Optimization simulation method of robot structure design based on finite element model

Abstract

The present invention relates to the technical field of finite element simulation, and in particular to a robot structural design optimization simulation method based on a finite element model, comprising: obtaining the stress vector of each division unit and the stress vector of each node during each motion simulation of the robot in a stationary state; determining a first grid density eigenvalue based on the similarity of stress stability indicators between each division unit and other surrounding division units and the similarity of stress vectors under the same motion simulation; determining a second grid density eigenvalue based on the difference in stress vectors between each division unit and each node during each motion simulation, combined with the stress stability indicator of each division unit; and adjusting the grid density of each structural area based on the first grid density eigenvalue and the second grid density eigenvalue to obtain an optimal grid density for each structural area, which effectively improves the accuracy of the grid division result and facilitates reliable finite element simulation of the robot.

Description

Technical Field

[0001] The present invention relates to the technical field of finite element simulation, and in particular to a robot structure design optimization simulation method based on a finite element model. Background Art

[0002] Finite element simulation can analyze in detail the mechanical properties of a robot's structure, such as stress, strain, and deformation, under different operating conditions. This can identify weaknesses in the robot's structural design, improve design quality, and reduce R&D costs and cycles. Finite element meshing is a crucial step in optimizing robot finite element simulation parameters, directly impacting the accuracy of subsequent numerical calculations. Furthermore, meshing also affects the computational complexity of subsequent finite element simulations, as each unit needs to be simulated. Therefore, a reasonable meshing approach can achieve accurate finite element simulations while also reducing computational complexity and increasing speed.

[0003] Mesh density is a key parameter in finite element meshing. Currently, when a robot model is designed and introduced into finite element analysis, a fixed mesh density is used. This fixed mesh density cannot represent the structural changes that should occur during the robot's dynamic process, and different structures in the robot require different mesh densities. Therefore, the traditional method of determining mesh density results in low mesh accuracy, which is not conducive to reliable finite element simulation of the robot. Summary of the Invention

[0004] In order to solve the technical problem that the mesh density determination method of the traditional finite element simulation results in low mesh division accuracy, the purpose of the present invention is to provide a robot structure design optimization simulation method based on a finite element model. The technical solution adopted is as follows:

[0005] Obtain each division unit in each structural area of the robot in a stationary state, perform several motion simulations on each structural area, and obtain the stress vector of each division unit and the stress vector of each node during each motion simulation;

[0006] According to each stress component in the stress vector of each divided unit, the stress component fluctuation in the same dimension during multiple motion simulations is analyzed to determine the stress stability index of each divided unit;

[0007] Determine the first grid density eigenvalue of each division unit based on the similarity of stress stability index between each division unit and other division units around it and the similarity of stress vectors under the same motion simulation;

[0008] According to the stress vector difference between each division unit and each node during each motion simulation, combined with the stress stability index of each division unit, the second mesh density eigenvalue of each division unit is determined;

[0009] The mesh density of each structural region is adjusted according to the first mesh density eigenvalue and the second mesh density eigenvalue to obtain the optimal mesh density of each structural region.

[0010] Furthermore, the stress component fluctuations in the same dimension during multiple motion simulations are analyzed based on the stress components in the stress vectors of the divided units to determine the stress stability index of each divided unit, including:

[0011] For any divided unit, a stress component sequence in the same dimension during multiple motion simulations is obtained, a stable value of the stress component sequence is calculated, and the stable value of each dimension is obtained;

[0012] The minimum value is selected from all the stability values as the stress stability index of the corresponding divided unit.

[0013] Furthermore, the determining of the first mesh density eigenvalue of each division unit according to the similarity of the stress stability index between each division unit and other surrounding division units and the similarity of the stress vectors under the same motion simulation includes:

[0014] Determine any divided unit as a unit to be analyzed, and use other divided units in the structural area except the unit to be analyzed as first comparison units;

[0015] Calculating a first similarity between the stress vector of the unit to be analyzed and the stress vectors of each first comparison unit in the same motion simulation, and obtaining each first similarity corresponding to the unit to be analyzed in each motion simulation;

[0016] Calculating a second similarity between the stress stability index of the unit to be analyzed and the stress stability index of each first comparison unit to obtain each second similarity corresponding to the unit to be analyzed;

[0017] For each motion simulation, each of the first similarities corresponding to the unit to be analyzed and each of the second similarities corresponding to the unit to be analyzed are fused to obtain a first mesh density eigenvalue of the unit to be analyzed.

[0018] Furthermore, obtaining each second similarity corresponding to the unit to be analyzed includes:

[0019] The absolute value of the difference between the stress stability index of the unit to be analyzed and the stress stability index of each first comparison unit is calculated, and the absolute value of the difference is negatively correlated to obtain a negative correlation value, and the negative correlation value is used as the second similarity.

[0020] Furthermore, the fusing of the first similarities corresponding to the unit to be analyzed and the second similarities corresponding to the unit to be analyzed to obtain a first grid density eigenvalue of the unit to be analyzed includes:

[0021] For any motion simulation, calculating the product of the first similarity and the second similarity for the same first comparison unit, accumulating all the products to obtain an accumulated value, and using the accumulated value as the stress consistency index between the unit to be analyzed and all the first comparison units in the motion simulation;

[0022] The stress consistency index between the unit to be analyzed and all first comparison units in each motion simulation is used to calculate the average value of all stress consistency indices to obtain the first mesh density characteristic value of the unit to be analyzed.

