Simulation test method and device for dexterous hand and medium

Abstract

The invention discloses a simulation test method for a dexterous hand. The simulation test method comprises the following steps: constructing a finite element simulation model based on the configuration of the dexterous hand; performing multi-physical-field multi-working-condition coupling simulation on the finite element simulation model to obtain simulation data; verifying the simulation data according to a pre-constructed bidirectional calibration mechanism and experimental data so as to correct the finite element simulation model; and when the corrected finite element simulation model meets a preset condition, outputting a dexterous hand performance evaluation result. By constructing a finite element simulation model, multi-physical field and multi-working-condition coupling simulation is carried out, so that the simulation model can output multi-dimensional performance data, and evaluation limitation is avoided. And meanwhile, aiming at a complex coupling environment in real operation, dynamic feedback optimization of the model is realized through a pre-constructed bidirectional calibration mechanism and experimental data verification, so that simulation data can be directly used for subsequent model iteration, and a stable and reliable dexterous hand simulation test process is realized.

Description

Technical Field

[0001] This invention relates to the field of dexterity hand simulation, specifically to a method, equipment, and medium for simulating and testing dexterity hands. Background Technology

[0002] As a core component for human-robot collaboration and precision operation, the dexterous hand is a key execution component for achieving complex operations and precise interactions. Its application scenarios have expanded from traditional industrial assembly to various fields such as medical surgery and service robots. In these scenarios, the dexterous hand must simultaneously meet multi-dimensional performance requirements, and the reliability of these requirements directly determines the operational safety and efficiency of the entire system.

[0003] Traditional robot dexterity hand performance verification primarily relies on building physical testing platforms to simulate operational scenarios under different working conditions and obtain performance data. However, real-world testing has significant limitations. This is not only because dexterity hands have complex structures, high manufacturing costs, and long production cycles, and repeated testing can easily lead to component wear and tear, but also because real-world testing struggles to cover special conditions such as extreme temperatures, high-frequency vibrations, and complex load couplings. Furthermore, its internal stress distribution, fatigue evolution, and other microscopic characteristics cannot be accurately captured, resulting in blind spots in performance evaluation.

[0004] Therefore, finite element simulation, due to its advantages of low cost, high flexibility, and repeatability, has gradually become a core method for performance pre-research and design optimization. For example, CN119310881A discloses a finite element simulation-based lifting force control method for climbing robots. This method establishes a finite element model of the gear transmission mechanism, obtains the gear geometry and material characteristics, and combines temperature and vibration to correct tooth root stress and friction coefficient, ultimately achieving dynamic control of the lifting force. This technology focuses on the force analysis of specific transmission components of climbing robots, with the core objective of optimizing the lifting force control accuracy. CN117621048A discloses a dynamic simulation analysis method for six-degree-of-freedom industrial robots. A three-dimensional model is established using Solidworks and imported into Adams software to perform individual dynamic simulations of each joint, providing a reference for motor and reducer selection. This technology emphasizes the kinematic and dynamic performance analysis of joints in multi-degree-of-freedom robots, with its core application being component selection and verification. Publication number CN110633509A discloses a simulation method for optimizing a cantilever arm in Cartesian coordinates based on the MOGA algorithm. This method optimizes the cantilever structure dimensions by establishing a finite element model, thereby reducing R&D costs and shortening the development cycle. This technology focuses on optimizing the structural parameters of a single component, particularly its strength and vibration characteristics. However, considering the complex characteristics of a robot's dexterous hand, the following key shortcomings still exist:

[0005] (1) The performance evaluation dimensions are too singular to meet the comprehensive needs. Existing technologies either focus on a single transmission component (such as gears), emphasize joint dynamics, or only optimize a single structure (such as cantilever), without covering the multi-dimensional core indicators required by dexterous hands, making it difficult to fully reflect the overall operational performance of dexterous hands.

[0006] (2) Lack of multi-condition coupling analysis capability, and the simulation scenario is out of touch with reality. In actual operation, dexterous hands need to face a variety of complex conditions, but the existing technology only simulates a single condition (such as lifting and joint movement), which makes the simulation results unable to truly reflect the performance in actual use.

[0007] (3) The simulation and physical experiment lack linkage, resulting in insufficient credibility of the results. Most existing technologies only stay at the simulation level, and the simulation results deviate from the actual performance, making it difficult to directly guide product design iteration.

[0008] (4) Lack of unified standardized procedures leads to poor comparability and generalizability of results. The simulation methods of different test scenarios and different R&D teams vary, making it difficult to compare test results horizontally, which is not conducive to technology promotion and industry collaboration.

[0009] Therefore, there is an urgent need for a new simulation testing method for dexterous hands to solve the above problems. Summary of the Invention

[0010] This invention provides a simulation testing method for dexterous hands. By constructing a finite element simulation model and conducting multi-physics, multi-condition coupled simulations, the simulation model can output multi-dimensional performance data, thereby avoiding evaluation limitations. Furthermore, considering the complex coupled environments encountered in real-world operations, a pre-built bidirectional calibration mechanism and experimental data verification enable dynamic feedback optimization of the model. This allows simulation data to be directly used for subsequent model iterations, achieving a stable and reliable dexterous hand simulation testing process.

