Industrial robot scale optimization design framework and implementation method

Abstract

The application discloses a kind of industrial robot scale optimization design framework and implementation method, design framework includes modeling module, performance analysis module and parameter optimization module;Modeling module includes topological structure conceptual model, mechanical structure scheme model and finite element parameterized model, for the simulation estimation of robot kinematics, rigid body dynamics and elastic dynamics performance under different scale and configuration conditions;Performance analysis module is used to analyze the global kinematics and dynamics performance of robot, and constructs the local performance index that can replace global performance analysis, and then reveals the change law of kinematics and dynamics performance with scale parameter;Parameter optimization module is used to optimize the scale parameter of robot based on multi-objective optimization algorithm, solves the coupling and competition problem between different performances.The application effectively solves the problem of industrial robot scale optimization design, reduces the design difficulty, improves the design effect and efficiency.

Description

Technical Field

[0001] This invention relates to the field of industrial robot technology, and in particular to a design framework and implementation method for optimizing the dimensions of industrial robots. Background Technology

[0002] Because the kinematic and dynamic properties of industrial robots conflict with each other and vary with their configuration, the optimization of their scale parameters is quite complex. This requires addressing two main issues: first, the coupling or conflict between different performance indicators; and second, the high computational cost in global performance analysis of industrial robots (especially elastic dynamics analysis).

[0003] Traditional methods involve establishing analytical or semi-analytical theoretical models of robot kinematics and dynamics, and then weighting and unifying various performance indicators into a single-objective optimization problem. The limitations of this approach are twofold: first, establishing the theoretical model requires designers to possess advanced mathematical and mechanical modeling knowledge, which is difficult and inconvenient for enterprise engineering designers; second, the single-objective optimization problem struggles to reflect the coupling or conflict between various performance characteristics of industrial robots, such as kinematics, rigid body dynamics, and elastic dynamics, and the determination of weights is subjective.

[0004] If computer-aided technologies such as CAD and CAE are used, commercial software only provides digital prototype modeling and simulation functions for specific fields or tasks. There are obstacles to the mapping and connection of models between different design stages, and it is difficult to effectively support the scale optimization design problem of industrial robots. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the existing technology, it is desirable to provide a framework and implementation method for the dimensional optimization design of industrial robots, which can effectively solve the problem of dimensional optimization design of industrial robots.

[0006] This invention provides a scale optimization design framework for industrial robots, including a modeling module, a performance analysis module, and a parameter optimization module; The modeling module includes a topological conceptual model, a mechanical structure scheme model, and a finite element parameterized model, which are used for simulation and prediction of the robot's kinematics, rigid body dynamics, and elastic dynamics performance under different scales and configurations. The performance analysis module is used to analyze the robot's global kinematics and dynamics performance, and to construct local performance indices that can replace global performance analysis, thereby revealing the variation law of kinematics and dynamics performance with scale parameters. The parameter optimization module is used to optimize the robot's scale parameters based on a multi-objective optimization algorithm, thereby solving the coupling and competition problems between different performance characteristics.

[0007] Furthermore, the topological conceptual model consists of a solid model and skeleton elements in the CAD system; the skeleton elements are a series of reference points, reference axes, and reference planes, used to describe the dimensional features, interface features, kinematic pair axis orientation features, and coordinate system features of the robot mechanism; the dimensional features are used to describe the basic dimensional information of the robot, the interface features are used to describe the assembly constraints between the solid models of the robot's various components, the kinematic pair axis orientation features are used to describe the axis orientation of each single-degree-of-freedom kinematic pair in the robot, and the coordinate system features are used to describe the pose information of each component in the robot relative to the reference coordinate system.

[0008] Furthermore, the mechanical structure scheme model and the finite element parametric model are integrated through CAD-CAE technology to achieve automatic transmission between the two in terms of scale information, pose information and finite element analysis information, and the supporting database is the finite element parametric command flow template file library.

