UUV intelligent bionic cooperative positioning method based on artificial chicken flock algorithm

Abstract

The invention discloses a UUV intelligent bionic cooperative positioning method based on an artificial chicken flock algorithm, and belongs to the field of multi-UUV cooperation. The problem that in an existing single UUV or simple cooperative system, geomagnetic matching depends on a complete magnetic field map, but only sporadic known points exist in an actual task area, so that the matching precision is insufficient or the applicability is limited is solved. The method comprises the steps that a plurality of UUVs are regarded as artificial chicken flocks, and the state of each UUV is expressed as a position vector; initializing artificial chicken flock parameters, and setting a target function and a precision requirement; iteratively updating UUV positions through an artificial chicken flock algorithm, including a chasing behavior, an aggregation behavior, a dispersion behavior and an escape behavior, to minimize a fitness function and realize cooperative positioning of a UUV group; and when the UUV approaches the target position, switching to a simulation collaborative navigation mode, and correcting the accumulative error of the inertial navigation system by using an artificial chicken flock algorithm. The method is mainly used in the large-scale ocean exploration field.

Description

Technical Field

[0001] This invention belongs to the field of multi-UUV collaboration, and in particular relates to a UUV intelligent bionic collaborative localization method based on artificial chicken flock algorithm. Background Technology

[0002] Marine scientific exploration and mineral resource exploration are continuously expanding into deep-sea areas, which increasingly highlights the limitations of single Unmanned Underwater Vehicles (UUVs) in complex tasks. For example, in deep-sea environmental monitoring or military reconnaissance, UUVs, due to their limited payload capacity, endurance, and sensing range, struggle to efficiently complete large-scale, multi-target operations. Therefore, multi-UUV collaborative systems are gradually becoming a key technology for marine exploration, resource development, and military applications. However, the core challenge of multi-UUV systems lies in the problem of precise navigation: when operating underwater, UUVs cannot directly rely on global satellite navigation systems (such as GPS) because satellite signals are severely attenuated or even completely ineffective underwater; while high-precision inertial navigation systems can provide short-term positioning, they are expensive, and their errors accumulate over time. For example, gyroscope drift and accelerometer deviations can cause positioning errors to continuously amplify, ultimately causing the UUV to deviate from its target during long-term missions.

[0003] In existing technologies, researchers have attempted to address UUV navigation problems by employing geomagnetic matching-assisted inertial navigation. This involves comparing real-time acquired geomagnetic field information with pre-measured magnetic field maps to correct positioning errors. For example, in some marine exploration projects, UUVs utilize factors such as total geomagnetic intensity and horizontal components for matching to compensate for the limitations of inertial navigation. However, this method has significant drawbacks: First, obtaining accurate magnetic field information over large areas is extremely difficult because the marine geomagnetic field is affected by factors such as geological structure and water depth variations, requiring costly and high-precision mapping operations, and real-time data updates are difficult to achieve. Second, in single UUVs or simple collaborative systems, geomagnetic matching often relies on complete magnetic field maps, but the actual mission area may only have scattered known points, leading to insufficient matching accuracy or limited applicability. Furthermore, while existing swarm intelligence algorithms such as particle swarm optimization have been applied in UUV collaborative navigation, they are prone to getting trapped in local extrema and do not fully consider the multi-dimensional optimization of UUV motion characteristics and geomagnetic elements, failing to effectively address the problem of long-term error accumulation.

[0004] These shortcomings directly limit the reliability and practicality of multi-UUV systems. For example, in long-distance navigation missions, when UUVs rely solely on low-precision inertial navigation systems, the accumulated error may reach several meters, making it impossible for UUVs to accurately reach the target point. In fixed-route navigation, even if some magnetic field information is obtained in advance, existing methods lack a dynamic correction mechanism, resulting in the inertial navigation error not being corrected in a timely manner. Summary of the Invention

