A six-dimensional force sensor elastic body optimization method

Abstract

A six-dimensional force sensor elastic body optimization method belongs to the field of structural optimization. It solves the problems of low optimization efficiency and easy non-convergence in the existing six-dimensional force sensor elastic body structural optimization method. The method of the present invention adopts a combination of finite element analysis and mathematical calculation to realize the screening of the main structural parameter variables that affect the condition number, and obtains the optimal elastic body safety factor fitting function, the optimal elastic body mass fitting function and the optimal condition number fitting function. At the same time, the main structural size parameters screened out are used as independent variables, the optimal elastic body safety factor fitting function is used as a constraint condition, and the weighted function value of the optimal condition number fitting function and the optimal mass fitting function is minimized as the optimization goal for optimization, which improves the optimization efficiency and makes the optimization process easy to converge. The present invention is mainly used in the field of force sensor elastic body structural optimization.

Description

Technical Field

[0001] The invention belongs to the field of structure optimization. Background Art

[0002] With the rapid development of humanoid robots, more and more types of humanoid robots are emerging. As one of the core components of humanoid robots, the six-dimensional force sensor must be adapted to humanoid robots of different types and sizes. This requires that the elastic structure of the six-dimensional force sensor can be quickly optimized according to demand.

[0003] Existing optimization methods for the elastic body structure of six-dimensional force sensors generally only use finite element analysis software to perform static simulation of the elastic body structure. By limiting the range of target parameters to be optimized (including but not limited to the elastic body strain beam strain, elastic body safety factor, and elastic body structure mass) that can be directly obtained by the finite element analysis software or manually setting the target parameter values ​​to be optimized, they do not consider the essence of sensor elastic body structure optimization and therefore cannot obtain the optimal solution. Alternatively, they use a single optimization algorithm combined with finite element analysis software for optimization. Although this method optimizes the elastic body structure based on the essence of sensor optimization, it still suffers from low optimization efficiency and is prone to non-convergence problems. Therefore, there is an urgent need for an optimization method that can achieve rapid optimization of the elastic body structure of a six-dimensional force sensor. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems of low optimization efficiency and easy non-convergence in the existing six-dimensional force sensor elastic body structure optimization method. The present invention provides a six-dimensional force sensor elastic body optimization method.

[0005] A six-dimensional force sensor elastic body optimization method, the method comprising:

[0006] Step 1: Import the three-dimensional model of the elastic body structure of the six-dimensional force sensor to be optimized into the finite element analysis software for parameterization, set multiple structural parameter variables that affect the elastic body structure strain, the parameter range of each structural parameter variable, and the elastic body safety factor range;

[0007] Step 2: Select parameter values ​​within the parameter range of each structural parameter variable in turn to obtain a set of structural parameters, thereby obtaining multiple sets of structural parameters, and then combine finite element analysis software and response surface method to eliminate weakly correlated structural parameter variables;

[0008] Step 3: Use central composite CCD or Box-Behnken to perform static simulation of the elastic body on each group of structural parameters after eliminating weak correlation, and obtain the elastic body safety factor, elastic body mass and strain beam strain corresponding to the corresponding components;

[0009] Step 4: Using the strain of the strain beam under each component, obtain the strain compliance matrix under the corresponding component and calculate the condition number of the strain compliance matrix; wherein the strain beam is a connecting beam with strain gauges attached to the elastic structure;

[0010] Step 5: Using four response surface model functions, fit the structural parameters of all components after eliminating weak correlations and the elastic body safety factors corresponding to all components to obtain four elastic body safety factor fitting functions;

[0011] Four response surface model functions were used to fit the structural parameters of all components after eliminating weak correlations and the corresponding elastomer masses of all components, and four elastomer mass fitting functions were obtained;

[0012] Four response surface model functions were used to fit the structural parameters of all components after eliminating weak correlations with the condition numbers of the strain compliance matrices corresponding to all components, and four condition number fitting functions were obtained.

