Hydro-muscle model simulation method and device, electronic equipment and storage medium

Abstract

The application provides a liquid muscle model simulation method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining detection parameters of a liquid muscle model to be simulated, wherein the material of the liquid muscle model comprises rubber, and the detection parameters at least comprise a nonlinear damping coefficient and a stiffness coefficient of the rubber; inputting the detection parameters into a physical engine based on multi-body dynamics to obtain an initial liquid muscle simulation model of the liquid muscle model, wherein the initial liquid muscle simulation model comprises N tendons connected to the same joint, and N is an integer greater than 1; in the initial liquid muscle simulation model, a muscle driver is configured for each simulation tendon respectively to obtain a liquid muscle simulation model, wherein the muscle driver is used to drive the simulation tendon to move. The application can realize liquid muscle simulation modeling based on multi-body dynamics, and can reduce modeling complexity and improve simulation accuracy.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and in particular to a method, device, electronic device and storage medium for simulating a hydraulic muscle model. Background Technology

[0002] Current robot simulations mostly employ traditional motor-driven models or simplified muscle models based on linear springs. However, traditional motor models cannot simulate the force-velocity relationship of biological muscles, and existing hydraulic muscle simulations rely on specialized fluid dynamics software such as Fluent, which has high computational complexity and is generally used for computational fluid dynamics rather than multibody dynamics, resulting in low accuracy in biological muscle simulations.

[0003] Therefore, there is an urgent need for a method to establish a hydrodynamic muscle model under multibody dynamics to solve the above problems. Summary of the Invention

[0004] In view of this, this application provides a method, device, electronic device and storage medium for simulating a hydraulic muscle model, which can realize the simulation modeling of hydraulic muscles based on multibody dynamics, and can reduce the modeling complexity and achieve high simulation accuracy.

[0005] A first aspect of this application provides a method for simulating a hydraulic muscle model, comprising: acquiring detection parameters of a hydraulic muscle model to be simulated, wherein the material of the target hydraulic muscle model includes rubber, and the detection parameters include at least the nonlinear damping coefficient and stiffness coefficient of the rubber; inputting the detection parameters into a physics engine based on multibody dynamics to obtain an initial hydraulic muscle simulation model of the hydraulic muscle model, wherein the initial hydraulic muscle simulation model includes N simulated tendons connected to the same joint, where N is an integer greater than 1; configuring a muscle actuator for each simulated tendon in the initial hydraulic muscle simulation model to obtain a hydraulic muscle simulation model, wherein the muscle actuator is used to drive the simulated tendon to move.

[0006] In one possible implementation, the detection parameters further include a first time required for the muscle to transition from relaxation to maximum contraction, a second time required for the muscle to recover from a contracted state to a relaxed state, and the maximum contractile force of the muscle; before inputting the detection parameters into a multibody dynamics-based physics engine, the method further includes: establishing a multibody dynamics behavior feature expression for the simulated tendon based on the first time, the second time, and the maximum contractile force of the muscle; inputting the detection parameters into the multibody dynamics-based physics engine to obtain the initial hydrodynamic muscle simulation model includes: inputting the multibody dynamics behavior feature expression and the detection parameters into the physics engine to obtain the initial hydrodynamic muscle simulation model.

[0007] In one possible implementation, the multibody dynamics behavior characteristic expression includes a first characteristic expression of the simulated tendon during the contraction phase and a second characteristic expression of the simulated tendon during the relaxation phase.

[0008] In one possible implementation, establishing the multibody dynamic behavior characteristic expression of the simulated tendon based on the first time, the second time, and the maximum contractile force of the muscle includes: setting the following formula as the first characteristic expression: F1(t)=force*activation(t)*(1-e^(-t1 / timeconst_contract)), where F1(t) is the contractile force of the simulated tendon during the contraction phase, force is the maximum contractile force of the muscle, t1 is the actual contraction time of the simulated tendon, and timeconst_contract is the first time; setting the following formula as the second characteristic expression: F2(t)=force*activation(t)*(1-e^(-t2 / timeconst_relax)), where F2(t) is the relaxation tension of the simulated tendon during the relaxation phase, force is the maximum contractile force of the muscle, t2 is the actual relaxation time of the simulated tendon, and timeconst_relax is the second time.