[0005] In view of this, the present invention aims to propose a UUV intelligent biomimetic cooperative positioning method based on the artificial chicken flock algorithm, in order to solve the problem that geomagnetic matching in existing single UUV or simple cooperative systems often depends on a complete magnetic field map, but the actual task area may only have a few known points, resulting in insufficient matching accuracy or limited applicability.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A UUV intelligent biomimetic cooperative localization method based on artificial chicken flock algorithm, the method comprising: Treating multiple UUVs as an artificial flock of chickens, the state of each UUV is represented as a position vector. ,in, For UUV numbering, This indicates a specific moment in the operation of the UUV; and Indicates the first i The coordinates of each UUV in two-dimensional space; Initialize the artificial chicken flock parameters, including the number of chickens N, the initial position of the artificial chickens, and the maximum number of iterations. Maximum number of clusters Maximum number of escape attempts The objective function and accuracy requirements are set; the objective function is the fitness function of the geomagnetic elements. The location of UUVs is iteratively updated using an artificial chicken flock algorithm, including chasing, gathering, dispersing, and escaping behaviors, in order to minimize the fitness function and achieve collaborative localization of the UUV group. When the UUV approaches the target location, it switches to the biomimetic cooperative navigation mode, using an artificial chicken flock algorithm to correct the accumulated error of the inertial navigation system.

[0007] Furthermore, a preferred method is proposed, wherein in the chasing behavior of the artificial chicken flock algorithm, the positional difference between the lead chicken and the center of the flock is defined as velocity. :

[0008] in, This indicates the distance between the lead hen and the center of the flock. It means the first chicken is x Directional coordinates It means the first chicken is y Directional coordinates Indicates the center of the chicken flock is x The position of direction, Indicates the center of the chicken flock is y The position of direction; During the chasing behavior, the position update formula for all artificial chickens is:

[0009] in, For the first i The location of each UUV The calculated speed.

[0010] Furthermore, a preferred method is proposed, wherein the speed of each artificial chicken in the aggregation behavior is: , .

[0011] Furthermore, a preferred method is proposed, wherein the speed of each artificial chicken in the dispersed behavior is: , .

[0012] Furthermore, a preferred method is proposed, wherein the speed of each artificial chicken during the escape behavior is: , , in, To be determined randomly x Direction and position To be determined randomly y Direction and position.

[0013] Furthermore, a preferred approach is proposed, wherein the specific iterative steps of the artificial chicken flock algorithm include: Step 1: Initialize the artificial chicken flock and calculate the fitness of each chicken in the artificial flock; Step 2: Select the artificial chicken with the lowest fitness as the leader chicken. Determine whether the leader chicken is an artificial chicken in the middle of the artificial chicken flock. If it is, proceed to step 3. If not, update the position based on the chasing behavior and then proceed to step 7. Step 3: Determine if the fitness of the first chicken meets the optimization accuracy requirement or reaches the maximum number of iterations. If so, stop the loop and output the position of the first chicken as the optimal solution; otherwise, determine the number of clusters. If the maximum number of gatherings has been reached, proceed to step 4. If not, update the position based on the gathering behavior, increment the gathering count by 1, calculate the fitness of each chicken, and determine whether the lead chicken is in the middle of the artificial flock. If yes, continue to step 3; otherwise, proceed to step 2. Step 4: Determine if the number of clusters is 0. If it is 0, proceed to step 5. Otherwise, update the position based on the dispersion behavior, decrement the number of clusters by 1, and then proceed to step 4. Step 5: Randomly select a chicken located on the periphery of the flock as... Proceed to step 6; Step 6: Update the location based on the escape behavior, calculate the fitness of each chicken, and determine whether half of the chickens in the flock have a fitness lower than that of others. If step 2 is executed, otherwise determine whether the maximum number of escape attempts has been reached. If the condition is met, proceed to step 2; otherwise, continue with the next step. Step 7: Determine whether the fitness of the first chicken meets the optimization accuracy requirement or reaches the maximum number of iterations. If so, stop the loop and output the position of the first chicken as the optimal solution. Otherwise, calculate the fitness of each chicken and execute Step 2 until the termination condition is met.