[0013] Step 6: Screening the four elastic body safety factor fitting functions, the four elastic body mass fitting functions, and the four condition number fitting functions to obtain the optimal elastic body safety factor fitting function, the optimal elastic body mass fitting function, and the optimal condition number fitting function;

[0014] Step seven, constrain the optimal elastic body safety factor fitting function with the parameter range of each structural parameter variable remaining after eliminating weak correlation and the set elastic body safety factor range, and use the constrained optimal elastic body safety factor fitting function as a constraint condition, and at the same time take the minimum value of the weighted function constructed by the optimal condition number fitting function and the optimal quality fitting function as the optimization goal, and use the optimization algorithm to optimize, and obtain the optimal value of each structural parameter variable remaining after eliminating weak correlation, as well as the optimal value of the elastic body safety factor, the optimal value of the strain beam strain, the optimal value of the elastic body mass and the optimal value of the condition number corresponding to the corresponding structural parameter variable remaining after eliminating weak correlation.

[0015] Preferably, in step 2, the method of eliminating weakly correlated structural parameter variables by combining finite element analysis software and response surface methodology includes:

[0016] Finite element analysis software is used to perform static simulation of the elastic body for each selected group of structural parameters to obtain the elastic body safety factor, elastic body mass and strain beam strain corresponding to the corresponding components;

[0017] According to all component structural parameters and all elastic body safety factors, elastic body mass and strain beam strain, the response surface methodology is used to determine the correlation between each structural parameter variable and the strain beam strain, elastic body mass and elastic body safety factor, and the weakly correlated structural parameter variables are eliminated from all component structural parameters.

[0018] Preferably, when the correlation values ​​of the corresponding structural parameter variable with the strain of the strain beam, the correlation values ​​with the mass of the elastic body, and the correlation values ​​with the safety factor of the elastic body are all less than the preset correlation values, the structural parameter variable is determined to be a weakly correlated structural parameter variable.

[0019] Preferably, the structural parameter variable is one or more of the diameter of the main structure of the elastomer, the diameter of the through hole on the elastomer, the length of the connecting beam, the width of the connecting beam and the thickness of the connecting beam.

[0020] Preferably, the four response surface model functions are linear, interactive, quadratic and pure quadratic response surface model functions.

[0021] Preferably, the fourth step, using the strain beam strain of each component to obtain the strain compliance matrix of the corresponding component, is implemented as follows:

[0022] Step 41: For the strain beams in the same component, only the strain calculation part is retained after the Wheatstone bridge group calculation to obtain the strain beam strain matrix ε;

[0023] Among them, ε=[ε1ε2…ε6];

[0024] ε1 is the strain of the strain beam under the action of force Fx, ε2 is the strain of the strain beam under the action of force Fy, ε3 is the strain of the strain beam under the action of force Fz, ε4 is the strain of the strain beam under the action of moment Mx, ε5 is the strain of the strain beam under the action of moment My, and ε6 is the strain of the strain beam under the action of moment Mz;

[0025] F = [FxFyFzMxMyMz] is the force applied to the six-dimensional force sensor to be optimized;

[0026] Fx, Fy and Fz are the component column vectors of the force applied to the six-dimensional force sensor to be optimized in the x-axis, y-axis and z-axis directions of its coordinate system, respectively; Mx, My and Mz are the component column vectors of the force applied to the optimized six-dimensional force sensor in the x-axis, y-axis and z-axis directions of its coordinate system, respectively;

[0027] Step 4.2: Use the strain matrix ε of the strain beam to obtain the strain compliance matrix C ε , and ε=C ε F -1 .

[0028] Preferably, step 4, calculating the condition number of the strain compliance matrix is ​​implemented as follows:

[0029] C0=||C ε ||||C ε -1 ||;

[0030] Among them, C ε is the strain compliance matrix, C0 is the condition number of the strain compliance matrix, and ||·|| represents the norm of the matrix.

[0031] Preferably, step six, obtaining the optimal elastic body safety factor fitting function, the optimal elastic body mass fitting function and the optimal condition number fitting function is implemented as follows:

[0032] Calculate the adjusted R-squared statistics of the four elastic body safety factor fitting functions, and take the elastic body safety factor fitting function corresponding to the largest adjusted R-squared statistic as the optimal elastic body safety factor fitting function;

[0033] Calculate the adjusted R-squared statistics of the four elastic body mass fitting functions, and take the elastic body mass fitting function corresponding to the largest adjusted R-squared statistic as the optimal elastic body mass fitting function;

[0034] Calculate the adjusted R-squared statistics of the four condition number fitting functions, and take the condition number fitting function corresponding to the largest adjusted R-squared statistic as the optimal condition number fitting function.

