Unmanned ship area coverage search path planning method

Abstract

The invention relates to an unmanned ship area coverage search path planning method, which comprises the following steps of: 1, calling environmental data such as submarine topography and hydrological environment of a task area by utilizing an environmental database, dispersing a sea area into a plurality of grids by adopting a grid method, and drawing a sounding map and a sound velocity profile map of the area, the motion space of the unmanned ship is simplified into a two-dimensional plane, under-actuated and high-inertia motion characteristics of the unmanned ship are considered, the unmanned ship is regarded as vector points with the direction and the speed, meanwhile, speed constraint is achieved by limiting the upper limit and the lower limit of the speed, and steering constraint is achieved by setting the maximum steering angle in unit time. According to the invention, the convergence capability, robustness and adaptability of a path planning algorithm are effectively improved; and the coverage efficiency of the target area is effectively improved.

Description

Technical field:

[0001] The present invention belongs to the technical field of unmanned boats, and in particular relates to an unmanned boat area coverage search path planning method taking into account the unevenness of sonar detection capabilities. Background technology:

[0002] The regional coverage mission for unmanned vehicles (UAVs) involves patrolling and surveying a specific sea area within a specific time and space. Optimizing the UAV's route allows the UAV to cover as much territory as possible within a limited mission timeframe. To achieve regional coverage, the UAV's detection model must be determined. Because sound waves attenuate minimally in the ocean, acoustic positioning has become the primary method for ocean exploration.

[0003] In existing research on area coverage for unmanned vehicles (USVs), sonar detection range is typically considered constant. However, environmental factors such as temperature, salinity, and bathymetry can interfere with sonar detection capabilities, leading to significant differences in detection range at different locations in real environments. Therefore, a method for planning area coverage search paths for USVs that accounts for the heterogeneity of sonar detection capabilities is urgently needed. Summary of the invention:

[0004] The technical problem to be solved by the present invention is to provide a method for planning an area coverage search path for an unmanned boat, which effectively solves the problem of uneven sonar detection capability in the prior art.

[0005] The technical solution of the present invention is to provide a method for planning an unmanned boat area coverage search path, comprising the following steps:

[0006] Step 1: Use the environmental database to retrieve environmental data such as the seabed topography and hydrological environment of the mission area, use the grid method to discretize the sea area into several grids, draw the bathymetric map and sound velocity profile of the area, and simplify the motion space of the unmanned vehicle into a two-dimensional plane. Considering its under-actuated and high-inertia motion characteristics, the unmanned vehicle is regarded as a vector point with direction and speed. At the same time, speed constraints are implemented by limiting the upper and lower speed limits, and steering constraints are implemented by setting the maximum steering angle per unit time;

[0007] Step 2: Obtain the sound velocity profile and sea depth data in the sea area, and perform hydrological assimilation based on the measured sound velocity profile. Select a suitable sound field calculation model to calculate the propagation loss of the observation point at the sound source depth in all horizontal directions. Calculate the sonar quality factor based on the sonar parameters. Obtain the maximum detection range of the observation point in all directions through matching. Calculate the average value of the maximum detection range on the left and right sides of the unmanned boat as the search sector.

[0008] Step 3: Convert the parameters in the solution space into individuals composed of genes through encoding. The individual represents the position of the unmanned boat at each moment, and the path is represented by two series of speed and heading; add the artificial potential field method in the initial population generation process as the starting point of optimization; and use coverage as the fitness function to evaluate the fitness of the individual; use the roulette algorithm to retain high-quality individuals and eliminate low-quality individuals, and select the operator to pass the optimized individuals to the next generation; the mutation operator enhances the local search ability of the genetic algorithm by changing the gene value at a specific gene site in the individual, and prevents the algorithm from premature convergence, so that it iterates in the direction of increasing fitness, and finally converges to the individual that is most adapted to the environment as the optimal solution for optimization.

[0009] Preferably, in step 1, the speed and steering angle of the unmanned boat satisfy the following formula:

[0010] V∈[V min ,V max ]

[0011] Where V min ,V max are the lower and upper limits of the unmanned boat speed respectively, V is the speed of the unmanned boat,

[0012]

[0013] Where Δt is the time step, and Δθ is the maximum turning angle of the USV within a single time step.

