Intelligent underwater wireless submerging and detecting mechanical bionic body

Abstract

The invention discloses an intelligent underwater wireless submerging and detecting mechanical bionic body, and belongs to the technical field of ocean exploration, and the intelligent underwater wireless submerging and detecting mechanical bionic body comprises a hardware system and a software system; the hardware system is composed of a shell structure, a Yuba fish structure, a propelling system module, a sensor module, a communication and control module and a wireless submerging module. The Yuba fish structure comprises a motion simulation unit, a swimming direction unit and a pressure-resistant cabin structure design unit. And the software system operates by taking the communication and control module in the hardware system as a base body and forms an autonomous decision. According to the underwater wireless underwater navigation and detection mechanical bionic body, underwater intelligent control operation can be achieved, autonomous decision making can be carried out, and meanwhile the underwater operation efficiency of the underwater wireless underwater navigation and detection mechanical bionic body can be effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of ocean exploration technology, and more particularly to an intelligent underwater wireless diving and detection mechanical bionic body. Background Art

[0002] An AUV (autonomous underwater vehicle) is an intelligent robot used for underwater exploration, mapping, scientific research, or military missions. Relying on internal energy and autonomous navigation systems, it operates independently without cables and is widely used in ocean exploration, resource surveys, environmental monitoring, and other fields.

[0003] When using AUVs, it is difficult to select suitable lightweight, high-strength, and corrosion-resistant materials at a given working depth. As a result, the AUV cannot have the functions of light weight, large load-bearing capacity, and high safety and reliability. Secondly, the structural failure form of AUV materials is complex, and it is necessary to consider the given scale to determine the laying angle, laying sequence, number of layers, rib spacing, rib cross-section, rib parameters, etc., making it difficult to minimize the shell structure stability failure pressure and the strength failure pressure in all directions.

[0004] In summary, AUVs have the problems of being unable to achieve intelligent control, difficulty in making autonomous decisions, and immature bionic technology, which makes the operation efficiency of AUVs low.

[0005] Therefore, in view of this, the existing structure is studied and improved to provide an intelligent underwater wireless diving and detection mechanical bionic body, in order to achieve a more practical purpose. Summary of the Invention

[0006] 1. Technical problems to be solved

[0007] In response to the problems existing in the prior art, the purpose of the present invention is to provide an intelligent underwater wireless navigation and detection mechanical bionic body, which can realize underwater intelligent control operations and make autonomous decisions. At the same time, it can effectively ensure the operating efficiency of the underwater wireless navigation and detection mechanical bionic body underwater.

[0008] 2. Technical solution

[0009] To solve the above problems, the present invention adopts the following technical solutions.

[0010] Intelligent underwater wireless navigation and detection mechanical bionic body, the underwater wireless navigation and detection mechanical bionic body includes hardware system and software system;

[0011] The hardware system consists of a shell structure, a Yanba structure, a propulsion system module, a sensor module, a communication and control module, and a wireless submersible module. The Yanba structure includes a motion simulation unit, a swimming direction unit, and a pressure cabin structure design unit.

[0012] The software system operates on the basis of the communication and control modules in the hardware system and forms autonomous decisions.

[0013] Furthermore, the shell structure is composed of epoxy resin and hollow glass microspheres, and the shell structure is optimized using a multi-objective optimization algorithm to form a ribbed composite material shell.

[0014] Furthermore, the Yanba fish structure is combined with the BCF mode and the MPF mode to propel the bionic body in water.

[0015] Furthermore, the steps of the working mode of the motion simulation unit are:

[0016] Step a1: Use the lateral wave equation of the fish body centerline to derive the swimming state of the bionic body;

[0017] Step a2: Using the Strouhal number, the swing rate of the bionic body is obtained;

[0018] Step a3: during pitch motion, obtain the basic values of the bionic body;

[0019] Step a4: Finally, through data integration, the specific swimming data of the bionic body is obtained;

[0020] In the swimming direction unit, a posture sensor is used to obtain the angle information of the robotic fish in real time, and a fuzzy control method is used to adjust the deviation between the robotic fish and the target direction to complete the directional turning of the bionic body;

[0021] In the pressure cabin structural design unit, a cylindrical structure is reinforced with ring ribs. The ring rib reinforced cylindrical pressure hull under external water pressure is simplified to a complex curved elastic foundation beam with both ends rigidly fixed on elastic supports.

