Biomimetic robotic fish preparation method based on combined approximation model

Abstract

The application discloses a kind of based on combination approximation model's bionic robot fish preparation method.This method combines combination approximation model with multi-objective particle swarm optimization algorithm and parallel subspace design method, fully considers the coupling between four subsystems of the hydrodynamic shape of multi-joint bionic robot fish, total layout, propulsion, energy, realizes the decoupling between subsystems by replacing coupling state variables with combination approximation model, so that each subsystem analysis can be executed in parallel, shorten the design cycle, evaluate the probability constraint through the one reliability analysis method, finally utilize multi-objective particle swarm optimization algorithm to execute optimization, obtain the final non-dominated solution set, realize the multi-objective multidisciplinary reliability optimization design of robot fish.The method can be used for the integrated conceptual design problem of multi-joint bionic robot fish system, and opens up a new way of thinking and method for the conceptual design of bionic robot fish.

Description

Technical Field

[0001] This invention relates to a manufacturing method, and more specifically to a method for preparing a biomimetic robotic fish based on a combined approximation model. Background Technology

[0002] Multi-jointed bionic robotic fish have higher maneuverability and stealth. In addition, traditional underwater vehicles often use propeller propulsion, which has low propulsion efficiency, only about 40%-50%. However, the movement of bionic robotic fish simulates the propulsion mode of fish, which has higher propulsion efficiency. Therefore, compared with the propeller method, multi-jointed bionic robotic fish can work underwater for a longer time.

[0003] Because the shape, overall layout, propulsion, and energy of a multi-jointed bionic robotic fish are integrated into a single system, they need to be considered holistically during the manufacturing process. However, current technologies for manufacturing robotic fish do not adequately consider these aspects. Consequently, the traditional spiral-like serial design method used in existing technologies for manufacturing bionic robotic fish has significant drawbacks, such as low efficiency, high design costs, and long design cycles. Moreover, it is difficult to obtain an overall optimal design solution. For example, the invention patent with patent number 202311796973.6, entitled "A Deep-Sea Cage Intelligent Cleaning Robotic Fish and Its Manufacturing Method," discloses a method for manufacturing a robotic fish. However, this method only aims to achieve the minimum shape resistance and minimal disturbance to the aquatic environment in the model's geometry, exploring an energy-efficient and high-performance bionic robotic fish shape. It does not fully consider the design of the entire robotic fish system, thus resulting in low efficiency, high design costs, long design cycles, and difficulty in obtaining an overall optimal design solution. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method for manufacturing multi-joint bionic robotic fish based on a combined approximation model, which overcomes the problems of traditional serial optimization design methods, fully considers the coupling effect between multiple disciplines or subsystems in the design of complex engineering systems, and improves the efficiency of optimization design of multi-joint bionic robotic fish.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for preparing a biomimetic robotic fish based on a combined approximation model, characterized by comprising the following steps:

[0006] Step 1: Plan the overall optimization design task of the multi-joint bionic robotic fish. Based on the multidisciplinary design optimization principle, the multi-joint bionic robotic fish is divided into four subsystems: hydrodynamic shape, overall layout, propulsion and energy.

[0007] Step 2: Analyze and model the four subsystems defined in Step 1: hydrodynamic shape, general layout, propulsion, and energy. Clarify the design variables and constraints of each subsystem, further analyze the uncertainties of the design variables in the subsystems, and determine the probabilistic constraints.

[0008] Step 3: Experiment with the hydrodynamic shape subsystem analysis model in ISIGHT, establish an input-output dataset, and construct a combined approximate model of the hydrodynamic shape analysis model to replace CFD numerical simulation.

[0009] Step four: Use the parallel subspace design method to handle the coupling relationship between disciplines or subsystems, replace the coupling state variables by combining approximate modules, and build a multi-objective and multi-disciplinary reliability optimization framework for a multi-joint biomimetic robotic fish with four subsystems: hydrodynamic shape, general arrangement, propulsion and energy in MATLAB. Analyze the probabilistic constraints in the optimization problem using the first 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 hydrodynamic shape subsystem optimized in Step 4 into the 3D printer. Use the 3D printer to create the rigid shell and stern of the robotic fish. Then install the main control board, power supply, and propulsion motor. Next, select a controller that communicates with the main control board. Based on the optimized design variables obtained from the overall layout and static balance subsystem, calculate the pitch angle of the robotic fish and input it into the controller. Then, based on the optimized design variables in the power supply subsystem, analyze the maximum torque required by the servo motor, select the required servo motor model, and install it into the shell. Finally, using the motion optimized 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 realize the forward movement of the robotic fish, thus completing the preparation of the robotic fish.