[0011] This invention is achieved through the following technical solution:

[0012] A simulation testing method for dexterous hands, comprising the following steps:

[0013] A finite element simulation model is constructed based on the configuration of a dexterous hand;

[0014] Multiphysics and multi-condition coupled simulations were performed on the finite element simulation model to obtain simulation data;

[0015] The simulation data is verified based on a pre-built two-way calibration mechanism and experimental data to correct the finite element simulation model;

[0016] When the modified finite element simulation model meets the preset conditions, the performance evaluation results of the dexterous hand are output.

[0017] Furthermore, before performing multiphysics multi-condition coupled simulation on the finite element simulation model, the following steps are included:

[0018] The stress distribution data of the finite element simulation model of the dexterous hand is obtained, and the stress distribution data is divided based on a preset stress threshold. The first feature region with an average stress value greater than or equal to the stress threshold is divided using a first mesh size. The second feature region with an average stress value less than the stress threshold is divided using a second mesh size. The first mesh size is smaller than the second mesh size.

[0019] Furthermore, the multi-physics multi-condition coupled simulation includes at least the following conditions: clamping load condition for grasping action, rotational torque condition for torsional action, and axial tensile force condition for vertical separation action.

[0020] Furthermore, the physical fields in the multiphysics multi-condition coupled simulation include at least one of mechanical field, thermal field, fluid field, and magnetic field; wherein the mechanical field includes at least one of static analysis field and dynamic analysis field, and the dynamic analysis field includes vibration analysis and impact analysis.

[0021] Furthermore, the simulation testing method includes: obtaining the target operation task of the dexterous hand and determining the coupled loads and boundary constraints of the corresponding physical model; wherein the coupled loads include a combination of at least two different types of loads, namely displacement loads, force loads and temperature loads.

[0022] Furthermore, the bidirectional calibration mechanism includes the following steps:

[0023] Based on the type of simulation data from coupled simulation, determine the type of experimental data to be compared;

[0024] Based on the type of experimental data to be compared, physical experiments are conducted on dexterous hands to obtain experimental data;

[0025] The simulation data is compared with the experimental data, and the parameters of the finite element simulation model are adjusted based on the comparison results.

[0026] Furthermore, the boundary conditions include combinations of at least two of the following constraint types: fixed constraints, sliding constraints, symmetric constraints, and far-end displacement constraints.

[0027] Furthermore, the performance evaluation results of the dexterous hand are output based on a unified evaluation index system, which includes at least three of the following evaluation indexes: maximum gripping force index, friction distribution index, equivalent stress index, temperature rise rate index, and fatigue life index.

[0028] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, characterized in that the processor performs the steps in the above method.

[0029] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0030] Compared with the prior art, the present invention has the following beneficial technical effects:

[0031] This invention provides a simulation testing method for dexterous hands. It utilizes a finite element simulation model built based on the dexterous hand configuration to perform multi-physics, multi-condition coupled simulations. By closely approximating actual working conditions, comprehensive simulation data is obtained, providing a complete data foundation for model calibration and performance evaluation. Simultaneously, a two-way calibration mechanism is employed, combined with experimental data, to verify the simulation data and correct the model. This effectively optimizes the rationality of the simulation model parameters and structure, improving the model's accuracy and reliability, and making the simulation results more closely resemble actual physical responses. Furthermore, performance evaluation results are output when the corrected model meets preset conditions, ensuring the objectivity and repeatability of the evaluation results and providing a reliable basis for dexterous hand structural analysis, performance verification, and optimization. This method enables fully automated and standardized operation from modeling, simulation, calibration to evaluation, effectively improving the overall efficiency and reliability of dexterous hand simulation testing. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating a simulation testing method for a dexterous hand according to a preferred embodiment of the present invention;

[0034] Figure 2 A flowchart of step 100 in a simulation testing method for a dexterous hand provided in a preferred embodiment of the present invention;

[0035] Figure 3 A flowchart of step 300 in a simulation testing method for a dexterous hand provided in a preferred embodiment of the present invention;

[0036] Figure 4 The flowchart shows step 400 in a simulation testing method for a dexterous hand provided in a preferred embodiment of the present invention. Detailed Implementation

[0037] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive. The terms "comprising" and "having," and any variations thereof, in the specification, claims, and the foregoing drawings, are intended to cover non-exclusive inclusion.

[0038] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0039] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0041] In existing dexterous hand simulations, the focus is often only on a portion of the structural components or transmission parts, or on a single aspect of the structure, failing to comprehensively evaluate the hand performance of the robot and lacking consideration for the overall multi-joint coordination of the dexterous hand. To address this issue, existing simulation methods need to be optimized, with a focus on optimizing the finite element simulation model while maintaining a complete dexterous hand model and transmission relationships.