[0022] Furthermore, in the propulsion system module, the power source for the bionic body's forward movement and other movements is the thrust control system of the propeller. By determining the thrust size of the propeller, the bionic body is allowed to track along a certain path at a desired speed and posture.

[0023] Furthermore, in the sensor module, the bionic body adopts a fiber Bragg grating (CTD) system and uses B / Ge co-doped optical fiber for sensitivity enhancement.

[0024] Furthermore, the communication and control module is in wireless communication mode, and estimates the status of itself and adjacent nodes using measurement information such as relative distance measurement and angle, instead of directly realizing information interaction through underwater acoustic broadcasting.

[0025] Furthermore, the wireless submersible module adopts wireless charging technology. The DC power supply is converted into high-frequency AC power through transmission. The receiving circuit picks up the power of the primary coil through the secondary coil, and then converts the high-frequency AC power into DC power through the rectifier circuit to power the load.

[0026] Furthermore, in the software system, an autonomous decision-making module is formed based on the autonomous decision-making system.

[0027] 3. Beneficial effects

[0028] Compared with the prior art, the advantages of the present invention are:

[0029] This solution consists of a hardware system and a software system for underwater wireless navigation and detection mechanical bionic bodies. Hardware design innovation is achieved through bionic fin or tail propulsion systems, extending battery life and reducing dependence on external energy sources. Path planning is optimized to plan the optimal path that meets specific mission requirements and accurately track the target trajectory. A bionic cluster underwater communication detection data collection system is used to complete data collection in a cyclical manner, providing support for marine operations and research. Deep learning target detection algorithms are used to achieve efficient data utilization. Distributed cluster control algorithms enable multi-bionic collaboration and division of labor for complex tasks, improving system flexibility and adaptability. A cavity design is used to store special equipment, combined with cluster control to achieve specific tasks, making underwater detection equipment intelligent and multifunctional, meeting the needs of multiple fields.

[0030] In summary, the underwater wireless submersible and detection mechanical bionic body can realize underwater intelligent control operations and make autonomous decisions. At the same time, it can effectively ensure the operational efficiency of the underwater wireless submersible and detection mechanical bionic body underwater. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the relationship between the maximum working depth and the weight-floating ratio of the shell structure of the intelligent underwater wireless diving and detection mechanical bionic body made of different materials in the present invention;

[0032] Figure 2 Schematic diagram of the coordinates of the bionic body in the present invention;

[0033] Figure 3 This is a block diagram of the bionic body fuzzy control structure in the present invention;

[0034] Figure 4 Schematic diagram of the decision framework of the bionic body manipulation scheme in the present invention;

[0035] Figure 5 Schematic diagram of the adaptive optimal parameter PID control principle in the present invention;

[0036] Figure 6A comparison of the computational and memory efficiency of LLM-A* and A* in the pathfinding process in the present invention Figure 1 ;

[0037] Figure 7 A comparison of the computational and memory efficiency of LLM-A* and A* in the pathfinding process in the present invention Figure 2 . DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] Example 1:

[0040] See also Figure 1 - Figure 7 , Intelligent underwater wireless diving and detection mechanical bionic body, the underwater wireless diving and detection mechanical bionic body includes a hardware system and a software system;

[0041] The hardware system consists of the shell structure, the Yanba structure, the propulsion system module, the sensor module, the communication and control module, and the wireless submersible module. The Yanba structure includes the motion simulation unit, the swimming direction unit, and the pressure cabin structure design unit.

[0042] The software system runs on the basis of the communication and control modules in the hardware system and makes autonomous decisions.

[0043] The underwater wireless navigation and detection mechanical bionic body is composed of a hardware system and a software system, which can realize underwater intelligent control operations and make autonomous decisions. At the same time, it can effectively ensure the operational efficiency of the underwater wireless navigation and detection mechanical bionic body underwater.

[0044] Example 2:

[0045] Based on the above embodiment 1, further description is given.

