Robot acceleration performance evaluation method and device

Abstract

The invention relates to the technical field of robot performance evaluation, in particular to a robot acceleration performance evaluation method and device, and the method comprises the steps: generating an acceleration mapping matrix of a robot according to a pre-constructed robot dynamic model; performing singular value decomposition on the acceleration mapping matrix to obtain at least one singular value representing the geometric features of the dynamic operable ellipsoid of the robot; and calculating at least one composite acceleration performance index of the robot according to the at least one singular value, and generating an acceleration performance evaluation result of the robot according to the at least one composite acceleration performance index. Therefore, the problem that the overall dynamic performance of the robot under the actual complex working condition cannot be accurately represented due to the fact that the evaluation result is easy to have one-sidedness due to the fact that a single acceleration related index is usually adopted as the evaluation basis in the related technology is solved.

Description

Technical Field

[0001] This application relates to the field of robot performance evaluation technology, and in particular to a method and apparatus for evaluating robot acceleration performance. Background Technology

[0002] In related technologies, the dynamic acceleration performance of robots is usually quantified by specific numerical indicators, such as maximum acceleration, acceleration time, or peak joint angular acceleration, so as to effectively evaluate the merits of different robot configurations and drive design schemes.

[0003] However, in related technologies, a single acceleration-related index is usually used as the evaluation basis, which fails to comprehensively reflect the dynamic response characteristics of the robot during startup, speed change and multi-joint coordinated movement. This can easily lead to one-sided or incomplete evaluation results, making it difficult to accurately characterize the overall dynamic performance of the robot under actual complex working conditions, which urgently needs to be addressed. Summary of the Invention

[0004] This application provides a method and apparatus for evaluating the acceleration performance of a robot, in order to solve the problem that in related technologies, the evaluation results are often one-sided and cannot accurately characterize the overall dynamic performance of the robot under actual complex working conditions, since related technologies usually use a single acceleration-related index as the evaluation basis.

[0005] The first aspect of this application provides a method for evaluating the acceleration performance of a robot, comprising the following steps: generating an acceleration mapping matrix of the robot based on a pre-constructed robot dynamics model; performing singular value decomposition on the acceleration mapping matrix to obtain at least one singular value characterizing the dynamic operable ellipsoidal geometric features of the robot; calculating at least one composite acceleration performance index of the robot based on the at least one singular value, and generating an acceleration performance evaluation result of the robot based on the at least one composite acceleration performance index.

[0006] Through the above technical means, the embodiments of this application can calculate composite acceleration performance indicators based on the singular values ​​of the acceleration mapping matrix, and then generate the robot's acceleration performance evaluation results. This can comprehensively reflect the robot's overall dynamic acceleration capability and its distribution characteristics in different principal directions in the task space, avoiding the one-sidedness caused by using only a single acceleration performance indicator for evaluation. At the same time, the evaluation results can take into account the influence of translational and rotational degrees of freedom on acceleration performance, improve the objectivity and comparability of the comparative analysis of robot acceleration performance under different configurations, different driving methods and different working postures, and provide a reliable basis for robot dynamic performance evaluation and optimization design.

[0007] Optionally, in one embodiment of this application, generating the acceleration performance evaluation result of the robot based on the at least one composite acceleration performance index includes: obtaining the at least one composite acceleration performance index in at least one pose; calculating the global acceleration index of the robot in the workspace based on the at least one composite acceleration performance index; and generating the acceleration performance evaluation result based on the global acceleration index.

[0008] Through the above technical means, the embodiments of this application can generate acceleration performance evaluation results based on global acceleration indicators, which can comprehensively reflect the overall dynamic acceleration performance level of the robot in the entire workspace, reduce the impact of single poses or local working conditions on the evaluation results, and thus improve the stability and representativeness of acceleration performance evaluation results.

[0009] Optionally, in one embodiment of this application, the formula for calculating the global acceleration index is: , in, This indicates the global acceleration metric. Represents discrete pose points. This represents the composite acceleration performance index value of the robot's end effector at each pose point.

[0010] Through the above technical means, the embodiments of this application quantify the global acceleration index, which can uniformly and quantitatively characterize the dynamic acceleration performance of the robot in the entire workspace. It integrates the evaluation results of local acceleration performance scattered at different pose points into numerical indicators with clear physical meaning, thereby realizing the quantitative comparison and analysis of the robot's overall acceleration capability level.

[0011] Optionally, in one embodiment of this application, calculating at least one composite acceleration performance index of the robot based on the at least one singular value includes: calculating at least one of the composite acceleration performance indexes based on the ratio of the volume of the dynamically operable ellipsoid to the square of the maximum semi-axis length based on the at least one singular value; and / or calculating at least one of the composite acceleration performance indexes based on the product of the volume of the dynamically operable ellipsoid and a geometric factor characterizing the compactness of the ellipsoid shape based on the at least one singular value.