[0023] Furthermore, the second mesh density eigenvalue of each division unit is determined based on the stress vector difference between each division unit and each node during each motion simulation, in combination with the stress stability index of each division unit, including:

[0024] Determine any node as a node to be analyzed, and obtain a division unit connected to the node to be analyzed as a second comparison unit;

[0025] For each motion simulation, the modulus of the vector difference between the stress vector of the node to be analyzed and the stress vector of each second comparison unit is calculated, and the average of the ratios of each modulus to the stress stability index of the corresponding second comparison unit is taken as the density deviation of the node to be analyzed;

[0026] The density deviation of each node during each motion simulation is obtained, and the second grid density eigenvalue of each divided unit is determined according to the density deviation of each node during each motion simulation.

[0027] Furthermore, determining the second mesh density characteristic value of each divided unit according to the density deviation of each node during each motion simulation includes:

[0028] Calculating the mean and standard deviation of the density deviation of the node to be analyzed during each motion simulation, performing negative correlation normalization processing on the product of the mean and the standard deviation to obtain a normalized value, and using the normalized value as the mesh density sub-feature of the node to be analyzed;

[0029] A second mesh density characteristic value of each division unit is determined according to the mesh density sub-characteristic of each node corresponding to each division unit.

[0030] Furthermore, determining the second mesh density characteristic value of each division unit according to the mesh density sub-characteristic of each node corresponding to each division unit includes:

[0031] Determine any divided unit as a unit to be analyzed, and determine all other divided units in the structural area except the unit to be analyzed as first comparison units; calculate the grid density sub-feature mean and grid density sub-feature standard deviation of all nodes corresponding to the unit to be analyzed, and the grid density sub-feature mean of all nodes corresponding to each first comparison unit;

[0032] Determine a second grid density eigenvalue of the unit to be analyzed based on a difference in the grid density sub-feature mean between the unit to be analyzed and all first comparison units, the grid density sub-feature mean and the grid density sub-feature standard deviation of the unit to be analyzed;

[0033] Among them, the difference in the mean of the grid density sub-feature, the standard deviation of the grid density sub-feature and the second grid density eigenvalue are negatively correlated, and the mean of the grid density sub-feature of the unit to be analyzed and the second grid density eigenvalue are positively correlated.

[0034] Furthermore, adjusting the mesh density of each structural region by using the first mesh density eigenvalue and the second mesh density eigenvalue to obtain the optimal mesh density of each structural region includes:

[0035] determining a grid density parameter for each structural region according to the first grid density eigenvalue and the second grid density eigenvalue of each division unit; setting a first density parameter threshold and a second density parameter threshold, wherein the first density parameter threshold is less than the second density parameter threshold;

[0036] For each structural region, if the grid density parameter of the structural region is less than the first density parameter threshold, the grid density of the structural region is increased until the grid density parameter of the structural region is within a numerical range corresponding to the first density parameter threshold and the second density parameter threshold, thereby obtaining an optimal grid density of the structural region;

[0037] If the grid density parameter of the structural area is greater than the second density parameter threshold, the grid density of the structural area is reduced until the grid density parameter of the structural area is within the numerical range corresponding to the first density parameter threshold and the second density parameter threshold, thereby obtaining the optimal grid density of the structural area.

[0038] Furthermore, determining the mesh density parameter of each structural region according to the first mesh density eigenvalue and the second mesh density eigenvalue of each division unit includes:

[0039] For each structural region, calculating the product of the first grid density eigenvalue and the second grid density eigenvalue of each division unit as a grid density parameter of the corresponding division unit;

[0040] The mean value of the grid density parameters of all division units in the same structural area is taken as the grid density parameter of the corresponding structural area.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a robot structural design optimization simulation method based on a finite element model. The method analyzes the stress changes of static subdivision units during multiple dynamic simulations of the robot, that is, determines the stress stability index of the subdivision units, thereby preventing the impact of the non-reproducibility of simulation process results on finite element mesh division. The method determines the first mesh density eigenvalue of each subdivision unit based on the similarity of the stress stability index between each subdivision unit and other surrounding subdivision units and the similarity of the stress vectors under the same motion simulation, thereby preventing the mesh density from being too large and the unit from being infinitely subdivided. The method obtains the mesh density characteristics represented by the nodes by analyzing the relationship between the unit stress and the node stress, and enhances the comprehensiveness of the mesh density characteristic analysis by combining the unit stress and the node stress to jointly reflect the mesh density characteristics. By adaptively determining the mesh density for each structural area corresponding to the robot, the accuracy of the mesh division results is effectively improved, which is conducive to reliable finite element simulation of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 A flowchart of a robot structure design optimization simulation method based on a finite element model proposed in one embodiment of the present invention;

[0045] Figure 2 Flowchart for implementing step S3 in an embodiment of the present invention;

[0046] Figure 3 This is a flowchart for implementing step S4 in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] To further illustrate the technical means and effects employed by the present invention to achieve its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementations, structures, features, and effects of the technical solutions proposed by the present invention. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0048] Unless defined otherwise, 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 invention belongs.