[0014] Furthermore, an optimal approach is proposed, wherein the objective function is:

[0015] in, For the first i The total intensity of the geomagnetic field measured by a UUV. For the first i The horizontal northward intensity of the geomagnetic field measured by a UUV For the first i The horizontal eastward intensity of the geomagnetic field measured by a UUV For the first i Vertical intensity of the geomagnetic field measured by a UUV To optimize the total intensity of the geomagnetic field at the target location, To optimize the horizontal northward intensity of the geomagnetic field at the target location, To optimize the eastward horizontal intensity of the geomagnetic field at the target location, To optimize the vertical intensity of the geomagnetic field at the target location.

[0016] Furthermore, a preferred method is proposed, which iteratively updates the UUV location using an artificial chicken flock algorithm, including: ,

[0017] in, For the first i The direction of UUV navigation for In the direction at the The distance traveled within a sampling period, For the first i The speed of a UUV's journey for In the direction at the The distance traveled within a sampling period.

[0018] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a UUV intelligent bionic cooperative localization method based on an artificial chicken flock algorithm according to any one of the above.

[0019] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of a UUV intelligent bionic cooperative localization method based on an artificial chicken flock algorithm as described above.

[0020] Compared with the prior art, the beneficial effects of the present invention are: 1. The method proposed in this invention addresses the problem that existing UUV navigation technologies mainly rely on improving the accuracy of single-unit navigation devices or on complete geomagnetic maps, resulting in high costs and limited applicability. It proposes a different technical concept, achieving a fundamental paradigm shift: treating multiple UUV systems as a whole (a flock of chickens), transforming the positioning problem into a collaborative optimization problem for the group. Each UUV is no longer an isolated individual but a collaborative artificial chicken within the group. They share group location information and geomagnetic measurement information to jointly optimize a group objective function based on geomagnetic matching. This paradigm only requires precise geomagnetic information of one or more discrete points within the task area as "anchor points," rather than a large-scale precise magnetic field map, greatly reducing reliance on prior information and solving the core technical challenge of the difficulty in obtaining precise magnetic field information over large areas. Through group collaborative optimization, the accumulated errors of the inertial navigation system over time are effectively corrected. Simulation experiments show that when the inertial navigation system operates alone, there is a large distance deviation between the UUV and the target location. However, after biomimetic collaborative navigation, the error is significantly reduced, verifying the effectiveness of this method in improving long-endurance, high-precision positioning capabilities.

[0021] 2. Existing particle swarm optimization algorithms mostly simulate the convergent behavior of flocks of birds and schools of fish, with relatively simple mechanisms that easily lead to the loss of population diversity and getting trapped in local optima. This invention takes a unique approach, meticulously simulating the complex, hierarchical social behaviors of chicken flocks in nature, such as chasing, gathering, dispersing, and fleeing. These four behaviors constitute a dynamically balanced optimization system: chasing ensures the population's global exploration ability as a whole to move towards the dominant direction (leading chicken); gathering and dispersing behaviors form a contradictory unity, respectively controlling the population's "mining" and "exploration" abilities, effectively avoiding premature convergence through dynamic parameter adjustment; fleeing behavior provides an effective random perturbation mechanism for the population to escape local optima. In complex and variable underwater geomagnetic environments, or when individual UUV sensors exhibit measurement noise, the population proposed in this invention can still maintain stable optimization performance through cooperative behavior, demonstrating excellent fault tolerance and anti-interference capabilities.

[0022] 3. This invention does not completely abandon inertial navigation systems, but proposes an intelligent switching strategy between coarse inertial navigation guidance and biomimetic fine search. During long-distance voyages, inertial navigation is used for general-directional, high-efficiency guidance; when approaching the target area and the accumulated error of the inertial navigation becomes significant, it switches to a biomimetic cooperative navigation mode based on an artificial chicken swarm algorithm for precise destination positioning or waypoint correction. This strategy fully leverages the long-term stability of inertial navigation and the precision of swarm biomimetic search, forming a complementary advantage. Furthermore, since precise correction can be achieved using only a few known geomagnetic points, eliminating the need for mapping large-scale, precise magnetic field maps, it significantly reduces the cost, time, and difficulty of system deployment. This makes the technology suitable for cost-sensitive or rapidly deployable applications, such as large-scale marine exploration and urgent military missions. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the artificial chicken flock algorithm described in this invention; Figure 2 Four sets of simulation experiments were conducted to demonstrate the biomimetic cooperative navigation of the artificial chicken flock algorithm described in this invention. Figure 3 The figures show four sets of simulation experiments and inertial navigation system positioning comparisons of the artificial chicken flock algorithm described in this invention for biomimetic cooperative navigation. In the figures, the horizontal axis represents longitude and the vertical axis represents latitude. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other, and the described embodiments are only some embodiments of the present invention, not all embodiments.