[0012] Through the above technical means, the embodiments of this application can obtain multiple composite acceleration performance indicators based on the geometric characteristics of the dynamic operable ellipsoid. These indicators can characterize the robot's overall acceleration capability level, directional distribution characteristics, and isotropic degree in the task space from different perspectives, thereby achieving a multi-dimensional description of the robot's dynamic acceleration performance. This provides a more comprehensive and reliable evaluation basis for the comparative analysis and optimization design of acceleration performance under different configurations, different driving methods, and different working postures.

[0013] Optionally, in one embodiment of this application, the expression for the robot dynamics model is: , in, For the Jacobian matrix of the robot, This is the joint driving torque vector. This is the robot's end-effector pose vector. These are the terminal velocity and acceleration, respectively. The inertia matrix mapped to the operation space. It is a matrix of nonlinear terms. For external force / torque, This is the gravity term.

[0014] Through the above technical means, the embodiments of this application can construct a robot dynamics model based on external force / torque terms, gravity terms, inertia terms, and Coriolis force terms, which can comprehensively describe the dynamic relationship between the driving input and motion response of the robot under different poses and working conditions, thereby providing an accurate and unified theoretical basis for the construction of the acceleration mapping matrix and the analysis of the robot's dynamic acceleration performance.

[0015] Optionally, in one embodiment of this application, performing singular value decomposition on the acceleration mapping matrix to obtain at least one singular value characterizing the dynamic maneuverable ellipsoidal geometry of the robot includes: extracting rows and columns corresponding to redundant degrees of freedom from the acceleration mapping matrix to obtain sub-matrices corresponding to end-effector degrees of freedom; dividing the sub-matrices into blocks according to physical dimensions to obtain translational and rotational degree-of-freedom sub-matrices characterizing the dynamic maneuverable ellipsoidal geometry of the robot; and performing singular value decomposition on the translational and rotational degree-of-freedom sub-matrices to obtain a first singular value of the translational degree-of-freedom sub-matrices and a second singular value of the rotational degree-of-freedom sub-matrices.

[0016] Through the above technical means, the embodiments of this application can extract the rows and columns corresponding to redundant degrees of freedom to obtain sub-matrices corresponding to the end-operation degrees of freedom, and then perform block processing. This can effectively eliminate the interference of internal redundant degrees of freedom unrelated to specific operation tasks on acceleration performance evaluation. At the same time, through the block processing, linear acceleration and angular acceleration can be modeled and analyzed independently, solving the evaluation deviation problem caused by the inconsistency of their physical dimensions, and avoiding the ambiguity of physical meaning caused by directly performing singular value decomposition on the hybrid acceleration mapping matrix. This ensures the rationality, interpretability and engineering application value of the acceleration performance evaluation results.

[0017] A second aspect of this application provides a robot acceleration performance evaluation device, comprising: a generation module for generating an acceleration mapping matrix of a robot based on a pre-built robot dynamics model; a decomposition module for performing singular value decomposition on the acceleration mapping matrix to obtain at least one singular value characterizing the dynamic operable ellipsoidal geometric features of the robot; and an evaluation module for calculating at least one composite acceleration performance index of the robot based on the at least one singular value, to generate an acceleration performance evaluation result of the robot based on the at least one composite acceleration performance index.

[0018] Optionally, in one embodiment of this application, the evaluation module includes: an acquisition unit, configured to acquire at least one composite acceleration performance index in at least one pose; a first calculation unit, configured to calculate the robot's global acceleration index in the workspace based on the at least one composite acceleration performance index; and a generation unit, configured to generate the acceleration performance evaluation result based on the global acceleration index.

[0019] Optionally, in one embodiment of this application, the formula for calculating the global acceleration index is: , in, This indicates the global acceleration metric. Represents discrete pose points. This represents the composite acceleration performance index value of the robot's end effector at each pose point.

[0020] Optionally, in one embodiment of this application, the evaluation module includes: a second calculation unit, configured to calculate at least one of the composite acceleration performance indicators based on the at least one singular value, according to the ratio of the volume of the dynamically operable ellipsoid to the square of the maximum semi-axis length; and / or a third calculation unit, configured to calculate at least one of the composite acceleration performance indicators based on the at least one singular value, according to the product of the volume of the dynamically operable ellipsoid and a geometric factor characterizing the compactness of the ellipsoid shape.

[0021] Optionally, in one embodiment of this application, the expression for the robot dynamics model is: , in, For the Jacobian matrix of the robot, This is the joint driving torque vector. This is the robot's end-effector pose vector. These are the terminal velocity and acceleration, respectively. The inertia matrix mapped to the operation space. It is a matrix of nonlinear terms. For external force / torque, This is the gravity term.