[0014] Preferably, in step 2, the received signal-to-noise ratio (SNR) formula in the detection mode is:

[0015] SNR 主动 =SL-2TL+TS-(NL-DI)

[0016] SNR 被动 =SL-TL-(NL-DI)

[0017] Where SL is the radiated sound source level, TS is the target intensity, TL is the propagation loss, NL is the ambient noise level, and DI is the receiver directivity coefficient. All units are decibels, and TL is obtained through the calculated sound field data.

[0018] Preferably, in step 2, the relationship between the detection probability, the false alarm probability, and the signal-to-noise ratio is expressed as follows:

[0019]

[0020] in is a chi-square distribution with ν degrees of freedom.

[0021] Preferably, in step 2, in order to improve the accuracy of the model and reduce the impact of steering on sonar detection capability, when the steering angle is within a specified range, the sonar detection capability is lost at a fixed ratio.

[0022] As a preference, in step three, the artificial potential field method:

[0023] F att =η·ρ(q,q0)

[0024]

[0025] Where η is the gain coefficient, q and q0 represent the distance between the UAV and the target (or obstacle), ρ(q,q0) represents the distance between the UAV and the target (or obstacle), and ρ0 is the distance affected by the obstacle. The resultant force calculated by the formula represents the tendency of the UAV to move in a certain direction under the influence of the target and the obstacle, F att is the gravitational force of the target on the unmanned boat, F rep It is the repulsive force of the target on the unmanned boat.

[0026] Preferably, in step 3, the optimization operation is performed using selection and mutation operators:

[0027]

[0028] Where n is the number of initial paths, and the fitness value corresponding to each path is T n .

[0029] Preferably, in step 3, the area coverage rate is used as the fitness of the area coverage task:

[0030]

[0031] Among them S cover is the sea area detected by sonar, S total To calculate the total area of ​​the sea.

[0032] Furthermore, in step 3, individual selection is achieved through the roulette algorithm:

[0033]

[0034] Among them, individual x i The fitness of f(x i ), the probability of the individual being selected is p(x i ), the cumulative probability is q(x i ).

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] Based on the consideration of the unmanned vehicle's own motion constraints, the limitations of sonar detection range, and the motion characteristics of underwater targets, the present invention proposes an unmanned vehicle area coverage search path planning method that takes into account the unevenness of sonar detection capabilities. This method effectively improves the convergence ability, robustness, and adaptability of the path planning algorithm; and effectively improves the coverage efficiency of the target area. Description of the drawings:

[0037] Figure 1 This is the kinematic model of the unmanned boat in the embodiment of the present invention.

[0038] Figure 2 Schematic diagram of sea area depth measurement and detection radius distribution in an embodiment of the present invention.

[0039] Figure 3 This is the sonar coverage area coverage index result of an embodiment of the present invention.

[0040] Figure 4 This is the comparison result of regional coverage of the embodiments of the present invention. Specific implementation method:

[0041] The present invention will be further described below with reference to the accompanying drawings:

[0042] The present invention discloses an unmanned boat area coverage search path planning method considering the unevenness of sonar detection capability. The unmanned boat area coverage path planning method specifically includes the following steps:

[0043] Step 1: The environmental database retrieves the seafloor topography and hydrological environment of the mission area. Using a grid method, the sea area is discretized into several grids, and a bathymetric map and sound velocity profile are drawn for the area. The motion space of the unmanned vehicle is simplified to a two-dimensional plane. Considering its underactuated and high-inertia motion characteristics, and assuming that the unmanned vehicle can only move forward and rotate, the unmanned vehicle is considered a vector point with direction and velocity. Furthermore, speed constraints are implemented by setting upper and lower speed limits, and steering constraints are implemented by setting a maximum steering angle per unit time.

[0044] Among them, the speed and steering angle of the unmanned boat must satisfy the following formula:

[0045] V∈[V min ,V max ]

[0046] Where V min ,V max are the lower and upper limits of the speed of the unmanned boat respectively, and V is the speed of the unmanned boat.