[0035] Preferably, in step seven, the implementation of constructing the weighting function using the optimal condition number fitting function and the optimal quality fitting function includes:

[0036] First, the optimal condition number fitting function f1 and the optimal quality fitting function f2 are used to standardize the optimal condition number fitting function and the optimal quality fitting function to obtain the standardized optimal condition number fitting function f 1norm and the standardized optimal quality fitting function f 2norm ;

[0037] f 1norm =(f1-f 1mean ) / f 1std ;

[0038] f 2norm =(f2-f 2mean ) / f 2std ;

[0039] Among them, f 1mean 、f 1std 、f 2mean and f 2std are the average value of the strain compliance matrix condition number corresponding to all component structural parameters, the standard deviation of the strain compliance matrix condition number corresponding to all component structural parameters, the average value of the elastic body mass corresponding to all component structural parameters, and the standard deviation of the elastic body mass corresponding to all component structural parameters before optimization by the optimization algorithm;

[0040] Secondly, according to f1norm and f 2norm Construct a weighting function f;

[0041] f=ω1·f 1norm +ω2·f 2norm ;

[0042] Among them, ω1 and ω2 are f 1norm With f 2norm The weight value of ω1+ω2=1.

[0043] Preferably, in step 2, the parameter values ​​of all structural parameter variables in each group of structural parameters are selected with the same step size.

[0044] Advantages of the present invention:

[0045] The method of the present invention adopts a combination of finite element analysis and mathematical calculation to realize the screening of main structural parameter variables that affect the condition number, and obtains the optimal elastic body safety factor fitting function and the optimal condition number fitting function. At the same time, the screened main structural size parameters are used as independent variables, the optimal elastic body safety factor fitting function is used as a constraint condition, and the optimization is performed with the minimum weighted function value of the optimal condition number fitting function and the optimal quality fitting function as the optimization goal. It can effectively reduce the elastic body strain compliance matrix condition number and the elastic body mass under the premise of meeting the safety factor requirements, thereby improving the optimization efficiency and the convergence and convergence speed of the optimization algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a six-dimensional force sensor elastic body optimization method described in the present invention.

[0047] Figure 2 It is a structural schematic diagram of the elastomer structure obtained after applying the optimization method of the present invention.

[0048] Figure 3 This is a group diversity graph. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0051] See also Figure 1This embodiment describes a method for optimizing an elastic body of a six-dimensional force sensor, the method comprising:

[0052] Step 1: Import the three-dimensional model of the elastic body structure of the six-dimensional force sensor to be optimized into the finite element analysis software for parameterization, set multiple structural parameter variables that affect the elastic body structure strain, the parameter range of each structural parameter variable, and the elastic body safety factor range;

[0053] Step 2: Select parameter values ​​within the parameter range of each structural parameter variable in turn to obtain a set of structural parameters, thereby obtaining multiple sets of structural parameters, and then combine finite element analysis software and response surface method to eliminate weakly correlated structural parameter variables;

[0054] Step 3: Use central composite CCD or Box-Behnken to perform static simulation of the elastic body on each group of structural parameters after eliminating weak correlation, and obtain the elastic body safety factor, elastic body mass and strain beam strain corresponding to the corresponding components;

[0055] Step 4: Using the strain of the strain beam under each component, obtain the strain compliance matrix under the corresponding component and calculate the condition number of the strain compliance matrix; wherein the strain beam is a connecting beam with strain gauges attached to the elastic structure;

[0056] Step 5, using four response surface model functions to fit the structural parameters of all components after removing weak correlations with the elastic body safety factors corresponding to all components, and obtain four elastic body safety factor fitting functions; using four response surface model functions to fit the structural parameters of all components after removing weak correlations with the elastic body masses corresponding to all components, and obtain four elastic body mass fitting functions; using four response surface model functions to fit the structural parameters of all components after removing weak correlations with the condition numbers of the strain compliance matrices corresponding to all components, and obtain four condition number fitting functions;

[0057] Step 6: Screening the four elastic body safety factor fitting functions, the four elastic body mass fitting functions, and the four condition number fitting functions to obtain the optimal elastic body safety factor fitting function, the optimal elastic body mass fitting function, and the optimal condition number fitting function;

[0058] Step seven, constrain the optimal elastic body safety factor fitting function with the parameter range of each structural parameter variable remaining after eliminating weak correlation and the set elastic body safety factor range, and use the constrained optimal elastic body safety factor fitting function as a constraint condition, and at the same time take the minimum value of the weighted function constructed by the optimal condition number fitting function and the optimal quality fitting function as the optimization goal, and use the optimization algorithm to optimize, and obtain the optimal value of each structural parameter variable remaining after eliminating weak correlation, as well as the optimal value of the elastic body safety factor, the optimal value of the strain beam strain, the optimal value of the elastic body mass and the optimal value of the condition number corresponding to the corresponding structural parameter variable remaining after eliminating weak correlation.