[0022] Optionally, in one embodiment of this application, the decomposition module includes: an extraction unit, configured to extract rows and columns corresponding to redundant degrees of freedom according to the acceleration mapping matrix to obtain a sub-matrix corresponding to the end-effector degrees of freedom; a block division unit, configured to divide the sub-matrix into blocks according to physical dimensions to obtain a translational degree-of-freedom sub-matrix and a rotational degree-of-freedom sub-matrix characterizing the dynamic operable ellipsoidal geometric features of the robot; and a decomposition unit, configured to perform singular value decomposition on the translational degree-of-freedom sub-matrix and the rotational degree-of-freedom sub-matrix to obtain a first singular value of the translational degree-of-freedom sub-matrix and a second singular value of the rotational degree-of-freedom sub-matrix.

[0023] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the robot acceleration performance evaluation method as described in the above embodiments.

[0024] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the robot acceleration performance evaluation method described above.

[0025] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the robot acceleration performance evaluation method described above.

[0026] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0027] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of a parallel robot configuration with a remote motion center according to one embodiment of this application; Figure 2 This is a flowchart of a robot acceleration performance evaluation method provided according to an embodiment of this application; Figure 3 This is a schematic diagram of workspace discrete sampling according to an embodiment of this application; Figure 4 This is a flowchart of a robot acceleration performance evaluation method according to an embodiment of this application; Figure 5 This is a block diagram of a robot acceleration performance evaluation device according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0028] Figure label: 10 Robot acceleration performance evaluation device; 100-Generation module, 200-Decomposition module, 300-Evaluation module; 601-Memory, 602-Processor, 603-Communication interface. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0030] The robot acceleration performance evaluation method and apparatus of this application are described below with reference to the accompanying drawings. To address the technical problem mentioned in the background that related technologies typically use a single acceleration-related index as the evaluation criterion, which can easily lead to one-sided evaluation results and thus fail to accurately characterize the overall dynamic performance of robots under complex working conditions, this application provides a robot acceleration performance evaluation method. In this method, singular value decomposition is performed on the acceleration mapping matrix to obtain singular values ​​representing the geometric characteristics of the robot's dynamic maneuverable ellipsoid. These singular values ​​are then used to calculate a composite acceleration performance index, thereby generating the robot's acceleration performance evaluation result. This method can simultaneously reflect the robot's dynamic acceleration capability and its distribution balance in different directions, avoiding the one-sidedness caused by relying solely on a single acceleration index, and improving the comprehensiveness and objectivity of the acceleration performance evaluation result. Furthermore, by introducing the geometric characteristics of the dynamic maneuverable ellipsoid as the evaluation criterion, the acceleration performance evaluation process corresponds to the robot's dynamic characteristics and kinematic constraints, enhancing the physical meaning and interpretability of the evaluation result. This method is applicable to the acceleration performance analysis of robots with different configurations, different driving methods, and different working postures, facilitating a unified evaluation of robot dynamic performance and providing a reliable quantitative reference for robot configuration design, trajectory planning, and drive system selection. This solves the problem that related technologies often use a single acceleration-related index as the evaluation basis, which can easily lead to one-sided evaluation results and thus fail to accurately characterize the overall dynamic performance of the robot under actual complex working conditions.

[0031] Before describing the robot acceleration performance evaluation method of the embodiments of this application, the system structure and application scenarios involved in the embodiments of this application will be described first.

[0032] As a concrete example, such as Figure 1As shown, a typical configuration of a parallel robot with a remote motion center may include, but is not limited to, a fixed base 1, two parallel planar branches, and an end effector. Branch one includes a tilting bracket 2, a bearing housing 4, a motor frame 6, and five link assemblies 8, 10, 12, 14, and 16. The tilting bracket 2 is rigidly connected to the fixed base 1, the bearing housing 4 is rigidly connected to the tilting bracket 2, and the motor frame 6 is connected to the bearing housing via a revolute joint. The first link 8 and the third link 12 are respectively connected to the motor frame via motors, and the connection points serve as two drive shafts. The first link 8 is connected to the second link 10, the third link 12 to the fourth link 14, the second link 10 to the fourth link 16, and the fourth link 14 to the fifth link 16 via revolute hinges. The fifth link 16 is connected to the end effector 18 via a linear bearing, forming a cylindrical joint. Branch two includes a tilting bracket 3, a bearing housing 5, a motor frame 7, and five link assemblies 9, 11, 13, 15, and 17. The connection method is similar to that of the first branch, with the fifth link 17 connected to the end effector 18 via a revolute joint. The end effector 18 must pass through the intersection of the axis of the first branch bearing seat 4 and the axis of the second branch bearing seat 5, which is the remote motion center.