[0025] Implementation Method 1: This implementation method addresses the problem that existing UUV navigation technologies mainly rely on improving the accuracy of single-unit navigation devices or on complete geomagnetic maps, resulting in high costs and limited applicability. It proposes a UUV intelligent bionic cooperative positioning method based on an artificial chicken flock algorithm. The method includes: Treating multiple UUVs as an artificial flock of chickens, the state of each UUV is represented as a position vector. ,in, For UUV numbering, This indicates a specific moment in the operation of the UUV; and Represents the coordinates of the UUV in two-dimensional space; Initialize the artificial chicken flock parameters, including the number of chickens N, the initial position of the artificial chickens, and the maximum number of iterations. Maximum number of clusters Maximum number of escape attempts The objective function and accuracy requirements are set; the objective function is the fitness function of the geomagnetic elements. The location of UUVs is iteratively updated using an artificial chicken flock algorithm, including chasing, gathering, dispersing, and escaping behaviors, in order to minimize the fitness function and achieve collaborative localization of the UUV group. When the UUV approaches the target location, it switches to the simulation cooperative navigation mode and uses the artificial chicken flock algorithm to correct the cumulative error of the inertial navigation system.

[0026] The method proposed in this implementation achieves a fundamental paradigm shift: treating multiple UUV systems as a whole (a flock of chickens), transforming the localization problem into a collaborative optimization problem for the group. Each UUV is no longer an isolated individual, but rather a cooperative "artificial chicken" within the flock. They jointly optimize a group objective function based on geomagnetic matching by sharing group location information and geomagnetic measurement information. This paradigm only requires precise geomagnetic information of one or more discrete points within the task area as "anchor points," rather than a large-scale precise magnetic field map, greatly reducing the dependence on prior information and solving the core technical obstacle of the great difficulty in obtaining precise magnetic field information over a large area.

[0027] Implementation Method 2, see below Figures 1 to 3 This embodiment describes the UUV intelligent biomimetic cooperative localization method based on the artificial chicken flock algorithm described in Example 1 above. Multiple UUVs are treated as an artificial flock of chickens, and the position of each UUV is used as the positional information in the artificial flock algorithm. The position of each UUV within each sampling interval is... x , y The displacements in two directions are used as velocity information in the artificial chicken flock algorithm, that is:

[0028]

[0029] in, For UUV numbering, This indicates a specific moment in the operation of the UUV; and Represents the coordinates of the UUV in two-dimensional space; Design an artificial chicken flock algorithm, including: Step 1: Initialize the number of chickens N in the artificial flock and their initial positions, and the maximum number of iterations. Maximum number of clusters Maximum number of escape attempts Set the objective function and accuracy requirements; the objective function is the fitness function of geomagnetic elements; calculate the fitness of each chicken in the artificial chicken flock; Step 2: Select the artificial chicken with the lowest (highest) fitness as the head chicken. Determine whether the head chicken is an artificial chicken in the middle of the artificial chicken flock. If it is, proceed to step 3. If not, update the position based on the chasing behavior and then proceed to step 7. Step 3: Determine if the fitness of the first chicken meets the optimization accuracy requirement or reaches the maximum number of iterations. If so, stop the loop and output the position of the first chicken as the optimal solution; otherwise, continue the process. Check if the maximum number of aggregations has been reached. If so, proceed to step 4; otherwise, update the position based on the aggregation behavior. Add 1, then calculate the fitness of each chicken, and determine whether the head chicken is in the middle of the artificial flock. If yes, continue to step 3; otherwise, proceed to step 2. Step 4: Determine If the value is 0, proceed to step 5; otherwise, update the position based on the scattering behavior and... Subtract 1 and then proceed to step 4; Step 5: Randomly select a chicken located on the periphery of the flock as... Proceed to step 6; Step 6: Update the location based on the escape behavior, calculate the fitness of each chicken, and determine whether half of the chickens in the flock have a fitness value less than (greater than) that of other chickens. If step 2 is executed, otherwise determine whether the maximum number of escape attempts has been reached. If the condition is met, proceed to step 2; otherwise, continue to step 6. Step 7: Determine whether the fitness of the first chicken meets the optimization accuracy requirement or reaches the maximum number of iterations. If so, stop the loop and output the position of the first chicken as the optimal solution. Otherwise, calculate the fitness of each chicken and execute Step 2 until the termination condition is met.

