Dynamic search probability estimation method for underwater moving target

Abstract

The invention provides a dynamic search probability estimation method for an underwater moving target, and belongs to the technical field of path planning of target search. Comprising the following steps: setting a search task, loading a map, and gridding a corresponding task area in the map; initializing a probability field and navigation parameters of a search platform, and calculating a time-varying dynamically updated probability field diffusion probability of the moving target in a task area grid; and the search platform obtains a navigation planning path according to the update content of the probability field diffusion probability. According to the method, the probability of the moving target in the task area in the searching process can be dynamically estimated, and then path planning of dynamic searching of the moving target is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning for target search, and in particular to a dynamic search probability estimation method for an underwater moving target. Background Art

[0002] In marine production activities, there is a need to search for moving targets, such as searching for underwater unmanned vehicles that are still sailing but have lost communication, and searching and monitoring high-value fish schools in fishing activities. Under the large-scale conditions of the ocean and underwater environment, the detection distance of unmanned vehicles or marine life with small body size and small target characteristics using commonly used acoustic means is also very limited, and searching for them takes a lot of time. For targets that are still moving and whose moving direction is unknown, the commonly used methods such as regional parallel search and bow search based on searching for fixed targets cannot effectively evaluate the probability of appearing in the searched area due to target movement during the dynamic search process.

[0003] Therefore, based on the above situation, it is very necessary to provide a dynamic search probability estimation method for underwater moving targets to realize dynamic estimation of the probability of ocean moving targets in the mission area. Summary of the invention

[0004] In view of this, the present invention proposes a method for estimating the probability of dynamic search of underwater mobile targets, which can realize dynamic estimation of the probability of marine mobile targets in the mission area and provide a reference for path planning decisions for dynamic search of mobile targets.

[0005] The present invention provides a method for estimating the dynamic search probability of an underwater moving target, comprising the following contents:

[0006] Set up search tasks, load the map, and grid the corresponding task areas in the map;

[0007] Initialize the probability field and navigation parameters of the search platform, and calculate the probability field diffusion probability of the moving target in the mission area grid, which is dynamically updated over time;

[0008] The search platform obtains the navigation planning path based on the updated content of the probability field diffusion probability.

[0009] On the basis of the above technical solution, preferably, the gridding of the corresponding task area in the map is to divide the task area into a plurality of plane grids, the plurality of plane grids are arranged in an array, and the sizes of the plurality of grids are exactly the same.

[0010] Preferably, the plurality of planar grids are rectangular grids.

[0011] Further preferably, the initialized probability field takes the boundary of the task area as the task boundary, and there is a search target in a moving state within the task boundary; the starting position of the search platform is set, and a detector is set at one end of the navigation direction of the search platform, and the detector has a fan-shaped detection range; during the navigation of the search platform, the area swept by the detector forms a detected envelope boundary; the area within the detection range at the current moment, because the search task is still in progress and the detector has not confirmed the target, forms an area that has not diffused to the probability; the target is within the task boundary and outside the detected envelope boundary, if it enters the detected envelope boundary due to movement, then a probability diffusion zone of the probability field diffusing to the detected envelope boundary is formed, and a 0 probability envelope boundary is formed between the area to which the probability has not diffused, and the 0 probability envelope boundary is located inside the detected envelope boundary.

[0012] More preferably, the calculation of the diffusion probability of the probability field dynamically updated over time of the moving target in the task area grid is based on the theory of the probability of material diffusion, and the moving target is regarded as a separate molecule. The probability change of the moving target during movement is deduced by referring to the flow process of the LBM method, and the line connecting the center of the task area grid and the midpoints of each side of the task area grid is made to point to the adjacent positive grid, and the line connecting the center of the task area grid and each vertex of the task area grid is made to point to the adjacent oblique grid. C0 indicates that the molecule stays in the current task area grid, C1, C2, C3 and C4 indicate the direction of the molecule to the adjacent positive task area grid, and C5, C6, C7 and C8 indicate the direction of the oblique task area grid adjacent to the vertex of the task area grid where the molecule is located; at each time step, the diffusion probability f of the molecule moving to each adjacent grid c for in ∑f ci =1; the basic equation for the collision process to reach equilibrium is: f i (x+c i Δt,t+Δt)-f i (x,t)=Ω i +F i The left side of the equation is the flow term, f i represents the normalized concentration of the substance, x represents the grid position of the current task area, x+c i Δt represents the direction c i The corresponding adjacent grid position, Δt represents the time step, t is the current time, F i is the external force term, Ω i is the collision term, which indicates the diffusion degree of the equilibrium state of the molecular motion during the fluid motion within the time Δt. The equilibrium concentration u is the macroscopic velocity vector of the task area grid; the relaxation time τ satisfies v is the kinematic viscosity.