[0033] The system structure provided in the above embodiments can realize the robot acceleration performance evaluation method of this application. The robot acceleration performance evaluation method of this application will be described in detail below.

[0034] Specifically, Figure 2 This is a flowchart illustrating a robot acceleration performance evaluation method provided in an embodiment of this application.

[0035] like Figure 2 As shown, the robot acceleration performance evaluation method includes the following steps: In step S201, the robot's acceleration mapping matrix is ​​generated based on the pre-built robot dynamics model.

[0036] As one possible approach, embodiments of this application can obtain the kinematic and dynamic parameters of the robot, including but not limited to kinematic parameters such as the pose parameters of each joint, link length, and joint type, as well as dynamic parameters such as the mass, driving torque, damping, and friction coefficient of each joint and link; it can also include motor rated power, maximum output torque, moment of inertia matching relationship related to the drive system, and sampling period acceleration constraint parameters related to the control system, to establish a dynamic model describing the mapping relationship between the robot joint driving force / torque and the end effector physical parameters.

[0037] Next, the embodiments of this application can simplify the dynamic model for any pose to be evaluated in the robot's workspace, construct a robot end-effector acceleration mapping relationship containing only inertial terms, and calculate the corresponding acceleration mapping matrix to uniformly map the acceleration information of each joint to the task space, thereby realizing a quantitative characterization of the robot's overall dynamic acceleration characteristics and providing a unified evaluation basis for the comparative analysis of the acceleration performance of robots with different configurations.

[0038] Optionally, in one embodiment of this application, the expression for the robot dynamics model can be: , in, For the Jacobian matrix of the robot, This is the joint driving torque vector. This is the robot's end-effector pose vector. These are the terminal velocity and acceleration, respectively. The inertia matrix mapped to the operation space. It is a matrix of nonlinear terms. For external force / torque, This is the term related to gravity. , , The coordinates of the actuator end relative to the remote motion center. The distance between the fifth link of the first branch and the end effector and the fifth link of the second branch and the end effector.

[0039] Next, the embodiments of this application can simplify the dynamic model by ignoring the velocity term. External force item W and gravity terms G(P) The dynamic model is simplified to a direct linear mapping between the driving torque and the terminal acceleration, which can be expressed as: , in, Let be the acceleration mapping matrix, and satisfy... Or its equivalent pseudo-inverse form.

[0040] Furthermore, the embodiments of this application can yield the following model: , In this embodiment, the selected pose points can be substituted to calculate the result. .

[0041] In the embodiments of this application, for non-redundant driven parallel robots, the inverse matrix of the inertial matrix can be used to construct the acceleration mapping matrix, and for redundant driven parallel robots, the pseudo-inverse matrix of the inertial matrix can be used to construct the acceleration mapping matrix. This enables unified modeling and objective evaluation of the dynamic acceleration performance of robots with different drive configurations while ensuring dynamic consistency.

[0042] In step S202, singular value decomposition is performed on the acceleration mapping matrix to obtain at least one singular value that characterizes the dynamic maneuverable ellipsoidal geometry of the robot.

[0043] Singular value decomposition refers to the process of decomposing any matrix into a form consisting of a left singular vector matrix, a singular value diagonal matrix, and the transpose of a right singular vector matrix. By analyzing the singular values, the magnification or reduction capability of the matrix in different directions can be characterized, thereby representing the dynamic performance differences of the robot in various motion directions and obtaining the dynamic operable ellipsoidal geometric features.

[0044] It can be noted that the geometric characteristics of the dynamically operable ellipsoid can be characterized by indicators such as volume and surface area in three dimensions, or by indicators such as area and perimeter in two dimensions. The two-dimensional or three-dimensional form used is determined by the number of degrees of freedom in which the robot actually participates in motion and evaluation in the task space. For example, when the corresponding translational or rotational degrees of freedom are three-dimensional, a three-dimensional volume indicator is used for characterization; when the corresponding degrees of freedom are two-dimensional or the motion is constrained by a plane, a two-dimensional area or perimeter indicator is used for characterization. These can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0045] Optionally, in one embodiment of this application, singular value decomposition is performed on the acceleration mapping matrix to obtain at least one singular value characterizing the dynamic maneuverable ellipsoidal geometry of the robot. This includes: extracting rows and columns corresponding to redundant degrees of freedom from the acceleration mapping matrix to obtain sub-matrices corresponding to the end effector degrees of freedom; dividing the sub-matrices into blocks according to physical dimensions to obtain translational and rotational degree-of-freedom sub-matrices characterizing the dynamic maneuverable ellipsoidal geometry of the robot; and performing singular value decomposition on the translational and rotational degree-of-freedom sub-matrices to obtain a first singular value of the translational degree-of-freedom sub-matrices and a second singular value of the rotational degree-of-freedom sub-matrices.