[0059] Specifically, optimization algorithms include, but are not limited to, traditional genetic algorithms and particle swarm optimization algorithms. Other traditional optimization algorithms and their improved optimization algorithms that can achieve target optimization should also be included. Structural parameter variables include, but are not limited to, the diameter of the elastomer body structure, the diameter of the through-holes in the elastomer, the length, width, and thickness of the connecting beam.

[0060] Traditional optimization methods use finite element analysis software to optimize sensors, but can only optimize the limited parameters provided by the finite element analysis software. They cannot perform matrix calculations, let alone calculate the condition number of the elastic body's strain compliance matrix. Therefore, they cannot achieve the joint optimization of the elastic body's strain compliance matrix condition number and the elastic body's mass.

[0061] This embodiment uses a combination of finite element analysis and mathematical calculations to screen the main structural parameter variables that affect the condition number, and obtain the optimal elastic body safety factor fitting function, the optimal elastic body safety factor fitting function and the optimal condition number fitting function. At the same time, the main structural size parameters screened out are used as independent variables, the optimal elastic body safety factor fitting function is used as a constraint condition, and the weighted function value of the optimal condition number fitting function and the optimal quality fitting function is minimized as the optimization goal. It can effectively reduce the elastic body strain compliance matrix condition number and the elastic body mass under the premise of meeting the safety factor requirements, thereby improving the optimization efficiency and the convergence and convergence speed of the optimization algorithm. And Figure 2 A schematic diagram of the optimized elastomer structure is given in FIG.

[0062] Furthermore, in step 2, the implementation method of eliminating weakly correlated structural parameter variables by combining finite element analysis software and response surface methodology includes:

[0063] Finite element analysis software is used to perform static simulation of the elastic body for each selected group of structural parameters to obtain the elastic body safety factor, elastic body mass and strain beam strain corresponding to the corresponding components;

[0064] According to all component structural parameters and all elastic body safety factors, elastic body mass and strain beam strain, the response surface methodology is used to determine the correlation between each structural parameter variable and the strain beam strain, elastic body mass and elastic body safety factor, and the weakly correlated structural parameter variables are eliminated from all component structural parameters.

[0065] Specifically, when the correlation values ​​of the corresponding structural parameter variable with the strain of the strain beam, the correlation values ​​with the mass of the elastic body, and the correlation values ​​with the safety factor of the elastic body are all less than the preset correlation values, the structural parameter variable is determined to be a weakly correlated structural parameter variable.

[0066] In this preferred embodiment, a method of eliminating weakly correlated structural parameter variables is provided, which can effectively reduce the number of selected structural parameters, retain relatively important structural parameters, significantly reduce the overall finite element simulation time, and improve optimization efficiency.

[0067] Furthermore, the four response surface model functions are linear, interactive, quadratic and pure quadratic response surface model functions.

[0068] Furthermore, in step 4, the strain beam strain of each component is used to obtain the strain compliance matrix of the corresponding component as follows:

[0069] Step 41: For the strain beams in the same component, only the strain calculation part is retained after the Wheatstone bridge group calculation to obtain the strain beam strain matrix ε;

[0070] Among them, ε=[ε1ε2…ε6];

[0071] ε1 is the strain of the strain beam under the action of force Fx, ε2 is the strain of the strain beam under the action of force Fy, ε3 is the strain of the strain beam under the action of force Fz, ε4 is the strain of the strain beam under the action of moment Mx, ε5 is the strain of the strain beam under the action of moment My, and ε6 is the strain of the strain beam under the action of moment Mz;

[0072] F = [FxFyFzMxMyMz] is the force applied to the six-dimensional force sensor to be optimized;

[0073] Fx, Fy and Fz are the component column vectors of the force applied to the six-dimensional force sensor to be optimized in the x-axis, y-axis and z-axis directions of its coordinate system, respectively; Mx, My and Mz are the component column vectors of the force applied to the optimized six-dimensional force sensor in the x-axis, y-axis and z-axis directions of its coordinate system, respectively;

[0074] Step 4.2: Use the strain matrix ε of the strain beam to obtain the strain compliance matrix C ε , and ε=C ε F -1 .

