Multi-AUV (Autonomous Underwater Vehicle) collaborative underwater data acquisition system and path optimization method

Abstract

The invention discloses a multi-AUV (Autonomous Underwater Vehicle) collaborative underwater data acquisition system and a path optimization method, and belongs to the technical field of underwater wireless sensor networks. The system comprises a fixed sensor node, a data acquisition layer, a data integration layer, a cross-medium transmission layer, a dynamic region division module and a path optimization module. The fixed sensor node is deployed in a shallow submerged reef sea area of the South China Sea, and transmits data to the peripheral AUV through single-hop underwater acoustic communication; the central AUV is interconnected with the peripheral AUV through a dynamic telescopic optical fiber link to fuse multi-source data; and the mobile AUV uses blue-green laser communication to enable the data to float upwards to the water surface base station to be transmitted back. The dynamic region division module divides a monitoring region into three fan-shaped sub-regions which are not overlapped with one another, and the geographic unit integrity constraint and the load balancing constraint are met; and the path optimization module plans a peripheral AUV path by adopting an information entropy enhanced ant colony algorithm, and path planning efficiency is remarkably improved by dynamically adjusting heuristic factors and triggering local search optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater wireless sensor networks, and in particular relates to a multi-AUV collaborative underwater data acquisition system and a path optimization method. Background Art

[0002] As human exploration of the ocean and underwater space becomes increasingly in-depth and diverse, scientific research and production activities—from deep-sea resource exploration and three-dimensional marine environmental monitoring to modern deep-sea aquaculture and fishing, to underwater facility inspection and safety assurance—are creating an unprecedented and urgent need for real-time perception, efficient transmission, and interconnection of underwater information. Against this backdrop, underwater sensor networks have emerged and are rapidly becoming a core technology for ocean observation and underwater informatization, garnering widespread attention from both academia and industry. By deploying multiple nodes, underwater sensor networks can overcome the limitations of complex underwater environments and acquire critical data such as temperature, salinity, ocean currents, and even geological activity in real time, providing precise support for resource exploration, ecological protection, and waterway safety.

[0003] Existing underwater data collection solutions primarily rely on fixed sensors connected by fiber optic cables to transmit information, or on underwater acoustic communication using multi-hop relay schemes to transmit information. However, these methods have the following major drawbacks:

[0004] (1) Fixed cable deployment is costly and has limited coverage. The current mainstream solution relies on fixed cable fiber optic sensors (such as seabed observation networks). Such systems have significant problems:

[0005] High deployment cost: submarine optical cables need to be laid, and the cost of deploying a single node exceeds US$5,000;

[0006] Poor terrain adaptability: In complex terrains such as the reef areas of the South China Sea, coverage is insufficient;

[0007] Insufficient scalability: The node position is fixed and the monitoring range cannot be adjusted dynamically.

[0008] (2) The efficiency of single AUV operation is low. The solution of using a single AUV (autonomous underwater vehicle) to traverse and collect data has exposed serious bottlenecks:

[0009] Lack of real-time performance: AUVs need to periodically return to the surface base station to unload data (the round trip time accounts for 60%-70% of the mission cycle), which cannot meet real-time monitoring needs such as disaster warning.

[0010] Path planning redundancy: The length of the global traversal path increases exponentially with the number of nodes. When the number of nodes reaches 1,000, traditional algorithms (such as genetic algorithms) take more than 24 hours to calculate;

[0011] Poor terrain adaptability: It is easy to fall into the local optimal path in reef areas, and the obstacle avoidance failure rate exceeds 30%.

[0012] (3) Multi-hop relay communication has poor reliability. The multi-hop relay solution based on underwater acoustic communication has fatal flaws:

[0013] Energy consumption imbalance: Relay nodes consume 8-10 times more energy than terminal nodes, shortening the network lifecycle by more than 50%;

[0014] Routing hole problem: When the node failure rate reaches 5%, the probability of network paralysis exceeds 90%;

[0015] High transmission delay: The end-to-end delay of multi-hop underwater acoustic communication exceeds 10 seconds, which cannot support high-precision ocean modeling.

[0016] (4) Insufficient existing multi-AUV collaborative solutions. Although some studies have attempted multi-AUV collaborative acquisition in recent years, the core issues have not been resolved:

[0017] Path coupling disaster: The strong coupling of multiple AUV paths leads to an explosion in the solution space dimension, and traditional optimization algorithms converge slowly;

[0018] Cross-media transmission delay: AUVs need to frequently surface and transmit data back, and the water-air cross-media communication delay accounts for 80% of the total delay;

[0019] Uncontrollable energy consumption: Nodes taking on relay tasks exacerbate uneven energy consumption, and the network lifecycle is less than three months.