[0046] Specifically, embodiments of this application can eliminate the acceleration mapping matrix. The rows and columns corresponding to redundant degrees of freedom are divided into submatrices, retaining only the submatrices corresponding to the final operational degrees of freedom. Then, the submatrices are divided into blocks according to physical dimensions to construct submatrices corresponding to the translational degrees of freedom. Submatrices corresponding to rotational degrees of freedom The specific model can be represented as:

[0047] in, For linear acceleration components, For angular acceleration components, and submatrix , All dimensions are no greater than 3.

[0048] Furthermore, the embodiments of this application can respectively address... , Perform singular value decomposition to obtain singular values. and .

[0049] As a concrete example, because the output vector contains variables related to redundant degrees of freedom within the mechanism. Therefore, the embodiments of this application can extract only the degrees of freedom of the robot's end effector space. The corresponding submatrix, that is, the matrix is ​​first divided into blocks: , The matrix is ​​then dimensionality reduced by removing redundant degrees of freedom. We can obtain: , because This corresponds to the translational degree of freedom at the end point, therefore no further degree of freedom decomposition is needed.

[0050] Furthermore, such as Figure 3 As shown, since the above decomposition yields 3-dimensional translational degrees of freedom, the sampling space can be 3-dimensional. For a specific robot implementation, its effective workspace is approximately conical. By uniformly sampling N end-effector pose points within this conical region, the following can be obtained: Figure 3 The sampled scatter plot shown represents a sampled end-effector pose point. Figure 3 A set of uniformly distributed points can serve as the discretized workspace of a robot.

[0051] Next, in this embodiment of the application, a pose point in any of the obtained sampling spaces can be selected, and the pose point can be substituted into the mapping submatrix. Singular value decomposition yields three singular values. , , : , in, Describes a left singular vector matrix. Let represent a diagonal matrix composed of singular values. This represents a right singular vector matrix.

[0052] In step S203, at least one composite acceleration performance index of the robot is calculated based on at least one singular value, so as to generate an acceleration performance evaluation result of the robot based on at least one composite acceleration performance index.

[0053] The composite acceleration performance index may include one or more sub-indices constructed based on a dynamically operable ellipsoid, including but not limited to: translational acceleration performance index characterizing the robot's overall translational acceleration capability, rotational acceleration performance index characterizing the robot's posture change capability, and isotropic index reflecting the uniformity of the robot's acceleration capability distribution in different directions. These can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0054] The acceleration performance evaluation results may include, but are not limited to, a comprehensive acceleration performance score that reflects the overall acceleration capability level of the robot, a directional evaluation result that characterizes the distribution of the robot's acceleration capability in different main directions, and a sub-item acceleration performance evaluation result that reflects the difference between the robot's translational and rotational acceleration capabilities. These results can be used for dynamic performance comparison and analysis under different robot configurations, different drive methods, or different control parameter configurations.

[0055] Optionally, in one embodiment of this application, calculating at least one composite acceleration performance index of the robot based on at least one singular value includes: calculating at least one of the composite acceleration performance indexes based on at least one singular value, according to the ratio of the volume of the dynamically operable ellipsoid to the square of the maximum semi-axis length; and / or calculating at least one of the composite acceleration performance indexes based on at least one singular value, according to the product of the volume of the dynamically operable ellipsoid and a geometric factor characterizing the compactness of the ellipsoid shape.

[0056] In the embodiments of this application, the composite acceleration performance index The specific calculation formula can be expressed as: , in, It is a singular value. The maximum singular value indicates that the robot has a stronger overall dynamic acceleration capability in the task space and a more balanced distribution of acceleration capability in different directions, thus enabling it to achieve higher motion response efficiency under dynamic conditions such as start-up, speed change, and attitude adjustment.

[0057] As one possible approach, when the acceleration map submatrix decomposes to obtain three singular values ​​(corresponding to three-dimensional space), the composite acceleration performance index... The specific calculation formula can be expressed as: , in, For a three-dimensional, dynamically operable ellipsoidal volume, It represents the surface area of ​​a three-dimensional, dynamically operable ellipsoid.

[0058] When the acceleration map submatrix decomposes to obtain two singular values ​​(corresponding to two-dimensional space), the composite acceleration performance index The specific calculation formula can be expressed as: , in, The area of ​​a two-dimensional dynamically operable ellipsoid. It is the perimeter of a two-dimensional, dynamically operable ellipsoid.