[0013] Further preferably, the search platform uses the following formula to calculate the derivation result f′ of the probability field diffusion probability based on the updated content of the probability field diffusion probability: c : U is the maximum speed of the moving target, which is determined by the moving target; L is the length of the task area grid, and the intermediate variable s satisfies

[0014] Further preferably, the search platform uses numerical experimental statistics to obtain the derivation result of the probability field diffusion probability based on the updated content of the probability field diffusion probability: within the 3×3 task area grid, within the central task area grid and the maximum speed range, randomly set the position, speed and direction of a point, calculate the position of the random point within a time step, repeat multiple times and count the proportion of the point falling into the surrounding task area grids until the proportion is stable, and the proportion is the derivation result f′ of the probability field diffusion probability c .

[0015] More preferably, when the intermediate parameter is 0.1<s<1, the derivation result of the probability field diffusion probability is obtained by using the formula; when 0<s<0.1, the derivation result of the probability field diffusion probability is obtained by using numerical test statistics.

[0016] Further preferably, the navigation planning path is obtained by iteratively updating the probability P of the task area grid with pointer i at time t+1 after obtaining the derivation result of the probability field diffusion probability. i，t+1 :P i，t+1 =P i,t -[f′ c ][P i，t -P inei,t ], P i，t represents the probability of the task area grid with pointer i at time t; [f′ c ] is the vector corresponding to the derivation result of the probability field diffusion probability of the task area grid with pointer i in all directions; P inei，t Represents the adjacent task area grid of the task area grid with pointer i at time t; obtains the dynamically updated probability field according to the formula, determines the navigation planning path optimization function, and obtains the final navigation planning path.

[0017] Further preferably, the optimization function of the route planning path takes the minimum time T to search for the moving target as the optimization goal: minT, satisfying Where A represents the action of the search platform, and the subscripts represent different moments; Φ(·) is the strategy function; P iRepresents the state of the task area grid at time i; S is the actual physical state of the environment, and the subscripts represent different moments; F represents the update of the probability field diffusion probability.

[0018] The method for estimating the dynamic search probability of an underwater moving target provided by the present invention has the following beneficial effects compared with the prior art:

[0019] (1) The present invention provides a method for estimating the probability of dynamic search of underwater mobile targets, which can be applied to scenarios where both the task boundary and the searched target are moving, provides input for path planning decisions for dynamic search of mobile targets, and realizes dynamic estimation of the probability of the mobile target being in the task area during the search process. The method can be applied to scenarios where both the task boundary and the searched target are moving;

[0020] (2) The present invention adopts a map gridding method, which can be expanded to a plane or three-dimensional environment, and can also be further expanded to the water surface, land, air and even cross-domain application environments outside the water surface to perform dynamic and rapid searches for dynamic targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of the working process of a dynamic search probability estimation method for an underwater moving target according to the present invention;

[0023] Figure 2 A schematic diagram of probability field diffusion of a dynamic search probability estimation method for an underwater moving target according to the present invention;

[0024] Figure 3 A schematic diagram of a probability diffusion model of a dynamic search probability estimation method for an underwater moving target according to the present invention with reference to an LBM method;

[0025] Figure 4 A schematic diagram of a route planning path of an embodiment of a method for estimating the probability of dynamic search of an underwater mobile target according to the present invention;

[0026] Figure 5 A schematic diagram of the probability of a mobile target being scanned in a method for estimating the probability of a dynamic search of an underwater mobile target according to the present invention;

[0027] Figure 6The schematic diagram is a decision process of a strategy function of a dynamic search probability estimation method for an underwater moving target according to the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] In view of this, if Figure 1-Figure 3 As shown, the present invention provides a method for estimating the dynamic search probability of an underwater moving target, comprising the following contents:

[0030] S1: Set the search task, load the map, and grid the corresponding task area in the map;

[0031] The corresponding task area in the map is gridded, specifically dividing the task area into a plurality of plane grids, the plurality of plane grids are arranged in an array, and the sizes of the plurality of grids are exactly the same. Preferably, the plurality of plane grids are rectangular grids.