[0075] In this preferred embodiment, the strain beam strain matrix ε realizes the unified processing of multiple strain beam strains, simplifies the calculation process, and is easy to expand to high-dimensional problems. The force F is composed of its six component column vectors on the six-dimensional force sensor, so that the force F has an inverse matrix. Finally, the strain compliance matrix C is obtained by matrix operation with the strain beam strain matrix ε. ε .

[0076] Furthermore, the implementation method of step 4, calculating the condition number of the strain compliance matrix is:

[0077] C0=||C ε ||||C ε -1 ||;

[0078] Among them, C ε is the strain compliance matrix, C0 is the condition number of the strain compliance matrix, and ||·|| represents the norm of the matrix.

[0079] Furthermore, in step six, the optimal elastic body safety factor fitting function, the optimal elastic body mass fitting function and the optimal condition number fitting function are obtained as follows:

[0080] Calculate the adjusted R-squared statistics of the four elastic body safety factor fitting functions, and take the elastic body safety factor fitting function corresponding to the largest adjusted R-squared statistic as the optimal elastic body safety factor fitting function;

[0081] Calculate the adjusted R-squared statistics of the four elastic body mass fitting functions, and take the elastic body mass fitting function corresponding to the largest adjusted R-squared statistic as the optimal elastic body mass fitting function;

[0082] Calculate the adjusted R-squared statistics of the four condition number fitting functions, and take the condition number fitting function corresponding to the largest adjusted R-squared statistic as the optimal condition number fitting function.

[0083] In this preferred embodiment, when selecting the optimal elastic body safety factor fitting function, the optimal elastic body mass fitting function and the optimal condition number fitting function, the four fitting methods are evaluated by adjusting the R-square statistic as an indicator. This method can effectively evaluate the fitting effect of the model while avoiding the overfitting problem caused by relying solely on the R-square statistic for evaluation.

[0084] Specifically, in step seven, the implementation method of constructing the weighting function using the optimal condition number fitting function and the optimal quality fitting function includes:

[0085] First, the optimal condition number fitting function f1 and the optimal quality fitting function f2 are used to standardize the optimal condition number fitting function and the optimal quality fitting function to obtain the standardized optimal condition number fitting function f1norm and the standardized optimal quality fitting function f 2norm ;

[0086] f 1norm =(f1-f 1mean ) / f 1std ;

[0087] f 2norm =(f2-f 2mean ) / f 2std ;

[0088] Among them, f 1mean 、f 1std 、f 2mean and f 2std are the average value of the strain compliance matrix condition number corresponding to all component structural parameters, the standard deviation of the strain compliance matrix condition number corresponding to all component structural parameters, the average value of the elastic body mass corresponding to all component structural parameters, and the standard deviation of the elastic body mass corresponding to all component structural parameters before optimization by the optimization algorithm;

[0089] Secondly, according to f 1norm and f 2norm Construct a weighting function f;

[0090] f=ω1·f 1norm +ω2·f 2norm ;

[0091] Among them, ω1 and ω2 are f 1norm With f 2norm The weight value of , and ω1+ω2=1.

[0092] In this preferred embodiment, the optimization is performed with the minimum weighted function value of the optimal condition number fitting function and the optimal quality fitting function as the optimization goal. This can effectively reduce the strain compliance matrix condition number and the mass of the elastic body while meeting the safety factor requirements, thereby improving the optimization efficiency and the convergence and convergence speed of the optimization algorithm.

[0093] In specific applications, in step 2, the principle for selecting parameter values ​​is: the parameter values ​​of all structural parameter variables in each group of structural parameters are selected with the same step size.

[0094] Specific embodiment 2: A six-dimensional force sensor elastomer optimization system described in this embodiment includes a storage device, a processor, and a computer program stored in the storage device and runnable on the processor, characterized in that the processor executes the computer program to implement a six-dimensional force sensor elastomer optimization method as described above.

[0095] Verification test:

[0096] Figure 3 It is a population diversity graph, which reflects the convergence of the optimization process.