[0030] The location of UUVs is iteratively updated using an artificial chicken flock algorithm, incorporating chasing, gathering, dispersing, and fleeing behaviors, to minimize the fitness function and achieve cooperative localization of the UUV population. This includes: Let the space for the chickens to seize insects be a single... European-style space, in which This represents the total number of artificially bred chickens in the flock. Let represent the number of variables to be optimized. A certain artificial chicken... The state can be represented as ,in It is the first The first artificially bred chicken is awaiting optimization. The position of the variable in the dimensional space. The objective function is called fitness. The artificial chicken with the lowest fitness in the flock is designated as the chicken that pecks at the insect, and is defined as the "head chicken" (since maxima and minima problems can be converted to each other, this implementation studies the minima problem, taking the chicken with the lowest fitness as the head chicken; the position of the head chicken is indicated by...). This indicates that, in studying the maxima problem, the chicken with the highest fitness is taken as the leader (artificial chicken). p and artificial chickens q The distance between them is used p and q The state difference is represented as... Furthermore, the flock of chickens is evenly and centrally symmetrically distributed.

[0031] Chase behavior: When the lead hen flees the flock, the non-lead hen follows it; this is called chase behavior. In this case, the artificial flock acts as a whole, and the distance between the artificial chickens... It remains unchanged. The speed at which the first chicken escapes in each iteration is expressed as: , k The moment in the iteration process can be obtained using the following formula: , in, This indicates the distance between the lead hen and the center of the flock. It means the first chicken is x Directional coordinates It means the first chicken is y Directional coordinates Indicates the center of the chicken flock is y The position of direction, This represents the location of the center of the chicken flock. During the chasing behavior, the position update formula for all artificial chickens is: .

[0032] in, For the first i The location of each UUV The calculated speed.

[0033] Aggregation behavior: The behavior of all artificial chickens gathering towards the center of the flock. At this time, the distance between the artificial chickens decreases. The iterative update process of each artificial chicken can be represented by the following formula: , .

[0034] Dispersion behavior: This refers to the behavior of all artificial chickens moving away from the flock, increasing the distance between them. Dispersion behavior can be seen as the inverse of aggregation behavior, and the iterative update process of each artificial chicken can be represented by the following formula: , .

[0035] Escape behavior: The directional movement of all artificial chickens in a certain direction while maintaining a constant distance between them. The iterative update process of each artificial chicken can be represented by the following formula: , , In the formula, This refers to a chicken randomly selected from the flock. To be determined randomly x Direction and position To be determined randomly y Direction and position.

[0036] When the UUV approaches the target location, it switches to the simulation cooperative navigation mode and uses the artificial chicken flock algorithm to correct the cumulative error of the inertial navigation system.