[0020] Currently, several AUV-assisted underwater information collection systems and energy optimization algorithms exist. These systems traverse nodes along a pre-set path to collect data, but they periodically return to a surface base station to offload and store the data, making them inadequate for real-time monitoring. The global traversal path of a single AUV is highly redundant, and computational complexity increases with scale. Furthermore, traditional algorithms are prone to local optima and struggle to adapt to complex terrain, such as reefs. Summary of the Invention

[0021] In order to overcome the problems in the background technology, the present invention provides a multi-AUV collaborative underwater data acquisition system and a path optimization method.

[0022] To achieve the above object, the present invention is implemented through the following technical solutions:

[0023] (1) System architecture construction

[0024] Fixed sensor nodes: The research object is the shallow reef area of ​​the South my country Sea, which includes three independent shoal units (CM, SD, and EF). Several submarine sensors are installed on the seabed. The submarine sensors are equipped with fixed sensor nodes. The fixed sensor nodes are used to monitor the target at all times and collect and upload the latest target location data.

[0025] Data collection layer: Multiple peripheral AUVs collect data from sensor nodes within their fan-shaped areas through underwater acoustic communication;

[0026] Data integration layer: The central AUV is fixed at the geometric center of the monitoring area and is connected to the peripheral AUVs through dynamically retractable fiber optic links to receive and fuse multi-source data;

[0027] Cross-medium transmission layer: The mobile AUV obtains data from the central AUV through blue-green laser communication and surfaces to the surface base station for backhaul;

[0028] Dynamic area division module: divides the monitoring area into three non-overlapping sector-shaped sub-areas to meet the constraints of geographic unit integrity and load balancing;

[0029] Path optimization module: uses information entropy enhanced ant colony algorithm to plan the movement path of peripheral AUV;

[0030] Among them, the sensor node transmits data to the peripheral AUV in the corresponding fan-shaped area through single-hop underwater acoustic communication. The node energy consumption is only positively correlated with the amount of data generated by itself, and it does not undertake relay forwarding tasks.

[0031] (2) Path optimization method

[0032] S1: Building a layered communication architecture:

[0033] The peripheral AUVs collect sector-shaped data through underwater acoustic communication. The peripheral AUVs are connected to the central AUV in real time via optical fiber. The maneuvering AUVs acquire data from the central AUV via blue-green lasers and surface to transmit it back. Based on the Shannon formula, the channel capacity of underwater electromagnetic waves can be expressed as:

[0034]

[0035] S2: Establish optimization problem A to balance node load while ensuring geographic integrity;

[0036] The optimization problem A is expressed as:

[0037]

[0038] K represents the preset density tolerance threshold, and the set of sensor nodes in the monitoring area is: S = {x1, x2, ... x N}, x i =(r i ,θ i ) represents the node polar coordinates, dividing the detection area into three sectors:

[0039]

[0040] Satisfying full area coverage: θ1+θ2+θ3=2π, the dark sand unit set is R={R1,R2,R3}, Indicates the sector node density.

[0041] S3: Solve the optimization problem A using dynamic angle adjustment and divide it into three sectors;

[0042] (1) Initial equal-angle division, the division formula is as follows:

[0043]

[0044] (2) Geographic integrity test: Each dark sand unit must fall completely within the corresponding sector area, and the minimum distance between its center point and the sector boundary must meet the safety threshold.

[0045] Calculate the minimum boundary distance for each shoal unit like The trigger angle is fine-tuned, and the adjustment formula is as follows:

[0046]

[0047] (3) Load balancing adjustment: The node density difference in each sector area needs to be controlled within the set threshold to achieve energy consumption balance.

[0048] Calculate the current density difference using the following formula:

[0049] Δρ j,j+1 =|ρ j -ρ j+1 |

[0050] right High-density sector expansion angle, Low density fan compression angle

[0051] (4) Iterate until the following conditions are met simultaneously:

[0052]

[0053] S4: Decoupling global path planning into three traveling salesman problem models;

[0054] S5: Using the improved ant colony algorithm to solve the optimization problem B, minimize the total length of the peripheral AUV path, the optimization problem B is expressed as:

[0055]

[0056] t ij It represents the time that the i-th AUV stays at the j-th node. The total system delay is the maximum time taken by the three AUVs to complete the acquisition task. Represents node x i The amount of data transferred in a single round, Represents node p i and node p i+1 The distance between them.