[0011] As a further improvement of the present invention, the modeling method of the hydrodynamic shape subsystem in step two is as follows: the shape of the biomimetic robotic fish is parametrically modeled using UG software, and the corresponding Parasolid geometric model file is automatically output. Then, the generated Parasolid file is read by ICEM software, a flow field calculation domain is established, the calculation domain is meshed, and the corresponding Mesh file is output. Finally, the generated Mesh file is read by Fluent software, and the flow field is calculated and analyzed to output the fluid resistance value.

[0012] As a further improvement of the present invention, the analysis and modeling method of the overall layout subsystem in step two is as follows: The overall layout analysis is modeled using MATLAB, the mass of each part of the bionic robotic fish is calculated, and the weight and buoyancy of the bionic robotic fish in the water are analyzed and calculated to balance buoyancy and gravity, and the pitch angle θ of the bionic robotic fish is considered.t As a constraint.

[0013] As a further improvement of the present invention, the analysis and modeling method of the propulsion subsystem in step two 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 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. The motion law of the bionic robotic fish is analyzed, and the optimization objective of maximizing the forward speed of the bionic robotic fish is taken as the output.

[0014] As a further improvement to the present invention, the specific steps for conducting experiments on the hydrodynamic shape subsystem analysis model in ISIGHT in step three are as follows:

[0015] Step 31: 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;

[0016] Step 32: Using the set of design points given in Step 31, perform hydrodynamic analysis, collect the corresponding fluid resistance output values, and establish an input-output value dataset;

[0017] Step 33: The optimal weighted combination approximation model is adopted as the approximation model for the hydrodynamic shape subsystem analysis model;

[0018] Steps three and four involve verifying the accuracy of the optimal weighted combination approximation model. The collected input-output dataset is divided into a sample set and a validation set. The optimal weighted combination approximation model is initialized using the sample set, and then the accuracy is verified using the validation set. The accuracy is then assessed to determine if it meets the requirements. If it does, it can be used to replace the hydrodynamic shape analysis model. If not, the number of independent approximation models is changed, and the optimal weighted combination approximation model is retrained until the accuracy requirements are met. As a further improvement of this invention, the specific method for handling the coupling relationship between disciplines or subsystems using the parallel subspace design method in step four is as follows: A multi-objective, multi-disciplinary reliability design optimization framework for a multi-joint biomimetic robotic fish based on four subsystems—hydrodynamic shape, overall arrangement, propulsion, and energy—is established in MATLAB. The lengths of the three connecting rods of the robotic fish are selected as random variables. The probabilistic constraints in the optimization problem are analyzed using a first-order reliability method, and the corresponding failure probabilities are calculated. Finally, a multi-objective particle swarm optimization algorithm is used as the optimizer to drive the entire optimization process, resulting in the final non-dominated solution set.

[0019] The beneficial effects of this invention are as follows: The method employs a parallel subspace design approach, using an optimal weighted combination approximation model to handle the coupling relationships between disciplines or subsystems, achieving decoupling between them. This allows for independent analysis of each subsystem, enabling parallel computation and significantly reducing the design cost of the biomimetic robotic fish, shortening the overall design cycle. Furthermore, it fully considers the coupling relationships between the various subsystems within the robotic fish, improving design quality and overcoming the shortcomings of traditional serial design methods that neglect inter-subsystem coupling. By introducing the optimal weighted combination approximation model, the computational load for overall system optimization is reduced while maintaining analytical accuracy. Additionally, a first-order reliability method is used to analyze the probabilistic constraints in the optimization problem and calculate the corresponding failure probabilities, ensuring the reliability of the design scheme. Based on this, a multi-objective particle swarm optimization algorithm is introduced to achieve multi-objective, multi-disciplinary reliability design optimization for multi-jointed biomimetic robotic fish. This method can be applied to the integrated conceptual design problems of hydrodynamic shape, overall layout, propulsion, and energy for multi-jointed biomimetic robotic fish, opening up new avenues for conceptual design and possessing significant reference value. Attached Figure Description

[0020] Figure 1 A flowchart illustrating the multi-objective, multi-disciplinary reliability design optimization of the multi-joint biomimetic robotic fish according to an embodiment of the present invention;

[0021] Figure 2 This is a flowchart illustrating the modeling process of the hydrodynamic shape subsystem analysis model according to an embodiment of the present invention.

