Bionic robotic fish preparation method based on combined approximation model

Abstract

The invention discloses a bionic robotic fish preparation method based on a combined approximation model. According to the method, a combined approximation model is combined with a multi-target particle swarm optimization algorithm and a parallel subspace design method, the coupling effect among four subsystems, namely the hydrodynamic shape subsystem, the total arrangement subsystem, the propulsion subsystem and the energy subsystem, of the multi-joint bionic robotic fish is fully considered, and decoupling among the subsystems is achieved by replacing coupling state variables through the combined approximation model. According to the multi-target and multi-disciplinary reliability optimization design method for the robotic fish, analysis of all subsystems can be executed in parallel, the design period is shortened, probability constraints are evaluated through a one-time reliability analysis method, finally optimization is executed through a multi-target particle swarm optimization algorithm, a final non-dominated solution set is obtained, and multi-target and multi-disciplinary reliability optimization design of the robotic fish is achieved. The method can be used for the integrated concept design problem of a multi-joint bionic robotic fish system, and a new thought and method are developed for the concept design of the bionic robotic fish.

Description

Technical Field

[0001] The present invention relates to a manufacturing method, and more specifically to a preparation method of a bionic robotic fish based on a combined approximation model. Background Art

[0002] Multi-joint bionic robotic fish have higher maneuverability and concealment. In addition, traditional underwater vehicles often use propellers for propulsion, and this propulsion method has low propulsion efficiency, only about 40%-50%. The movement of bionic robotic fish simulates the propulsion mode of fish and has higher propulsion efficiency. Therefore, compared with the propeller propulsion method, multi-joint bionic robotic fish have a longer working time underwater.

[0003] Since the shape, overall layout, propulsion, and energy of multi-joint bionic robotic fish are an integrated system, they need to be considered uniformly during the preparation process. However, in the current existing technologies, the preparation methods for robotic fish do not fully consider the shape, overall layout, propulsion, and energy. Thus, the traditional spiral linear serial design method for the production process of bionic robotic fish in the existing technologies has obvious disadvantages, such as low efficiency, high design cost, long design cycle, etc., and it is difficult to obtain an overall optimal design scheme. For example, in the prior art, there is a patent with the patent number 202311796973.6 and the title of an invention patent for a deep-water cage intelligent cleaning robotic fish and its manufacturing method, which discloses a manufacturing method of a robotic fish. In the disclosed preparation method, only the shape resistance with the smallest geometric shape of the model and the smallest disturbance to the water area environment are achieved, and the shape of an energy-saving and efficient bionic robotic fish is explored. The entire system of the robotic fish is not fully considered in the design. Therefore, there are problems such as low efficiency, high design cost, long design cycle, etc., and it is difficult to obtain an overall optimal design scheme. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technologies, the purpose of the present invention is to provide a manufacturing method of a multi-joint bionic robotic fish based on a combined approximation model, which overcomes the problems existing in the traditional serial optimization design method, fully considers the coupling effects between multiple disciplines or subsystems in the design of complex engineering systems, and improves the efficiency of the optimization design of multi-joint bionic robotic fish.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A preparation method of a bionic robotic fish based on a combined approximation model, characterized in that it includes the following steps:

[0006] Step 1, plan the overall optimization design task of the multi-joint bionic robotic fish, and divide the multi-joint bionic robotic fish into four subsystems: hydrodynamic shape, overall layout, propulsion, and energy according to the principle of multidisciplinary design optimization;

[0007] Step 2: Analyze and model the four subsystems divided in Step 1: the hydrodynamic shape, general arrangement, propulsion, and energy subsystems, clarify the design variables and constraint conditions of each subsystem, further analyze the uncertainties existing in the design variables of the subsystems, and determine the probabilistic constraint conditions;

[0008] Step 3: Conduct experiments on the analysis model of the hydrodynamic shape subsystem in ISIGHT, establish an input-output data set, and construct a combined approximation model of the hydrodynamic shape analysis model to replace the CFD numerical simulation;