[0059] It can be noted that the geometric features of the dynamically operable ellipsoid can be calculated using the following numerical approximation method: The surface area of ​​a three-dimensional dynamically operable ellipsoid can be approximated using the Knud-Thomsen formula: , in, , This represents the surface area of ​​a three-dimensional dynamically operable ellipsoid. This represents singular values.

[0060] The perimeter of a two-dimensional dynamically operable ellipsoid can be approximated using the Ramanujan formula: , in, This represents the perimeter of a two-dimensional dynamically operable ellipsoid.

[0061] Optionally, in one embodiment of this application, generating an acceleration performance evaluation result for a robot based on at least one composite acceleration performance index includes: acquiring at least one composite acceleration performance index in at least one pose; calculating a global acceleration index for the robot in the workspace based on the at least one composite acceleration performance index; and generating an acceleration performance evaluation result based on the global acceleration index.

[0062] Among them, pose can be the position and attitude information used to determine the operation state of the robot's end effector, and serve as the input for constructing the acceleration mapping matrix and evaluating dynamic acceleration performance.

[0063] The embodiments of this application can evaluate the acceleration performance of a robot based on composite acceleration performance indicators under a single or multiple poses.

[0064] Specifically, the global acceleration index can be defined as: the ratio of the integral of the composite acceleration performance index value within the workspace domain to the total volume of the workspace domain, which can be expressed as: , in, For the robot's global acceleration performance indicators, For the robot's workspace domain, For pose variables, The composite acceleration index is given by the robot's end-effector pose.

[0065] Furthermore, the global acceleration performance index can be estimated using a discretized numerical method, specifically including: based on the translational and rotational degrees of freedom defined above, respectively, within the robot's workspace... The internal random uniform sampling method or grid partitioning method is used. Calculate the composite acceleration performance index of the robot's end effector at each discrete pose point; for each discrete pose point, obtain the composite acceleration performance index value corresponding to that pose point. The summation of the index values ​​of all sampling points and the average value are used as an approximation of the global acceleration performance index.

[0066] Optionally, in one embodiment of this application, the formula for calculating the global acceleration index can be expressed as: , in, This indicates the global acceleration index. Represents discrete pose points. This represents the composite acceleration performance index value of the robot's end effector at each pose point.

[0067] The embodiments of this application can perform weighted average calculation on all index values ​​to obtain an approximate global acceleration performance index, which is used to characterize the overall dynamic acceleration performance level of the robot in the entire workspace, thereby reducing the impact of a single pose or local working condition on the acceleration performance evaluation results and improving the stability and representativeness of the evaluation results.

[0068] The following is a specific example, such as Figure 4 As shown, the robot acceleration performance evaluation method of this application embodiment is further described; it may include the following steps: In step S401, the dynamic equations are established: The embodiments of this application can construct dynamic equations including but not limited to inertia matrix, Coriolis force term and gravity term to reflect the dynamic characteristics of the robot in different poses.

[0069] In step S402, the acceleration mapping matrix is ​​constructed: Furthermore, embodiments of this application can construct an acceleration mapping matrix based on dynamic equations to describe the mapping relationship between driving input and task space acceleration, so as to realize the mapping from joint space or driving space to end-effector linear acceleration and / or angular acceleration.

[0070] In step S403, matrix dimensionality reduction and block partitioning: Specifically, based on the requirements of the end-effector operation task, the embodiments of this application can decompose the acceleration mapping matrix into degrees of freedom and extract the sub-matrices corresponding to the translational and / or rotational degrees of freedom of the end-effector; when the acceleration in the task space is constrained by a plane, the sub-matrices are reduced in dimensionality to obtain two-dimensional or three-dimensional acceleration mapping sub-matrices.

[0071] In step S404, the workspace is discretized and sampled: As one possible approach, embodiments of this application can discretize the robot's workspace and perform random uniform sampling within the workspace to approximate the distribution of the robot's dynamic acceleration performance across the entire workspace.

[0072] In step S405, the matrix singular value decomposition is performed: For each pose sampling point, the embodiments of this application can perform singular value decomposition on the corresponding acceleration mapping submatrix to obtain a set of singular values; the singular values ​​are used to construct a two-dimensional or three-dimensional dynamic manipulable ellipsoid to characterize the robot's dynamic acceleration capability in each principal direction at that pose point.

[0073] In step S406, the acceleration performance index is calculated: Next, embodiments of this application can calculate the corresponding composite acceleration performance index based on the geometric features of the dynamically operable ellipsoid corresponding to the pose sampling point.

[0074] In step S407, the global acceleration performance index is calculated: Finally, the embodiments of this application can perform a weighted average calculation on the composite acceleration performance index corresponding to all pose sampling points to obtain an approximate global acceleration performance index, which can be used to comprehensively characterize the overall dynamic acceleration performance of the robot in the entire workspace.