[0009] In one possible implementation, the method further includes: for each of the simulated tendons, deploying one end of the simulated tendon to a first target node and deploying the other end of the simulated tendon to a second target node; wherein the first target node and the second target node are located on different structures of the hydrodynamic muscle simulation model.

[0010] In one possible implementation, the physics engine is the MuJoCo physics engine.

[0011] In one possible implementation, the detection parameters further include: the length limit range of the simulated tendon, the visual muscle diameter of the simulated tendon, the length of the first tendon when the simulated tendon is fully contracted, and the length of the second tendon when the simulated tendon is fully relaxed.

[0012] Secondly, this application also provides a hydraulic muscle model simulation device, comprising: an acquisition module, an input module, and a configuration module; the acquisition module is used to acquire detection parameters of the hydraulic muscle model to be simulated, wherein the material of the target hydraulic muscle model includes rubber, and the detection parameters include at least the nonlinear damping coefficient and stiffness coefficient of the rubber; the input module is used to input the detection parameters into a physics engine based on multibody dynamics to obtain an initial hydraulic muscle simulation model of the hydraulic muscle model, wherein the initial hydraulic muscle simulation model includes N simulated tendons connected to the same joint, where N is an integer greater than 1; the configuration module is used to configure muscle actuators for each simulated tendon in the initial hydraulic muscle simulation model to obtain a hydraulic muscle simulation model, wherein the muscle actuators are used to drive the simulated tendons to move.

[0013] Thirdly, embodiments of this application also provide an electronic device, the electronic device including a processor and a memory, the memory being used to store instructions, and the processor being used to call the instructions in the memory, causing the electronic device to execute the hydraulic muscle model simulation method as described in the first aspect.

[0014] Fourthly, embodiments of this application also provide a storage medium that stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the hydraulic muscle model simulation method as described in the first aspect.

[0015] Compared with related technologies, the embodiments of this application have at least the following advantages: By obtaining the detection parameters of the hydraulic muscle model to be simulated, since the material of the hydraulic muscle model includes rubber, and the detection parameters include the nonlinear damping coefficient and stiffness coefficient of the rubber, and since rubber itself is a hyperelastic material, considering its anisotropic deformation would greatly increase the amount of computation and the computation speed. The nonlinear damping coefficient and stiffness coefficient are more suitable for dynamic simulation. Therefore, inputting the detection parameters into a physics engine based on multibody dynamics can improve the computational efficiency of the physics engine, thereby reducing the modeling complexity of the initial hydraulic muscle simulation model and enabling the initial hydraulic muscle simulation model to accurately simulate the effect of muscle contraction pulling the joint. By setting N simulated tendons connected to the same joint in the initial hydraulic muscle simulation model, and configuring a muscle actuator for each simulated tendon, multiple simulated tendons can drive the same joint in parallel, resulting in high simulation accuracy of the hydraulic muscle simulation model.

[0016] The technical effects achieved by the second, third, and fourth aspects mentioned above are similar to those achieved by the corresponding technical means in the first aspect, and will not be repeated here. Attached Figure Description

[0017] Figure 1This is a flowchart illustrating one step of a simulation method for a hydraulic muscle model provided in an embodiment of this application.

[0018] Figure 2 Another flowchart of the simulation method for a hydraulic muscle model provided in an embodiment of this application.

[0019] Figure 3 This is a functional block diagram of a hydraulic muscle model simulation device provided in an embodiment of this application.