[0057] The optimal path for collecting data from the peripheral AUV and the total time required to collect one cycle of data can be summarized into the following important steps:

[0058] (1) Initialization parameters:

[0059] Set the maximum number of iterations, the number of ants, the pheromone heuristic factor α and the path heuristic factor β, and the pheromone volatility ρ.

[0060] (2) Constructing paths and calculating information entropy

[0061] Each ant constructs a complete probability based on the state transition probability. The state transition probability calculation formula is as follows:

[0062]

[0063] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij (t)

[0064] τ ij is the pheromone concentration from city i to j, η ij is the heuristic function from city i to j, ρ represents the volatility of pheromone; Δτ ij Expressed as the change from city i to city j at time t.

[0065] Calculate the population information entropy, the calculation formula is as follows:

[0066]

[0067] In order to adapt to the dynamic adjustment requirements of subsequent algorithm parameters, the population entropy value needs to be standardized. After normalizing the population information entropy, we can obtain:

[0068] n represents the total number of cities, represents the information entropy of ant k in state i at time t, s k (t) represents the information entropy of an individual ant completing a round trip, and s(t) represents the average information entropy of the entire ant population.

[0069] (3) Dynamically adjust algorithm parameters

[0070] In order to make the algorithm flexible to adapt to the changes in the actual problem in the initial exploration stage and the later convergence stage, the population information entropy indicator is introduced to adjust the parameters adaptively. The pheromone heuristic factor and path heuristic factor are updated as follows:

[0071]

[0072]

[0073] L best The reference optimal solution to the TSP problem is usually the currently recognized international optimal solution.

[0074] (4) Group information update

[0075] Using a pheromone update method based on group cooperation, we emphasize the leading role of outstanding ants in the population. By highlighting these "elite" individuals, we can improve the overall activity and exploration ability of the population. The path enhancement formula is as follows:

[0076]

[0077] L(S max ()) refers to the length of the path taken by the ant with the largest entropy value, ω(t) represents the dynamic weight factor, ρ(t) represents the pheromone volatilization factor, γ represents the memory factor, and N represents the current number of iterations.

[0078] In order to strengthen the positive feedback effect of the optimal path obtained in each iteration on the population and promote the rapid and stable convergence of the algorithm, it is necessary to appropriately increase the additional pheromone concentration for the current optimal path after each round of iteration. The specific formula for global optimal enhancement is as follows:

[0079]

[0080] L best Indicates the length of the optimal path obtained in the current iteration.

[0081] (5) Triggering local search optimization

[0082] When the population information entropy drops to a certain threshold, the local search strategy is triggered to eliminate the path crossing problem and perform hierarchical optimization. 2-opt is used first to eliminate path crossing. If 2-opt cannot be further improved, the automatic insertion search method is enabled. If the above two methods cannot continue to optimize, the more advanced Or-opt method is introduced and repeated until the path length remains unchanged.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] Fixed sensor nodes collect data from the monitored area in real time. When an AUV moves along an optimized path into the communication range of a node, the node transmits the data to the corresponding AUV. The AUV then transmits the data back to the central AUV via an optical fiber link for real-time fusion processing. The maneuverable AUV periodically dives to the central AUV, receives and integrates the data using near-field blue-green lasers, and then surfaces to the surface base station for backhaul. This collaborative mechanism achieves three major technological breakthroughs:

[0085] Dynamic division of mission areas: The monitoring sea area is divided into three mutually exclusive sector-shaped sub-areas, each of which is managed by an independent peripheral AUV. This decouples the global path planning into three parallel local optimization problems, significantly reducing the path complexity.

[0086] Cross-media near-real-time transmission: The mobile AUV receives data from the central AUV underwater and surfaces to transmit it back, eliminating the latency overhead of frequent AUV returns in traditional solutions.

[0087] Heterogeneous communications collaborate to reduce energy consumption: The "underwater acoustic-fiber-blue-green laser" layered architecture matches different scenarios: underwater acoustics achieves wide-area coverage, fiber ensures low-latency relay, and blue-green laser supports high-speed cross-media transmission. The three work together to significantly reduce system energy consumption and end-to-end latency. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative work.

[0089] Figure 1 is a flow chart of the method of the present invention;

[0090] Figure 2 It is a system architecture diagram of the present invention;

[0091] Figure 3 This is a schematic diagram of the distribution of shoals and reefs in the South China Sea according to the present invention;

[0092] Figure 4 is a schematic diagram of the layered communication architecture of the present invention;

[0093] Figure 5 This is the peripheral AUV path planning result diagram of the present invention;

[0094] Figure 6 This is the peripheral AUV path planning result diagram of the traditional ant colony algorithm. DETAILED DESCRIPTION

[0095] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, 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 any creative efforts shall fall within the scope of protection of the present invention.