[0022] Figure 3 This is a geometric schematic diagram of a multi-jointed biomimetic robotic fish according to an embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram of the various parts of the multi-jointed bionic robotic fish according to an embodiment of the present invention.

[0024] Figure 5 This is a simplified diagram of the multi-joint biomimetic robotic fish in a two-dimensional plane according to an embodiment of the present invention.

[0025] Figure 6 A flowchart illustrating the establishment of a combined approximation model for embodiments of the present invention;

[0026] Figure 7 This is a flowchart of the DOE (Direction of Effect) hydrodynamic analysis performed by ISIGHT according to an embodiment of the present invention;

[0027] Figure 8 This is a diagram showing the coupling relationships between the subsystems of the multi-joint biomimetic robotic fish according to an embodiment of the present invention.

[0028] Figure 9This is a multi-objective, multi-disciplinary reliability design optimization framework diagram for embodiments of the present invention. Detailed Implementation

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

[0030] like Figure 1 As shown, this invention discloses a method for preparing a biomimetic robotic fish based on a combined approximation model. The method specifically includes the following steps:

[0031] Step 1:

[0032] The optimization design requirements for the multi-jointed bionic robotic fish are clearly defined. Based on multidisciplinary design optimization principles, the multi-jointed bionic robotic fish is decomposed into four subsystems: hydrodynamic shape, overall layout, propulsion, and energy. The overall optimization design objectives are also clearly defined. Specifically, the hydrodynamic shape subsystem mainly analyzes the fluid resistance experienced by the robotic fish and designs a bionic robotic fish shape with minimal fluid resistance; the overall layout subsystem mainly analyzes the buoyancy-gravity balance of the bionic robotic fish and calculates its pitch angle; the propulsion subsystem mainly analyzes its forward speed using a dynamic model of the bionic robotic fish, with maximizing forward speed as the optimization objective; and the energy subsystem mainly analyzes the endurance of the bionic robotic fish, with maximizing endurance as the optimization objective.

[0033] Step Two:

[0034] The analysis and modeling of four subsystems—hydrodynamics, general layout, propulsion, and energy—determines the design variables and constraints required for each subsystem. Further analysis of the uncertainties in the design variables within the subsystems is conducted to determine probabilistic constraints.

[0035] 1): Analysis and modeling of the hydrodynamic shape subsystem. The modeling process for the hydrodynamic shape subsystem analysis is as follows: Figure 2 As shown, the first step is parametric modeling of the geometric shape of the biomimetic robotic fish, using parameters affecting its shape as design variables. The shape of the robotic fish is as follows: Figure 3 As shown, where qf1, qf2, qa1, qa2 and L pShape parameters were selected as design variables for the hydrodynamic subsystem. An executable program obtained through secondary development of UG was used to parametrically model the shape of the biomimetic robotic fish. When the input design variable parameters changed, the geometric model of the robotic fish also changed simultaneously, outputting a corresponding Parasolid geometric model file. Then, ICEM read the generated Parasolid geometric model file, established the computational domain for the entire flow field, meshed the computational domain, and output the final Mesh file. Finally, Fluent. read the Mesh file and performed flow field calculations and analysis to solve for the fluid resistance value experienced by the robotic fish. The optimized fluid resistance value of the robotic fish was 11.973 N, with the corresponding optimized design variable values ​​being qf1 = 2, qf2 = 0, qa1 = 3.6777, qa2 = 20, and L... p =25.3912cm.