[0009] Furthermore, the performance analysis module includes a performance global analysis submodule, a feature configuration optimization submodule, and a performance variation law analysis submodule, and its supporting database is a kinematic performance analysis algorithm library and a multi-feature clustering analysis algorithm library; The performance global analysis submodule is used to analyze the global distribution law of the robot's motion / force transmission performance and elastic dynamics performance by means of CAD technology and CAD-CAE integration technology; The feature configuration optimization submodule is used to determine several feature configurations that can represent the performance of many reference configurations in the field by means of a multi-feature hard clustering algorithm, and then construct local performance indicators. The performance variation analysis submodule is used to analyze the variation of local performance indicators of the robot with scale parameters, and then determine the main design parameters that play a dominant role in performance, as well as the correlation and competition relationships of various performance parameters.

[0010] Furthermore, the parameter optimization module includes an experimental design submodule, a response surface fitting submodule, and a parameter optimization submodule, supported by an optimization algorithm model library. The experimental design submodule is used to determine the minimum number of analysis samples required to construct the response surface model using the optimal super-Latin algorithm. The response surface fitting submodule is used to fit the second-order response surface model between the main design parameters and various performance parameters. The parameter optimization submodule is used to determine the Pareto optimal non-dominated solution that satisfies engineering constraints using optimization algorithms in the optimization algorithm model library, and then selects the optimal design scheme based on the principle of minimizing the residual between the Pareto solution and the ideal performance.

[0011] In addition, the present invention also provides an implementation method for the dimensional optimization design of industrial robots, comprising the following steps: 1) Establish the topological conceptual model (4), mechanical structure scheme model (5), and finite element parameterized model (6) of the robot. 2) Analyze the distribution pattern of the robot's motion / force transmission performance throughout the workspace, and the distribution pattern of the robot's low-order natural frequencies in the workspace; 3) Using the clustering analysis algorithm in the multi-feature clustering analysis algorithm library (25), several feature configurations that can represent the performance of many reference configurations in the field are determined (S5), and local performance indices are constructed based on the average values ​​of kinematic and elastic dynamic performance under the feature configurations (S6). 4) Update the scale parameters of the topological conceptual model (4), mechanical structure scheme model (5) and finite element parameterized model (6), analyze the variation law of robot kinematics and elastic dynamics performance at different scales (S7), and determine the main design parameters that play a dominant role in performance and the correlation and competition relationship of various performances (S8). 5) Determine the minimum number of analysis samples for constructing the response surface model. Given the range of variation of the master design parameters, use the optimal super-Latin algorithm to determine the minimum number of analysis samples for constructing the response surface model, and analyze the robot's kinematics and elastic dynamics performance at a given scale (S9). 6) Fit a second-order response surface model between the main design parameters and various performance parameters (S10), and use multiple coefficients of determination to determine whether the fitting accuracy of the response surface model meets the requirements (S11); if not, return to step 5); if yes, proceed to the next step. 7) Establish the robot scale parameter optimization problem; determine the feasible region of the design variables; solve for the Pareto optimal non-dominated solution that satisfies the geometric and engineering constraints; select the optimal design scheme; 8) Determine the robot's design parameters based on the optimal design scheme.

[0012] Furthermore, in step 1), a topological conceptual model of the robot is established (4), and the initial dimensional parameters of the robot mechanism and the orientation information of the kinematic axis are given, laying the foundation for kinematic performance analysis (S1). Based on topological and scale information, and with the help of the finite element parameterized command flow template file library (61), the mechanical structure scheme model (5) and finite element parameterized model (6) of the robot are established, laying the foundation for elastic dynamic performance analysis (S2).

[0013] Furthermore, in step 2), kinematic performance indicators are selected from the kinematic performance analysis algorithm library (24) using CAD-based kinematic performance analysis technology to analyze the distribution law of the robot's motion / force transmission performance in the entire workspace (S3). Then, by using CAD-CAE integration technology, the transmission of scale, pose and finite element analysis information between the mechanical structure scheme model (5) and the finite element parameterized model (6) is established, and the distribution law of the robot's low-order natural frequency in the workspace is analyzed (S4).