[0049] The application scenarios targeted by the present invention may be:

[0050] On the one hand, the meshing density of different robot structures varies, such as the arm and fingers. The fingers are more flexible than the arms and require more refined finite element meshing. On the other hand, existing finite element meshing methods do not account for structural changes during robot movement, such as the raising and lowering of the arms and the extension and retraction of the fingers. These two factors lead to low accuracy in existing finite element meshing methods.

[0051] In order to improve the accuracy of finite mesh division results, an embodiment of the present invention provides a robot structure design optimization simulation method based on a finite element model, such as Figure 1 As shown, the following steps are included:

[0052] S1, obtaining each division unit in each structural area of the robot in a stationary state, performing several motion simulations on each structural area, and obtaining the stress vector of each division unit and the stress vector of each node in each motion simulation.

[0053] The above step S1 can be implemented through steps S11 to S12 (not shown):

[0054] S11, obtaining each divided unit in each structural area of the robot when it is in a stationary state.

[0055] First, the robot structure to be simulated is imported into finite element analysis software. Based on the robot's structural information, the robot is divided into regions, such as the head and torso. Once the robot's structural information is grouped, the robot's material data is obtained and imported to ensure that the robot structure possesses the required mechanical properties.

[0056] Structural regions can be composed of multiple structural components or parts of a component. For example, the hand can be divided into a single structural region, or it can be divided into multiple finger regions and a palm region. The specific division is determined by the actual robot simulation requirements. After the entire robot is divided into regions, each structural region is analyzed as a whole. For example, when operating with the robot palm, the hand region needs to be refined, while the robot's kinematic balance can be directly considered as the hand as a whole.

[0057] Then, without applying any external forces, the finite element software is used to obtain the robot's static finite element mesh. This includes the individual meshing units within each structural region. These units are tetrahedral structures composed of several interconnected nodes. Traditional finite element meshing of robots relies on the robot's structural information during static conditions. Meshing in the static state primarily utilizes the robot's material and structural data.

[0058] It's worth noting that the meshing results above may not reflect the actual robot's operating conditions. For example, static simulation results in a denser meshing of the robot's body, but in reality, the robot's body can be considered a single unit, resulting in a larger mesh and a lower density. Therefore, the meshing should be combined with the actual motion of the robot structure to determine the most appropriate mesh density for each structural region.

[0059] At this point, the finite element mesh division of each structural area of the robot in a stationary state is completed, and each division unit in each structural area is obtained.

[0060] S12, performing several motion simulations on each structural region to obtain the stress vectors of each divided unit and the stress vector of each node during each motion simulation.

[0061] After completing the initial meshing of the structural area, finite element software is used to perform motion simulations based on the motion characteristics of each structural area of the robot. These simulations include finger bending, torso translation, and arm extension and retraction, among other tasks. During the simulations, the force conditions of each element within the structural area are determined during motion. The force conditions of each element and each node can be represented by a vector, namely a stress vector. The elements of the stress vector represent stress components in different directions.

[0062] The number of motion simulations can be set by the implementer according to the actual situation without any specific limitation, such as 10 times; there are 6 stress components in three dimensions, 3 directional stresses and 3 shear stresses.

[0063] At this point, by performing finite element simulation on the units in the divided structural area, the force conditions of each divided unit and each node can be determined, which is helpful for subsequent robot mechanical analysis.

[0064] S2, based on the stress components in the stress vector of each divided unit, analyze the stress component fluctuations in the same dimension during multiple motion simulations to determine the stress stability index of each divided unit.

[0065] After multiple finite element simulations, the final simulation analysis results should be stable. For example, after multiple simulations of a robot's finger bending motion, the simulation results should be reproducible. However, when the mesh is inaccurate, the divided units cannot accurately describe the mechanical behavior of the robot's structural motion, resulting in unstable stress calculations. The unstable unit stresses lead to large differences in the results of multiple simulations. Conversely, if the finite element mesh division results are appropriate, the mechanical properties of the robot's motion process can be accurately captured in multiple simulations, and the unit stress results of different motion simulations are relatively stable.

[0066] For any division unit, the above step S2 can be implemented through steps S21 to S22 (not shown):

[0067] S21, obtaining a stress component sequence in the same dimension during multiple motion simulations, calculating a stable value of the stress component sequence, and obtaining a stable value of each dimension.

[0068] In this embodiment, each division unit has its corresponding stress component sequence in different dimensions, and the stability of the stress component sequence can represent the stability of the simulation results during multiple motion simulations.

[0069] As an example implementation, it includes:

[0070] Obtain the stress components in the i-th dimension during multiple motion simulations to form a stress component sequence; calculate the standard deviation of the stress component sequence; and use exp(-) to negatively normalize the standard deviation of the stress component sequence to obtain a normalized value, which serves as the stable value of the i-th dimension. Here, i is a positive integer representing the dimension number, the normalized value ranges from 0 to 1, and the stress component data is scalar.

[0071] Yet another exemplary embodiment includes:

[0072] Calculate the average value of the stress component sequence of the i-th dimension; make the difference between each stress component in the stress component sequence and the average value to obtain each difference value; take the reciprocal of the non-zero difference value, and take the average value of all the reciprocals obtained as the stable value of the i-th dimension.

[0073] S22, select the minimum value from all the stable values as the stress stability index of the corresponding divided unit.

[0074] In this embodiment, based on the calculation process of the stability value of the i-th dimension, the stability value of each dimension is obtained, and then the minimum stability value is used as the stress stability index of the corresponding divided unit.