[0036] 2): Analysis and modeling of the overall layout subsystem, including the various parts of the biomimetic robotic fish, such as... Figure 4As shown, a long, rigid shell (1) is provided, with a pectoral fin (10) on the side of the rigid shell (1) and a stern (16) and a tail fin (17) on the tail of the rigid shell (1). A control board (9) is arranged and installed in sequence from head to tail inside the rigid shell (1). A battery (8) is placed below the control board (9) together with the control board (9). Next are a connecting plate (2), a first servo motor (3), a second servo motor (4), a third servo motor (5), and a joint link (6). The first servo motor (3), the second servo motor (4), and the third servo motor (5) are connected by the joint link (6) to drive the tail fin (17). Multiple parallel ring ribs (7) are coaxially sleeved on the joint link (6). A corresponding counterweight (11), counterweight (12), and counterweight (13) are set below each servo motor. Three motor bases (14) are set on the joint link (6) for corresponding installation of the servo motors. Finally, the ring ribs (7) are covered with... The cover (15) serves as a seal and camouflage. Therefore, based on the above components, the rigid shell (1), pectoral fin (10), stern (16), tail fin (17), and ring ribs (7) constitute the hydrodynamic shape subsystem. The counterweights (11), (12), and (13) and the motor base (14) constitute the overall layout subsystem. The three servo motors, connecting plate (2), and joint linkage (6) constitute the propulsion subsystem. The battery (8) and control board (9) constitute the energy subsystem. During operation, the robot fish is powered by the battery (8) to the control board (9). The control board (9) controls the operation of the three servo motors to achieve the twisting of the robot fish body formed by the ring ribs (7), thereby achieving the swimming effect. Through the action of the three counterweights, the robot fish can maintain the calculated pitch angle for forward movement. Finally, the overall layout subsystem is mathematically modeled using MATLAB software. Let the output axis of the servo motor i (i = 1, 2, 3) be the i-th joint of the robot fish. i Using the link length l1 between joints o1 and o2, the link length l2 between joints o2 and o3, and the length l3 from joint o3 to the tail as design variables, the mass of each part of the biomimetic robotic fish was analyzed, and the weight and buoyancy of the biomimetic robotic fish in water were calculated. The balance between buoyancy and gravity, and the pitch angle of the biomimetic robotic fish were used as constraints for this subsystem. The final optimized weight of the robotic fish was 3.5743 kg, with a corresponding pitch angle of 1.7639 degrees. The corresponding optimized design variable values ​​were l1 = 11.3912 cm, l2 = 7 cm, and l3 = 5 cm. 3): Analysis and modeling of the propulsion subsystem, such as... Figure 5 As shown, the entire multi-joint bionic robotic fish is simplified into a multi-stage linkage mechanism in a two-dimensional plane. Then, the dynamic model of the multi-joint bionic robotic fish is established using the Newton-Euler method. The motion of each joint is defined as follows:

[0037]

[0038] In the formula, θ i Let A represent the motion angle of the i-th joint. i and ω i Let represent the amplitude and angular frequency of the i-th joint motion, respectively (the amplitude and angular frequency of all joints remain consistent). This represents the phase of joint motion. It is expressed using the motion parameter A for each joint. i ,ω i and And the length L of each link i As design variables, the lengths of the connecting rods between joints o1 and o2 (l1), o2 and o3 (l2), and o3 to the tail (l3) were selected as random variables. The motion law of the bionic robotic fish was analyzed using formula (1), and the forward speed of the bionic robotic fish was calculated. Finally, the forward swimming speed of the robotic fish optimized by the proposed method was 0.6189 m / s. The final optimized design variable values ​​were w = 3.8616 rad / s, A = 0.5236 rad,

[0039] 4) Energy Subsystem Analysis and Modeling: A lithium battery is used as the power source to provide the required energy for the robotic fish. The energy subsystem is mathematically modeled using MATLAB software. The motion parameters of each joint and the length of the connecting rod are used as design variables. By analyzing the motion law of each joint and the load of each joint, the maximum torque of the servo motor is estimated, and the power of the servo motor is estimated. Thus, the total power of the robotic fish is analyzed, and the endurance of the robotic fish is calculated. With the maximum torque as a constraint, the final optimized total endurance of the robotic fish is 3.7415 hours, and the corresponding maximum torque is 0.5462 N·m.

[0040] Step 3:

[0041] A combined approximate model for the hydrodynamic shape subsystem analysis model is established, such as... Figure 6

[0042] 1): Using the optimal Latin hypercube experimental design method in ISIGHT software, design points for design variables in a set of hydrodynamic shape subsystem analysis models are given.

[0043] 2): For example Figure 6 As shown, the process of performing hydrodynamic shape subsystem analysis in ISIGHT is described, the corresponding fluid resistance output values ​​are collected, and an input-output value dataset is established.

[0044] 3): For example Figure 7 As shown, an optimal weighted combination approximation model is established and used as an approximation model for the hydrodynamic shape subsystem analysis model.

[0045] 4): To verify the accuracy of the optimal weighted combination approximation model, the collected input-output dataset is divided into a sample set and a validation set. The sample set is used to construct the initial optimal weighted combination approximation model, and the validation set is used to test the accuracy of the established optimal weighted combination approximation model. This model is then used to replace the analysis model of the hydrodynamic shape subsystem.