[0042] The following explains a simulation testing method for a dexterous hand provided by an embodiment of the present invention. This embodiment uses a three-finger industrial dexterous hand as the test object, focusing on gripping, rotation, and thermal environment coupling in industrial assembly scenarios. The aim is to verify the accuracy, efficiency, and feasibility of the method and equipment of the present invention. Any adjustments to the test object, working conditions, and parameters made by those skilled in the art according to actual needs fall within the protection scope of this invention. Figure 1 The diagram shows a flowchart of a simulation testing method for a dexterous hand according to a preferred embodiment of the present invention. The method includes the following steps:

[0043] S100: Finite element simulation model based on the configuration of a dexterous hand;

[0044] S200: Perform multi-physics multi-condition coupled simulation on the finite element simulation model to obtain simulation data;

[0045] S300: Verify the simulation data based on a pre-built bidirectional calibration mechanism and experimental data to correct the finite element simulation model;

[0046] S400: When the modified finite element simulation model meets the preset conditions, output the performance evaluation results of the dexterous hand.

[0047] This invention constructs a corresponding finite element simulation model based on the configuration of the dexterous hand to be simulated. Then, it performs multiphysics, multi-condition coupled simulation on the finite element simulation model of the dexterous hand. Based on a pre-built bidirectional calibration mechanism, the simulation data and experimental data are verified to correct the finite element simulation model. The performance evaluation results are output only when the corrected finite element simulation model meets preset conditions. This method solves the shortcomings of existing dexterous hand simulation technologies, such as single evaluation dimensions, disconnected scenarios, insufficient credibility, and lack of unified standards, ensuring the authenticity and reliability of the simulation results. This method provides reliable technical support for the performance pre-research, design optimization, and mass production of dexterous hands, effectively improving the operational safety and efficiency of products. It is applicable to the research and testing of dexterous hands in various fields such as medical surgery, industrial assembly, and service robots.

[0048] In some preferred embodiments of the present invention, such as Figure 2 As shown, preprocessing is performed before constructing the finite element simulation model of the dexterous hand. Specifically, S100 includes the following steps:

[0049] S110: Retain the transmission and force-bearing structures in the dexterous hand configuration, and remove non-critical features.

[0050] To address the force and transmission characteristics of dexterous hands, non-critical features that have no impact on simulation results are purposefully removed. By eliminating redundant features that do not affect transmission and force, the model focuses on its core functional structure, which is crucial for ensuring high fidelity and efficiency. This geometric reduction in model complexity avoids difficulties in subsequent mesh generation and computational redundancy, resulting in reduced computational load and improved convergence in the mesh generation and solution calculation stages. This ensures the quality of the mesh elements while also making the mesh element shape more regular, the stiffness matrix more stable, and the subsequent iteration process smoother, making it easier to achieve convergence accuracy.

[0051] The transmission structure of the dexterous hand includes joints, links, and drive shafts. This transmission structure ensures the simulation accuracy of transmission ratios and frictional torque. By retaining the force-bearing structure of the dexterous hand (including fingertips and pads), the simulation accuracy of stress and strain is ensured. Furthermore, ensuring that the transmission ratio and force transmission path of the model are consistent with those of a real dexterous hand guarantees no loss of simulation accuracy.

[0052] In a preferred embodiment of the present invention, step S110 includes the following steps:

[0053] S111: The core contact surfaces have been refined, including the rubber contact surfaces of the fingertips and pads.

[0054] By eliminating non-critical features such as the micro-textures on the surface of dexterous fingertips and fingertips, the stability of contact simulation and the accuracy of load transfer calculations are improved. This eliminates the risk of contact mesh distortion, thereby ensuring the accuracy of contact stress calculations and the stability of contact convergence. This allows contact stress data to be directly used to evaluate operational accuracy indicators such as dexterity hand grasping, compensating for the problem of a single evaluation dimension. Utilizing simulated contact force data from smooth contact surfaces reduces the deviation from the measured data of fingertip force sensors on physical testing platforms, improving calibration efficiency and model reliability.

[0055] S112: Refinement of connection and opening features, including removal of non-critical bolts and holes that do not affect the main load-bearing capacity on the dexterous hand.

[0056] By removing non-critical connections and openings, the geometric continuity of the structure is prevented from being disrupted by such redundant features. At the same time, the artificial effects of excessive local mesh refinement and stress concentration caused by these features are eliminated, ensuring the accuracy of stiffness simulation for the core load-bearing structure.

[0057] S113: Refinement of peripheral additional structures, including the elimination of non-core additional structures and the equivalent simplification of complex key components.

[0058] The dexterous hand simulation model undergoes structural adjustments: Non-core additional structures that do not affect core motion and load transfer, such as shells, non-load-bearing decorative parts, and redundant fasteners, are removed to reduce invalid meshes and computational load. Components such as motors and chips, although belonging to the core motion / force system, but with complex internal structures, are simplified using equivalent methods. Their kinematic topology (such as motor output shafts and transmission connections with joints) and core transmission / force transfer characteristics are preserved, simplifying complex internal details. By reducing model complexity without altering the core transmission logic and force transfer characteristics, the computational speed and convergence of multiphysics coupling simulations are improved.

[0059] Through the above preprocessing steps, the number of meshes and the simulation cycle for multiple working conditions and multiple physics fields are effectively shortened while ensuring transmission and force characteristics. At the same time, by optimizing the model, the engineering applicability of the simulation results is improved, so that the calibrated model can directly guide the subsequent optimization of the dexterous hand model structure.