[0014] Furthermore, in step 7), with the goal of optimizing kinematic and elastic dynamic performance and with geometric and engineering constraints as constraints, a robot scale parameter optimization problem is established (S12). Given the boundary values ​​of each constraint, determine the feasible region of the design variables (S13). Select the appropriate multi-objective optimization algorithm from the optimization algorithm model library (34) and solve for the Pareto optimal non-dominated solution that satisfies the geometric and engineering constraints (S14). Then, based on the principle of minimizing the residual between the Pareto solution and the ideal performance, the optimal design scheme is selected (S15).

[0015] Furthermore, in step 8), based on the optimal design scheme, the mechanical structure scheme model is updated (5), rigid body dynamics analysis is performed in the CAD system (S16), the robot's actuator parameters are determined, and the corresponding drive motor and reducer models are selected (S17).

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs a scale optimization design framework for industrial robots. This framework integrates technologies such as skeleton modeling, CAD-CAE integration, clustering, and multi-objective optimization, and incorporates the process and method for optimizing the scale parameters of industrial robots. It can be applied to kinematic and dynamic modeling, performance analysis and prediction, feature configuration optimization, and multi-objective optimization problems of industrial robots with different topology types.

[0017] This design framework allows designers to build virtual models of industrial robots without resorting to complex mathematical and mechanical derivations. It enables rapid analysis of the robot's kinematic and dynamic performance variations with configuration and dimensional parameters. Furthermore, by employing methods such as feature configuration optimization, global performance local substitution, and Pareto front optimization, it addresses the challenges of handling performance coupling and competition, and the time-consuming nature of global performance analysis found in existing industrial robot design methods. This framework effectively solves the problem of dimensional optimization design for industrial robots in both academia and industry, reducing the difficulty of the design process and improving design effectiveness and efficiency.

[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A structural diagram of a framework for optimizing the industrial robot's scale. Figure 2 A schematic diagram of the topological structure concept of an industrial robot; Figure 3 A flowchart for the implementation method of dimensional optimization design for industrial robots. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Please refer to Figures 1-2 The embodiments of the present invention provide an industrial robot scale optimization design framework, including a modeling module 1, a performance analysis module 2 and a parameter optimization module 3; The modeling module 1 includes a topological conceptual model 4, a mechanical structure scheme model 5, and a finite element parameterized model 6, which are used for simulation and prediction of the robot's kinematics, rigid body dynamics, and elastic dynamics performance under different scales and configurations. The performance analysis module 2 is used to analyze the robot's global kinematics and dynamics performance, and to construct local performance indicators that can replace global performance analysis, thereby revealing the variation law of kinematics and dynamics performance with scale parameters. The parameter optimization module 3 is used to optimize the robot's scale parameters based on a multi-objective optimization algorithm, thereby solving the coupling and competition problems between different performance characteristics.

[0023] In a preferred embodiment, such as Figure 2As shown, the topological conceptual model 4 is composed of a solid model and skeleton elements in the CAD system. The skeleton elements are a series of reference points, reference axes, and reference planes, used to describe the dimensional features 41, interface features 42, kinematic pair axis orientation features 43, and coordinate system features 44 of the robot mechanism. The dimensional features 41 are used to describe the basic dimensional information of the robot, the interface features 42 are used to describe the assembly constraints between the solid models of the robot's various components, the kinematic pair axis orientation features 43 are used to describe the axis orientation of each single-degree-of-freedom kinematic pair in the robot, and the coordinate system features 44 are used to describe the pose information of each component in the robot relative to the reference coordinate system.

[0024] In a preferred embodiment, such as Figure 1 As shown, the mechanical structure scheme model 5 and the finite element parametric model 6 achieve automatic transmission between scale information, pose information and finite element analysis information through CAD-CAE integration technology, and the supporting database is the finite element parametric command flow template file library 61.