[0075] The robot acceleration performance evaluation method proposed in this application obtains singular values ​​representing the geometric characteristics of the robot's dynamic maneuverable ellipsoid by performing singular value decomposition on the acceleration mapping matrix. These singular values ​​are then used to calculate a composite acceleration performance index, generating the robot's acceleration performance evaluation result. This method simultaneously reflects the robot's dynamic acceleration capability and its distribution uniformity in different directions, avoiding the bias caused by relying solely on a single acceleration index and improving the comprehensiveness and objectivity of the acceleration performance evaluation result. Furthermore, by introducing the geometric characteristics of the dynamic maneuverable ellipsoid as an evaluation basis, the acceleration performance evaluation process corresponds to the robot's dynamic characteristics and kinematic constraints, enhancing the physical meaning and interpretability of the evaluation result. This method is applicable to the acceleration performance analysis of robots with different configurations, driving methods, and working postures, facilitating a unified evaluation of robot dynamic performance and providing a reliable quantitative reference for robot configuration design, trajectory planning, and drive system selection.

[0076] Next, the robot acceleration performance evaluation device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0077] Figure 5 This is a block diagram of a robot acceleration performance evaluation device according to an embodiment of this application.

[0078] like Figure 5 As shown, the robot acceleration performance evaluation device 10 includes: a generation module 100, a decomposition module 200, and an evaluation module 300.

[0079] The generation module 100 is used to generate the robot's acceleration mapping matrix based on a pre-built robot dynamics model.

[0080] The decomposition module 200 is used to perform singular value decomposition on the acceleration mapping matrix to obtain at least one singular value that characterizes the dynamic maneuverable ellipsoidal geometry of the robot.

[0081] Evaluation module 300 is used to calculate at least one composite acceleration performance index of the robot based on at least one singular value, so as to generate an acceleration performance evaluation result of the robot based on at least one composite acceleration performance index.

[0082] Optionally, in one embodiment of this application, the evaluation module 300 includes: an acquisition unit, a first calculation unit, and a generation unit.

[0083] The acquisition unit is used to acquire at least one composite acceleration performance index under at least one pose.

[0084] The first computing unit is used to calculate the robot's global acceleration index in the workspace based on at least one composite acceleration performance index.

[0085] The generation unit is used to generate acceleration performance evaluation results based on global acceleration metrics.

[0086] Optionally, in one embodiment of this application, the formula for calculating the global acceleration index is: , in, This indicates the global acceleration index. Represents discrete pose points. This represents the composite acceleration performance index value of the robot's end effector at each pose point.

[0087] Optionally, in one embodiment of this application, the evaluation module 300 includes a second calculation unit and a third calculation unit.

[0088] The second calculation unit is used to calculate at least one of the composite acceleration performance indicators based on at least one singular value, according to the ratio of the volume of the dynamically operable ellipsoid to the square of the maximum semi-axis length; and / or The third calculation unit is used to calculate at least one of the composite acceleration performance indices based on at least one singular value, according to the product of the volume of the dynamically operable ellipsoid and a geometric factor characterizing the compactness of the ellipsoid shape.

[0089] Optionally, in one embodiment of this application, the expression for the robot dynamics model is: , in, For the Jacobian matrix of the robot, This is the joint driving torque vector. This is the robot's end-effector pose vector. These are the terminal velocity and acceleration, respectively. The inertia matrix mapped to the operation space. It is a matrix of nonlinear terms. For external force / torque, This is the gravity term.

[0090] Optionally, in one embodiment of this application, the decomposition module 200 includes: an extraction unit, a block division unit, and a decomposition unit.

[0091] The extraction unit is used to extract the rows and columns corresponding to the redundant degrees of freedom based on the acceleration mapping matrix, so as to obtain the submatrix corresponding to the end operation degrees of freedom. The block unit is used to divide the sub-matrix according to the physical dimensions to obtain the translational degree of freedom sub-matrix and rotational degree of freedom sub-matrix that characterize the dynamic maneuverable ellipsoidal geometry of the robot. The decomposition unit is used to perform singular value decomposition on the translational and rotational degree-of-freedom submatrices to obtain the first singular value of the translational degree-of-freedom submatrices and the second singular value of the rotational degree-of-freedom submatrices.

[0092] It should be noted that the foregoing explanation of the robot acceleration performance evaluation method embodiment also applies to the robot acceleration performance evaluation device of this embodiment, and will not be repeated here.