[0075] As an example, the calculation formula for the stress stability index of the partitioned unit can be:

[0076] ; In the formula, a represents the stress stability index of the divided unit, min represents the minimum value function, exp represents the exponential function with the natural constant e as the base, and exp (-) is used to achieve negative correlation normalization processing. It represents the standard deviation function. Represents the stress component sequence of the i-th dimension in the stress vector of the divided element during multiple motion simulations. I represents the number of dimensions and can be set to 6.

[0077] When the minimum stability values are large, it means that each stress component has high stress stability under multiple motion simulations, which further indicates that the stress calculation stability of the divided unit is good, the difference between the simulation results of different motion simulations is small, and the numerical accuracy of the initial mesh density of the divided unit may be high.

[0078] At this point, the stress stability index of each divided unit in each structural area is obtained.

[0079] S3, determining a first mesh density eigenvalue of each division unit based on similarities in stress stability indicators between each division unit and other surrounding division units and similarities in stress vectors under the same motion simulation.

[0080] Although a high mesh density can produce relatively accurate simulation results, the increased number of divided elements makes it time-consuming to obtain the final robot structure simulation results. In addition, for some non-stress concentration areas, increasing the mesh density does not improve the robot structure simulation. A slightly smaller mesh density can also produce more accurate robot structure simulation results. Among them, the non-stress concentration area can be the robot body.

[0081] In this embodiment, for any divided unit, it is necessary to compare with the surrounding divided units to determine whether the grid density of the divided unit is too large, that is, to analyze the grid density characteristics of the divided unit, which is recorded as the first grid density characteristic value. Specifically, in the process of finite element software simulating the robot structure, the stress changes continuously in space. If the grid division is too fine, the size and shape of adjacent units will be very small and similar, so the load and boundary conditions of the divided unit will not differ much in the local range. As for the reason that the sizes of adjacent units are similar, the finite element method itself is an approximate method. When the grid density reaches a certain level, the stress calculation results of adjacent units tend to be consistent due to local similarity.

[0082] The above step S3 can be Figure 2 The steps S31 to S34 shown implement:

[0083] S31 , determining any divided unit as a unit to be analyzed, and taking other divided units in the structural area except the unit to be analyzed as first comparison units.

[0084] For the convenience of description, any division unit in any structural area is selected and recorded as the unit to be analyzed, and all division units in the structural area except the unit to be analyzed are regarded as division units adjacent to the selected unit and recorded as the first comparison units of the unit to be analyzed.

[0085] S32, calculating the first similarities between the stress vector of the unit to be analyzed and the stress vectors of each first comparison unit in the same motion simulation, and obtaining each first similarity corresponding to the unit to be analyzed in each motion simulation.

[0086] In this embodiment, the higher the stress similarity between adjacent divided units, the more dense the grid density of the current structural area is, that is, the grid density is too large. On the contrary, the worse the stress similarity between adjacent divided units, the more significant the stress variation between the divided units is, and the grid density of the current structural area is too sparse, that is, the grid density is too small.

[0087] Specifically, for the stress vector of the unit to be analyzed and the stress vectors of each first comparison unit under the same motion simulation, the cosine similarity between the stress vector of the unit to be analyzed and the stress vector of each first comparison unit is calculated as the first similarity. The process of calculating the cosine similarity is prior art and is beyond the scope of protection of the present invention and will not be elaborated on here.

[0088] S33 , calculating the second similarities between the stress stability index of the unit to be analyzed and the stress stability index of each first comparison unit, and obtaining each second similarity corresponding to the unit to be analyzed.

[0089] In this embodiment, while the stress similarity between adjacent divided units is high, the stress stability between the unit to be analyzed and the first comparison unit is similar. This can indicate that the stress consistency between the unit to be analyzed and other divided units in the structural area is stronger, so it is necessary to determine the second similarity.

[0090] As an example implementation, it includes:

[0091] The absolute value of the difference between the stress stability index of the unit to be analyzed and the stress stability index of each first comparison unit is calculated, and the absolute value of the difference is negatively correlated to obtain a negative correlation value, which is used as the second similarity.

[0092] Specifically, the reciprocal of the absolute value of the difference is taken, and the reciprocal value is used as the second similarity. In extreme cases, if the difference between the stress stability index of the unit to be analyzed and the stress stability index of the first comparison unit is zero, the second similarity analysis is not performed on the first comparison unit, or a non-zero constant, such as 0.01, is added to the denominator of the fraction.

[0093] S34 , for each motion simulation, fusing the first similarities corresponding to the unit to be analyzed and the second similarities corresponding to the unit to be analyzed to obtain a first grid density characteristic value of the unit to be analyzed.

[0094] In this embodiment, after obtaining the first similarity and the second similarity, the network density characteristics are quantified from two perspectives: stress similarity and stress stability similarity.

[0095] If the network density feature is too large, it means that the grid density is too large and the division unit size is too small; if the network density feature is too small, it means that the grid density is too small and the division unit size is too large.

[0096] The above step S34 can be implemented through steps S341 to S342 (not shown):

[0097] S341, for any motion simulation, calculate the product of the first similarity and the second similarity under the same first comparison unit, accumulate all the products to obtain a cumulative value, and use the cumulative value as the stress consistency index between the unit to be analyzed and all the first comparison units in the motion simulation.