[0060] In a preferred embodiment of the present invention, step S100 includes the following steps:

[0061] S120: Import the configuration of the dexterous hand into the finite element simulation software to assign material properties, define contact relationships, and establish joint kinematic pairs;

[0062] By assigning material values, defining contacts, and establishing kinematic pairs, the material properties, contact characteristics, and transmission logic of the dexterous hand are reconstructed. This avoids large deviations between the model and the actual structure, ensuring the physical realism and mechanical validity of subsequent simulations. It also provides a foundation for subsequent multi-physics simulations involving mechanics, heat, vibration, and other fields, ensuring the feasibility of coupled simulations.

[0063] In a preferred embodiment of the present invention, step S120 includes the following steps:

[0064] S121: Based on the parameterized material properties of each component of the dexterous hand.

[0065] Based on the actual simulation of the dexterous hand, corresponding materials are selected for different components of the dexterous hand. By clarifying the material type and key parameters of each part of the dexterous hand, the consistency between the simulation materials and the physical materials is ensured.

[0066] The material values ​​for different components can be assigned using data provided by suppliers or measured parameters obtained through tensile and hardness tests. The process involves creating a material library and assigning values ​​to components in batches or individually. This ensures the accuracy of the material properties for each component and avoids simulation deviations caused by idealized material parameters.

[0067] S122: Define contact relationships based on dexterous hands to obtain high-confidence initial values ​​for contact parameters.

[0068] The contact surfaces between the dexterous hand and other objects are set to establish contact relationships. These relationships include the contact between the fingertips and the manipulated object, and the contact between the transmission components within the finger joints. Parameters such as the coefficient of friction are obtained during contact experiments; the coefficient of friction is acquired by measuring the static and dynamic coefficients of friction, thus ensuring the reliability of the simulation results.

[0069] This step ensures high accuracy of the finite element model at the contact mechanics level, reducing systematic errors caused by inaccurate parameters. The obtained measurement results can serve as reliable initial values ​​or benchmark references for parameters to be corrected during the S300 bidirectional calibration process.

[0070] S123: Establish joint kinematic pairs to recreate the transmission relationship of the dexterous hand in the simulation software.

[0071] Based on the configuration of a dexterous hand, kinematic pairs are established in the corresponding joints, including the rotation centers of interphalangeal and metacarpophalangeal joints, as well as joints that may involve sliding or complex movements. The kinematic pairs are defined according to the actual situation, thereby reconstructing the motion and force transmission chain in the simulation. This step ensures that the simulation model can realistically reproduce the actual motion and dynamics of the dexterous hand, which is a prerequisite for subsequent multiphysics coupling simulations to simulate complex operating conditions.

[0072] In actual simulation, the execution order of S122 and S123 is not mandatory. In this embodiment, S123 can be executed first, followed by S122, to avoid contact pair failure caused by subsequent adjustments to the kinematic pair. This step-by-step setup effectively improves operational efficiency and simulation convergence.

[0073] Before performing multiphysics multi-condition coupled simulation on the finite element simulation model, the following steps are also included:

[0074] S130: Obtain stress distribution data from the finite element simulation model of the dexterous hand, and divide the stress distribution data based on a preset stress threshold;

[0075] The equivalent stress value, as an equivalent mechanical index used in the finite element model to characterize the overall stress level of the dexterous hand structure, can comprehensively reflect the stress concentration and stress level of the structure under multi-physics loads. This equivalent stress value is calculated by multi-physics multi-condition coupled simulation; it is used for data verification and model correction in the two-way calibration mechanism, and serves as an important basis for judging whether the simulation model meets the preset conditions and for evaluating the strength and reliability of the dexterous hand structure.

[0076] The stress distribution data is used to characterize the magnitude and spatial distribution of stress in various parts of the dexterous hand, reflecting the stress transmission path and stress concentration location within the structure. The equivalent stress value is a quantitative representation of the stress distribution data, which in turn provides distributional support for the equivalent stress value. Together, they constitute the core content related to structural stress in the simulation data, used for bidirectional calibration of the simulation model and performance evaluation of the dexterous hand structure.

[0077] The first feature region with an average stress value greater than or equal to the stress threshold is divided using a first grid size; the second feature region with an average stress value less than the stress threshold is divided using a second grid size; wherein the first grid size is smaller than the second grid size.

[0078] By refining the mesh at joints and contact surfaces, a coarser mesh can be used in other areas with relatively low stress or simple structures to improve computational efficiency. Specifically, a denser first mesh size is used in high-stress areas (the first characteristic region) to ensure computational accuracy, while a sparser second mesh size is used in low-stress areas (the second characteristic region). To improve computational efficiency while ensuring accuracy in critical areas, an adaptive mesh generation strategy is adopted to shorten simulation time and achieve a balance between simulation accuracy and efficiency.

[0079] By using fine meshes in high-stress regions, the gradient changes in stress and temperature can be accurately captured, avoiding abrupt changes in physical quantities caused by excessively coarse meshes and ensuring convergence of coupled simulations. Using coarse meshes in low-stress regions can reduce the solution burden and avoid simulation divergence caused by excessive mesh counts, which is especially suitable for the complex scenarios of multi-condition coupling in the subsequent S200.