[0020] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0022] The following description sets forth many specific details to provide a full understanding of this application. The described embodiments are only some, not all, of the embodiments of this application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0024] It should be further noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0025] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0026] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] For ease of understanding, exemplary descriptions of some concepts related to the embodiments of this application are provided for reference.

[0028] MuJoCo Physics Engine: A high-performance physics engine focused on robotics, biomechanics, and machine learning. Its core design integrates innovative technologies from dynamics simulation and contact mechanics. It pioneered the combination of generalized coordinates from robotics with modern contact dynamics optimization methods, solving soft contact constraints through convex optimization. This avoids the instability of traditional spring-damped models, significantly improving the accuracy and stability of simulations of complex joint systems. It supports tendon modeling (3D path constraints, entanglement simulation) and general actuator models (unified modeling of motors / hydraulics / biological muscles), meeting diverse physical interaction needs.

[0029] Muscle actuators: These are biomimetic actuators that mimic the contraction and relaxation mechanisms of biological muscles. They are mainly divided into two major directions: biological muscle drive principles and artificial muscle technology. The core goal is to provide robots, medical devices, and other devices with compliant and efficient power output that is close to that of biological muscles.

[0030] Hydraulic muscle: This is a soft actuator that uses hydraulic or pneumatic pressure to drive flexible materials, simulating the contraction of biological muscles. Its core principle is to use fluid pressure to deform an elastic structure and generate mechanical motion. It consists of an elastic inner liner (rubber / silicone) and a braided sleeve. After fluid is injected, the internal pressure increases, and the sleeve's constraint causes axial contraction and outputs tensile force. The deformation rate can reach 20%-30%.

[0031] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of an embodiment of the hydraulic muscle model simulation method of this application. Depending on different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0032] It should be noted that the hydraulic muscle model simulation method of this application embodiment can be applied to robot simulation scenarios, and its execution subject can be a hydraulic muscle model simulation device. For example, in a robot simulation scenario, the simulation of a robot can be achieved through a hydraulic muscle model simulation device. Of course, the hydraulic muscle model simulation method can also be applied to other scenarios that require robot simulation, and this application does not specifically limit it in this regard.

[0033] The specific process of this embodiment is as follows: Figure 1 As shown, it includes the following steps: S101, obtain the detection parameters of the hydraulic muscle model to be simulated, wherein the material of the hydraulic muscle model includes rubber, and the detection parameters include at least the nonlinear damping coefficient and stiffness coefficient of the rubber.

[0034] In some embodiments, a physical hydraulic muscle model is created using rubber, and then the hydraulic muscle model is tested to obtain test parameters.

[0035] S102, input the detection parameters into the physics engine based on multibody dynamics to obtain the initial hydraulic muscle simulation model of the hydraulic muscle model, wherein the initial hydraulic muscle simulation model includes N simulated tendons connected to the same joint.

[0036] In some embodiments, the physics engine is the MuJoCo physics engine. Inputting the detection parameters into the MuJoCo physics engine yields the initial hydrodynamic muscle simulation model.

[0037] In some embodiments, the detection parameters further include: the length limit range of the simulated tendon, the visualized muscle diameter of the simulated tendon, the length of the first tendon when the simulated tendon is fully contracted, and the length of the second tendon when the simulated tendon is fully relaxed.

[0038] It should be noted that the calculation of the length limit range, the length of the first tendon, and the length of the second tendon are based on the contraction rate of biological muscles. Here, the length limit range is a 1:1 reproduction of the length change during actual muscle contraction, which can further improve the simulation accuracy of the initial hydrodynamic muscle simulation model.

[0039] In some embodiments, N is an integer greater than 1. This embodiment does not impose specific limitations on the value of N, and it can be set according to actual needs.

[0040] In some embodiments, for each simulated tendon, one end of the simulated tendon is deployed on a first target node, and the other end of the simulated tendon is deployed on a second target node; wherein the first target node and the second target node are located on different structures of the hydrodynamic muscle simulation model.