[0046] Specifically, the intelligent underwater wireless diving and detection mechanical bionic body is composed of epoxy resin and hollow glass microspheres, and the ribbed composite shell is optimized using a multi-objective optimization algorithm.

[0047] This material's relatively stable chemical properties not only effectively ensure the safety of deep-sea equipment but also reduce product weight, meeting the deep-sea equipment's requirements for deep-sea pressure resistance and long-term corrosion resistance. Furthermore, the composite material exhibits excellent sound and wave absorption properties.

[0048] See Figure 1 The results show that compared with aluminum alloy and titanium alloy materials, the use of carbon fiber composite materials can reduce weight by 30% to 45% under the same conditions. Considering the lightweight and sufficient buoyancy, the density of the buoyancy material should not exceed 420g / m 3 .

[0049] Specifically, the Yanba fish structure combines the BCF mode and the MPF mode to propel the bionic body in water.

[0050] The yamba fish has a spherical appearance and mainly uses its pectoral fins and large tail to swim. The BCF mode mainly uses the fluctuation of the fish body and the swing of the tail fin to propel the entire fish forward. In this mode, the fish has good instantaneous acceleration performance and strong endurance, but its maneuverability is relatively poor.

[0051] The MPF mode mainly relies on the swinging of the pectoral fins, pelvic fins, dorsal fins and anal fins of biological fish to generate fish propulsion. In this mode, the fish has good maneuverability and strong swimming stability, but its instantaneous acceleration performance is poor and the swimming speed is low.

[0052] The Yanba fish structure adopts the shape of the fish, which helps to combine the two modes, facilitates better propulsion of the bionic body in the water, and can also relatively better avoid the corresponding defects.

[0053] See Figure 2 ,Specifically, the steps of the working mode of the motion simulation unit are:

[0054] Step a1: Use the lateral wave equation of the fish body centerline to derive the swimming state of the bionic body;

[0055] Step a2: Using the Strouhal number, the swing rate of the bionic body is obtained;

[0056] Step a3: during pitch motion, obtain the basic values of the bionic body;

[0057] Step a4: Finally, through data integration, the specific swimming data of the bionic body is obtained.

[0058] This allows for motion simulation that is closer to the real-life movement of fish than conventional bionic body simulations. The Navier-Stokes equations are also used to simulate the autonomous swimming of fish.

[0059] (1) Fish achieve swimming performance by swinging their bodies to bend. Based on observation, Viddle obtained the lateral wave equation of the fish's centerline. At this time, a Fourier series function is established, and the expression is as follows:

[0060] y(x, t) = a(x)sin(kx-wt)

[0061] a(x)=a0+a1x+a2x 2

[0062] Among them, all length physical quantities are dimensionless through the fish body centerline length L;

[0063] The y direction is parallel to the body trunk direction, and the x direction is perpendicular to the body trunk direction;

[0064] y(x, t) is the displacement of the fish centerline at time t;

[0065] t represents time; k is the wave number, w is the oscillation frequency;

[0066] a0, a1, and a2 are constant coefficients.

[0067] Then, the Strouhal number is used to reflect the relationship between the swing amplitude, swing frequency and swimming speed. It also has a significant impact on the wake vortex structure generated by swimming. The expression is as follows:

[0068]

[0069] Then, the pitch motion of the tail fin around its own swing axis is calculated using the following expression:

[0070] θ(t)=θ max sin(2πAt+Ψ)

[0071] Among them, θ max is the amplitude of the tail fin pitching motion, A is the swing frequency of the dolphin's body, and Ψ is the phase difference between the tail fin pitching motion and the sinking and floating motion.

[0072] See Figure 2 According to Lighthill's theory, the expression of the fish's midline when it is moving is as follows:

[0073] y(x, t)=(c1x+c2x 2 )sin(kx+wt)

[0074] Among them, c1 and c2 represent the amplitude envelopes of the primary and secondary waves of the fish body swing, respectively, k = 2Π / λ, the λ distribution represents the number of fish body fluctuations and the length of fish body fluctuations, ω represents the angular velocity of the fish body fluctuations, and Φ is the angle of the fish body movement.