[0080] In actual simulation, a stress threshold is first determined, which can be set based on the actual task and engineering experience. Alternatively, a rated load can be applied to the finite element simulation model with a preliminary coarse mesh for pre-simulation calculations, thereby obtaining a rough preliminary stress distribution of the model. Specifically, the minimum stress value in the high-frequency stress concentration region is set as the stress threshold. If the high-stress region accounts for too large a proportion of the model volume, the threshold is appropriately lowered to avoid excessive fine mesh areas leading to wasted computational power; if the high-stress region accounts for too small a proportion of the model volume, the threshold is appropriately increased to avoid coarse meshes covering critical areas.

[0081] In this embodiment, the first feature region can be a stress concentration area, including joint bearings, fingertip contact surfaces, and gear meshing areas; the second feature region can be a region with less stress or a simpler structure, including the main body of the finger bone and non-load-bearing parts of the palm. By setting the first mesh size to 1:3-10 of the second mesh size, the calculation accuracy of stress concentration areas is ensured, while reducing the computational power consumption of non-critical areas. Mesh quality checks are then performed to prevent simulation non-convergence or result distortion due to poor mesh quality.

[0082] In previous simulation technologies, grasping, rotation, impact, and heat transfer conditions were often simulated separately. Load application and model building typically only addressed a specific condition, failing to reflect the coupling effects of dexterity in complex usage environments. Furthermore, the complex movements of the dexterity hand were simplified to simple loads, lacking the ability to perform coupled analysis of multiple conditions. To address this issue, in a preferred embodiment of the present invention, step S200 performs multiphysics, multi-condition coupled simulation on the finite element simulation model to obtain simulation data. Step S200 includes the following steps:

[0083] S210: Based on the first feature region of the dexterous hand, multiple indicators are defined simultaneously.

[0084] Based on the stress distribution data of the finite element simulation model of the dexterous hand and the preset stress threshold, the high stress concentration area (first characteristic region) of the dexterous hand is divided using a first mesh size. Multi-dimensional indicators, including stress, strain, friction, gripping force, rotational torque, thermal stress, and contact surface temperature, are observed simultaneously. Monitoring points, force transmission paths, and a full-area monitoring surface are set in the first characteristic region using simulation software to achieve simultaneous data acquisition of indicators under multiple working conditions such as gripping, rotation, impact, vibration, and heat transfer, laying a data foundation for subsequent bidirectional calibration and performance evaluation.

[0085] S211: Obtain the target operation task of the dexterous hand and determine the coupled loads and boundary constraints of the corresponding physical model. The coupled loads include a combination of at least two different types of loads: displacement loads, force loads, and temperature loads.

[0086] The target manipulation tasks of the dexterous hand (such as grasping, rotating, clamping, lifting, etc.) are obtained, and complex hand movements are simulated by applying various coupled loads. At the same time, boundary constraints are applied, including adjusting boundary conditions and constraint methods (such as defining contact pairs, fixing the base, and restricting the lateral displacement of the object) to improve the convergence of the model.

[0087] In this preferred embodiment, taking the dexterous hand grasping a cylinder as an example, the finger posture needs to be set in advance during the simulation to shorten the finger movement process at the start of the simulation, saving computing power. Then, a displacement load is applied to the motor linkage of each finger to make the dexterous hand close and grasp the object. After the fingertips establish contact with the object, the motor displacement is canceled and the motor force is reapplied to more closely resemble the actual grasping situation.

[0088] In other embodiments of the invention, displacement loads are applied at the joints to achieve closure, and then switched to force loads after contact. By superimposing multiphysics loads (temperature loads combined with force loads), the coupling effect of complex movements and the environment is reproduced. Through load switching and the superposition of multiple loads, the simulation of the dexterous hand is made to be as close to reality as possible, ensuring the effectiveness of multiphysics coupling simulation and improving the credibility of simulation results.

[0089] S212: Set boundary conditions for the target operation task based on the dexterous hand.

[0090] Based on the target manipulation task of the dexterous hand, the position of the dexterous hand and the manipulated object are fixed. Different boundary conditions are used depending on the contact situation. For example, solid supports are set up to support the manipulated object, and contact is added between the manipulated object and the solid supports. Alternatively, supports are directly applied to the manipulated object to prevent movement. In some cases, remote displacement needs to be applied to the manipulated object to restrict its degrees of freedom.

[0091] In this preferred embodiment, by fixing the base of the dexterous hand, support constraints are set according to the state of the manipulated object. Through physical support combined with contact definition / direct support / remote displacement constraints, coupled loads are adapted to avoid model instability, thereby preventing model instability or solution divergence.

[0092] S213: Based on the working conditions that the dexterous hand needs to simulate, perform simulation settings, including convergence criteria, load step size, etc.

[0093] The dexterous hand that completes the load and constraint configuration adapts to complex coupled working conditions (such as dynamic impacts requiring small steps to capture instantaneous responses, and static clamping allowing for large steps to improve efficiency) through differentiated step sizes and dual convergence criteria, thereby reducing errors in subsequent output data.