[0009] Step 4: Use the parallel subspace design method to handle the coupling relationships between disciplines or subsystems, replace the coupled state variables through the combined approximation model, and construct a multi-objective and multi-disciplinary reliability optimization framework of the multi-joint bionic robot fish for the four subsystems of hydrodynamic shape, general arrangement, propulsion, and energy in MATLAB. Analyze the probabilistic constraints in the optimization problem through the first-order reliability method, calculate the corresponding failure probability, and finally use the multi-objective particle swarm optimization algorithm to perform the optimization;

[0010] Step 5: Input the design variables obtained from the optimized hydrodynamic shape subsystem in Step 4 into a 3D printer, use the 3D printer to fabricate the rigid shell and stern of the robot fish, then install the main control board, energy, and propulsion motors. Then select a controller that communicates with the main control board, and based on the optimized design variables obtained from the general arrangement and static balance subsystems, calculate the magnitude of the trim angle of the robot fish and input it into the controller. Then, based on the optimized design variables in the energy subsystem, analyze the maximum torque required by the servo, select the required servo model and install it into the shell. Finally, taking the motion optimization design variables obtained from the propulsion subsystem as the control target, adjust the output mode of the controller signal to use a proportional-integral-derivative controller to achieve the forward movement of the robot fish and complete the preparation of the robot fish.

[0011] As a further improvement of the present invention, the modeling method for the hydrodynamic shape subsystem in Step 2 is as follows: Use UG software to perform parametric modeling on the shape of the bionic robot fish, and automatically output the corresponding Parasolid geometric model file. Then, use ICEM software to read the generated Parasolid file, establish a flow field calculation domain, perform mesh division on the calculation domain, and output the corresponding Mesh file. Finally, use Fluent software to read the generated Mesh file and perform flow field calculation and analysis, and output the fluid resistance value.

[0012] As a further improvement of the present invention, the analysis and modeling method for the general arrangement subsystem in Step 2 is as follows: Use MATLAB to model the general arrangement analysis, calculate the mass of each part of the bionic robot fish, and analyze and calculate the weight of the bionic robot fish and the buoyancy in water to balance the buoyancy and gravity and the trim angle θ of the bionic robot fisht As a constraint condition.

[0013] As a further improvement of the present invention, the analysis and modeling method of the propulsion subsystem in the second step is as follows: The multi-joint bionic robotic fish is simplified into a two-dimensional planar multi-link mechanism, and the dynamic model of the bionic robotic fish is established by using the Newton-Euler method. The motion parameters of each joint and the length of each link are used as design variables, and the link length is selected as a random variable to analyze the motion law of the bionic robotic fish, and the maximization of the forward speed of the obtained bionic robotic fish is used as the optimization objective of the output.

[0014] As a further improvement of the present invention, the specific steps of testing the hydrodynamic shape subsystem analysis model in ISIGHT in the third step are as follows:

[0015] Step 3-1: Give a set of design points of the design variables in the hydrodynamic subsystem through the optimal Latin hypercube experimental design method in ISIGHT;

[0016] Step 3-2: Through a set of design points given in Step 3-1, perform hydrodynamic analysis, collect the corresponding fluid resistance output values, and establish a data set of input-output values;

[0017] Step 3-3: Adopt the optimal weighted combination approximation model as the approximation model of the hydrodynamic shape subsystem analysis model;

[0018] Step 3-4: Verify the accuracy of the optimal weighted combination approximation model. Divide the collected input-output data set into a sample set and a validation set. Initialize the optimal weighted combination approximation model with the sample set, and then use the validation set to verify the accuracy of the established optimal weighted combination approximation model; judge whether the accuracy meets the requirements. If the accuracy meets the requirements, it can be used to replace the hydrodynamic shape analysis model. If the accuracy does not meet the requirements, change the number of independent approximation models and retrain the optimal weighted combination approximation model until the accuracy requirements are met. As a further improvement of the present invention, the specific method of using the parallel subspace design method to handle the coupling relationship between disciplines or subsystems in the fourth step is as follows: Establish a multi-objective and multi-disciplinary reliability design optimization framework of a multi-joint bionic robotic fish based on four subsystems of hydrodynamic shape, general arrangement, propulsion, and energy in MATLAB. Select the lengths of three links of the robotic fish as random variables, analyze the probability constraints in the optimization problem by the first-order reliability method, calculate the corresponding failure probability, and finally use the multi-objective particle swarm optimization algorithm as the optimizer to drive the entire optimization process to obtain the final non-dominated solution set.