[0075] The bionic body is based on the autonomous swimming of fish. The governing equations are the two-dimensional unsteady incompressible Navier-Stokes equations. A dynamic mesh model suitable for the computational domain that changes with flexible motion is established. The governing equations are expressed as follows:

[0076]

[0077] in represents divergence, U is the incoming flow velocity;

[0078] Then, the mass and momentum conservation calculations are performed, and the expressions are:

[0079]

[0080] See Figure 3 Specifically, in order to ensure the accuracy of the swimming direction, the bionic body uses a posture sensor to obtain the angle information of the robotic fish in real time, and uses the fuzzy control method to adjust the deviation between the robotic fish and the target direction to achieve the directional turning of the bionic body.

[0081] The error value between the fish head and the target direction and the error change rate △P are taken as the input of fuzzy control, and the actual angle of the robot fish after it returns to the straight swimming state is taken as the output u. The offset variable σ of the robot fish is adjusted in real time. i Adjust the fish head movement to return it to the target direction.

[0082] When the actual angle of the robot fish is greater than the target angle, bi Take the opposite number;

[0083] When the actual angle of the robot fish is smaller than the target angle, the value remains unchanged.

[0084] e(k) and △e(k) represent the steering angle error and error change rate of the bionic body at the kth discrete moment, respectively. The definition expression is:

[0085] e(k)=θ(k)-θ ref

[0086] Δe(k)=e(k)-e(k-1)

[0087] Among them, θ ref Indicates the target direction angle of the robotic fish;

[0088] θ(k) represents the turning angle of the robot fish at that moment.

[0089] The basic domains of the three definitions of the angle error value e(k), the error change rate △e(k) and the output control quantity u(k) are [-e L , e H ]、[-ec L ,ec H ] and [-μ L , μ H ].

[0090] Implement fuzzy processing on input and output variables separately:

[0091] After fuzzy processing, e(k) is A: {NB, NS, ZE, PS, PB}, which correspond to the angle difference {negative large, negative small, moderate, positive small, positive large} respectively;

[0092] After fuzzification, △e(k) is B: {N, Z, P}, corresponding to {negative, moderate, positive} respectively;

[0093] The fuzzy set of the output variable u(k) is C: {NB, NS, ZE, PS, PB}, corresponding to {negative large, negative small, moderate, positive small, positive large} respectively.

[0094] Specifically, in the pressure cabin structural design unit, the bionic body's pressure cabin adopts a cylindrical structure reinforced with ring ribs, which fully utilizes the shape advantages of the Yanba fish. The ring rib reinforced cylindrical pressure shell under external water pressure can be simplified into a complex curved elastic foundation beam with both ends rigidly fixed on elastic supports.

[0095] The characteristic quantities that represent the strength of the ring-stiffened cylindrical pressure shell structure include the shell plate stress σ1 at the mid-span of adjacent ribs, the shell plate axial stress σ2 at the ribs, and the rib stress σ3. The expressions are as follows:

[0096]

[0097] Among them, P j Represents the calculated pressure in MPa; usually 1.5 times the actual pressure of the pressure chamber;

[0098] R represents the average radius of the pressure hull, in mm;

[0099] t represents the thickness of the pressure hull, in mm;

[0100] The buckling pressure of the spherical shell plate is calculated according to the following formula:

[0101] p cr =C S C Z p e MPa

[0102] Wherein, CS is determined by the parameter σe / ReH, which is determined by the Rules for Classification and Construction of Diving Systems and Submersibles;

[0103] CZ—determined by the parameter σe / ReH, as specified in the Rules for Classification and Construction of Diving Systems and Submersibles;

[0104] ReH—material yield strength, in MPa;

[0105] σe=Pe / 2C, unit is MPa;

[0106] Pe=0.84EC2,unit is MPa;

[0107] E—elastic modulus, in GPa;

[0108] C—is determined by the ratio t / R, as specified in the Rules for Classification and Construction of Diving Systems and Submersibles;

[0109] D—Outer diameter of cylindrical cabin, in mm.