[0093] The robot acceleration performance evaluation device proposed in this application performs singular value decomposition on the acceleration mapping matrix to obtain singular values ​​that characterize the geometric features of the robot's dynamic maneuverable ellipsoid. This is used to calculate a composite acceleration performance index, thereby generating the robot's acceleration performance evaluation result. This device can simultaneously reflect the robot's dynamic acceleration capability and its distribution uniformity in different directions, avoiding the bias caused by relying solely on a single acceleration index and improving the comprehensiveness and objectivity of the acceleration performance evaluation result. Furthermore, by introducing the geometric features of the dynamic maneuverable ellipsoid as the evaluation basis, the acceleration performance evaluation process corresponds to the robot's dynamic characteristics and kinematic constraints, enhancing the physical meaning and interpretability of the evaluation result. It is applicable to the acceleration performance analysis of robots with different configurations, different driving methods, and different working postures, facilitating a unified evaluation of the robot's dynamic performance and providing a reliable quantitative reference for robot configuration design, trajectory planning, and drive system selection.

[0094] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0095] When the processor 602 executes the program, it implements the robot acceleration performance evaluation method provided in the above embodiments.

[0096] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0097] The memory 601 is used to store computer programs that can run on the processor 602.

[0098] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0099] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0100] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0101] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0102] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the robot acceleration performance evaluation method described above.

[0103] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the robot acceleration performance evaluation method provided in this application.

[0104] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is 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, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0105] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0106] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0107] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0108] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0109] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0110] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0111] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating the acceleration performance of a robot, characterized in that, Includes the following steps: Generate the robot's acceleration mapping matrix based on a pre-built robot dynamics model; Singular value decomposition is performed on the acceleration mapping matrix to obtain at least one singular value that characterizes the dynamic maneuverable ellipsoidal geometry of the robot. Calculate at least one composite acceleration performance index of the robot based on the at least one singular value, and generate an acceleration performance evaluation result of the robot based on the at least one composite acceleration performance index.

2. The method according to claim 1, characterized in that, The step of generating the acceleration performance evaluation result of the robot based on the at least one composite acceleration performance index includes: Obtain the at least one composite acceleration performance index under at least one pose; Calculate the robot's global acceleration index in the workspace based on the at least one composite acceleration performance index; The acceleration performance evaluation result is generated based on the global acceleration index.

3. The method according to claim 2, characterized in that, The formula for calculating the global acceleration index is as follows: ， in, This indicates the global acceleration metric. N Represents discrete pose points. This represents the composite acceleration performance index value of the robot's end effector at each pose point.

4. The method according to claim 1, characterized in that, The calculation of at least one composite acceleration performance index of the robot based on the at least one singular value includes: Based on the at least one singular value, calculate at least one of the composite acceleration performance indices according to the ratio of the volume of the dynamically operable ellipsoid to the square of the maximum semi-axis length; and / or Based on the at least one singular value, at least one of the composite acceleration performance indices is calculated according to the product of the volume of the dynamically operable ellipsoid and a geometric factor characterizing the compactness of the ellipsoid shape.

5. The method according to claim 1, characterized in that, The expression for the robot dynamics model is: ， in, J For the Jacobian matrix of the robot, This is the joint driving torque vector. P This is the robot's end-effector pose vector. These are the terminal velocity and acceleration, respectively. M ( P Let be the inertia matrix mapped to the operation space. It is a matrix of nonlinear terms. W External force / torque term G(P) This is the gravity term.

6. The method according to claim 1, characterized in that, The step of performing singular value decomposition on the acceleration mapping matrix to obtain at least one singular value characterizing the dynamic maneuverable ellipsoidal geometry of the robot includes: Based on the acceleration mapping matrix, extract the rows and columns corresponding to the redundant degrees of freedom to obtain the submatrix corresponding to the end-operation degrees of freedom; The sub-matrix is ​​divided into blocks according to physical dimensions to obtain translational and rotational degree-of-freedom sub-matrixes that characterize the dynamic maneuverable ellipsoidal geometry of the robot. Singular value decomposition is performed on the translational degree-of-freedom submatrix and the rotational degree-of-freedom submatrix to obtain the first singular value of the translational degree-of-freedom submatrix and the second singular value of the rotational degree-of-freedom submatrix.

7. A robot acceleration performance evaluation device, characterized in that, include: The generation module is used to generate the robot's acceleration mapping matrix based on a pre-built robot dynamics model; The decomposition module is used to perform singular value decomposition on the acceleration mapping matrix to obtain at least one singular value that characterizes the dynamic operable ellipsoidal geometry of the robot. An evaluation module is used to calculate at least one composite acceleration performance index of the robot based on the at least one singular value, so as to generate an acceleration performance evaluation result of the robot based on the at least one composite acceleration performance index.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the robot acceleration performance evaluation method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the robot acceleration performance evaluation method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the robot acceleration performance evaluation method as described in any one of claims 1-6.

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