[0025] In a preferred embodiment, such as Figure 1 As shown, the performance analysis module 2 includes a performance global analysis submodule 21, a feature configuration optimization submodule 22, and a performance variation law analysis submodule 23. Its supporting database is a kinematic performance analysis algorithm library 24 and a multi-feature clustering analysis algorithm library 25. The performance global analysis submodule 21 is used to analyze the global distribution law of the robot's motion / force transmission performance and elastic dynamics performance by means of CAD technology and CAD-CAE integration technology. The feature configuration optimization submodule 22 is used to determine several feature configurations that can represent the performance of many reference configurations in the field by means of a multi-feature hard clustering algorithm, and then construct local performance indicators. The performance variation law analysis submodule 23 is used to analyze the variation law of the robot's local performance index with scale parameters, and then determine the main design parameters that play a dominant role in performance, as well as the correlation and competition relationships of various performances.

[0026] In a preferred embodiment, such as Figure 1As shown, the parameter optimization module 3 includes an experimental design submodule 31, a response surface fitting submodule 32, and a parameter optimization submodule 33, with its supporting database being an optimization algorithm model library 34. The experimental design submodule 31 is used to determine the minimum number of analysis samples for constructing the response surface model using the optimal super-Latin algorithm. The response surface fitting submodule 32 is used to fit the second-order response surface model between the main design parameters and various performance parameters. The parameter optimization submodule 33 is used to determine the Pareto optimal non-dominated solution that satisfies the engineering constraints using the optimization algorithms in the optimization algorithm model library 34, and then select the optimal design scheme based on the principle of minimizing the residual between the Pareto solution and the ideal performance.

[0027] Also, please refer to Figure 3 The embodiments of the present invention also provide an implementation method for the dimensional optimization design of industrial robots, comprising the following steps: 1) Enter the modeling module 1, establish the topological conceptual model 4 of the robot, and provide the initial dimensional parameters of the robot mechanism and the orientation information of the kinematic axis to lay the foundation for kinematic performance analysis S1; 2) Based on topological and scale information, and with the help of the finite element parameterized command flow template file library 61, the mechanical structure scheme model 5 and the finite element parameterized model 6 of the robot are established, laying the foundation for elastic dynamic performance analysis S2. 3) Enter the full-domain performance analysis submodule 21, and with the help of CAD-based kinematic performance analysis technology, select kinematic performance indicators from the kinematic performance analysis algorithm library 24 to analyze the distribution law of the robot's motion / force transmission performance in the full domain of the workspace S3. Then, with the help of CAD-CAE integration technology, establish the transmission of scale, pose and finite element analysis information between the mechanical structure scheme model 5 and the finite element parameterized model 6, and analyze the distribution law of the robot's low-order natural frequencies in the workspace S4. 4) Enter the feature configuration optimization submodule 22, and use the clustering analysis algorithm in the multi-feature clustering analysis algorithm library 25 to determine several feature configurations S5 that can represent the performance of many reference configurations in the field, and construct the local performance index S6 based on the average value of the kinematic and elastic dynamic performance under the feature configurations. 5) Enter the performance variation law analysis submodule 23, update the scale parameters of the topology conceptual model 4, mechanical structure scheme model 5 and finite element parameterized model 6, analyze the variation law of robot kinematics and elastic dynamics performance at different scales S7, and determine the main design parameters that play a dominant role in performance and the correlation and competition relationships of various performances S8. 6) Enter the experimental design submodule 31, give the range of variation of the main design parameters, use the optimal super-Latin algorithm to determine the minimum analysis samples for constructing the response surface model, and analyze the robot's kinematics and elastic dynamics performance S9 at a given scale; 7) Enter the response surface fitting submodule 32, fit the second-order response surface model S10 between the main design parameters and various performances, and use multiple coefficients of determination to determine whether the fitting accuracy of the response surface model meets the requirements S11. If not, return to the experimental design submodule 31 of step 6) and re-complete the experimental design process S9. If yes, proceed to the next step. 8) Enter the parameter optimization submodule 33, with the goal of optimizing kinematic and elastic dynamic performance, and with geometric and engineering constraints as constraints, establish the robot scale parameter optimization problem S12, give the boundary values ​​of each constraint, determine the feasible region S13 of the design variables, select the corresponding multi-objective optimization algorithm from the optimization algorithm model library 34, solve the Pareto optimal non-dominated solution S14 that satisfies the geometric and engineering constraints, and then select the optimal design scheme S15 based on the principle of minimizing the residual between the Pareto solution and the ideal performance. 9) Based on the optimal design scheme, update the mechanical structure scheme model 5, perform rigid body dynamics analysis in the CAD system S16, determine the robot's actuator parameters, and select the corresponding drive motor and reducer models S17.