[0037] The artificial chicken flock algorithm designed in this embodiment includes: Step 1: Initialize the number of chickens N in the artificial flock and their initial positions, and the maximum number of iterations. Maximum number of clusters Maximum number of escape attempts Set the objective function and accuracy requirements, and calculate the fitness of each chicken in the artificial flock; Step 2: Select the artificial chicken with the lowest (highest) fitness as the head chicken. Determine whether the head chicken is an artificial chicken in the middle of the artificial chicken flock. If it is, proceed to step 3. If not, update the position based on the chasing behavior and then proceed to step 7. Step 3: Determine if the fitness of the first chicken meets the optimization accuracy requirement or reaches the maximum number of iterations. If so, stop the loop and output the position of the first chicken as the optimal solution; otherwise, continue the process. Check if the maximum number of aggregations has been reached. If so, proceed to step 4; otherwise, update the position based on the aggregation behavior. Add 1, then calculate the fitness of each chicken, and determine whether the head chicken is in the middle of the artificial flock. If yes, continue to step 3; otherwise, proceed to step 2. Step 4: Determine If the value is 0, proceed to step 5; otherwise, update the position based on the scattering behavior and... Subtract 1 and then proceed to step 4; Step 5: Randomly select a chicken located on the periphery of the flock as... Proceed to step 6; Step 6: Update the location based on the escape behavior, calculate the fitness of each chicken, and determine whether half of the chickens in the flock have a fitness value less than (greater than) that of other chickens. If step 2 is executed, otherwise determine whether the maximum number of escape attempts has been reached. If the condition is met, proceed to step 2; otherwise, continue to step 6. Step 7: Determine whether the fitness of the first chicken meets the optimization accuracy requirement or reaches the maximum number of iterations. If so, stop the loop and output the position of the first chicken as the optimal solution. Otherwise, calculate the fitness of each chicken and execute Step 2 until the termination condition is met.

[0038] A chicken in the middle of an artificial flock will not become the leader unless it reaches the optimal position or a local extreme. Therefore, we can determine whether the chicken has reached the optimal position or fallen into a local extreme based on the chicken in the middle of the artificial flock, and take appropriate phase actions accordingly.

[0039] In this embodiment, the objective function is the fitness function of the geomagnetic elements. Considering that describing the magnetic field information of a certain point requires more than three independent magnetic field elements, four magnetic field elements are selected as variables in the optimization function: total geomagnetic field intensity F, horizontal northward intensity X, horizontal eastward intensity Y, and vertical intensity Z. The objective function is written as:

[0040] in, For the first i The total intensity of the geomagnetic field measured by a UUV. For the firsti The horizontal northward intensity of the geomagnetic field measured by a UUV For the first i The horizontal eastward intensity of the geomagnetic field measured by a UUV For the first i Vertical intensity of the geomagnetic field measured by a UUV To optimize the total intensity of the geomagnetic field at the target location, To optimize the horizontal northward intensity of the geomagnetic field at the target location, To optimize the eastward horizontal intensity of the geomagnetic field at the target location, To optimize the vertical intensity of the geomagnetic field at the target location.

[0041] The positions of each UUV are updated according to the artificial chicken flock algorithm. The conversion relationship between the speed and heading of the UUV and the speed in the artificial chicken flock algorithm is determined by the following formula: ,

[0042] in, For the first i The direction of UUV navigation for In the direction at the The distance traveled within a sampling period, For the first i The speed of a UUV's journey for In the direction at the The distance traveled within a sampling period.

[0043] Because the positioning error of the UUV's inertial navigation system accumulates over time when operating underwater for extended periods, and underwater GPS signals attenuate significantly, GPS cannot be used to correct the inertial navigation system. This embodiment proposes a positioning correction method for the inertial navigation system based on biomimetic cooperative navigation. This method only requires prior knowledge of the precise magnetic field information of a certain location within the UUV's mission area. When the UUV navigates a long distance towards a fixed target with known magnetic field information, the inertial navigation system provides general directional search. Initially, the inertial navigation system is used for navigation. However, as the UUV approaches the target, due to the accumulated error over time, the inertial navigation system inevitably accumulates a certain amount of error. At this point, the inertial navigation system can no longer accurately reach the destination, and a biomimetic cooperative navigation method is used instead of the inertial navigation system to search for the target location.

[0044] When a UUV travels on a fixed route, it can obtain precise magnetic field information at several points along the route in advance. When the inertial navigation error accumulates too much, it can use a biomimetic cooperative navigation method to search for a known position and use this position to correct the accumulated error of the inertial navigation.