[0047]

[0048] Where Δt is the time step, and Δθ is the maximum turning angle of the USV within a single time step.

[0049] Step 2: Acquire sound velocity profile and depth data for the sea area and perform hydrographic assimilation based on the measured sound velocity profile. Select an appropriate acoustic field calculation model to calculate the propagation loss in all horizontal directions at the observation point at the sound source depth. Calculate the sonar quality factor based on the sonar parameters. By matching, obtain the maximum detection range of the observation point in all directions. Calculate the average of the maximum detection ranges on the left and right sides of the unmanned boat as the search sector. To improve the accuracy of the model and minimize the impact of steering on sonar detection capability, a fixed percentage of sonar detection capability is lost when the steering angle is within a certain range. To improve the accuracy of the model and minimize the impact of steering on sonar detection capability, a fixed percentage of sonar detection capability is lost when the steering angle is within a certain range.

[0050] The receiving signal-to-noise ratio (SNR) in active and passive detection modes is:

[0051] SNR 主动 =SL-2TL+TS-(NL-DI)

[0052] SNR 被动 =SL-TL-(NL-DI)

[0053] Where SL is the radiated sound source level, TS is the target intensity, TL is the propagation loss, NL is the ambient noise level, and DI is the receiver directivity, all in decibels. TL is obtained from the calculated sound field data.

[0054] The relationship between detection probability, false alarm probability and signal-to-noise ratio is expressed as:

[0055]

[0056] in is a chi-square distribution with ν degrees of freedom.

[0057] Step 3: The parameters in the solution space are converted into individuals composed of genes through encoding. The individual represents the position of the unmanned boat at each moment, and the path is represented by two series of speed and heading. The artificial potential field method is added in the initial population generation process as the starting point of optimization, and the coverage rate is used as the fitness function to evaluate the fitness of the individual. The roulette algorithm is used to retain high-quality individuals and eliminate low-quality individuals. The selection operator inherits the optimized individuals to the next generation. The mutation operator enhances the local search ability of the genetic algorithm by changing the gene values ​​at certain gene sites in the individual, and prevents the algorithm from premature convergence, so that it iterates in the direction of increasing fitness, and finally converges to the individual that is most adapted to the environment as the optimal solution for optimization.

[0058] Use the artificial potential field method to select the initial population:

[0059] F att =η·ρ(q,q0)

[0060]

[0061] Where η is the gain coefficient, q and q0 represent the distance between the unmanned boat and the target (or obstacle), ρ(q,q0) represents the distance between the unmanned boat and the target (or obstacle), ρ0 is the distance of the obstacle affecting the target or obstacle in the area, F att is the gravitational force of the target on the unmanned boat, F rep It is the repulsive force of the target on the unmanned boat.

[0062] Perform optimization operations using selection and mutation operators:

[0063]

[0064] Where n is the number of initial paths, and the fitness value corresponding to each path is T n

[0065] The area coverage rate is used as the fitness of the area coverage task:

[0066]

[0067] Among them S cover is the sea area detected by sonar, S total To calculate the total area of ​​the sea.

[0068] Figure 3 It is the coverage index result of the sonar coverage area.

[0069] Individual selection is achieved through the roulette algorithm:

[0070]

[0071] Among them, individual x i The fitness of f(x i The probability of this individual being selected is p(x i ), the cumulative probability is q(x i ).

[0072] See also Figure 1 and Figure 2 , the present invention mainly includes sonar detection of unevenness and improved genetic algorithm, wherein,

[0073] Sonar detection non-uniformity is caused by the fact that the propagation of acoustic signals in the ocean is significantly affected by the speed of sound in the sea and boundary conditions, resulting in differences in sonar detection capabilities at different locations. The present invention uses a towed sonar to detect underwater targets. The formula for the received signal-to-noise ratio (SNR) in its active and passive detection modes is:

[0074] SNR 主动 =SL-2TL+TS-(NL-DI)

[0075] SNR 被动 =SL-TL-(NL-DI)

[0076] Where: SL is the radiated sound source level, TS is the target intensity, TL is the propagation loss, NL is the ambient noise level, and DI is the receiver directivity, all in decibels. TL is obtained from the calculated sound field data.