[0098] In this embodiment, the first similarity and the second similarity have the same dimension, both being data between 0 and 1. When performing the multiplication calculation, the product calculation is performed on the first similarity and the second similarity of the same first comparison unit.

[0099] As an example, the calculation formula for the stress consistency index between the unit to be analyzed and all first comparison units in any motion simulation can be:

[0100] Where, represents the stress consistency index between the unit to be analyzed and all the first comparison units, K represents the number of the first comparison units, k represents the sequence number of the first comparison unit, represents the stress stability index of the unit to be analyzed, represents the stress stability index of the kth first comparison unit of the unit to be analyzed, represents the absolute value function, cos represents the cosine similarity function, represents the stress vector of the element to be analyzed, Represents the stress vector of the kth first comparison unit of the unit to be analyzed.

[0101] Represents the second similarity, which is used to characterize the stress stability similarity between the unit to be analyzed and the kth first comparison unit. If If it is equal to 0, add a non-zero constant, such as 0.01, to the denominator of the fraction; Represents the first similarity, which is used to characterize the stress vector similarity between the unit to be analyzed and the kth first comparison unit. By determining the stress stability similarity and stress vector similarity between the unit to be analyzed and each first comparison unit, the stress consistency between the unit to be analyzed and all the divided units in the same structural area to which it belongs can be quantified. If the stress consistency index is too large, it means that the mesh density of the corresponding structural area in the same motion simulation is too large and the mesh density needs to be reduced.

[0102] S342 , calculating an average value of all stress consistency indices by the stress consistency indices between the unit to be analyzed and all first comparison units during each motion simulation, and obtaining a first mesh density characteristic value of the unit to be analyzed.

[0103] In this embodiment, the aforementioned step S341 is implemented to obtain the stress consistency index corresponding to the unit to be analyzed during each motion simulation, obtain a stress consistency index sequence, and calculate the average value of the stress consistency index sequence as the first mesh density eigenvalue. If the unit to be analyzed has high stress consistency during each motion simulation, it indicates that the similarity between the unit to be analyzed and other divided units in the structural area during the robot finite element simulation process is too great, and the initial mesh density is too high, and the mesh density needs to be reduced.

[0104] Referring to the calculation process of the first grid density eigenvalue of the unit to be analyzed, the first grid density eigenvalue of each divided unit in each structural area can be obtained, and the steps of determining the first grid density eigenvalue of each divided unit will not be repeated here.

[0105] So far, this embodiment has obtained the first grid density characteristic value of each division unit in each structural area.

[0106] S4, determining a second mesh density eigenvalue of each division unit according to the stress vector difference between each division unit and each node during each motion simulation and in combination with the stress stability index of each division unit.

[0107] The above step S3 considers that the simulation efficiency of the robot structure design is low due to the grid density being too high. However, a grid density that is too low will result in low simulation accuracy of the robot structure design. Specifically, the stability of the division unit depends on the position of the division unit in the robot structure. When the division unit appears in a special position, such as in the body structure area, it is not sensitive to the stress changes in the simulation process. Although the grid density division accuracy of the body structure area is low, it will not affect its high stress stability; when the division unit appears in an area with a large stress gradient, such as the sharp corners of the robot joints and the bolted joints, if the grid density is too small, the stress of the division unit may underestimate the local peak stress, and the node stress is closer to the true value through extrapolation, resulting in a significant difference between the unit stress and the node stress.

[0108] The large difference between the unit stress and the nodal stress in this example indicates that the internal stress distribution undergoes dramatic changes during the robot's simulated motion. For example, when the robot's finger joints bend, stress concentrates in the contact area. If the mesh density is too low, the unit stress will show a low average value, while the nodal stress may capture local peaks through extrapolation, requiring an increase in the mesh density.

[0109] The above step S4 can be Figure 3 The steps S41 to S43 shown implement:

[0110] S41 , determining any node as a node to be analyzed, and obtaining a division unit connected to the node to be analyzed as a second comparison unit.

[0111] In this embodiment, any node in the robot structure is taken as an example for analysis, and any node is used as the node to be analyzed, and the division unit connected to the node to be analyzed is used as the comparison unit of the node to be analyzed. In order to distinguish it from the first comparison unit, it is recorded as the second comparison unit.

[0112] S42, for each motion simulation, calculating the modulus of the vector difference between the stress vector of the node to be analyzed and the stress vector of each second comparison unit, and taking the average of the ratios of each modulus to the stress stability index of the corresponding second comparison unit as the density deviation of the node to be analyzed.

[0113] In this embodiment, the density deviation of the finite element node division can reflect whether the mesh density needs to be increased. If the density deviation is too large, it indicates that the robot structure's mesh division is too sparse, that is, the mesh density is too low, and the mesh density needs to be increased. If the difference between the unit stress and the node stress is large and the unit stress stability is low, it indicates that the current finite element mesh division is inaccurate and the density deviation is large.

[0114] As an example, the calculation formula for the density deviation of the node to be analyzed can be:

[0115] Where, represents the density deviation of the node to be analyzed, E represents the mean function, represents the stress vector of the mth second comparison element of the node to be analyzed, represents the stress vector of the node to be analyzed, It means to find the modulus of a vector. represents the stress stability index of the mth second comparison unit of the node to be analyzed, M represents the number of the second comparison units of the node to be analyzed, and m represents the sequence number of the second comparison unit.