[0094] S214: Based on the finite element simulation model of the dexterous hand, perform simulation solutions to obtain simulation data.

[0095] After simulation, the simulation data is output and used directly as the main input for subsequent two-way calibration. The model is then corrected by comparing it with the actual experimental data, ultimately providing accurate data support for performance evaluation.

[0096] This invention is based on a unified finite element simulation model and mesh generation. By applying multiple coupled loads such as displacement, force, and temperature during the simulation process, and employing flexible boundary condition strategies, it ensures model convergence and stability. This avoids errors and repetitive work caused by switching between multiple software programs and models. Nonlinear solutions guarantee simulation accuracy under complex scenarios such as large deformation, contact friction, and thermo-mechanical coupling, making the simulation results more realistic and reliable. This invention achieves comprehensive evaluation of multiple indicators by simultaneously monitoring key metrics such as gripping force, anti-slip force, rotational torque, stress distribution, thermal stress, and fatigue life. Through simulation-based scheme selection, it reduces the number of physical tests, significantly lowering R&D costs and improving performance stability. It ensures that the simulation scenario closely resembles reality, solving the problem of the disconnect between simulation and real-world scenarios.

[0097] Currently, most methods remain at the stage of pure simulation or pure experimentation, making it difficult to guarantee the consistency between simulation results and actual performance. S300 of this invention: Verifies the simulation data based on a pre-built bidirectional calibration mechanism and experimental data to correct the finite element simulation model. This solves the problem of the disconnect between simulation and physical experiment in existing technologies. Furthermore, through bidirectional verification from simulation to experiment, the model is corrected to further improve the accuracy and reliability of subsequent model simulations.

[0098] In a preferred embodiment of the present invention, such as Figure 3 As shown: S300 includes the following steps:

[0099] S310: Based on the type of simulation data in coupled simulation, determine the type of experimental data to be compared.

[0100] Simulation data from multiphysics and multi-condition coupled simulations using finite element simulation models is acquired. Simultaneously, a pre-built bidirectional calibration mechanism is used to determine the alignment dimensions between the simulation and experimental data, ensuring direct comparison of their data types. For example, the maximum fingertip force in the simulation data is correlated with the measured value of the maximum fingertip force in the experimental data.

[0101] S320: Based on the type of experimental data to be compared, conduct physical experiments on the dexterous hand to obtain experimental data.

[0102] Based on the multiphysics multi-condition coupled simulation, a physical testing platform was built to conduct physical experiments on the dexterous hand to obtain experimental data. In this embodiment, the multiphysics multi-condition coupled simulation of the dexterous hand includes grasping, rotation, and thermal environment. In the physical experiment, pressure sensors, torque sensors, and temperature sensors were used to collect experimental data in the benchmark dimensions. This ensures the consistency between the experimental conditions and the simulation conditions. By replacing large-scale testing with a small number of physical experiments, experimental costs are reduced while providing reliable data for calibration, achieving a good balance between experimental cost and data reliability.

[0103] S330: Compare the simulation data with the experimental data, and adjust the parameters of the finite element simulation model based on the comparison results.

[0104] The finite element simulation model parameters that need to be corrected include at least one of the following: material property parameters, contact definition parameters, and boundary condition parameters, until the error between the simulation data and experimental data meets a preset threshold. This invention is based on a two-way calibration mechanism, which calculates the deviation between simulation data and experimental data and then specifically corrects the model parameters. When the deviation originates from material properties, the elastic modulus, Poisson's ratio, etc., are adjusted; when the deviation originates from contact characteristics, the friction coefficient is corrected; and when the deviation originates from constraint logic, the boundary conditions are optimized. Through targeted correction of parameters such as the material model, friction coefficient, and boundary conditions, a data closed loop is formed. By accurately locating the root cause of model deviations, blind corrections are avoided, effectively improving model optimization efficiency. This mechanism allows for rapid iteration during the simulation phase, and the model can be corrected with only a few physical experiments, significantly shortening the R&D cycle and improving production quality consistency.

[0105] After correcting the finite element simulation model, the following post-processing steps are also included:

[0106] S340: Update the parameters of the corrected finite element simulation model to the finite element simulation model, and then perform multiphysics multi-condition coupled simulation of the dexterous hand to obtain new simulation data; compare the new simulation data with the experimental data; until the error meets the preset threshold.

[0107] The corrected parameters are re-input into the finite element simulation model, and the previous multiphysics, multi-condition coupled simulation is repeated. The newly obtained simulation data is compared with the experimental data until the error between the simulation and experimental data meets the preset threshold. In the actual bidirectional calibration and correction process, if the error between the simulation and experimental data does not meet the preset threshold, the above steps are continuously repeated until the error meets the preset threshold. Through continuous iterative correction and comparison, a stable data closed loop is formed until the error of all benchmark indicators is satisfied.

[0108] The preset threshold can be adaptively selected based on actual working conditions. For example, if the preset threshold is set to 5%, and the error between the simulation data and the experimental data is less than or equal to 5%, it is considered to be satisfied, and the correction parameter will be continuously maintained in subsequent simulation iterations.