[0096] Example 1: System deployment and dynamic area division

[0097] 1. Technical parameters

[0098] Monitoring area: South China Sea reef area (coordinate range 3°58′20″N112°16′53″E), the core area water depth range is 17-32m;

[0099] Sensor nodes: N = 900, randomly distributed in the reef area;

[0100] Maximum telescopic range of optical fiber: 900m;

[0101] Safety threshold: d safe =50m (to ensure the integrity of the reef unit);

[0102] Load tolerance: K = 0.0002 (density difference between sectors ≤ 10%);

[0103] Adjustment coefficient: k = 0.05 (angle fine-tuning step).

[0104] 2. Work process

[0105] (1) Initial equal-angle division

[0106] According to the formula

[0107]

[0108] Divide into sectors.

[0109] (2) Geographical integrity verification

[0110] Calculate the minimum distance d from the boundary of each dark sand unit to the fan-shaped dividing line min =32m <d safe , trigger adjustment; adjustment amount After updating, 120°+0.9°=120.9°.

[0111] (3) Load balancing adjustment

[0112] Calculate sector density:

[0113] ρ1=325 / 854500≈0.00038

[0114] ρ2=283 / 854500≈0.00033

[0115] ρ3=292 / 854500≈0.00034

[0116] Adjustment operation: low-density sector 2 expands 1°, high-density sector 1 compresses 1°.

[0117] (4) Iterative convergence

[0118] Repeat steps (2)-(3) until both of the following conditions are met:

[0119]

[0120]

[0121] Final segmentation angles: θ1 = 118.8°, θ2 = 119.55°, θ3 = 121.65°.

[0122] 3. Effect verification

[0123] Geographic integrity: the boundary of each shoal unit is ≥50m from the dividing line;

[0124] Load balancing: |ρ2-ρ3|≈0.00001

[0125] Computational efficiency: Convergence occurs within 7 iterations.

[0126] Example 2: Path optimization and ant colony algorithm implementation

[0127] 1. Technical parameters

[0128] Peripheral AUVs: 3, speed 5m / s;

[0129] Ant colony parameters: α = 1.0, β = 3.5, ρ = 0.1, the number of ants is 50;

[0130] Information entropy threshold: 0.2, below 0.2 triggers local search;

[0131] Node residence time: d i,j represents the data volume of the jth node in the i-th sector area, and C(d) is the underwater acoustic channel capacity at distance d).

[0132] 2. Work process

[0133] (1) State transfer and information entropy calculation

[0134] The probability of each ant moving from node i to node j is: The pheromone concentration from city i to city j is: τ ij (t+1)=(1-ρ)τ ij(t)+Δτ ij (t)

[0135] (2) Calculate population information entropy:

[0136] The information entropy of ant k in state i at time t is:

[0137]

[0138] The information entropy of an ant completing a tour is: The average information entropy of the entire ant population is: Normalized entropy value:

[0139] (3) Dynamic parameter adjustment

[0140] The population information entropy indicator is introduced to adjust the parameters adaptively. The pheromone heuristic factor and path heuristic factor are updated according to the formula:

[0141]

[0142] L bes t represents the reference optimal solution of the TSP problem, and the optimal solution 50778 in the pcb442 problem is selected as the benchmark.

[0143] Example: After 10 iterations, α increases from 1.2 to 1.418, and β decreases from 2.5 to 2.181.

[0144] (4) Local search trigger

[0145] When the population information entropy drops to 0.2, local search is triggered:

[0146] Prioritize 2-opt: eliminate path intersections;

[0147] If there is no improvement, start the automatic insertion search: insert the unvisited nodes into the optimal position;

[0148] If there is still no improvement, use Or-opt: swap a sequence of 3 consecutive nodes.

[0149] (5) Global Optimal Enhancement

[0150] Elite Path Pheromone Increment:

[0151]

[0152] The specific formula for global optimal enhancement is as follows:

[0153] (6) Effect comparison

[0154] Comparison of path planning effects:

[0155] Taking the number of sensor nodes as 150, the path length is optimized from 8514.8m to 7455.4m, as shown in Figure 5 Figure 6 shown.