[0116] In general, There is no possibility of zero. If an extreme case occurs, then in the fraction Add a non-zero constant, such as 0.01, to the denominator.

[0117] S43, obtaining the density deviation of each node during each motion simulation, and determining the second grid density characteristic value of each divided unit according to the density deviation of each node during each motion simulation.

[0118] In this embodiment, after obtaining the density deviation of the node to be analyzed during any motion simulation, the density deviation of each node during each motion simulation is obtained by referring to the implementation process of steps S41 to S42. Subsequently, based on the density deviation of each node during each motion simulation, the second mesh density eigenvalue of each partitioned unit is quantified.

[0119] The above step S43 can be implemented through steps S431 to S432 (not shown):

[0120] S431, calculating the mean and standard deviation of the density deviation of the node to be analyzed during each motion simulation, performing negative correlation normalization processing on the product of the mean and the standard deviation to obtain a normalized value, and using the normalized value as the mesh density sub-feature of the node to be analyzed.

[0121] In this embodiment, if the same node has a high density deviation during each motion simulation, and the node density deviation changes are unstable, it means that subtle parameter changes during these motion simulations may cause large errors in the robot structural analysis results, resulting in non-convergence of the node simulation results. The resulting simulation results are of no reference value and seriously interfere with the subsequent simulation optimization of the robot. The reason for the high node density deviation and poor density stability under different motion simulations is that the initial mesh parameters are too small and the division unit is too large, resulting in complex internal stress in each division unit. Treating a unit with complex stress as a whole for structural optimization can cause large errors in the simulation process. In this case, the mesh density needs to be increased.

[0122] However, in order to correspond to the logical meaning of the first grid density eigenvalue, that is, if the network density feature is too large, it means that the grid density is too large and the division unit size is too small, and the grid density needs to be reduced. After calculating the mean and standard deviation of the density deviation of the node to be analyzed, the product of the mean and standard deviation is negatively correlated to ensure that the physical meaning of the second grid density eigenvalue determined subsequently is consistent with that of the first grid density eigenvalue.

[0123] As an example, the calculation formula for the mesh density sub-feature of the node to be analyzed can be:

[0124] Where, Represents the grid density sub-feature of the node to be analyzed, exp represents the exponential function with the natural constant as the base, and exp (-) is used to achieve negative correlation normalization processing. It represents the mean value of the density deviation of the nodes to be analyzed during all motion simulations. It represents the standard deviation of the density deviation of the nodes to be analyzed during all motion simulations. It represents the density deviation of the node to be analyzed during the lth motion simulation, L represents the number of motion simulations, and l represents the sequence number of different motion simulations.

[0125] S432 : Determine a second mesh density characteristic value of each division unit according to the mesh density sub-characteristic of each node corresponding to each division unit.

[0126] In this embodiment, under normal circumstances, the mesh density of the robot body is relatively low. However, if the body is damaged during a simulation, such as a tiny hole or crack, the defect has a limited impact on the entire unit due to the large division unit. As a result, the stress change of the division unit during the motion simulation is not obvious. However, because only the stress change of a specific node is considered, the stress change of the node is significant. Therefore, it is necessary to combine the mesh density sub-features of each node to analyze the mesh density characteristics of the division unit.

[0127] First, any division unit is determined as the unit to be analyzed, and all other division units in the structural area except the unit to be analyzed are determined as the first comparison units; the grid density sub-feature mean and grid density sub-feature standard deviation of all nodes corresponding to the unit to be analyzed, as well as the grid density sub-feature mean of all nodes corresponding to each first comparison unit are calculated.

[0128] For the unit to be analyzed, if the nodal stress consistency of the unit to be analyzed is similar to the nodal stress consistency of all units, it means that the nodal stress of the unit to be analyzed and the nodal stress of other adjacent units are consistent during the simulation process. The unit to be analyzed and its surrounding units can be appropriately merged to avoid the occurrence of too large a grid density and resulting in too small a divided unit; on the contrary, if the nodal stress consistency of the unit to be analyzed is significantly different from the nodal stress consistency of all units, it means that the grid density of the unit to be analyzed is too small and the unit needs to be further subdivided.

[0129] Secondly, the second grid density eigenvalue of the unit to be analyzed is determined according to the difference in grid density sub-feature means between the unit to be analyzed and all first comparison units, the grid density sub-feature mean and the grid density sub-feature standard deviation of the unit to be analyzed.

[0130] Among them, the difference in grid density sub-feature means, the grid density sub-feature standard deviation and the second grid density eigenvalue are negatively correlated, and the grid density sub-feature mean of the unit to be analyzed and the second grid density eigenvalue are positively correlated.

[0131] As an example, the calculation formula for the second grid density eigenvalue of the unit to be analyzed can be:

[0132] ; Where p represents the second grid density eigenvalue of the unit to be analyzed, norm represents the linear normalization function, represents the mean value of the grid density sub-characteristic of the unit to be analyzed, represents the mean value of the grid density sub-features among all first comparison units, represents the difference in the mean value of the grid density sub-feature between the unit to be analyzed and all the first comparison units, Represents the standard deviation of the grid density sub-characteristic of the cell to be analyzed.