[0077] The detection probability can be calculated based on the signal-to-noise ratio (SNR). The relationship between the detection probability, false alarm probability, and signal-to-noise ratio is expressed as:

[0078]

[0079] in is a chi-square distribution with ν degrees of freedom.

[0080] Considering the fluctuating nature of the sound field, the signal-to-noise ratio (SNR) at a 50% detection probability is typically used as a criterion for determining sonar detection capabilities. Substituting this SNR into the sound source level, combined with other parameters such as the ambient noise level, yields the sound propagation loss threshold that the sonar can detect. This propagation loss threshold is then used in subsequent sound field calculations, and the sound field model is then used to determine the sonar's detection radius.

[0081] Improved genetic algorithm

[0082] Referring to the concept of genetic algorithms, genetic algorithms start with an initial population and optimize the population through selection, mutation, and crossover operators, iterating it in the direction of increasing fitness, and ultimately converging to the individual that is most adapted to the environment as the optimal solution. The improved genetic algorithm uses the artificial potential field method to improve the initial population, so that the initial path is oriented towards nodes with a high detection radius, thereby accelerating subsequent optimization. The relevant formula is as follows:

[0083] F att =η·ρ(q,q0)

[0084]

[0085] Where η is the gain coefficient, q and q0 represent the distance between the unmanned boat and the target (or obstacle), ρ(q,q0) represents the distance between the unmanned boat and the target (or obstacle), ρ0 is the distance of the obstacle affecting the target or obstacle in the area, F att is the gravitational force of the target on the unmanned boat, F rep It is the repulsive force of the target on the unmanned boat.

[0086] In the optimization process of the genetic algorithm, the selection and mutation operators are used to perform the optimization operation. The selection operator is implemented by the roulette algorithm. Assuming that there are n initial paths, the fitness value corresponding to each path is T n , then the probability of an initial path being selected is:

[0087]

[0088] This selection process makes initial paths with higher fitness values ​​more likely to be selected. In the mutation process, a path point is randomly selected, and the speed and heading series at that point and all subsequent path points are randomly varied as a whole. Because the speed and steering angle of the unmanned vehicle are constrained, the mutation process must keep the variation within these constraints.

[0089] In the context of area coverage missions, the unmanned boat cannot obtain information about the target, but only knows that there are underwater targets in the sea area or is conducting routine searches. In this case, the criterion for measuring the quality of the route is the size of the effective area that the sonar can cover within the mission time. This paper uses the area coverage rate as the adaptability of the area coverage mission, which is defined as:

[0090]

[0091] Where: S cover is the sea area detected by sonar, S total To calculate the total area of ​​the sea.

[0092] Figure 4 This is the comparison result of the regional coverage in this embodiment.

[0093] The genetic algorithm starts with an initial population and optimizes the population through selection, mutation, and crossover operators, iterating it in the direction of increasing fitness, and eventually converging to the individual that is most adapted to the environment as the optimal solution. The algorithm is implemented as follows:

[0094] S1: The parameters in the solution space are converted into individuals composed of genes through encoding. The individuals represent the position of the unmanned boat at each moment, and the path is represented by two series: speed and heading.

[0095] S2: The artificial potential field method is added in the initial population generation process as the starting point of optimization.

[0096] S3: Use coverage as the fitness function to evaluate the fitness of individuals.

[0097] S4: Use the roulette wheel algorithm to retain high-quality individuals and eliminate low-quality individuals, and the selection operator will pass the optimized individuals to the next generation.

[0098] S5: The mutation operator enhances the local search capability of the genetic algorithm by changing the gene values ​​at certain gene loci in the individual, and prevents the algorithm from premature convergence, so that it iterates in the direction of increasing fitness, and finally converges to the individual that is most adapted to the environment as the optimal solution for optimization.

[0099] The above description is only for the preferred embodiment of the present invention, which should not be understood as limiting the claims. Any equivalent process changes made using the present invention description are included in the patent protection scope of the present invention.