[0156] Taking the number of sensor nodes as 900, the comparison results are as follows:

[0157] index Traditional ant colony algorithm Algorithm of the present invention Total path length 45.7km 21.5km Convergence iterations 194 122

[0158] The comparison of latency and energy consumption effects is shown in the following table:

[0159] Compared with the traditional ant colony algorithm method after using the LEACH network clustering algorithm

[0160]

[0161] Through the triple technological innovation of "layered communication architecture + dynamic area division + information entropy ant colony algorithm", this invention systematically solves the four core problems of underwater data collection: deployment cost, real-time performance, energy consumption and path planning. All indicators surpass the existing solutions in the background technology.

[0162] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-AUV collaborative underwater data acquisition system, characterized in that: include: Fixed sensor nodes: These include three independent shoal units (CM, SD, and EF) deployed in the shallow reef areas of the South China Sea, which monitor targets and collect data in real time. Data collection layer: Multiple peripheral AUVs collect data from sensor nodes within their fan-shaped areas through underwater acoustic communication; Data integration layer: The central AUV is fixed at the geometric center of the monitoring area and is connected to the peripheral AUVs through dynamically retractable fiber optic links to receive and fuse multi-source data; Cross-medium transmission layer: The mobile AUV obtains data from the central AUV through blue-green laser communication and surfaces to the surface base station for backhaul; Dynamic area division module: divides the monitoring area into three non-overlapping sector-shaped sub-areas to meet the constraints of geographic unit integrity and load balancing; Path optimization module: uses information entropy enhanced ant colony algorithm to plan the movement path of peripheral AUV; Among them, the sensor node transmits data to the peripheral AUV in the corresponding fan-shaped area through single-hop underwater acoustic communication. The node energy consumption is only positively correlated with the amount of data generated by itself, and it does not undertake relay forwarding tasks.

2. The system according to claim 1, wherein: The central AUV is integrated with a fiber optic recovery device to retract all fiber optic links at the end of the mission cycle; The peripheral AUVs automatically return to the initial connection position of the central AUV according to the homing path generated by the path optimization module.

3. The system according to claim 1, wherein: The dynamic area division module performs the following operations: Initial equal-angle division formula: Geographic integrity check, calculate the minimum boundary distance for each shoal unit like The trigger angle is fine-tuned, and the adjustment formula is as follows: Load balancing adjustment, calculate the current density difference, the calculation formula is as follows: Dr. j,j+1 =|ρ j -r j+1 | right High-density sector expansion angle, Low density fan compression angle Iterate until both:

4. The system according to claim 1, wherein: The path optimization module adopts an improved ant colony algorithm, including: State transition probability calculation: t ij (t+1)=(1-ρ)τ ij (t)+Δτ ij (t) τ ij is the pheromone concentration from city i to j, η ij is the heuristic function from city i to j, ρ represents the volatility of pheromone; Δτ ij It is expressed as the change from city i to city j at time t; Population information entropy calculation and normalization: After normalizing the population information entropy, we can obtain: Where n represents the total number of cities, represents the information entropy of ant k in state i at time t, s k (t) represents the information entropy of an individual ant completing one round trip, and s(t) represents the average information entropy of the entire ant population; Dynamically adjust the heuristic factor:

5. A multi-AUV collaborative underwater data acquisition path optimization method, characterized in that: The following steps are involved: S1: Build a layered communication architecture: Peripheral AUVs collect sector-shaped data through underwater acoustic communication. Peripheral AUVs are connected to the central AUV in real time via optical fiber. Maneuverable AUVs acquire data from the central AUV using blue-green lasers and surface to transmit data back. S2: Establish optimization problem A to balance node load while ensuring geographic integrity; S3: Solve the optimization problem A using dynamic angle adjustment and divide it into three sectors; S4: Decoupling global path planning into three traveling salesman problem models; S5: Use the improved ant colony algorithm to solve the optimization problem B and minimize the total length of the peripheral AUV path.

6. The method according to claim 5, characterized in that The optimization problem A is defined as: K represents the preset density tolerance threshold, and the set of sensor nodes in the monitoring area is: S = {x1, x2, ... x N }, x i =(r i ,θ i ) represents the node polar coordinates, dividing the detection area into three sectors:

7. The method according to claim 5, characterized in that The optimization problem B is defined as: t ij It represents the time that the i-th AUV stays at the j-th node. The total system delay is the maximum time taken by the three AUVs to complete the acquisition task. Represents node x i The amount of data transferred in a single round, Represents node p i and node p i+1 The distance between them.

8. The method according to claim 5, characterized in that The improved ant colony algorithm in S5 includes local search optimization: When the population information entropy is lower than the threshold, the 2-opt, automatic insertion search, and Or-opt methods are triggered in sequence to eliminate path intersections; The optimization is repeated until the path length remains constant.

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