[0041] It is worth noting that by deploying the two ends of the simulated muscle on different structures of the hydraulic muscle simulation model, the effect of muscle pulling across joints can be simulated, thereby further improving the simulation accuracy of the initial hydraulic muscle simulation model.

[0042] Specifically, in this embodiment, one endpoint of the simulated tendon is deployed to the first target node, and the other endpoint of the simulated tendon is deployed to the second target node. This can be achieved by creating the first target node under the first parent link code and creating the second target node under the second parent link code, which is different from the first.

[0043] S103, in the initial hydraulic muscle simulation model, muscle actuators are configured for each simulated tendon to obtain the hydraulic muscle simulation model.

[0044] Specifically, muscle actuators are used to drive simulated tendons to move. By setting up N simulated tendons connected to the same joint and configuring a muscle actuator for each simulated tendon, multiple simulated tendons can drive the same joint in parallel, which is more in line with the actual movement of biological muscles, thereby further improving the simulation accuracy of the hydraulic muscle simulation model.

[0045] Compared with related technologies, the embodiments of this application have at least the following advantages: By obtaining the detection parameters of the hydraulic muscle model to be simulated, since the material of the hydraulic muscle model includes rubber, and the detection parameters include the nonlinear damping coefficient and stiffness coefficient of the rubber, and since rubber itself is a hyperelastic material, considering its anisotropic deformation would greatly increase the amount of computation and the computation speed. The nonlinear damping coefficient and stiffness coefficient are more suitable for dynamic simulation. Therefore, inputting the detection parameters into a physics engine based on multibody dynamics can improve the computational efficiency of the physics engine, thereby reducing the modeling complexity of the initial hydraulic muscle simulation model and enabling the initial hydraulic muscle simulation model to accurately simulate the effect of muscle contraction pulling the joint. By setting N simulated tendons connected to the same joint in the initial hydraulic muscle simulation model, and configuring a muscle actuator for each simulated tendon, multiple simulated tendons can drive the same joint in parallel, resulting in high simulation accuracy of the hydraulic muscle simulation model.

[0046] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating one embodiment of the hydraulic muscle model simulation method of this application. Depending on different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted. This hydraulic muscle model simulation method can be applied to the aforementioned hydraulic muscle model simulation device, but is not limited thereto, and the embodiments of this application do not limit it in this regard.

[0047] This embodiment is a further improvement on the aforementioned embodiment. The main improvement lies in that: in this embodiment, the detection parameters also include the first time required for the muscle to go from relaxation to maximum contraction, the second time required for the muscle to recover from a contracted state to a relaxed state, and the maximum contractile force of the muscle. Furthermore, a multi-body dynamic behavior characteristic expression of the simulated tendon is input into the physics engine. This multi-body dynamic behavior characteristic expression is established based on the first time, the second time, and the maximum contractile force of the muscle. In this way, the simulation accuracy of the initial hydrodynamic muscle simulation model can be further improved.

[0048] The specific process of this embodiment is as follows: Figure 2 As shown, it includes the following steps: S201, Obtain the detection parameters of the hydraulic muscle model to be simulated, wherein the material of the hydraulic muscle model includes rubber, and the detection parameters include at least the nonlinear damping coefficient and stiffness coefficient of the rubber.

[0049] S202, establish the multibody dynamic behavior characteristic expression of the simulated tendon based on the first time, the second time, and the maximum muscle contraction force.

[0050] In some embodiments, the multibody dynamics behavior characteristic expression includes a first characteristic expression of the simulated tendon during the contraction phase and a second characteristic expression of the simulated tendon during the relaxation phase.

[0051] In some embodiments, the following formula is set as the first characteristic expression: F1(t) = force * activation(t) * (1 - e^(-t1 / timeconst_contract)), where F1(t) is the contractile force of the simulated tendon during the contraction phase, force is the maximum contractile force of the muscle, t1 is the actual contraction time of the simulated tendon, and timeconst_contract is the first time. Set the following formula as the second characteristic expression: F2(t) = force * activation(t) * (1 - e^(-t2 / timeconst_relax)), where F2(t) is the tension of the simulated tendon during the relaxation phase, force is the maximum contractile force of the muscle, t2 is the actual relaxation time of the simulated tendon, and timeconst_relax is the second time.