[0110] Through the buckling pressure expression:

[0111] P cr ≥P j

[0112] According to the theory of material mechanics, the static strength of the frame should be checked according to the fourth strength theory. The Von Mises stress value in the stress data obtained by finite element simulation software is calculated based on the fourth strength theory.

[0113] Therefore, by comparing the Von Mises stress values of the frame under dangerous load conditions, it can be concluded that the strength performance of the frame is very high.

[0114] Then, the expression is calculated according to the fourth strength theory:

[0115]

[0116] Where: cr4 Represents Von Mises equivalent stress, unit is MPa;

[0117] σ i represents the principal stress (i=1, 2, 3), in MPa;

[0118] σ s Represents yield stress, unit is MPa.

[0119] Specifically, in the propulsion system module, the main power source for the bionic body's forward movement is the thrust control system of its propeller. To make the bionic body follow a certain path at the desired speed and posture, it is necessary to determine the thrust of the propeller. The motion state of the boundary layer fluid will affect the magnitude of the frictional resistance coefficient. By calculating the Renault R e To determine the fluid motion state, when cruising in a straight line, the expression for calculating the Reynolds number is:

[0120]

[0121] Where γ is the kinematic viscosity of seawater, which is 1.141 × 10⁻⁶ m s / ², L is the length of the robot, and v is the speed. When the bionic body travels at its maximum speed of 2 m / s, the Reynolds number is calculated to be 3.5 × 10⁶, indicating turbulent flow.

[0122] The bionic body uses the Lagrange equation method to establish and analyze the dynamic model of the robotic fish during steady-state swimming. The expression based on the Lagrange equation method is:

[0123]

[0124] The bionic body system defines four generalized coordinates, namely X1, θ 10 ,θ 21 ,θ 32 , so according to the above Lagrange equation, the dynamic mathematical model of the robotic fish is obtained, which is expressed as:

[0125]

[0126] Among them, Q1, Q2, Q3, and Q4 are the generalized coordinates θ 10 ,θ 21 ,θ 32 , the generalized force or moment corresponding to X1.

[0127] By analyzing the dynamic equation, the expression of the fish body swing amplitude is obtained as follows:

[0128]

[0129] Specifically, in the sensor module, the bionic body adopts a fiber Bragg grating (CBG) CTD system and uses B / Ge co-doped fiber for sensitivity enhancement. The sensitivity enhancement principle is that the temperature coefficient of the long-period fiber Bragg grating is related to the change of the refractive index difference between the core and the cladding with temperature. Other elements are doped into the core to make the effective refractive index of the core close to the effective refractive index of the cladding, thereby reducing the impact of the difference in refractive index between the core and the cladding on the temperature characteristics of the grating.

[0130] Numerical aperture is a parameter that indicates the ability of an optical fiber to focus light. The refractive index of the core, n1, is slightly larger than the refractive index of the cladding, n2. The square difference between the two refractive indices can be expressed as the numerical aperture NA of the optical fiber, which is expressed as:

[0131]

[0132] The temperature sensitivity of long-period fiber gratings should be studied based on the most basic equation of long-period fiber gratings, that is, the phase matching condition. The expression of long-period fiber gratings is:

[0133]

[0134] Due to the existence of thermo-optical effect, when the temperature of the external environment changes, the refractive index of the core and cladding materials of the optical fiber will change accordingly, thereby causing the change of the difference in the effective refractive index of the core fundamental mode and the cladding mode respectively.

[0135] At the same time, due to thermal expansion and contraction, the grating period of the fiber Bragg grating will also change to a certain extent. Since the thermal expansion coefficient a of silica material is about 5x10 -7 (degrees Celsius), which is much smaller than Γ temp value, so it can generally be ignored.

[0136] Specifically, the communication and control module is a wireless communication mode, which uses measurement information such as relative distance and angle to estimate the status of itself and adjacent nodes, instead of directly realizing information interaction through underwater acoustic broadcasting.