[0097] The elastic body optimization method of the six-dimensional force sensor of the present invention is used to optimize the elastic body structure of the six-dimensional force sensor to be measured. When the optimization algorithm adopts the genetic algorithm, the population diversity Figure 3 It can be seen that when the number of generations reaches about 30, the average distance tends to 0, which indicates that the population tends to be concentrated and the objective function value has reached the convergence condition. At this point, the joint optimization of the elastic body strain compliance matrix condition number and the elastic body mass is completed. Since the method of the present invention utilizes the strain beam strain under each component to obtain the strain compliance matrix under the corresponding component and calculates the condition number of the strain compliance matrix, it can also achieve the joint optimization of the elastic body strain compliance matrix condition number and the elastic body mass, so that the objective function value converges quickly. Figure 3 The effectiveness of the present invention is proved.

[0098] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the invention. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be employed in conjunction with other described embodiments.

Claims

1. A six-dimensional force sensor elastic body optimization method, characterized in that: The method includes: Step 1: Import the three-dimensional model of the elastic body structure of the six-dimensional force sensor to be optimized into the finite element analysis software for parameterization, set multiple structural parameter variables that affect the elastic body structure strain, the parameter range of each structural parameter variable, and the elastic body safety factor range; Step 2: Select parameter values ​​within the parameter range of each structural parameter variable in turn to obtain a set of structural parameters, thereby obtaining multiple sets of structural parameters, and then combine finite element analysis software and response surface method to eliminate weakly correlated structural parameter variables; Step 3: Use central composite CCD or Box-Behnken to perform static simulation of the elastic body on each group of structural parameters after eliminating weak correlation, and obtain the elastic body safety factor, elastic body mass and strain beam strain corresponding to the corresponding components; Step 4: Using the strain of the strain beam under each component, obtain the strain compliance matrix under the corresponding component and calculate the condition number of the strain compliance matrix; wherein the strain beam is a connecting beam with strain gauges attached to the elastic structure; Step 5: Using four response surface model functions, fit the structural parameters of all components after eliminating weak correlations and the elastic body safety factors corresponding to all components to obtain four elastic body safety factor fitting functions; Four response surface model functions were used to fit the structural parameters of all components after eliminating weak correlations and the corresponding elastomer masses of all components, and four elastomer mass fitting functions were obtained; Four response surface model functions were used to fit the structural parameters of all components after eliminating weak correlations with the condition numbers of the strain compliance matrices corresponding to all components, and four condition number fitting functions were obtained. Step 6: Screening the four elastic body safety factor fitting functions, the four elastic body mass fitting functions, and the four condition number fitting functions to obtain the optimal elastic body safety factor fitting function, the optimal elastic body mass fitting function, and the optimal condition number fitting function; Step seven, constrain the optimal elastic body safety factor fitting function with the parameter range of each structural parameter variable remaining after eliminating weak correlation and the set elastic body safety factor range, and use the constrained optimal elastic body safety factor fitting function as a constraint condition, and at the same time take the minimum value of the weighted function constructed by the optimal condition number fitting function and the optimal quality fitting function as the optimization goal, and use the optimization algorithm to optimize, and obtain the optimal value of each structural parameter variable remaining after eliminating weak correlation, as well as the optimal value of the elastic body safety factor, the optimal value of the strain beam strain, the optimal value of the elastic body mass and the optimal value of the condition number corresponding to the corresponding structural parameter variable remaining after eliminating weak correlation.

2. The six-dimensional force sensor elastic body optimization method according to claim 1, characterized in that: In step 2, the implementation methods of combining finite element analysis software and response surface methodology to eliminate weakly correlated structural parameter variables include: Finite element analysis software is used to perform static simulation of the elastic body for each selected group of structural parameters to obtain the elastic body safety factor, elastic body mass and strain beam strain corresponding to the corresponding components; According to all component structural parameters and all elastic body safety factors, elastic body mass and strain beam strain, the response surface methodology is used to determine the correlation between each structural parameter variable and the strain beam strain, elastic body mass and elastic body safety factor, and the weakly correlated structural parameter variables are eliminated from all component structural parameters.

3. The six-dimensional force sensor elastic body optimization method according to claim 2, characterized in that: When the correlation values ​​of the corresponding structural parameter variable with the strain beam strain, the correlation values ​​with the elastic body mass, and the correlation values ​​with the elastic body safety factor are all less than the preset correlation values, the structural parameter variable is determined to be a weakly correlated structural parameter variable.