[0109] A pre-built two-way calibration mechanism is key to improving the reliability of simulation results. It goes beyond simply comparing simulation and experimental data; instead, it uses a predefined, iterative calibration process to correct the simulation model using experimental data. This forms a dynamic feedback loop of simulation experiment – ​​experimental verification – model correction, effectively enhancing the reliability of the simulation model and solving the problem of simulation results being difficult to directly use for product iteration.

[0110] In a preferred embodiment of the present invention, after obtaining preliminary simulation data, a small-scale simulation experiment is conducted under actual simulation conditions to determine the parameters to be compared, such as motor output and fingertip force. Then, the experimental data is compared with the simulation data. When the simulation data differs significantly from the experimental data, the finite element simulation model is corrected. The corrected finite element simulation model is then used to re-simulate, yielding new simulation data. The new simulation data is then compared with the experimental data. If the difference is less than 3%, no further correction is needed, and the next step of the simulation process can proceed.

[0111] In this invention, the credibility and verifiability of simulation results are greatly improved by bidirectional calibration of multi-physics field multi-condition coupled simulation and a small amount of physical experimental data, and the simulation results can be verified and corrected without large-scale repeated experiments.

[0112] In a preferred embodiment of the present invention, such as Figure 4 As shown, after high-precision and stable calibration based on a two-way calibration mechanism, a comparable performance evaluation is output through a unified index system to guide product iteration and mass production. Specifically, S400: When the corrected finite element simulation model meets preset conditions, the performance evaluation results of the dexterous hand are output. This is followed by the following steps:

[0113] S410: Determine whether the finite element simulation model meets the preset conditions.

[0114] In this embodiment, the preset conditions include accuracy conditions and stability conditions. Accuracy conditions refer to whether the error between the simulation data and experimental data for a unified evaluation index meets the error threshold. Stability conditions refer to the finite element simulation model exhibiting no penetration, solution divergence, or abnormal data fluctuations during repeated multi-physics multi-condition coupled simulations, and meeting the convergence criteria for force convergence tolerance and displacement convergence tolerance.

[0115] By judging whether the modified model meets the above preset conditions, if both of the above conditions are met, the finite element simulation model is judged to be up to standard.

[0116] S420: Based on a unified evaluation index system, calculate each evaluation index; including at least three of the following evaluation indexes: maximum gripping force index, friction force distribution index, equivalent stress index, temperature rise rate index, and fatigue life index, etc.

[0117] Specifically, the maximum gripping force index is calculated by taking the peak value of the resultant force under the gripping conditions in the simulation and removing abnormal fluctuations, such as the instantaneous peak value at the moment of contact. The friction force distribution index outputs the spatial distribution cloud map and mean value of the friction force on the contact surface, and calculates the average friction force using the area-weighted method. The equivalent stress index extracts the stress peak values ​​in key areas such as joints and contact surfaces, and compares them with the allowable stress of the material. The temperature rise rate index calculates the difference between the steady-state temperature and the initial temperature under thermo-mechanical coupling conditions, and divides it by the simulation time. The fatigue life index calculates the cycle life based on stress-strain data.

[0118] S430: Adjust the weight of each evaluation according to the application scenario of the dexterous hand, and output the performance evaluation results of the dexterous hand.

[0119] To address the limitation of using a single evaluation standard for all scenarios, this invention adjusts the weights of indicators based on the application scenario of the dexterous hand, achieving better environmental adaptability. The evaluation results are visualized through data charts such as stress distribution cloud maps, friction distribution heat maps, fatigue life curves, and temperature rise trend graphs. If the actual output still fails to meet the standards, the finite element simulation model returns to the above-mentioned S330 step to correct the model parameters. If the parameters still fail to meet the standards after three or more corrections, then the system is considered to return to S100 to adjust the configuration.

[0120] This invention addresses the problems of limited evaluation dimensions and lack of cross-sectional comparability in existing technologies by establishing a unified evaluation index system encompassing statics, dynamics, thermal analysis, and durability. This not only facilitates quick assessment by those in the field of structural or material improvements but also promotes efficient cross-project collaboration, accelerating the mass production and iterative upgrades of robotic dexterity hands.

[0121] In a preferred embodiment of the present invention, an electronic device is also provided, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the simulation testing method for a dexterous hand as described in the above embodiments.

[0122] In this preferred embodiment, the provided electronic device may include, but is not limited to, a smartphone, tablet computer, laptop computer, or desktop computer.

[0123] The processor may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor can be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may also include a main processor and coprocessors. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning. The processor implements the dexterous hand simulation method by moving or executing computational processes stored in the memory and by accessing data stored in the memory.

[0124] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback, image playback, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0125] In this embodiment, the memory is used to store at least the following computer program, which, after being loaded and executed by the processor, is capable of implementing the relevant steps of the dexterous hand simulation testing method disclosed in any of the foregoing embodiments. Additionally, the resources stored in the memory may also include an operating system and data, and the storage method may be temporary or permanent storage. The operating system may include Windows, Unix, Linux, etc.