[0019] Advantages of the present invention: The method of the present invention adopts a parallel subspace design method, and processes the coupling relationship between disciplines or subsystems through an optimal weighted combination approximation model to achieve decoupling between disciplines or subsystems, enabling independent analysis of each subsystem and realizing parallel computing. This greatly reduces the design cost of the bionic robot fish, shortens the overall design cycle, fully considers the coupling relationship between subsystems in the robot fish, improves the design quality, overcomes the shortcoming of ignoring the coupling relationship between subsystems in the traditional serial design method, and reduces the computational amount of the overall system optimization while ensuring the analysis accuracy by introducing an optimal weighted combination approximation model. In addition, the probability constraints in the optimization problem are analyzed through the first-order reliability method to calculate the corresponding failure probability, ensuring the reliability of the design scheme. On this basis, by introducing a multi-objective particle swarm optimization algorithm, the multi-objective and multi-disciplinary reliability design optimization of the multi-joint bionic robot fish is realized. This method can be used for the conceptual design problems of the hydrodynamic shape, overall layout, propulsion, and energy integration of the multi-joint bionic robot fish, opening up new ideas for the conceptual design of the multi-joint bionic robot fish and having important reference value. Brief Description of the Drawings

[0020] Figure 1 is the flowchart of the multi-objective and multi-disciplinary reliability design optimization of the multi-joint bionic robot fish according to the embodiment of the present invention;

[0021] Figure 2 is the flowchart of modeling the analysis model of the hydrodynamic shape subsystem according to the embodiment of the present invention;

[0022] Figure 3 is the geometric schematic diagram of the multi-joint bionic robot fish according to the embodiment of the present invention;

[0023] Figure 4 is the schematic diagram of each part of the multi-joint bionic robot fish according to the embodiment of the present invention;

[0024] Figure 5 is the simplified diagram of the multi-joint bionic robot fish in a two-dimensional plane according to the embodiment of the present invention;

[0025] Figure 6 is the flowchart of establishing the combined approximation model according to the embodiment of the present invention;

[0026] Figure 7 is the flowchart of the ISIGHT executing the DOE of hydrodynamic analysis according to the embodiment of the present invention;

[0027] Figure 8 is the coupling relationship diagram between subsystems of the multi-joint bionic robot fish according to the embodiment of the present invention;

[0028] Figure 9This is the framework diagram of the multi-objective and multi-disciplinary reliability design optimization for the embodiments of the present invention. Specific embodiments

[0029] The present invention will be further described in detail below with reference to the embodiments shown in the drawings.

[0030] As Figure 1 shown, the present invention discloses a preparation method of a bionic robotic fish based on a combined approximation model. The preparation method specifically includes the following steps:

[0031] Step 1:

[0032] Clarify the optimization design task requirements of the multi-joint bionic robotic fish. According to the principle of multidisciplinary design optimization, the multi-joint bionic robotic fish is decomposed into four subsystems: hydrodynamic shape, overall layout, propulsion, and energy. Define the overall optimization design objectives. Among them, the hydrodynamic shape subsystem mainly analyzes the fluid resistance received by the robotic fish and designs the shape of the bionic robotic fish with the minimum fluid resistance; the main task of the overall layout subsystem is to analyze the buoyancy-gravity balance of the bionic robotic fish and calculate its longitudinal inclination angle; the propulsion subsystem mainly analyzes its forward speed through the dynamic model of the bionic robotic fish, with the maximization of the forward speed as the optimization objective; while the energy subsystem mainly analyzes the endurance of the bionic robotic fish, with the maximization of the endurance as the optimization objective.

[0033] Step 2:

[0034] Analyze and model the four subsystems of hydrodynamic, overall layout, propulsion, and energy to determine the design variables required for each subsystem and the existing constraint conditions. Further analyze the uncertainty of the design variables in the subsystem and determine the probabilistic constraint conditions.