[0032] S2: Initialize the probability field and navigation parameters of the search platform, and calculate the probability field diffusion probability of the moving target in the mission area grid, which is dynamically updated over time;

[0033] S21: Initialize the probability field, taking the boundary of the task area as the task boundary, that is, Figure 2 The outermost rectangle in the image; there is a moving search target within the mission boundary; the starting position of the search platform is set, and a detector is set at one end of the navigation direction of the search platform, and the detector has a fan-shaped detection range; during the navigation of the search platform, the area swept by the detector forms the detected envelope boundary; the area within the detection range at the current moment, because the search mission is still in progress and the detector has not confirmed the target, forms an area that has not diffused to the probability; the target that is within the mission boundary and outside the detected envelope boundary, if it enters the detected envelope boundary due to movement, forms a probability diffusion area where the probability field diffuses to the detected envelope boundary, and forms a 0 probability envelope boundary between the area where the probability has not diffused to, that is, Figure 2 In the dashed part, the 0 probability envelope boundary is located inside the detected envelope boundary.

[0034] S22: Calculate the probability of diffusion of the dynamic updated probability field of the mobile target in the task area grid over time. It refers to the theory of the probability of material diffusion, takes the mobile target as a separate molecule, and refers to the flow process of the LBM method to infer the probability change of the mobile target during movement. Let the line connecting the center of the task area grid and the midpoint of each side of the task area grid point to the adjacent positive grid, let the line connecting the center of the task area grid and each vertex of the task area grid point to the adjacent oblique grid, C0 represents the molecule staying in the current task area grid, C1, C2, C3 and C4 represent the direction of the molecule to the adjacent positive task area grid, C5, C6, C7 and C8 represent the direction of the oblique task area grid adjacent to the vertex of the task area grid where the molecule is located; at each time step, the diffusion probability f of the molecule moving to each adjacent grid c for in ∑f ci =1; the basic equation for the collision process to reach equilibrium is: f i (x+c i Δt,t+Δt)-f i (x,t)=Ω i +F i , the left side of the equation is the flow term, f i represents the normalized concentration of the substance, x represents the grid position of the current task area, x+c i Δt represents the direction c i The corresponding adjacent grid position, Δt represents the time step, t is the current time, F i is the external force term, Ω i is the collision term, which indicates the diffusion degree of the equilibrium state of the molecular motion during the fluid motion within the time Δt. The equilibrium concentration u is the macroscopic velocity vector of the task area grid; the relaxation time τ satisfies v is the kinematic viscosity. It is assumed that the movement of molecules in the grid follows the Maxwell distribution and the macroscopic average velocity is the speed of sound.

[0035] This method refers to the LBM method, that is, the lattice Boltzmann method to compare the search probability of mobile targets within the task area, regard the mobile target as a separate molecule, characterize the statistical probability of the mobile target appearing in each grid with probability, and use the flow process of the LBM method to compare the probability change of the mobile target during movement. The LBM method is a technical means often used to deal with boundary conditions. The present invention has made further improvements on the basis of this method. After setting the task area grid, take the right side as the starting point, pass through each boundary in a counterclockwise direction, and the diffusion probability to the direction adjacent to the edge is Taking the upper right vertex as the starting vertex, the diffusion probability of each vertex in the task area grid in a clockwise direction is The probability of a molecule staying in the current task area grid is

[0036] S3: The search platform obtains the navigation planning path based on the updated content of the probability field diffusion probability.

[0037] S31: Due to the physical differences in the diffusion of substances in the fluid, f c The update content needs to be adjusted according to the parameters of the task and search platform. The update content of the search platform based on the probability of diffusion of the probability field can be updated in the following two ways:

[0038] Method 1: Use the following formula to calculate the derived result f′ of the probability field diffusion probability: c : U is the maximum speed of the moving target, which is determined by the moving target; L is the length of the task area grid, and the intermediate variable S satisfies

[0039] Method 2: Use numerical experiments to obtain the derived results of the probability field diffusion probability: within the 3×3 task area grid, within the central task area grid and the maximum speed range, randomly set the position, speed and direction of a point, calculate the position of the random point within a time step, repeat it many times and count the proportion of the point falling into the surrounding task area grid until the proportion is stable. This proportion is the derived result f′ of the probability field diffusion probability. c .

