtained by solving the traveling salesman problem; a series of optimal sequences are obtained by traversing all points in all divided areas, and the path length of each sequence is compared to obtain the optimal sequence for deploying the AUV; based on the optimal sequence, the USV is controlled to deploy the AUV when the horizontal distance between the USV and the buoy reaches a set distance range during the process of sailing to the next buoy; The third determination unit is used to determine the optimal heave and sink path of the AUV considering kinematic constraints under different target diving depths after the buoy dives; The fourth determining unit is used to determine the path planning of each area according to the location of the buoy and the relevant data collected by each buoy; The recovery unit is used to trigger the USV to recover the AUV and return home when the AUV floats to the surface.

9. The device according to claim 8, characterized in that The third determining unit is further configured to: The AUV is controlled to dive in a spiral trajectory at a fixed pitch angle. After the AUV is close to the target depth, the AUV is controlled to dive in a parabolic trajectory. After reaching the target depth, the pitch angle of the AUV is adjusted to 0. If the AUV does not reach the information collection range of the buoy, the AUV is controlled to navigate in a straight trajectory. Based on the trajectory homotopy algorithm, a path set of the AUV pitch angle and the target depth is constructed; wherein, the pitch angle of the AUV during the dive does not exceed a first set threshold; The path is evaluated based on the path length and the maximum acceleration of the AUV's pitch angle, and the optimal diving path of the AUV considering kinematic constraints at different target diving depths is obtained. The diving path parameters at different depths are stored. After the AUV completes the information collection task, it is controlled to surface and wait for recovery.

10. The device according to claim 9, characterized in that The third determining unit is further configured to: According to the set communication distance between the AUV and the buoy, the underwater acoustic channel capacity at this distance is obtained; Calculate the minimum information collection time for each buoy based on the amount of information on each buoy; According to the AUV's working depth, the placement depth of the buoy, and the set communication distance, the AUV's movement information around each buoy at the working depth is calculated, and the circumference radius of the circle is collected; Path planning is performed based on the Dubins path principle. The planned path consists of a straight path and a circular path. The straight path is the tangent direction of the AUV to the next buoy information collection circle; the circular path is the AUV information collection path. When the AUV's circular path navigation time is greater than the minimum information collection time, the AUV searches for the tangent between the current information collection circle and the next buoy information collection circle on the circular path and uses the tangent as the straight path.

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