[0028] This invention constructs a dimensional optimization design framework for industrial robots, which solves the obstacles to the mapping and connection of models between various design stages. It can effectively support the dimensional optimization design of industrial robots, reduce the difficulty of design operations, and improve design effect and efficiency.

[0029] In the description of this specification, the terms "one embodiment," "some embodiments," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0030] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A scale-optimized design framework for industrial robots, comprising a modeling module, a performance analysis module, and a parameter optimization module; The modeling module includes a topological conceptual model, a mechanical structure scheme model, and a finite element parameterized model, which are used for simulation and prediction of the robot's kinematics, rigid body dynamics, and elastic dynamics performance under different scales and configurations. The performance analysis module is used to analyze the robot's global kinematics and dynamics performance, and construct local performance indicators that can replace global performance analysis, thereby revealing the variation law of kinematics and dynamics performance with scale parameters. The performance analysis module includes a global performance analysis submodule, a feature configuration optimization submodule, and a performance variation law analysis submodule with scale. Its supporting database is a kinematic performance analysis algorithm library and a multi-feature clustering analysis algorithm library. The performance global analysis submodule is used to analyze the global distribution law of the robot's motion / force transmission performance and elastic dynamics performance by means of CAD technology and CAD-CAE integration technology; The feature configuration optimization submodule is used to determine several feature configurations that can represent the performance of many reference configurations in the field by means of a multi-feature hard clustering algorithm, and then construct local performance indicators. The performance variation analysis submodule is used to analyze the variation of local performance indicators of the robot with scale parameters, and then determine the main design parameters that play a dominant role in performance, as well as the correlation and competition relationships of various performance parameters. The parameter optimization module is used to optimize the robot's scale parameters based on a multi-objective optimization algorithm, thereby solving the coupling and competition problems between different performance characteristics.

2. The industrial robot dimensional optimization design framework according to claim 1, characterized in that, The topological conceptual model consists of a solid model in the CAD system and a series of skeletal elements; The skeleton elements are a series of reference points, reference axes and reference planes, used to describe the dimensional features, interface features, kinematic pair axis orientation features and coordinate system features of the robot mechanism; The scale feature is used to describe the basic scale information of the robot, the interface feature is used to describe the assembly constraints between the physical models of the robot's components, the kinematic pair axis orientation feature is used to describe the axis orientation of each single-degree-of-freedom kinematic pair in the robot, and the coordinate system feature is used to describe the pose information of each component in the robot relative to the reference coordinate system.

3. The industrial robot dimensional optimization design framework according to claim 1, characterized in that, The mechanical structure scheme model and the finite element parametric model are integrated through CAD-CAE technology to achieve automatic transmission between scale information, pose information and finite element analysis information. The supporting database is the finite element parametric command flow template file library.