[0035] 1): Analysis and modeling of the hydrodynamic shape subsystem. The modeling process of the hydrodynamic shape subsystem analysis is as Figure 2 shown. First, it is the parametric modeling of the geometric shape of the bionic robotic fish. The parameters affecting the shape of the bionic robotic fish are used as design variables. The shape of the robotic fish is as Figure 3 shown, where qf1, qf2, qa1, qa2, and L pAs the shape parameter is selected as the design variable of the hydrodynamic subsystem, the parametric modeling of the bionic robot fish's shape is carried out through the executable program obtained by secondary development of UG. When the input design variable parameters change, the geometric model of the robot fish also changes simultaneously, and the corresponding Parasolid geometric model file is output. Then, the generated Parasolid geometric model file is read by ICEM, and the computational domain of the entire flow field is established. The computational domain is meshed, and the final Mesh file is output. Finally, Fluent is used to read the Mesh file, and then the flow field is calculated and analyzed to solve the fluid resistance value received by the robot fish. The fluid resistance value of the bionic robot fish obtained by optimization is 11.973 N, and the corresponding optimized design variable values are qf1 = 2, qf2 = 0, qa1 = 3.6777, qa2 = 20, L p = 25.3912 cm.

[0036] 2): Analysis and modeling of the general layout subsystem. Each part of the bionic robot fish, such as Figure 4As shown in the figure, there is a long and strip-shaped rigid shell (1), pectoral fins (10) arranged on the side of the rigid shell (1), and a stern part (16) and a caudal fin (17) arranged at the tail of the rigid shell (1). Inside the rigid shell (1), a control board (9) is arranged in sequence from head to tail. The battery (8) is together with the control board (9) and is below the control board (9). Then there are a connecting plate (2), a first servo motor (3), a second servo motor (4), a third servo motor (5), and a joint connecting rod (6). The first servo motor (3), the second servo motor (4), and the third servo motor (5) are connected through the joint connecting rod (6) to drive the caudal fin (17). A plurality of mutually parallel ring ribs (7) are coaxially sleeved on the joint connecting rod (6). Corresponding counterweight blocks (11), (12), and (13) are arranged below each servo motor. Three motor bases (14) are arranged on the joint connecting rod (6) for correspondingly installing the servo motors. Finally, a skin (15) is covered on the ring ribs (7) to achieve the effects of sealing and camouflage. Therefore, based on the above components, the rigid shell (1), pectoral fins (10), stern part (16), caudal fin (17), and ring ribs (7) constitute a hydrodynamic shape subsystem. The counterweight blocks (11), (12), and (13) and the motor bases (14) constitute a general layout subsystem. The three servo motors, connecting plate (2), and joint connecting rod (6) constitute a propulsion subsystem. The battery (8) and the control board (9) constitute an energy subsystem. During the operation of the robotic fish, the battery (8) supplies power to the control board (9). The control board (9) controls the operation of the three servo motors to realize the twisting of the fish body composed of the ring ribs (7) of the robotic fish, and then realizes the swimming effect. Through the action of the three counterweight blocks, the robotic fish can maintain the calculated longitudinal inclination angle and move forward. Finally, through the MATLAB software, a mathematical model is established for the analysis of the general layout subsystem. Let the output shaft of the servo motor i (i = 1, 2, 3) be the i-th joint ο of the robotic fish. i , taking the link length l1 between joint ο1 and joint ο2, the link length l2 between joint ο2 and joint ο3, and the length l3 from joint ο3 to the tail as design variables, analyzing the mass of each part of the bionic robotic fish, calculating the weight of the bionic robotic fish and its buoyancy in water, and taking the balance between buoyancy and gravity and the longitudinal inclination angle value of the bionic robotic fish as the constraint conditions of this subsystem. Finally, the optimized weight of the robotic fish is 3.5743 kg, the corresponding longitudinal inclination angle value is 1.7639 degrees, and the corresponding optimized design variable values are l1 = 11.3912 cm, l2 = 7 cm, l3 = 5 cm. 3): Analysis and modeling of the propulsion subsystem. As Figure 5 shown, the entire multi-joint bionic robotic fish is simplified into a multi-stage link mechanism in a two-dimensional plane, and then the dynamic model of the multi-joint bionic robotic fish is established through the Newton-Euler method. The movement of each joint is defined as follows:

[0037]

[0038] In the formula, θ i represents the motion angle of the i-th joint, A i and ω i represent the amplitude and angular frequency of the i-th joint motion respectively (the amplitudes and angular frequencies of all joints are kept consistent), represents the phase of the joint motion. Using the motion parameters A i , ω i and of each joint, as well as the length L i of each connecting rod as design variables, and taking the connecting rod length l1 between joint ο1 and joint ο2, the connecting rod length l2 between joint ο2 and joint ο3, and the length l3 from joint ο3 to the tail as random variables, analyze the motion law of the bionic robot fish through formula (1), solve the forward speed of the bionic robot fish. Finally, the forward speed of the robot fish optimized by the proposed method is 0.6189 m / s, and the values of the optimized design variables obtained are w = 3.8616 rad / s, A = 0.5236 rad,

[0039] 4): Analysis and modeling of the energy subsystem: Use a lithium battery as the power source to provide the required energy for the robot fish. Conduct mathematical modeling of the energy subsystem analysis through MATLAB software. Taking the motion parameters of each joint and the length of the connecting rod as design variables, estimate the maximum torque of the servo motor by analyzing the motion law of each joint and the load of each joint, estimate the power of the servo motor, thereby analyzing the total power of the robot fish, solve the endurance time of the robot fish, and taking the maximum torque as the constraint condition. Finally, the total endurance of the optimized robot fish is 3.7415 hours, and the corresponding maximum torque is 0.5462 N·m.

[0040] Step 3:

[0041] Establish a combined approximate model for the hydrodynamic shape subsystem analysis model, such as Figure 6

[0042] 1): Use the optimal Latin hypercube experimental design method in ISIGHT software to give a set of design points of the design variables in the hydrodynamic shape subsystem analysis model.

[0043] 2): As Figure 6 shown, execute the process of hydrodynamic shape subsystem analysis in ISIGHT, collect the corresponding fluid resistance output values, and establish a data set of input-output values.

[0044] 3): As Figure 7 shown, establish an optimal weighted combined approximate model and use it as the approximate model of the hydrodynamic shape subsystem analysis model.

[0045] 4): Examine the accuracy of the optimal weighted combination approximation model. Divide the collected input-output data set into a sample set and a validation set. Use the sample set to construct the initial optimal weighted combination approximation model, and then test the accuracy of the established optimal weighted combination approximation model through the validation set, and use it to replace the analysis model of the hydrodynamic shape subsystem.

[0046] 5): If the accuracy of the optimal weighted combination approximation model is insufficient, it is necessary to change the number of independent approximation models and retrain the optimal weighted combination approximation model until the accuracy requirement is met.

[0047] Step 4:

[0048] Analyze the coupling relationships between subsystems. As Figure 8 shown, where l i (i = 1, 2, 3) are the shared design variables between all subsystem analysis models, ω, A are the shared design variables between the propulsion subsystem and the energy subsystem, qf1, qf2, qa1, qa2 are the local design variables of the hydrodynamic shape subsystem, is the local design variable of the propulsion subsystem, F drag , v e , H en are the three optimization objectives. The coupling relationships between the analysis models of each subsystem show two characteristics: orderliness and contradiction. Different subsystems are coupled together through the output state variables of their respective analysis models. When performing system analysis in this optimization problem, the hydrodynamic shape subsystem is associated with other subsystems, and its output state variables are the input variables of the analysis models of the other three subsystems. There is an orderly coupling relationship between the general arrangement and static balance subsystem and the hydrodynamic shape subsystem and the propulsion subsystem. The output state variables V wf , V wa , x fb , x ab of the hydrodynamic shape subsystem are the input variables of the general arrangement and static balance subsystem, while the output state variables m fh , m3, x fhg , x m3g of the general arrangement and static balance subsystem are the input variables of the propulsion subsystem. The energy or subsystem is respectively associated with the hydrodynamic shape subsystem and the general arrangement and static balance subsystem. The output state variables V wf , V wa , S a of the hydrodynamic shape subsystem and the output state variables m fh , m3, x m3gIt is an input variable of the energy subsystem analysis model. The relationship between the propulsion subsystem analysis model and the energy subsystem analysis model is contradictory. The optimization objectives v e and H en show opposite trends of change. As the forward speed of the robotic fish increases, its overall endurance will decrease accordingly. In view of the coupling relationship between subsystems, the parallel subspace design method is used to handle the coupling relationship between subsystems. By combining approximate models to replace the coupling state variables, decoupling is achieved between the hydrodynamic shape subsystem and the general arrangement subsystem, the propulsion subsystem and the energy subsystem, and at the same time, decoupling is achieved between the general arrangement and the propulsion subsystem. This enables the independent analysis of each subsystem and realizes parallel computing. Then, as Figure 9 shown, a multi-objective and multi-disciplinary reliability optimization design framework of a multi-joint bionic robotic fish based on the four subsystems of hydrodynamic shape, general arrangement, propulsion, and energy is built in MATLAB. The probability constraints in the optimization problem are analyzed by the first-order reliability method, the corresponding failure probability is calculated, and the multi-objective particle swarm optimization algorithm is used as the optimizer to drive the entire optimization process, so as to obtain the final non-dominated solution set and realize the multi-objective and multi-disciplinary reliability design optimization of the multi-joint bionic robotic fish. After the optimization is completed, based on the optimized design variables in the hydrodynamic shape subsystem, poly(lactic acid) material is used, and through 3D printing technology based on fused deposition modeling, the rigid shell and the stern of the robotic fish are fabricated. Then, based on the optimized design variables obtained from the general arrangement and static balance subsystem, the longitudinal inclination angle of the robotic fish is calculated and brought into the constraint conditions for verification. On this basis, based on the optimized design variables in the energy subsystem, the maximum torque required to be provided by the servo is analyzed, the required servo model is selected, and the selected servo is installed into the rigid shell. Finally, taking the optimized design variables of the motion obtained from the propulsion subsystem as the control target, based on the Arduino UNO R3 control board, a proportional-integral-derivative controller is used to realize the forward swimming motion of the robotic fish. In this way, the preparation of the bionic fish is completed. During the process of the controller controlling the robotic fish, the controller will fully consider the longitudinal inclination angle of the robotic fish and at the same time achieve precise control of the robotic fish through the proportional-integral-derivative control method. The controller described in this embodiment is the control board inside the robotic fish.

[0049] The above is only the preferred embodiment of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A preparation method of a bionic robotic fish based on a combined approximation model, characterized in that: It includes the following steps: Step 1: Plan the overall optimization design task of the multi-joint bionic robotic fish. According to the principle of multidisciplinary design optimization, divide the multi-joint bionic robotic fish into four subsystems: hydrodynamic shape, general arrangement, propulsion, and energy. Step 2: Analyze and model the four subsystems divided in Step 1: the hydrodynamic shape, general arrangement, propulsion, and energy subsystems respectively. Clearly define the design variables and constraint conditions of each subsystem, further analyze the uncertainty of the design variables in the subsystem, and determine the probabilistic constraint conditions. Step 3: Conduct experiments on the analysis model of the hydrodynamic shape subsystem in ISIGHT, establish an input-output data set, and construct a combined approximation model of the hydrodynamic shape analysis model to replace the CFD numerical simulation. Step 4: Use the parallel subspace design method to handle the coupling relationship between disciplines or subsystems. Replace the coupled state variables through the combined approximation model, and construct a multi-objective and multidisciplinary reliability optimization framework of the multi-joint bionic robotic fish for the four subsystems of hydrodynamic shape, general arrangement, propulsion, and energy in MATLAB. Analyze the probabilistic constraints in the optimization problem through the first-order reliability method, calculate the corresponding failure probability, and finally use the multi-objective particle swarm optimization algorithm to perform the optimization. Step 5: Input the design variables obtained from the optimized hydrodynamic shape subsystem in Step 4 into a 3D printer. Use the 3D printer to manufacture the rigid shell and stern of the robotic fish, then install the main control board, energy, and propulsion motor. Then select a controller that communicates with the main control board, and based on the optimized design variables obtained from the general arrangement and static balance subsystems, calculate the magnitude of the longitudinal inclination angle of the robotic fish and input it into the controller. Then, based on the optimized design variables in the energy subsystem, analyze the maximum torque required to be provided by the servo, select the required servo model and install it into the shell. Finally, taking the motion optimization design variables obtained from the propulsion subsystem as the control target, adjust the output mode of the controller signal to use the proportional-integral-derivative controller to achieve the forward movement of the robotic fish and complete the preparation of the robotic fish.