[0040] When the intermediate parameter is 0.1<s<1, the formula is used to calculate the derivation result of the probability field diffusion probability, that is, method 1; when 0<s<0.1, the numerical test statistics are used to obtain the derivation result of the probability field diffusion probability, that is, method 2.

[0041] S32: Obtaining the navigation planning path is to iteratively update the probability P of the task area grid with pointer i at time t+1 after obtaining the derivation result of the probability field diffusion probability. i，t+1 :P i，t+1 =P i,t -[f′ c ][P i，t -P inei,t ], P i，t represents the probability of the task area grid with pointer i at time t; [f′ c ] is the vector corresponding to the derivation result of the probability field diffusion probability of the task area grid with pointer i in all directions; P inei，trepresents the adjacent task area grid of the task area grid with pointer i at time t; according to the formula, the dynamically updated probability field is obtained, the navigation planning path optimization function is determined, and the final navigation planning path is obtained. i，t+1 In order to improve the calculation speed, the kinematic viscosity v and the macroscopic velocity u of the task area grid can be set to 0.

[0042] Among them, the optimization function of the route planning path takes the minimum time T to search for the moving target as the optimization goal: minT, satisfying Where A represents the action of the search platform, which mainly includes speed, heading information or other physical quantities that can be converted into this information, and the subscripts represent different moments; φ(·) is the strategy function; P i represents the state of the task area grid at time i; S is the actual physical state of the environment, including the position of the aircraft, the change of the obstacle position in the map, etc., and the subscripts represent different moments; F indicates that the probability field diffusion probability is updated by any method in step 31. Figure 6 A flowchart showing the decision-making process of the policy function.

[0043] It should be noted that for a certain patrol mission, the path can also be planned in advance. The path optimization method and strategy function for searching strategies or updating navigation parameters can use conventional algorithms such as greedy algorithms, or artificial intelligence methods such as reinforcement learning, decision-making transformers, long short-term memory networks, and generative networks. Regardless of which method is used, the optimization function can use the optimization function of the route planning path proposed in the present invention.

[0044] Embodiment: A movable super-large marine aquaculture cage with a diameter of 4km, which breeds certain high-value fish seafood. In order to ensure the quality of seafood and reduce construction costs, it is not appropriate to use centralized large-scale active sonar or more small sonars to conduct real-time detection of the situation in the cage, so two unmanned monitoring ships are used, each carrying a small sonar to continuously detect and patrol within the cage range. The cruising speed of the seafood and the moving speed of the cage platform are both 1 knot, and the speed of the patrol unmanned ship is 1.5 knots. The effective detection distance of the seafood is 300m. Unmanned ship 1 and unmanned ship 2 are assigned to the search task area on the left and right of the main ship's moving direction. The diffusion of the probability field is dynamically calculated according to this method. The length of the rectangular grid is L = 200 meters, and the time step Δt is 40 seconds. Without pre-training, a greedy algorithm is used for decision-making. The relative paths of the two unmanned monitoring ships within a period of time with the main ship as the reference system are obtained as follows: Figure 4 As shown in the figure, the probability of all marine targets being scanned once within this time range is as follows: Figure 5 As shown, the probability of finding each high-value target in the cage after approaching after 1750 time steps is about 99%, indicating the effectiveness of the method.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating the dynamic search probability of an underwater moving target, characterized in that: It includes the following: Set up search tasks, load the map, and grid the corresponding task areas in the map; Initialize the probability field and navigation parameters of the search platform, and calculate the probability field diffusion probability of the moving target in the mission area grid, which is dynamically updated over time; The search platform obtains the navigation planning path based on the updated content of the probability field diffusion probability.

2. The method for estimating the dynamic search probability of an underwater moving target according to claim 1, characterized in that: The gridding of the corresponding task area in the map is to divide the task area into a plurality of plane grids, and the plurality of plane grids are arranged in an array, and the sizes of the plurality of grids are exactly the same.

3. The method for estimating the dynamic search probability of an underwater moving target according to claim 2, characterized in that: Some of the plane grids are rectangular grids.