[0052] It is worth noting that by establishing the first and second characteristic expressions, the asymmetric response characteristics of the hydraulic muscle can be physically represented, with contraction being faster than relaxation, thus enabling the initial hydraulic muscle simulation model to better reproduce the real situation of the hydraulic muscle.

[0053] S203: Input the multibody dynamics behavior characteristic expression and detection parameters into the physics engine to obtain the initial hydrodynamic muscle simulation model.

[0054] S204. In the initial hydraulic muscle simulation model, muscle actuators are configured for each simulated tendon to obtain the hydraulic muscle simulation model.

[0055] S201 and S204 in this embodiment are similar to S101 and S103 in the previous embodiment. To avoid repetition, they will not be described again here.

[0056] Compared with related technologies, the embodiments of this application have at least the following advantages: By obtaining the detection parameters of the hydraulic muscle model to be simulated, since the material of the hydraulic muscle model includes rubber, and the detection parameters include the nonlinear damping coefficient and stiffness coefficient of the rubber, and since rubber itself is a hyperelastic material, considering its anisotropic deformation would greatly increase the amount of computation and the computation speed. The nonlinear damping coefficient and stiffness coefficient are more suitable for dynamic simulation. Therefore, inputting the detection parameters into a physics engine based on multibody dynamics can improve the computational efficiency of the physics engine, thereby reducing the modeling complexity of the initial hydraulic muscle simulation model and enabling the initial hydraulic muscle simulation model to accurately simulate the effect of muscle contraction pulling the joint. By setting N simulated tendons connected to the same joint in the initial hydraulic muscle simulation model, and configuring a muscle actuator for each simulated tendon, multiple simulated tendons can drive the same joint in parallel, resulting in high simulation accuracy of the hydraulic muscle simulation model.

[0057] Based on the same idea as the hydraulic muscle model simulation method in the above embodiments, this application also provides a hydraulic muscle model simulation device, which can be used to execute the above-described hydraulic muscle model simulation method. For ease of explanation, the structural schematic diagram of the hydraulic muscle model simulation device embodiment only shows the parts related to the embodiments of this application. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the device, and it may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0058] like Figure 3 As shown, the hydraulic muscle model simulation device 30 includes an acquisition module 301, an input module 302, and a configuration module 303. In some embodiments, the above modules can be programmable software instructions stored in memory and executable by a processor. It is understood that in other embodiments, the above modules can also be program instructions or firmware embedded in the processor.

[0059] The acquisition module 301 is used to acquire the detection parameters of the hydraulic muscle model to be simulated, wherein the material of the hydraulic muscle model includes rubber, and the detection parameters include at least the nonlinear damping coefficient and stiffness coefficient of the rubber. Input module 302 is used to input the detection parameters into a physics engine based on multibody dynamics to obtain an initial hydraulic muscle simulation model of the hydraulic muscle model, wherein the initial hydraulic muscle simulation model includes N simulated tendons connected to the same joint, where N is an integer greater than 1; The configuration module 303 is used to configure a muscle actuator for each of the simulated tendons in the initial hydraulic muscle simulation model to obtain a hydraulic muscle simulation model, wherein the muscle actuator is used to drive the simulated tendons to move.

[0060] The hydraulic muscle model simulation device 30 provided in the above embodiments can realize the technical solutions described in the above hydraulic muscle model simulation method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above hydraulic muscle model simulation method embodiments, and will not be repeated here.

[0061] Please refer to point 4. Figure 4 This is a schematic diagram of an embodiment of the electronic device of this application.

[0062] In some embodiments, processor 401 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 402 or process data, such as the hydraulic muscle model simulation method of the present invention.