[0137] Given the complex underwater environment and the non-negligible noise disturbances in the communication link, we introduce the AKF to achieve adaptive compensation for external noise disturbances. Specifically:

[0138] First, a state space model is established under the communication-measurement framework. The expression of communication-measurement is:

[0139]

[0140] in: and Bionic body i The state and measurement vectors at time k; are the state transfer matrix and measurement function of the system respectively, and the expression of relative distance is:

[0141]

[0142] The expression for angle calculation is:

[0143]

[0144] Secondly, the statistical characteristics of process noise and measurement noise satisfy the following conditions:

[0145]

[0146] Where: q k and r k are the mean of process noise and measurement noise, Q k and R k is the corresponding variance matrix.

[0147] Then, finally, an adaptive time-varying noise statistical recursive estimator is established, and the expression of the time-varying noise statistical recursive estimator is:

[0148]

[0149] Here: H i is the Jacobian matrix of the measurement function at the origin; d k-1=(1 - b) / (1 - b k ), where 0 < b < 1 is the forgetting factor. The expression of the matching AKF is:

[0150]

[0151] Specifically, in the wireless submersible module, to improve the endurance of the intelligent underwater wireless submersible and detection bionic body, a wireless charging technology based on the principle of electromagnetic induction is adopted, which mainly consists of four parts: a DC power supply, a transmitting circuit, a receiving circuit, and a load.

[0152] Among them, the transmitting circuit consists of an inverter circuit, a compensation topology, and a primary coil, and the receiving circuit consists of a secondary coil, a compensation topology, and a rectifier circuit.

[0153] The DC power supply is converted into high-frequency alternating current through the transmitter. Based on the principle of electromagnetic induction, the receiving circuit picks up the electrical energy of the primary coil through the secondary coil, and then converts the high-frequency alternating current into direct current through the rectifier circuit to supply power to the load.

[0154] According to the mutual inductance model, for the primary coil and the secondary coil, the coupling coefficient k is defined to represent the coupling degree between the primary coil and the secondary coil. The expression of the primary coil is:

[0155] U1 = (R1 + jωL1)I j + jωMI2

[0156] The expression of the mutual inductance of the secondary coil is:

[0157] U2 = (R2 + jωL2)I2 + jωMI1

[0158] The expression of the coupling degree between the primary coil and the secondary coil is:

[0159]

[0160] In wireless submersibles, a cavity design can be carried out to store special equipment such as micro bombs, and rapid aggregation can be achieved through cluster control when necessary.

[0161] Refer to Figure 4 , specifically, in the software system, based on the autonomous decision-making system, an autonomous decision-making module is formed. Autonomous decision-making is a decision-making for the bionic body motion control scheme under the constraints of multiple factors such as the environment,潜行规则 (it seems there is a wrong word here, maybe "diving rules" or something else), and manipulation motion characteristics.

[0162] Refer to Figure 5, classify the environment according to decision requirements and decompose it into various elements, and establish a mathematical model of the elements. In actual use, the navigation data driven model is obtained by shipboard equipment, and then combined into a traffic environment that can be recognized by the computer program; a maneuvering model is established; and finally, the parameters of the adaptive optimal PID control can change with the working environment and ship speed, and K is adjusted in real time based on the ship's motion state. p , K i , K d 3 parameters, real-time feedback and optimization adjustment, precise control of heading;

[0163] Obstacle avoidance uses a hybrid adaptive preference method based on an improved artificial potential field (HAP-IAPF) approach. This method takes into account the practical constraints of the underwater collaborative capture mission and establishes a mission model that includes underwater static and dynamic obstacles, bionic sensor interaction distance limits, bionic body speed changes, target confrontation strategies, and other influencing factors. The target gravity function is defined as:

[0164]

[0165] Where: R T is the capture convergence distance parameter; ρ(X i , X T ) is the Euclidean distance between the target and the i-th bionic body. In the traditional APF method, when an individual is affected by the attractive and repulsive forces around the obstacle, the resultant force may change positively or negatively at the next moment the bionic body moves. Therefore, the path planned by the bionic body may oscillate repeatedly, resulting in energy waste and time consumption. To solve this problem, an obstacle avoidance preference strategy with improved virtual force is adopted to enable the multi-bionic body group to pass through the obstacle area quickly and stably. The expression of the neighbor action function on the individual bionic body is:

[0166]