[0133] It should be noted that the second mesh density eigenvalue p of the element to be analyzed is obtained through node features. This can be used to prevent excessive mesh density. A larger second mesh density eigenvalue indicates excessive mesh density in the finite element meshing. By referring to the calculation process for the second mesh density eigenvalue of the element to be analyzed, the second mesh density eigenvalue of each element can be obtained.

[0134] So far, this embodiment has obtained the second grid density characteristic value of each division unit in each structural area.

[0135] S5, adjusting the mesh density of each structural region according to the first mesh density eigenvalue and the second mesh density eigenvalue to obtain an optimal mesh density for each structural region.

[0136] In this embodiment, the first and second mesh density eigenvalues are used to characterize the mesh density characteristics of the partition unit, which can be used to measure the rationality of the mesh density setting of the partition unit. After determining the rationality of the mesh density setting, the mesh density of each structural region is adaptively adjusted based on the rationality of the setting to obtain the optimal mesh density for each structural region.

[0137] The above step S5 can be implemented through steps S51 to S52 (not shown):

[0138] S51 , determining a grid density parameter of each structural region according to a first grid density eigenvalue and a second grid density eigenvalue of each division unit; and setting a first density parameter threshold and a second density parameter threshold.

[0139] Specifically, for each structural area, the product of the first grid density eigenvalue and the second grid density eigenvalue of each division unit is calculated as the grid density parameter of the corresponding division unit; the average of the grid density parameters of all division units in the same structural area is taken as the grid density parameter of the corresponding structural area.

[0140] In this embodiment, the first density parameter threshold is smaller than the second density parameter threshold. The two density parameter thresholds are used to represent the standard for dividing the grid density. The first density parameter threshold can take an empirical value of 0.45, and the second density parameter threshold can take an empirical value of 0.55. The implementer can set the size of the first density parameter threshold and the second density parameter threshold according to the specific grid division standard requirements without making specific restrictions.

[0141] S52 , for each structural region, comparing the size relationship between the grid density parameter of the structural region and the first density parameter threshold and the second density parameter threshold, and adjusting the grid density of the structural region based on the comparison result.

[0142] If the mesh density parameter of the structural region is less than the first density parameter threshold, the mesh density of the structural region is increased until the mesh density parameter of the structural region is within the numerical range corresponding to the first density parameter threshold and the second density parameter threshold, thereby obtaining the optimal mesh density of the structural region.

[0143] If the mesh density parameter of the structural area is greater than the second density parameter threshold, the mesh density of the structural area is reduced until the mesh density parameter of the structural area is within the numerical range corresponding to the first density parameter threshold and the second density parameter threshold, thereby obtaining the optimal mesh density of the structural area.

[0144] It is worth noting that when adjusting the mesh density of the structural area, the finite element software has different mesh density level options. When the mesh density of the structural area is increased, the mesh density level is increased; when the mesh density of the structural area is decreased, the mesh density level is decreased.

[0145] After increasing or decreasing the grid density level, the grid density parameter of the structural area after the grid density is updated is determined again, and compared with the density parameter threshold. The above grid density parameter determination and grid density adjustment operations are repeated until the grid density parameter of the structural area is within the numerical range corresponding to the first density parameter threshold and the second density parameter threshold. The adjusted grid density at this time is used as the optimal grid density of the corresponding structural area.

[0146] Thus far, this embodiment has completed the optimization simulation of the robot structure design using the improved finite element model.

[0147] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A robot structure design optimization simulation method based on finite element model, characterized in that: The following steps are involved: Obtain each division unit in each structural area of the robot in a stationary state, perform several motion simulations on each structural area, and obtain the stress vector of each division unit and the stress vector of each node during each motion simulation; According to each stress component in the stress vector of each divided unit, the stress component fluctuation in the same dimension during multiple motion simulations is analyzed to determine the stress stability index of each divided unit; Determine the first grid density eigenvalue of each division unit based on the similarity of stress stability index between each division unit and other division units around it and the similarity of stress vectors under the same motion simulation; According to the stress vector difference between each division unit and each node during each motion simulation, combined with the stress stability index of each division unit, the second mesh density eigenvalue of each division unit is determined; The mesh density of each structural region is adjusted according to the first mesh density eigenvalue and the second mesh density eigenvalue to obtain the optimal mesh density of each structural region.

2. The robot structure design optimization simulation method based on the finite element model according to claim 1 is characterized in that: The step of analyzing the stress component fluctuations in the same dimension during multiple motion simulations based on the stress components in the stress vectors of the divided units to determine the stress stability index of each divided unit includes: For any divided unit, a stress component sequence in the same dimension during multiple motion simulations is obtained, a stable value of the stress component sequence is calculated, and the stable value of each dimension is obtained; The minimum value is selected from all the stability values as the stress stability index of the corresponding divided unit.

3. The robot structure design optimization simulation method based on finite element model according to claim 1, characterized in that: The determining of the first mesh density eigenvalue of each division unit according to the similarity of the stress stability index between each division unit and other surrounding division units and the similarity of the stress vectors under the same motion simulation includes: Determine any divided unit as a unit to be analyzed, and use other divided units in the structural area except the unit to be analyzed as first comparison units; Calculating a first similarity between the stress vector of the unit to be analyzed and the stress vectors of each first comparison unit in the same motion simulation, and obtaining each first similarity corresponding to the unit to be analyzed in each motion simulation; Calculating a second similarity between the stress stability index of the unit to be analyzed and the stress stability index of each first comparison unit to obtain each second similarity corresponding to the unit to be analyzed; For each motion simulation, each of the first similarities corresponding to the unit to be analyzed and each of the second similarities corresponding to the unit to be analyzed are fused to obtain a first mesh density eigenvalue of the unit to be analyzed.