[0063] In some embodiments, processor 401 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.

[0064] In some embodiments, memory 402 may be an internal storage unit of electronic device 400, such as a hard disk or memory of electronic device 400. In other embodiments, memory 402 may also be an external storage device of electronic device 400, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 400.

[0065] Furthermore, the memory 402 may include both internal storage units of the electronic device 400 and external storage devices. The memory 402 is used to store application software and various types of data installed on the electronic device 400.

[0066] In some embodiments, display 403 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 403 is used to display information from electronic device 400 and to display a visual user interface. Components 401-403 of electronic device 400 communicate with each other via a system bus.

[0067] In one embodiment, when the processor 401 executes the hydraulic muscle model simulation program in the memory 402, the following steps can be implemented: Obtain the detection parameters of the hydraulic muscle model to be simulated, wherein the material of the hydraulic muscle model includes rubber, and the detection parameters include at least the nonlinear damping coefficient and stiffness coefficient of the rubber. The detection parameters are input into a physics engine based on multibody dynamics to obtain an initial hydraulic muscle simulation model of the hydraulic muscle model, wherein the initial hydraulic muscle simulation model includes N simulated tendons connected to the same joint, where N is an integer greater than 1. In the initial hydrodynamic muscle simulation model, a muscle actuator is configured for each simulated tendon to obtain a hydrodynamic muscle simulation model, wherein the muscle actuator is used to drive the simulated tendon to move.

[0068] It should be understood that when the processor 401 executes the hydraulic muscle model simulation program in the memory 402, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0069] Furthermore, this embodiment of the invention does not specifically limit the type of electronic device 400 mentioned. Electronic device 400 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the invention, electronic device 400 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0070] Accordingly, this application also provides a storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions in the hydraulic muscle model simulation methods provided in the above-described method embodiments.

[0071] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0072] The above provides a detailed description of the hydraulic muscle model simulation method, device, electronic equipment, and storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A simulation method for a hydraulic muscle model, characterized in that, include: Obtain the detection parameters of the hydraulic muscle model to be simulated, wherein the material of the hydraulic muscle model includes rubber, and the detection parameters include at least the nonlinear damping coefficient and stiffness coefficient of the rubber. The detection parameters are input into a physics engine based on multibody dynamics to obtain an initial hydraulic muscle simulation model of the hydraulic muscle model, wherein the initial hydraulic muscle simulation model includes N simulated tendons connected to the same joint, where N is an integer greater than 1. In the initial hydrodynamic muscle simulation model, a muscle actuator is configured for each simulated tendon to obtain a hydrodynamic muscle simulation model, wherein the muscle actuator is used to drive the simulated tendon to move. The detection parameters also include the first time required for the muscle to go from relaxed to maximum contraction, the second time required for the muscle to recover from a contracted state to a relaxed state, and the maximum contractile force of the muscle. Before inputting the detection parameters into the multibody dynamics-based physics engine, the following steps are also included: Based on the first time, the second time, and the maximum contractile force of the muscle, a multi-body dynamic behavior characteristic expression of the simulated tendon is established. The multi-body dynamic behavior characteristic expression includes a first characteristic expression of the simulated tendon in the contraction phase and a second characteristic expression of the simulated tendon in the relaxation phase. The step of inputting the detection parameters into a multibody dynamics-based physics engine to obtain the initial hydrodynamic muscle simulation model of the hydrodynamic muscle model includes: The multibody dynamics behavior feature expression and the detection parameters are input into the physics engine to obtain the initial hydrodynamic muscle simulation model; The step of establishing the multibody dynamics characteristic expression of the simulated tendon based on the first time, the second time, and the maximum muscle contraction force includes: Set the following formula as the first characteristic expression: F1(t) = force × activation(t) × (1 - e^(-t1 / timeconst_contract)), where F1(t) is the contractile force of the simulated tendon during the contraction phase, force is the maximum contractile force of the muscle, t1 is the actual contraction time of the simulated tendon, and timeconst_contract is the first time. Set the following formula as the second characteristic expression: F2(t) = force × activation(t) × (1 - e^(-t2 / timeconst_relax)), where F2(t) is the relaxation tension of the simulated tendon during the relaxation phase, force is the maximum contractile force of the muscle, t2 is the actual relaxation time of the simulated tendon, and timeconst_relax is the second time.