[0046] 5): If the accuracy of the optimal weighted combination approximation model is insufficient, the number of independent approximation models needs to be changed, and the optimal weighted combination approximation model needs to be retrained until the accuracy requirements are met.

[0047] Step Four:

[0048] Analyze the coupling relationships between subsystems, such as Figure 8 As shown, where l i (i = 1, 2, 3) are shared design variables among all subsystem analysis models, ω and A are shared design variables between the propulsion subsystem and the energy subsystem, and qf1, qf2, qa1, qa2 are local design variables of the hydrodynamic shape subsystem. To advance the local design variables of the subsystem, F drag ,v e H en There are three optimization objectives. The coupling relationships between the analysis models of each subsystem exhibit both sequential and contradictory characteristics. 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 related to all other subsystems, and its output state variable is the input variable of the analysis models of the other three subsystems. The overall arrangement and static equilibrium subsystem has a sequential coupling relationship with the hydrodynamic shape subsystem and the propulsion subsystem. The output state variable V of the hydrodynamic shape subsystem... wf V wa ,x fb ,x ab is the input variable of the overall arrangement and static balance subsystem, while m is the output state variable of the overall arrangement and static balance subsystem. fh ,m3,x fhg ,x m3g These are the input variables of the propulsion subsystem. The energy or subsystem is related to both the hydrodynamic shape subsystem and the overall arrangement and static balance subsystem, respectively. The output state variable V of the hydrodynamic shape subsystem... wf V wa ,S a The output state variable m of the overall layout and static balance subsystem fh ,m3,x m3gThese are the input variables for the energy subsystem analysis model. The relationship between the propulsion subsystem analysis model and the energy subsystem analysis model is contradictory; the optimization objective v of these two subsystem analysis models... e and H en The opposite trend is observed: as the robotic fish's forward speed increases, its overall range decreases accordingly. To address the coupling relationships between subsystems, a parallel subspace design method is used. By combining approximate modules to replace coupled state variables, decoupling is achieved between the hydrodynamic shape subsystem and the overall layout subsystem, as well as between the propulsion and energy subsystems. Simultaneously, decoupling is achieved between the overall layout and the propulsion subsystem. This allows for independent analysis of each subsystem, enabling parallel computation. Then, as... Figure 9 As shown, a multi-objective, multi-disciplinary reliability optimization design framework for a multi-joint biomimetic robotic fish based on four subsystems—hydrodynamic shape, overall arrangement, propulsion, and energy—is built in MATLAB. The probabilistic constraints in the optimization problem are analyzed using a single reliability method to calculate the corresponding failure probabilities. A multi-objective particle swarm optimization algorithm is used as the optimizer to drive the entire optimization process, thereby obtaining the final non-dominated solution set and achieving multi-objective, multi-disciplinary reliability design optimization for the multi-joint biomimetic robotic fish. After optimization, based on the optimized design variables in the hydrodynamic shape subsystem, a rigid shell and stern of the robotic fish are fabricated using polylactide material and fused deposition modeling (FDM) 3D printing technology. Then, based on the optimized design variables obtained from the overall arrangement and static equilibrium subsystems, the pitch angle of the robotic fish is calculated and verified against the constraints. Furthermore, based on the optimized design variables in the energy subsystem, the maximum torque required by the servo motors is analyzed, the required servo motor model is selected, and the selected servo motor is installed into the rigid shell. Finally, using the motion optimization design variables obtained from the propulsion subsystem as the control target, a proportional-integral-derivative controller is used on the Arduino UNO R3 control board to realize the forward swimming motion of the robotic fish, thus completing the preparation of the biomimetic fish. During the process of the controller controlling the robotic fish, the controller will fully consider the pitch angle of the robotic fish, and at the same time, it will achieve precise control of the robotic fish through the proportional-integral-derivative control method. The controller mentioned in this embodiment is the control board inside the robotic fish.