[0167] Where n is the number of captured bionic bodies; ρ(X i , X j ) is the Euclidean distance between the i-th bionic body and the j-th neighboring individual within the sensing range. Assuming that there are m obstacles or equivalent obstacles within the sensing range of the i-th bionic body, the expression of the obstacle force function is

[0168]

[0169] See Figure 6 、 Figure 7Path planning uses the novel LLM-A* path planning algorithm. First, two sets are created: the OPEN set and the CLOSED set. The algorithm core is to select the optimal node (the one with the smallest f value is optimal, or if f is the same, the one with the smallest h value is more optimal) from the OPEN set and place it in the CLOSED set. Then, the node's successor is placed in the OPEN set. This operation is repeated until the target is reached or the OPEN set is empty. Finally, in the CLOSED set, the node is searched in reverse order according to the previous nodes contained in the target G, and finally reaches the starting point S. The reverse order of this link is the optimal path. An obstacle state variable, obs, is also introduced.

[0170] The TARGET list must satisfy two important constraints: first, it must contain the actual start and end points, and second, all target points must not be located within obstacles. A similar heuristic function h(s), cost function g(s), and openlist and closelist are used.

[0171] Example 3:

[0172] Based on the above-mentioned embodiment 1 and embodiment 2, further description is given.

[0173] Integrating machine vision, data analysis, wireless transmission and cluster control technologies into underwater mechanical bionics plays a key role in multiple marine fields, brings many benefits and has significant innovations.

[0174] The specific areas are as follows:

[0175] 1. Marine Research and Monitoring: High-resolution cameras, combined with a variety of sensors, comprehensively collect data on marine life, environmental parameters, and seawater composition, providing rich information for marine ecological research and facilitating long-term monitoring of ecological changes. Data fusion and chemical analysis algorithms provide in-depth analysis of environmental conditions, enabling timely detection of anomalies such as excessive pollutants or ecological imbalances, providing a basis for decision-making on marine environmental protection.

[0176] 2. Marine aquaculture management: Real-time monitoring of aquaculture organism health and population density, early warning of abnormalities, reducing aquaculture losses and ensuring farmers' profits. Improve aquaculture efficiency and aquatic product quality by optimizing aquaculture environment management.

[0177] 3. Military Applications: Highly concealed bionic bodies can perform reconnaissance missions and gather intelligence. Cluster control enables coordinated operations among multiple bionic bodies, enhancing military operational capabilities, such as jamming and attack missions, and improving operational flexibility and combat effectiveness.

[0178] 4. Improve operational efficiency and safety: Cluster control improves detection efficiency, reducing time and costs. Automatic intervention mechanisms respond to anomalies, protecting equipment and personnel, and reducing risks.

[0179] Example 4

[0180] Based on the above-mentioned embodiment 1, embodiment 2, and embodiment 3, further description is given.

[0181] When wirelessly diving underwater and testing the actual operation of mechanical bionic bodies, the following features are specifically mentioned:

[0182] (1) Hardware design innovation: Bionic fin or tail propulsion system improves movement efficiency, reduces energy consumption, and enhances maneuverability; energy recovery device uses micro turbine or piezoelectric material to recover ocean energy, extend endurance, and reduce dependence on external energy.

[0183] (2) Optimized path planning: By establishing mechanical equations and dynamic equations to describe the underwater force and motion state of the bionic body, comprehensively considering environmental factors such as obstacle distribution and depth limit, as well as goals such as path length and time limit, using optimization solvers and MPC optimization control methods, the optimal path that meets specific task requirements is planned to achieve accurate tracking of the target trajectory.

[0184] (3) Efficient data collection and processing: The bionic cluster underwater communication detection data collection system uses the Qlearning algorithm to plan the data collection path, collects perception data and environmental information along the way, calculates the obstacle position information by analyzing the reflected echo signal, and completes the data collection work in a cycle, providing rich data support for marine operations and research.