4. The industrial robot dimensional optimization design framework according to claim 1, characterized in that, The parameter optimization module includes an experimental design submodule, a response surface fitting submodule, and a parameter optimization submodule, and its supporting database is an optimization algorithm model library; The experimental design submodule is used to determine the minimum number of analysis samples required to construct the response surface model using the optimal super-Latin algorithm. The response surface fitting submodule is used to fit a second-order response surface model between the main design parameters and various performance parameters. The parameter optimization submodule is used to determine the Pareto optimal non-dominated solution that satisfies the engineering constraints by using optimization algorithms in the optimization algorithm model library, and then selects the optimal design scheme based on the principle of minimizing the residual between the Pareto solution and the ideal performance.

5. A method for optimizing the dimensional design of an industrial robot, characterized in that, The steps include the following: 1) Establish the robot's topological conceptual model, mechanical structure scheme model, and finite element parameterized model; 2) Analyze the distribution pattern of the robot's motion / force transmission performance throughout the workspace, and the distribution pattern of the robot's low-order natural frequencies in the workspace; 3) Using clustering analysis algorithms in the multi-feature clustering analysis algorithm library, several characteristic configurations that can represent the performance of many reference configurations in the field are determined, and local performance indices are constructed based on the average values ​​of kinematic and elastic dynamic performance under the characteristic configurations. 4) Update the scale parameters of the topological conceptual model, mechanical structure scheme model and finite element parameterized model, analyze the variation law of robot kinematics and elastic dynamics performance at different scales, and determine the main design parameters that play a dominant role in performance and the correlation and competition relationships of various performances; 5) Determine the minimum number of analysis samples for constructing the response surface model. Given the range of variation of the master design parameters, use the optimal hyper-Latin algorithm to determine the minimum number of analysis samples for constructing the response surface model, and analyze the kinematic and elastic dynamic performance of the robot at a given scale. 6) Fit a second-order response surface model between the main design parameters and various performance parameters, and use multiple coefficients of determination to determine whether the fitting accuracy of the response surface model meets the requirements; if not, return to step 5); if yes, proceed to the next step. 7) Establish the robot dimensional parameter optimization problem; Determine the feasible region of the design variables; solve for the Pareto optimal non-dominated solution that satisfies the geometric and engineering constraints; select the optimal design scheme; 8) Determine the robot's design parameters based on the optimal design scheme.

6. The method for optimizing the dimensional design of an industrial robot according to claim 5, characterized in that, In step 1), a topological conceptual model of the robot is established, and the initial dimensional parameters of the robot mechanism and the orientation information of the kinematic pairs are given, laying the foundation for kinematic performance analysis. Based on topological and scale information, and with the help of a finite element parameterized command flow template file library, a mechanical structure model and a finite element parameterized model of the robot are established, laying the foundation for elastic dynamic performance analysis.

7. The method for implementing dimensional optimization design of an industrial robot according to claim 5, characterized in that, In step 2), kinematic performance analysis technology based on CAD is used to select kinematic performance indicators from the kinematic performance analysis algorithm library to analyze the distribution pattern of the robot's motion / force transmission performance in the entire workspace. Then, by using CAD-CAE integration technology, the transmission of scale, pose and finite element analysis information between the mechanical structure scheme model and the finite element parameterized model is established, and the distribution law of the robot's low-order natural frequencies in the workspace is analyzed.

8. The method for implementing dimensional optimization design of an industrial robot according to claim 5, characterized in that, In step 7), with the goal of optimizing kinematic and elastic dynamic performance and with geometric and engineering constraints as constraints, a robot scale parameter optimization problem is established. Given the boundary values ​​of each constraint, determine the feasible region of the design variables; Select the appropriate multi-objective optimization algorithm from the optimization algorithm model library and solve for the Pareto optimal non-dominated solution that satisfies the geometric and engineering constraints. Then, the optimal design scheme is selected based on the principle of minimizing the residual between the Pareto solution and the ideal performance.

9. The method for implementing dimensional optimization design of an industrial robot according to claim 5, characterized in that, In step 8), based on the optimal design scheme, the mechanical structure scheme model is updated, rigid body dynamics analysis is performed in the CAD system, the robot's actuator parameters are determined, and the corresponding drive motor and reducer models are selected.

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