[0049] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for preparing a biomimetic robotic fish based on a combined approximation model, characterized in that: Includes the following steps: Step 1: Plan the overall optimization design task of the multi-joint bionic robotic fish. Based on the multidisciplinary design optimization principle, the multi-joint bionic robotic fish is divided into four subsystems: hydrodynamic shape, overall layout, propulsion and energy. Step 2: Analyze and model the four subsystems defined in Step 1: hydrodynamic shape, general layout, propulsion, and energy. Clarify the design variables and constraints of each subsystem, further analyze the uncertainties of the design variables in the subsystems, and determine the probabilistic constraints. Step 3: Experiment with the hydrodynamic shape subsystem analysis model in ISIGHT, establish an input-output dataset, and construct a combined approximate model of the hydrodynamic shape analysis model to replace CFD numerical simulation. Step four: Use the parallel subspace design method to handle the coupling relationship between disciplines or subsystems, replace the coupling state variables by combining approximate modules, and build a multi-objective and multi-disciplinary reliability optimization framework for a multi-joint biomimetic robotic fish with four subsystems: hydrodynamic shape, general arrangement, propulsion and energy in MATLAB. Analyze the probabilistic constraints in the optimization problem using the first 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 hydrodynamic shape subsystem optimized in Step 4 into the 3D printer. Use the 3D printer to create the rigid shell and stern of the robotic fish. Then install the main control board, power supply, and propulsion motor. Next, select a controller that communicates with the main control board. Based on the optimized design variables obtained from the overall layout and static balance subsystem, calculate the pitch angle of the robotic fish and input it into the controller. Then, based on the optimized design variables in the power supply subsystem, analyze the maximum torque required by the servo motor, select the required servo motor model, and install it into the shell. Finally, using the motion optimized 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 realize the forward movement of the robotic fish, thus completing the preparation of the robotic fish.

2. The method for preparing a biomimetic robotic fish based on a combined approximation model according to claim 1, characterized in that: The modeling method for the hydrodynamic shape subsystem in step two is as follows: The shape of the biomimetic robotic fish is parametrically modeled using UG software, and the corresponding Parasolid geometric model file is automatically output. Then, the generated Parasolid file is read by ICEM software, a flow field calculation domain is established, the calculation domain is meshed, and the corresponding Mesh file is output. Finally, the generated Mesh file is read by Fluent software, and the flow field is calculated and analyzed to output the fluid resistance value.

3. The method for preparing a biomimetic robotic fish based on a combined approximation model according to claim 1 or 2, characterized in that: The analysis and modeling method for the overall layout subsystem in step two is as follows: The overall layout analysis is modeled using MATLAB, the mass of each part of the bionic robotic fish is calculated, and the weight and buoyancy of the bionic robotic fish in the water are analyzed and calculated to balance buoyancy and gravity, and the pitch angle θ of the bionic robotic fish is considered. t As a constraint.

4. The method for preparing a biomimetic robotic fish based on a combined approximation model according to claim 1 or 2, characterized in that: The analysis and modeling method of the propulsion subsystem in step two is as follows: the multi-joint bionic robotic fish is simplified into a two-dimensional planar multi-link mechanism. The dynamic model of the bionic robotic fish is established 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. The motion law of the bionic robotic fish is analyzed, and the optimization objective of maximizing the forward speed of the bionic robotic fish is taken as the output.

5. The method for preparing a biomimetic robotic fish based on a combined approximation model according to claim 1 or 2, characterized in that: The specific steps for conducting experiments on the hydrodynamic shape subsystem analysis model in ISIGHT in step three are as follows: Step 31: 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 32: Using the set of design points given in Step 31, perform hydrodynamic analysis, collect the corresponding fluid resistance output values, and establish an input-output value dataset; Step 33: The optimal weighted combination approximation model is adopted as the approximation model for the hydrodynamic shape subsystem analysis model; Steps three and four involve verifying the accuracy of the optimal weighted combination approximation model. The collected input-output dataset is divided into a sample set and a validation set. The optimal weighted combination approximation model is initialized using the sample set, and then the accuracy of the established optimal weighted combination approximation model is verified using the validation set. It is then determined 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, the number of independent approximation models is changed, and the optimal weighted combination approximation model is retrained until the accuracy requirements are met.

6. The method for preparing a biomimetic robotic fish based on a combined approximation model according to claim 1 or 2, characterized in that: The specific method for handling the coupling relationship between disciplines or subsystems using the parallel subspace design method in step four is as follows: In MATLAB, a multi-objective, multi-disciplinary reliability design optimization framework for a multi-joint biomimetic robotic fish based on four subsystems—hydrodynamic shape, overall arrangement, propulsion, and energy—is established. The lengths of the three connecting rods of the robotic fish are selected as random variables. The probabilistic constraints in the optimization problem are analyzed using a first-order reliability method to calculate the corresponding failure probability. Finally, a multi-objective particle swarm optimization algorithm is used as the optimizer to drive the entire optimization process, resulting in the final non-dominated solution set.

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