[0185] (4) Convenient research on marine biological behavior: Deep learning target detection algorithms, using CNN combined with YOLO or SSD, can accurately and quickly identify marine biological anomalies, improving monitoring accuracy and real-time performance. Data fusion and machine learning chemical analysis algorithms can comprehensively process multi-source data, provide comprehensive and accurate environmental information, and realize intelligent monitoring and early warning. Efficient data transmission protocols ensure real-time and reliable data transmission, support remote monitoring and command, and realize efficient data utilization. Distributed cluster control algorithms realize the collaboration of multiple bionic bodies, the division of labor and cooperation in complex tasks, and improve system flexibility and adaptability.

[0186] (5) Functional integration innovation: The cavity design can store special equipment, and combined with cluster control, it can achieve specific tasks, such as military strikes or clearing marine obstacles. The integration of machine vision, data analysis, wireless transmission and cluster control can realize the intelligent and multifunctional underwater detection equipment to meet the needs of multiple fields.

[0187] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any person skilled in the art who, within the technical scope disclosed by the present invention, makes equivalent substitutions or modifications based on the technical solutions and improved concepts of the present invention shall be covered by the scope of protection of the present invention.

Claims

1. Intelligent underwater wireless diving and detection mechanical bionic body, characterized by: The underwater wireless diving and detection mechanical bionic body includes a hardware system and a software system; The hardware system consists of a shell structure, a yanba structure, a propulsion system module, a sensor module, a communication and control module, and a wireless submersible module; The Yanba fish structure includes a motion simulation unit, a swimming direction unit, and a pressure cabin structure design unit; The software system operates on the basis of the communication and control modules in the hardware system and forms autonomous decisions.

2. The intelligent underwater wireless diving and detection mechanical bionic body according to claim 1, characterized in that: The shell structure is composed of epoxy resin and hollow glass microspheres, and the shell structure is optimized using a multi-objective optimization algorithm to form a ribbed composite material shell.

3. The intelligent underwater wireless diving and detection mechanical bionic body according to claim 1, characterized in that: The Yanba fish structure combines the BCF mode and the MPF mode to propel the bionic body in water.

4. The intelligent underwater wireless diving and detection mechanical bionic body according to claim 1, characterized in that: The steps of the working mode of the motion simulation unit are: Step a1: Use the lateral wave equation of the fish body centerline to derive the swimming state of the bionic body; Step a2: Using the Strouhal number, the swing rate of the bionic body is obtained; Step a3: during pitch motion, obtain the basic values of the bionic body; Step a4: Finally, through data integration, the specific swimming data of the bionic body is obtained; In the swimming direction unit, a posture sensor is used to obtain the angle information of the robotic fish in real time, and a fuzzy control method is used to adjust the deviation between the robotic fish and the target direction to complete the directional turning of the bionic body; In the pressure cabin structural design unit, a cylindrical structure is reinforced with ring ribs. The ring rib reinforced cylindrical pressure hull under external water pressure is simplified to a complex curved elastic foundation beam with both ends rigidly fixed on elastic supports.

5. The intelligent underwater wireless diving and detection mechanical bionic body according to claim 1, characterized in that: In the propulsion system module, the power source for the bionic body's forward movement and other movements is the thrust control system of the propeller. By determining the thrust size of the propeller, the bionic body is allowed to track along a certain path at a desired speed and posture.

6. The intelligent underwater wireless diving and detection mechanical bionic body according to claim 1, characterized in that: In the sensor module, the bionic body adopts a fiber Bragg grating (CTD) system and uses B / Ge co-doped optical fiber for sensitivity enhancement.

7. The intelligent underwater wireless diving and detection mechanical bionic body according to claim 1, characterized in that: The communication and control module is a wireless communication mode, which uses measurement information such as relative distance measurement and angle to estimate the status of itself and adjacent nodes, instead of directly realizing information interaction through underwater acoustic broadcasting.

8. The intelligent underwater wireless diving and detection mechanical bionic body according to claim 1, characterized in that: The wireless submersible module adopts wireless charging technology. The DC power supply is converted into high-frequency AC power through transmission. The receiving circuit picks up the power of the primary coil through the secondary coil, and then converts the high-frequency AC power into DC power through the rectifier circuit to power the load.

9. The intelligent underwater wireless diving and detection mechanical bionic body according to claim 1, characterized in that: In the software system, an autonomous decision-making module is formed based on the autonomous decision-making system.

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