4. The six-dimensional force sensor elastic body optimization method according to claim 1, characterized in that: The structural parameter variables are one or more of the diameter of the elastic body main structure, the diameter of the through hole on the elastic body, the length of the connecting beam, the width of the connecting beam and the thickness of the connecting beam.

5. The six-dimensional force sensor elastic body optimization method according to claim 1, characterized in that: The four response surface model functions are linear, interactive, quadratic and pure quadratic response surface model functions.

6. The six-dimensional force sensor elastic body optimization method according to claim 1, characterized in that: Step 4: Using the strain beam strain of each component, the strain compliance matrix of the corresponding component is obtained as follows: Step 41: For the strain beams in the same component, only the strain calculation part is retained after the Wheatstone bridge group calculation to obtain the strain beam strain matrix ε; Among them, ε=[ε1ε2…ε6]; ε1 is the strain of the strain beam under the action of force Fx, ε2 is the strain of the strain beam under the action of force Fy, ε3 is the strain of the strain beam under the action of force Fz, ε4 is the strain of the strain beam under the action of moment Mx, ε5 is the strain of the strain beam under the action of moment My, and ε6 is the strain of the strain beam under the action of moment Mz; F = [FxFyFzMxMyMz] is the force applied to the six-dimensional force sensor to be optimized; Fx, Fy and Fz are the component column vectors of the force applied to the six-dimensional force sensor to be optimized in the x-axis, y-axis and z-axis directions of its coordinate system, respectively; Mx, My and Mz are the component column vectors of the force applied to the optimized six-dimensional force sensor in the x-axis, y-axis and z-axis directions of its coordinate system, respectively; Step 4.2: Use the strain matrix ε of the strain beam to obtain the strain compliance matrix C ε , and ε=C ε F -1 .

7. The six-dimensional force sensor elastic body optimization method according to claim 1, characterized in that: Step 4: Calculate the condition number of the strain compliance matrix as follows: C0=||C ε ||||C ε -1 ||; Among them, C ε is the strain compliance matrix, C0 is the condition number of the strain compliance matrix, and ||·|| represents the norm of the matrix.

8. The six-dimensional force sensor elastic body optimization method according to claim 1, characterized in that: Step 6: The implementation method of obtaining the optimal elastic body safety factor fitting function, the optimal elastic body mass fitting function and the optimal condition number fitting function is as follows: Calculate the adjusted R-squared statistics of the four elastic body safety factor fitting functions, and take the elastic body safety factor fitting function corresponding to the largest adjusted R-squared statistic as the optimal elastic body safety factor fitting function; Calculate the adjusted R-squared statistics of the four elastic body mass fitting functions, and take the elastic body mass fitting function corresponding to the largest adjusted R-squared statistic as the optimal elastic body mass fitting function; Calculate the adjusted R-squared statistics of the four condition number fitting functions, and take the condition number fitting function corresponding to the largest adjusted R-squared statistic as the optimal condition number fitting function.

9. The six-dimensional force sensor elastic body optimization method according to claim 1, characterized in that: In step seven, the implementation method of constructing the weighting function using the optimal condition number fitting function and the optimal quality fitting function includes: First, the optimal condition number fitting function f1 and the optimal quality fitting function f2 are used to standardize the optimal condition number fitting function and the optimal quality fitting function to obtain the standardized optimal condition number fitting function f 1norm and the standardized optimal quality fitting function f 2norm ; in 1norm =(f1-f 1mean ) / f 1std ; f 2norm =(f2-f 2mean ) / f 2std ; Among them, f 1mean 、f 1std 、f 2mean and f 2std are the average value of the strain compliance matrix condition number corresponding to all component structural parameters, the standard deviation of the strain compliance matrix condition number corresponding to all component structural parameters, the average value of the elastic body mass corresponding to all component structural parameters, and the standard deviation of the elastic body mass corresponding to all component structural parameters before optimization by the optimization algorithm; Secondly, according to f 1norm and f 2norm Construct a weighting function f; f=ω1·f 1norm +ω2·f 2norm ; Among them, ω1 and ω2 are f 1norm With f 2norm The weight value of , and ω1+ω2=1.

10. The six-dimensional force sensor elastic body optimization method according to claim 1, characterized in that: In step 2, the parameter values ​​of all structural parameter variables in each group of structural parameters are selected with the same step size.

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