2. The simulation method for a hydraulic muscle model according to claim 1, characterized in that, The method further includes: For each of the simulated tendons, one end of the simulated tendon is deployed at a first target node, and the other end of the simulated tendon is deployed at a second target node; The first target node and the second target node are located on different structures of the hydraulic muscle simulation model.

3. The simulation method for a hydraulic muscle model according to claim 1, characterized in that, The physics engine is the MuJoCo physics engine.

4. The simulation method for a hydrodynamic muscle model according to any one of claims 1 to 3, characterized in that, The detection parameters also include: The length limit range of the simulated tendon, the visible muscle diameter of the simulated tendon, the length of the first tendon when the simulated tendon is fully contracted, and the length of the second tendon when the simulated tendon is fully relaxed.

5. A hydraulic muscle model simulation device, characterized in that, include: The module includes an acquisition module, an input module, and a configuration module. The acquisition module is used to acquire the detection parameters of the hydraulic muscle model to be simulated, wherein the material of the hydraulic muscle model includes rubber, and the detection parameters include at least the nonlinear damping coefficient and stiffness coefficient of the rubber. The input module is used to input the detection parameters into a physics engine based on multibody dynamics to obtain an initial hydraulic muscle simulation model of the hydraulic muscle model, wherein the initial hydraulic muscle simulation model includes N simulated tendons connected to the same joint, where N is an integer greater than 1; The configuration module is used to configure a muscle actuator for each simulated tendon in the initial hydraulic muscle simulation model to obtain a hydraulic muscle simulation model, wherein the muscle actuator is used to drive the simulated tendon to move. The detection parameters also include the first time required for the muscle to go from relaxed to maximum contraction, the second time required for the muscle to recover from a contracted state to a relaxed state, and the maximum contractile force of the muscle. Before inputting the detection parameters into the multibody dynamics-based physics engine, the following steps are also included: Based on the first time, the second time, and the maximum contractile force of the muscle, a multi-body dynamic behavior characteristic expression of the simulated tendon is established. The multi-body dynamic behavior characteristic expression includes a first characteristic expression of the simulated tendon in the contraction phase and a second characteristic expression of the simulated tendon in the relaxation phase. The step of inputting the detection parameters into a multibody dynamics-based physics engine to obtain the initial hydrodynamic muscle simulation model of the hydrodynamic muscle model includes: The multibody dynamics behavior feature expression and the detection parameters are input into the physics engine to obtain the initial hydrodynamic muscle simulation model; The step of establishing the multibody dynamics characteristic expression of the simulated tendon based on the first time, the second time, and the maximum muscle contraction force includes: Set the following formula as the first characteristic expression: F1(t) = force × activation(t) × (1 - e^(-t1 / timeconst_contract)), where F1(t) is the contractile force of the simulated tendon during the contraction phase, force is the maximum contractile force of the muscle, t1 is the actual contraction time of the simulated tendon, and timeconst_contract is the first time. Set the following formula as the second characteristic expression: F2(t) = force × activation(t) × (1 - e^(-t2 / timeconst_relax)), where F2(t) is the relaxation tension of the simulated tendon during the relaxation phase, force is the maximum contractile force of the muscle, t2 is the actual relaxation time of the simulated tendon, and timeconst_relax is the second time.

6. An electronic device, the electronic device comprising a processor and a memory, characterized in that, The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the hydraulic muscle model simulation method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the hydraulic muscle model simulation method as described in any one of claims 1 to 4.

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