[0126] In some embodiments, the electronic device may further include multimedia components, audio components, input / output (I / O) interfaces, sensor components, and communication components.

[0127] The multimedia component includes a screen that provides an output interface between the electronic device and the user. In this embodiment, the screen may include a liquid crystal display panel and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In this embodiment, the multimedia component includes a front-facing camera and / or a rear-facing camera. When the device is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera can receive external multimedia data. Each front-facing camera and rear-facing camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

[0128] The audio component is configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC) configured to receive external audio signals when the device is in an operating mode, such as call mode, recording mode, or voice recognition mode. The received audio signals may be further stored in memory or transmitted via a communication component. In this embodiment, the audio component also includes a speaker for outputting audio signals.

[0129] The I / O interface provides an interface between the processor and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0130] The sensor assembly includes one or more sensors for providing status assessments of various aspects of the device. For example, the sensor assembly can detect the device's on / off state, the relative positioning of components (e.g., a display panel and keypad of an electronic device), and can also detect changes in the position of the device or a component thereof, the presence or absence of user contact with the device, the orientation or acceleration / deceleration of the electronic device, and temperature changes of the device. The sensor assembly may also include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In this preferred embodiment, the sensor assembly may further include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0131] The communication component is configured to facilitate wired or wireless communication between electronic devices and other devices. Electronic devices can access wireless networks based on communication standards, and wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultrawideband) connections, and other currently known or future-developed wireless connection methods.

[0132] In this preferred embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In this embodiment, the communication component also includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra-Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies. In an exemplary embodiment, the electronic device may be implemented using one or more Application-Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field-Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the dexterous hand simulation method steps described above.

[0133] In a preferred embodiment of the present invention, a computer-readable storage medium is also provided, storing a computer program that, when executed by a processor, implements the above-described dexterity hand simulation testing method, specifically including: constructing a dexterity hand finite element model; performing multiphysics multi-condition coupled simulation; correcting the model based on a bidirectional calibration mechanism; and outputting performance evaluation results.

[0134] In various embodiments, the provided computer-readable storage medium may be, for example, flash memory, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (Radio Frequency), etc., or any suitable combination thereof.

[0135] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0136] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0137] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A simulation testing method for dexterous hands, characterized in that, The simulation testing method includes the following steps: A finite element simulation model is constructed based on the configuration of a dexterous hand; Multiphysics and multi-condition coupled simulations were performed on the finite element simulation model to obtain simulation data; The simulation data is verified based on a pre-built two-way calibration mechanism and experimental data to correct the finite element simulation model; When the modified finite element simulation model meets the preset conditions, the performance evaluation results of the dexterous hand are output.

2. The simulation testing method for a dexterous hand according to claim 1, characterized in that, Before performing multiphysics multi-condition coupled simulation on the finite element simulation model, the following steps are included: The stress distribution data of the finite element simulation model of the dexterous hand is obtained, and the stress distribution data is divided based on a preset stress threshold. The first feature region with an average stress value greater than or equal to the stress threshold is divided using a first mesh size. The second feature region with an average stress value less than the stress threshold is divided using a second mesh size. The first mesh size is smaller than the second mesh size.

3. The simulation testing method for a dexterous hand according to claim 1, characterized in that, The multi-physics multi-condition coupled simulation includes at least the following conditions: clamping load condition for grasping action, rotational torque condition for torsional action, and axial tensile force condition for vertical separation action.

4. The simulation testing method for a dexterous hand according to claim 1, characterized in that, The physical fields in the multiphysics multi-condition coupled simulation include at least one of mechanical field, thermal field, fluid field, and magnetic field; wherein, the mechanical field includes at least one of static analysis field and dynamic analysis field, and the dynamic analysis field includes vibration analysis and impact analysis.

5. The simulation testing method for a dexterous hand according to claim 1, characterized in that, The simulation testing method includes: obtaining the target operation task of the dexterous hand and determining the coupled loads and boundary constraints of the corresponding physical model; wherein, the coupled loads include a combination of at least two different types of loads, namely displacement loads, force loads and temperature loads.

6. The simulation testing method for a dexterous hand according to claim 1, characterized in that, The bidirectional calibration mechanism includes the following steps: Based on the type of simulation data from coupled simulation, determine the type of experimental data to be compared; Based on the type of experimental data to be compared, physical experiments are conducted on dexterous hands to obtain experimental data; The simulation data is compared with the experimental data, and the parameters of the finite element simulation model are adjusted based on the comparison results.

7. The simulation testing method for a dexterous hand according to claim 5, characterized in that, The boundary constraints include combinations of at least two of the following constraint types: fixed constraints, sliding constraints, symmetric constraints, and far-end displacement constraints.

8. The simulation testing method for a dexterous hand according to claim 1, characterized in that, The performance evaluation results of the dexterous hand are output based on a unified evaluation index system, which includes at least three of the following evaluation indexes: maximum gripping force index, friction distribution index, equivalent stress index, temperature rise rate index, and fatigue life index.

9. An electronic device, characterized in that, The method includes a memory and a processor, the memory storing a computer program executable on the processor, characterized in that the processor performs the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

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