4. The robot structure design optimization simulation method based on finite element model according to claim 3 is characterized in that: The obtaining of each second similarity corresponding to the unit to be analyzed includes: The absolute value of the difference between the stress stability index of the unit to be analyzed and the stress stability index of each first comparison unit is calculated, and the absolute value of the difference is negatively correlated to obtain a negative correlation value, and the negative correlation value is used as the second similarity.

5. The robot structure design optimization simulation method based on finite element model according to claim 4 is characterized in that: The fusing the first similarities corresponding to the unit to be analyzed and the second similarities corresponding to the unit to be analyzed to obtain a first grid density eigenvalue of the unit to be analyzed includes: For any motion simulation, calculating the product of the first similarity and the second similarity for the same first comparison unit, accumulating all the products to obtain an accumulated value, and using the accumulated value as the stress consistency index between the unit to be analyzed and all the first comparison units in the motion simulation; The stress consistency index between the unit to be analyzed and all first comparison units in each motion simulation is used to calculate the average value of all stress consistency indices to obtain the first mesh density characteristic value of the unit to be analyzed.

6. The robot structure design optimization simulation method based on finite element model according to claim 1, characterized in that: The second mesh density eigenvalue of each division unit is determined based on the stress vector difference between each division unit and each node during each motion simulation and in combination with the stress stability index of each division unit, including: Determine any node as a node to be analyzed, and obtain a division unit connected to the node to be analyzed as a second comparison unit; For each motion simulation, the modulus of the vector difference between the stress vector of the node to be analyzed and the stress vector of each second comparison unit is calculated, and the average of the ratios of each modulus to the stress stability index of the corresponding second comparison unit is taken as the density deviation of the node to be analyzed; The density deviation of each node during each motion simulation is obtained, and the second grid density eigenvalue of each divided unit is determined according to the density deviation of each node during each motion simulation.

7. The robot structure design optimization simulation method based on finite element model according to claim 6, characterized in that: The determining of the second grid density characteristic value of each divided unit according to the density deviation of each node during each motion simulation includes: Calculating the mean and standard deviation of the density deviation of the node to be analyzed during each motion simulation, performing negative correlation normalization processing on the product of the mean and the standard deviation to obtain a normalized value, and using the normalized value as the mesh density sub-feature of the node to be analyzed; A second mesh density characteristic value of each division unit is determined according to the mesh density sub-characteristic of each node corresponding to each division unit.

8. The robot structure design optimization simulation method based on finite element model according to claim 7, characterized in that: The determining, according to the mesh density sub-feature of each node corresponding to each division unit, a second mesh density eigenvalue of each division unit includes: Determine any divided unit as a unit to be analyzed, and determine all other divided units in the structural area except the unit to be analyzed as first comparison units; calculate the grid density sub-feature mean and grid density sub-feature standard deviation of all nodes corresponding to the unit to be analyzed, and the grid density sub-feature mean of all nodes corresponding to each first comparison unit; Determine a second grid density eigenvalue of the unit to be analyzed based on a difference in the grid density sub-feature mean between the unit to be analyzed and all first comparison units, the grid density sub-feature mean and the grid density sub-feature standard deviation of the unit to be analyzed; Among them, the difference in the mean of the grid density sub-feature, the standard deviation of the grid density sub-feature and the second grid density eigenvalue are negatively correlated, and the mean of the grid density sub-feature of the unit to be analyzed and the second grid density eigenvalue are positively correlated.

9. The robot structure design optimization simulation method based on finite element model according to claim 1, characterized in that: The step of adjusting the mesh density of each structural region by using the first mesh density eigenvalue and the second mesh density eigenvalue to obtain an optimal mesh density for each structural region includes: determining a grid density parameter for each structural region according to the first grid density eigenvalue and the second grid density eigenvalue of each division unit; setting a first density parameter threshold and a second density parameter threshold, wherein the first density parameter threshold is less than the second density parameter threshold; For each structural region, if the grid density parameter of the structural region is less than the first density parameter threshold, the grid density of the structural region is increased until the grid density parameter of the structural region is within a numerical range corresponding to the first density parameter threshold and the second density parameter threshold, thereby obtaining an optimal grid density of the structural region; If the grid density parameter of the structural area is greater than the second density parameter threshold, the grid density of the structural area is reduced until the grid density parameter of the structural area is within the numerical range corresponding to the first density parameter threshold and the second density parameter threshold, thereby obtaining the optimal grid density of the structural area.

10. The robot structure design optimization simulation method based on finite element model according to claim 9, characterized in that: The determining of the mesh density parameter of each structural region according to the first mesh density eigenvalue and the second mesh density eigenvalue of each division unit includes: For each structural region, calculating the product of the first grid density eigenvalue and the second grid density eigenvalue of each division unit as a grid density parameter of the corresponding division unit; The mean value of the grid density parameters of all division units in the same structural area is taken as the grid density parameter of the corresponding structural area.

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