2. The preparation method of the bionic robotic fish based on the combined approximation model according to claim 1, characterized in that: The modeling method for the hydrodynamic shape subsystem in Step 2 is as follows: Use UG software to perform parametric modeling on the shape of the bionic robotic fish and automatically output the corresponding Parasolid geometric model file. Then, use ICEM software to read the generated Parasolid file, establish the flow field calculation domain, perform mesh division on the calculation domain, and output the corresponding Mesh file. Finally, use Fluent software to read the generated Mesh file and perform flow field calculation and analysis to output the fluid resistance value.

3. The preparation method of the bionic robotic fish based on the combined approximation model according to claim 1 or 2, characterized in that: The analysis and modeling method of the general layout subsystem in the second step is as follows: Use MATLAB to model the general layout analysis, calculate the mass of each part of the bionic robot fish, and analyze and calculate the weight of the bionic robot fish and the buoyancy in water, with the buoyancy-gravity balance and the longitudinal inclination angle θ of the bionic robot fish t as the constraint conditions.

4. The preparation method of the bionic robotic fish based on the combined approximation model according to claim 1 or 2, characterized in that: The analysis and modeling method for the propulsion subsystem in Step 2 is as follows: Simplify the multi-joint bionic robotic fish into a two-dimensional planar multi-link mechanism, use the Newton-Euler method to establish the dynamic model of the bionic robotic fish, take the motion parameters of each joint and the length of each link as design variables, and select the link length as a random variable to analyze the motion law of the bionic robotic fish, and take the maximization of the forward speed of the obtained bionic robotic fish as the optimization target for output.

5. The preparation method of the bionic robotic fish based on the combined approximation model according to claim 1 or 2, characterized in that: The specific steps for testing the hydrodynamic shape subsystem analysis model in ISIGHT in Step 3 are as follows: Step 3-1: In ISIGHT, use the optimal Latin hypercube experimental design method to give a set of design points for the design variables in the hydrodynamic subsystem; Step 3-2: Through the set of design points given in Step 3-1, perform hydrodynamic analysis, collect the corresponding fluid resistance output values, and establish a data set of input-output values; Step 3-3: Use the optimal weighted combination approximation model as the approximation model of the hydrodynamic shape subsystem analysis model; Step 3-4: Verify the accuracy of the optimal weighted combination approximation model. Divide the collected input-output data set into a sample set and a validation set. Initialize the optimal weighted combination approximation model with the sample set, and then use the validation set to verify the accuracy of the established optimal weighted combination approximation model. Judge whether the accuracy meets the requirements. If the accuracy meets the requirements, it can be used to replace the hydrodynamic shape analysis model. If the accuracy does not meet the requirements, change the number of independent approximation models and retrain the optimal weighted combination approximation model until the accuracy requirements are met.

6. The preparation method of the bionic robotic fish based on the combined approximation model according to claim 1 or 2, characterized in that: The specific method for using the parallel subspace design method to handle the coupling relationship between disciplines or subsystems in Step 4 is as follows: Establish a multi-objective and multi-disciplinary reliability design optimization framework of a multi-joint bionic robotic fish based on four subsystems: hydrodynamic shape, general arrangement, propulsion, and energy in MATLAB. Select the lengths of three connecting rods of the robotic fish as random variables, analyze the probability constraints in the optimization problem through the first-order reliability method, calculate the corresponding failure probability, and finally use the multi-objective particle swarm optimization algorithm as the optimizer to drive the entire optimization process to obtain the final non-dominated solution set.

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