4. The method for estimating the dynamic search probability of an underwater moving target according to claim 3, characterized in that: The initialization probability field is based on the boundary of the task area as the task boundary, and there is a search target in a moving state within the task boundary; the starting position of the search platform is set, and a detector is set at one end of the navigation direction of the search platform, and the detector has a fan-shaped detection range; during the navigation of the search platform, the area swept by the detector forms a detected envelope boundary; the area within the detection range at the current moment, because the search task is still in progress and the detector has not confirmed the target, forms an area that has not diffused to the target in terms of probability; if the target is within the task boundary and outside the detected envelope boundary enters the detected envelope boundary due to movement, a probability diffusion zone of the probability field diffusing to the detected envelope boundary is formed, and a 0 probability envelope boundary is formed between the area to which the probability has not diffused, and the 0 probability envelope boundary is located inside the detected envelope boundary.

5. The method for estimating the dynamic search probability of an underwater moving target according to claim 4, characterized in that: The calculation of the probability field diffusion probability of the mobile target in the task area grid that is dynamically updated with time is based on the theory of the probability of material diffusion. The mobile target is regarded as a separate molecule, and the probability change of the mobile target during movement is inferred through the flow process of the LBM method. The line connecting the center of the task area grid and the midpoints of each side length of the task area grid is made to point to the adjacent positive grid, and the line connecting the center of the task area grid and each vertex of the task area grid is made to point to the adjacent oblique grid. C0 represents that the molecule stays in the current task area grid, C1, C2, C3 and C4 represent the direction of the molecule to the adjacent positive task area grid, and C5, C6, C7 and C8 represent the direction of the oblique task area grid adjacent to the vertex of the task area grid where the molecule is located; at each time step, the diffusion probability f of the molecule moving to each adjacent grid c for in Σf ci =1; the basic equation for the collision process to reach equilibrium is: f i (x+c i Δt, t+Δt)-f i (x, t) = Ω i +F i , the left side of the equation is the flow term, f i represents the normalized concentration of the substance, x represents the grid position of the current task area, x+c i Δt represents the direction c i The corresponding adjacent grid position, Δt represents the time step, t is the current time, F i is the external force term, Ω i is the collision term, which indicates the diffusion degree of the equilibrium state of the molecular motion during the fluid motion within the time Δt. The equilibrium concentration u is the macroscopic velocity vector of the task area grid; the relaxation time τ satisfies v is the kinematic viscosity.

6. The method for estimating the dynamic search probability of an underwater moving target according to claim 5, characterized in that: The search platform is based on the updated content of the probability field diffusion probability, and the derived result f′ of the probability field diffusion probability is calculated using the following formula: c : U is the maximum speed of the moving target, which is determined by the moving target; L is the length of the task area grid, and the intermediate variable S satisfies 7. A method for estimating the dynamic search probability of an underwater moving target according to claim 6, characterized in that: The search platform is based on the updated content of the probability field diffusion probability, and uses numerical experimental statistics to obtain the derivation result of the probability field diffusion probability: within the 3×3 task area grid range, within the central task area grid and the maximum speed range, randomly set the position, speed and direction of a point, calculate the position of the random point within a time step, repeat multiple times and count the proportion of the point falling into the surrounding task area grid until the proportion is stable. This proportion is the derivation result f′ of the probability field diffusion probability c .

8. The method for estimating the dynamic search probability of an underwater moving target according to claim 7, characterized in that: When the intermediate parameter is 0.1<s<1, the derived result of the probability field diffusion probability is calculated using the formula; When 0<s<0.1, the derivation result of the probability field diffusion probability is obtained by numerical experimental statistics.

9. The method for estimating the dynamic search probability of an underwater moving target according to claim 7, characterized in that: The navigation planning path is obtained by iteratively updating the probability P of the task area grid with pointer i at time t+1 after obtaining the derivation result of the probability field diffusion probability. i，t+1 :P i，t+1 =P i，t -[f′ c ][P i，t -P inei，t ], P i，t represents the probability of the task area grid with pointer i at time t; [f′ c ] is the vector corresponding to the derivation result of the probability field diffusion probability of the task area grid with pointer i in all directions; P inei，t Represents the adjacent task area grids of the task area grid with pointer i at time t; The dynamically updated probability field is obtained according to the formula, the navigation planning path optimization function is determined, and the final navigation planning path is obtained.

10. The method for estimating the dynamic search probability of an underwater moving target according to claim 9, characterized in that: The optimization function of the route planning path takes the minimum time T to search for the moving target as the optimization goal: min T, satisfying Where A represents the action of the search platform, and the subscripts represent different moments; Φ(·) is the strategy function; P i Represents the state of the task area grid at time i; S is the actual physical state of the environment, and the subscripts represent different moments; F represents the update of the probability field diffusion probability.

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