[0045] This implementation method also simulates the actual geomagnetic field environment with reference to the literature "Establishment and Analysis of the Main Magnetic Field Model in China from 1950 to 1980", and performs simulation verification in Matlab. Specifically: The x-axis component (X), y-axis component (Y), and z-axis component (Z) of the geomagnetic field are selected as navigation cues, corresponding to each search sub-target. In the simulation, nine UUVs form a flock and participate in cooperative navigation.

[0046] Without considering measurement noise, the following experiment was conducted: Initial positions were randomly selected, and the vehicles moved towards a predetermined target location in a geomagnetic environment. The gyroscope zero drift of the inertial navigation system (INS) of each UUV was 0.01° / h, with a random drift of 0.03°. Initially, navigation was performed using the INS. Once the INS reached the "target location," due to the accumulation of errors over time, a significant error occurred between the INS and the actual target location. At this point, a cooperative biomimetic navigation method was used for precise positioning, enabling multiple UUVs to accurately reach their destination. Figure 3 It can be seen that at the end of the inertial navigation, each UUV is far from the target location. After the biomimetic cooperative navigation, the error is significantly reduced, which verifies the effectiveness of the method proposed in this invention.

[0047] Existing particle swarm optimization algorithms mostly simulate the convergent behavior of flocks of birds and schools of fish, with relatively simple mechanisms that easily lead to loss of population diversity and getting trapped in local optima. This invention takes a unique approach, meticulously simulating the complex, hierarchical social behaviors of chicken flocks in nature, such as chasing, gathering, dispersing, and fleeing. These four behaviors constitute a dynamically balanced optimization system: chasing ensures the population's global exploration ability as a whole to move towards the dominant direction (leading chicken); gathering and dispersing behaviors form a contradictory unity, respectively controlling the population's "mining" and "exploration" abilities, effectively avoiding premature convergence through dynamic parameter adjustment; fleeing behavior provides an effective random perturbation mechanism for the population to escape local optima. In complex and variable underwater geomagnetic environments, or when individual UUV sensors exhibit measurement noise, the population proposed in this invention can still maintain stable optimization performance through cooperative behavior, demonstrating excellent fault tolerance and anti-interference capabilities.

[0048] The method proposed in this invention does not completely abandon inertial navigation systems, but innovatively proposes an intelligent switching strategy between coarse inertial navigation guidance and biomimetic fine search. During long-distance transit, inertial navigation is used for general-directional and efficient guidance; when approaching the target area and the accumulated error of the inertial navigation becomes significant, it switches to a biomimetic cooperative navigation mode based on an artificial chicken swarm algorithm for precise destination positioning or waypoint correction. This strategy fully leverages the long-term stability of inertial navigation and the precision of swarm biomimetic search, forming a complementary advantage and significantly improving the overall navigation accuracy and reliability of the system without excessive reliance on expensive hardware.

[0049] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the published pending claims.

Claims

1. A UUV intelligent biomimetic cooperative localization method based on an artificial chicken flock algorithm, characterized in that, The method includes: Treating multiple UUVs as an artificial flock of chickens, the state of each UUV is represented as a position vector. ,in, For UUV numbering, This indicates a specific moment in the operation of the UUV; and Indicates the first i The coordinates of each UUV in two-dimensional space; Initialize the artificial chicken flock parameters, including the number of chickens N, the initial position of the artificial chickens, and the maximum number of iterations. Maximum number of clusters Maximum number of escape attempts The objective function and accuracy requirements are set; the objective function is the fitness function of the geomagnetic elements. The location of UUVs is iteratively updated using an artificial chicken flock algorithm, including chasing, gathering, dispersing, and escaping behaviors, in order to minimize the fitness function and achieve collaborative localization of the UUV group. When the UUV approaches the target location, it switches to the biomimetic cooperative navigation mode, using an artificial chicken flock algorithm to correct the accumulated error of the inertial navigation system.

2. The UUV intelligent biomimetic cooperative localization method based on the artificial chicken flock algorithm according to claim 1, characterized in that, In the chasing behavior of the artificial chicken flock algorithm, the positional difference between the lead chicken and the center of the flock is defined as velocity. : in, This indicates the distance between the lead hen and the center of the flock. It means the first chicken is x Directional coordinates It means the first chicken is y Directional coordinates Indicates the center of the chicken flock is x The position of direction, Indicates the center of the chicken flock is y The position of direction; During the chasing behavior, the position update formula for all artificial chickens is: in, For the first i The location of each UUV The calculated speed.