Claims

1. A method for planning an unmanned boat's area coverage search path, characterized by: The following steps are included: Step 1: Use the environmental database to call the environmental data of the mission area, use the grid method to discretize the sea area into several grids, draw the bathymetric map and sound velocity profile of the area, and simplify the motion space of the unmanned vehicle into a two-dimensional plane. The unmanned vehicle is regarded as a vector point with direction and speed. At the same time, speed constraints are implemented by setting upper and lower speed limits, and steering constraints are implemented by setting the maximum steering angle per unit time. Step 2: Obtain the sound velocity profile and sea depth data in the sea area, and perform hydrological assimilation based on the measured sound velocity profile. Select a suitable sound field calculation model to calculate the propagation loss of the observation point at the sound source depth in all horizontal directions. Calculate the sonar quality factor based on the sonar parameters. Obtain the maximum detection range of the observation point in all directions through matching. Calculate the average value of the maximum detection range on the left and right sides of the unmanned boat as the search sector. Step 3: Convert the parameters in the solution space into individuals composed of genes through encoding. The individual represents the position of the UAV at each moment, and the path is represented by two series: speed and heading. In the process of initial population generation, the artificial potential field method is added as the starting point of optimization. The coverage rate is used as the fitness function to evaluate the fitness of the individual. The roulette wheel algorithm is used to retain high-quality individuals and eliminate low-quality individuals, and the selection operator inherits the optimized individuals to the next generation; the mutation operator enhances the local search ability of the genetic algorithm by changing the gene value at a specific gene site in the individual, and prevents the algorithm from premature convergence, so that it iterates in the direction of increasing fitness, and finally converges to the individual that is most adapted to the environment as the optimal solution for optimization.

2. The unmanned boat area coverage search path planning method according to claim 1, characterized in that: In step 1, the speed and steering angle of the unmanned boat satisfy the following formula: V∈[V min ,V max ] Where V min ,V max are the lower and upper limits of the unmanned boat speed respectively, V is the speed of the unmanned boat, Where Δt is the time step, and Δθ is the maximum turning angle of the USV within a single time step.

3. The method for planning an unmanned vehicle area coverage search path according to claim 1, characterized in that: In step 2, the formula for the received signal-to-noise ratio in detection mode is: SNR 主动 =SL-2TL+TS-(NL-DI) SNR 被动 =SL-TL-(NL-DI) Where SL is the radiated sound source level, TS is the target intensity, TL is the propagation loss, NL is the ambient noise level, DI is the receiver directivity coefficient, and TL is obtained by calculating the sound field data.

4. The method for planning an unmanned vehicle area coverage search path according to claim 1, characterized in that: In step 2, the relationship between the detection probability, false alarm probability, and signal-to-noise ratio is expressed as: in is a chi-square distribution with ν degrees of freedom.

5. The unmanned boat area coverage search path planning method according to claim 1, characterized in that: In step 2, when the steering angle is within the specified range, the sonar detection capability is lost at a fixed ratio.

6. The method for planning an unmanned boat area coverage search path according to claim 1, characterized in that: In step 3, the artificial potential field method is used to select the initial population: F att =η·ρ(q,q0) Where η is the gain coefficient, q and q0 represent the UAV and the target respectively, ρ(q,q0) represents the distance between the UAV and the target, ρ0 is the obstacle influence distance, F att is the gravitational force of the target on the unmanned boat, F rep It is the repulsive force of the target on the unmanned boat.

7. The method for planning an unmanned vehicle area coverage search path according to claim 1, characterized in that: In step 3, optimization operations are performed using selection and mutation operators: Where n is the number of initial paths, and the fitness value corresponding to each path is T n .

8. The method for planning an unmanned boat area coverage search path according to claim 1, characterized in that: In step 3, the area coverage rate is used as the fitness of the area coverage task: Among them S cover is the sea area detected by sonar, S total To calculate the total area of ​​the sea.

9. The unmanned boat area coverage search path planning method according to claim 1, characterized in that: In step 3, individual selection is achieved through the roulette algorithm: Among them, individual x i The fitness of f(x i ), the probability of the individual being selected is p(x i ), the cumulative probability is q(x i ).

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