3. The UUV intelligent biomimetic cooperative localization method based on the artificial chicken flock algorithm according to claim 2, characterized in that, The speed of each artificial chicken in the aggregation behavior is: ， 。 4. The UUV intelligent biomimetic cooperative localization method based on the artificial chicken flock algorithm according to claim 2, characterized in that, The speed of each artificial chicken in the dispersed behavior is: ， 。 5. The UUV intelligent biomimetic cooperative localization method based on the artificial chicken flock algorithm according to claim 2, characterized in that, The speeds of the artificial chickens during the escape were: ， ， in, To be determined randomly x Direction and position To be determined randomly y Direction and position.

6. The UUV intelligent biomimetic cooperative localization method based on artificial chicken flock algorithm according to claim 1, characterized in that, The specific iterative steps of the artificial chicken flock algorithm include: Step 1: Initialize the artificial chicken flock and calculate the fitness of each chicken in the artificial flock; Step 2: Select the artificial chicken with the lowest fitness as the leader chicken. Determine whether the leader chicken is an artificial chicken in the middle of the artificial chicken flock. If it is, proceed to step 3. If not, update the position based on the chasing behavior and then proceed to step 7. Step 3: Determine if the fitness of the first chicken meets the optimization accuracy requirement or reaches the maximum number of iterations. If so, stop the loop and output the position of the first chicken as the optimal solution; otherwise, determine the number of clusters. If the maximum number of gatherings has been reached, proceed to step 4. If not, update the position based on the gathering behavior, increment the gathering count by 1, calculate the fitness of each chicken, and determine whether the lead chicken is in the middle of the artificial flock. If yes, continue to step 3; otherwise, proceed to step 2. Step 4: Determine if the number of clusters is 0. If it is 0, proceed to step 5. Otherwise, update the position based on the dispersion behavior, decrement the number of clusters by 1, and then proceed to step 4. Step 5: Randomly select a chicken located on the periphery of the flock as... Proceed to step 6; Step 6: Update the location based on the escape behavior, calculate the fitness of each chicken, and determine whether half of the chickens in the flock have a fitness lower than that of others. If step 2 is executed, otherwise determine whether the maximum number of escape attempts has been reached. If the condition is met, proceed to step 2; otherwise, continue with the next step. Step 7: Determine whether the fitness of the first chicken meets the optimization accuracy requirement or reaches the maximum number of iterations. If so, stop the loop and output the position of the first chicken as the optimal solution. Otherwise, calculate the fitness of each chicken and execute Step 2 until the termination condition is met.

7. The UUV intelligent biomimetic cooperative localization method based on the artificial chicken flock algorithm according to claim 1, characterized in that, The objective function is: in, For the first i The total intensity of the geomagnetic field measured by a UUV. For the first i The horizontal northward intensity of the geomagnetic field measured by a UUV For the first i The horizontal eastward intensity of the geomagnetic field measured by a UUV For the first i Vertical intensity of the geomagnetic field measured by a UUV To optimize the total intensity of the geomagnetic field at the target location, To optimize the horizontal northward intensity of the geomagnetic field at the target location, To optimize the eastward horizontal intensity of the geomagnetic field at the target location, To optimize the vertical intensity of the geomagnetic field at the target location.

8. The UUV intelligent biomimetic cooperative localization method based on the artificial chicken flock algorithm according to claim 1, characterized in that, UUV locations are iteratively updated using an artificial chicken flock algorithm, including: ， in, For the first i The direction of UUV navigation for In the direction at the The distance traveled within a sampling period, For the first i The speed of a UUV's journey for In the direction at the The distance traveled within a sampling period.

9. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a UUV intelligent bionic cooperative localization method based on an artificial chicken flock algorithm according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the UUV intelligent bionic cooperative localization method based on the artificial chicken flock algorithm as described in any one of claims 1-8.

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