Parameter identification method, device, electronic device and storage medium for robotic arm

Abstract

The present application provides a parameter identification method, device, electronic device and storage medium for a robotic arm, and relates to the field of robotic arm control technology. The method includes: obtaining the dynamic inertia parameters, nonlinear friction parameters, joint motion information and dynamic total torque of the robotic arm; splitting the dynamic total torque according to the dynamic inertia parameters, nonlinear friction parameters and joint motion information to obtain split inertia torque and split friction torque; according to the split inertia torque and split friction torque, cyclically iteratively optimizing the dynamic inertia parameters and nonlinear friction parameters until the preset convergence conditions are met; obtaining the target dynamic inertia parameters and target nonlinear friction parameters when the preset convergence conditions are met, and the target dynamic inertia parameters and target nonlinear friction parameters are used to dynamically control the robotic arm. The present application can identify dynamic inertia parameters that conform to physical characteristics and nonlinear friction parameters that better fit joint friction.

Description

Technical Field

[0001] The present application relates to the field of robotic arm control technology, and more specifically, to a parameter identification method, device, electronic device, and storage medium for a robotic arm. Background Art

[0002] In the field of artificial intelligence, the application of robotic arms is becoming more and more extensive and in-depth. Based on this, the requirements for the control accuracy of robotic arms are also becoming higher and higher. Accurate dynamic models are the basis for high-precision control of robotic arms.

[0003] Existing methods for identifying the dynamic parameters of robotic arms are divided into parameter set methods and neural network training methods. Among them, the parameter set method can only identify inertia parameters and friction parameters separately, resulting in large parameter identification errors. The neural network training method has a large demand for training data, making it difficult to accurately predict and control the robotic arm. Summary of the Invention

[0004] The purpose of this application is to address the deficiencies in the above-mentioned prior art and provide a parameter identification method, device, electronic device and storage medium for a robotic arm, so as to identify dynamic inertia parameters that conform to physical properties and nonlinear friction parameters that better fit joint friction.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0006] In a first aspect, an embodiment of the present application provides a parameter identification method for a robotic arm, the method comprising:

[0007] Acquiring dynamic inertia parameters of the robotic arm, nonlinear friction parameters of the robotic arm, joint motion information of the robotic arm, and total dynamic torque;

[0008] Splitting the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter, and the joint motion information to obtain split inertia torque and split friction torque;

[0009] According to the split inertia moment and the split friction moment, the dynamic inertia parameter and the nonlinear friction parameter are cyclically iteratively optimized until a preset convergence condition is satisfied;

[0010] Target dynamic inertia parameters and target nonlinear friction parameters are acquired when the preset convergence condition is satisfied. The target dynamic inertia parameters and the target nonlinear friction parameters are used to perform dynamic control on the robotic arm.

[0011] Optionally, splitting the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter, and the joint motion information to obtain split inertia torque and split friction torque includes:

[0012] determining the split friction torque from the total dynamic torque according to the dynamic inertia parameter and the joint motion information;

[0013] The split inertia moment is determined from the total dynamic torque according to the split friction torque, the nonlinear friction parameter and the friction regression matrix of the joint motion information.

[0014] Optionally, determining the split inertia moment from the total dynamic torque according to the split friction torque, the nonlinear friction parameter and a friction regression matrix of the joint motion information includes:

[0015] Calculating a first linear friction parameter according to the split friction torque and the friction regression matrix;

[0016] The split inertia moment is determined from the total dynamic torque according to the first linear friction parameter and the friction regression matrix.

[0017] Optionally, the performing cyclic iterative optimization on the dynamic inertia parameter and the nonlinear friction parameter according to the split inertia moment and the split friction moment until a preset convergence condition is satisfied includes:

[0018] performing outer loop iterative optimization on the dynamic inertia parameters according to the dynamic inertia parameters, the joint motion information, and the split inertia moment until a first convergence condition is satisfied;

[0019] Re-splitting the total dynamic torque according to the optimized dynamic inertia parameters and the joint motion information to obtain a new split friction torque;

[0020] Each time the dynamic inertia parameter is optimized, the nonlinear friction parameter is iteratively optimized in an inner loop according to the nonlinear friction parameter, the joint motion information and the new split friction torque until a second convergence condition is met.

[0021] Optionally, performing inner loop iterative optimization on the nonlinear friction parameter according to the nonlinear friction parameter, the joint motion information, and the new split friction torque until a second convergence condition is satisfied includes:

[0022] Calculating a second linear friction parameter according to the friction regression matrix and the new split friction torque;

[0023] The nonlinear friction parameter is optimized iteratively according to the second linear friction parameter, the friction regression matrix and the new split friction torque until the second convergence condition is met.

[0024] Optionally, the method further includes:

[0025] The split inertia moment is re-determined from the total dynamic moment according to the optimized nonlinear friction parameter.

[0026] Optionally, the method further includes:

[0027] Construct a friction model of the robotic arm based on nonlinear friction parameters, joint motion information and linear friction parameters;

[0028] The friction regression matrix is ​​determined according to the mechanical arm friction model.

[0029] In a second aspect, an embodiment of the present application further provides a parameter identification device for a robotic arm, the device comprising:

[0030] An information acquisition module, configured to acquire the dynamic inertia parameters of the robotic arm, the nonlinear friction parameters of the robotic arm, the joint motion information of the robotic arm, and the total dynamic torque;

[0031] a torque splitting module, configured to split the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter, and the joint motion information to obtain split inertia torque and split friction torque;

[0032] an iterative optimization module, configured to perform cyclic iterative optimization on the dynamic inertia parameter and the nonlinear friction parameter according to the split inertia moment and the split friction moment, until a preset convergence condition is satisfied;

[0033] The parameter acquisition module is used to acquire target dynamic inertia parameters and target nonlinear friction parameters when the preset convergence condition is met, and the target dynamic inertia parameters and the target nonlinear friction parameters are used to perform dynamic control on the robotic arm.

[0034] Optionally, the torque splitting module is specifically used to determine the split friction torque from the total dynamic torque based on the dynamic inertia parameters and the joint motion information; and determine the split inertia torque from the total dynamic torque based on the split friction torque, the nonlinear friction parameters and the friction regression matrix of the joint motion information.

[0035] Optionally, the torque splitting module is further used to calculate a first linear friction parameter based on the split friction torque and the friction regression matrix; and determine the split inertia moment from the total dynamic torque based on the first linear friction parameter and the friction regression matrix.

[0036] Optionally, the iterative optimization module is specifically used to perform outer loop iterative optimization on the dynamic inertia parameters according to the dynamic inertia parameters, the joint motion information and the split inertia torque until the first convergence condition is met; re-split the total dynamic torque according to the dynamic inertia parameters after each optimization and the joint motion information to obtain a new split friction torque; each time the dynamic inertia parameters are optimized, the nonlinear friction parameters are performed inner loop iterative optimization according to the nonlinear friction parameters, the joint motion information and the new split friction torque until the second convergence condition is met.

[0037] Optionally, the iterative optimization module is also used to calculate a second linear friction parameter based on the friction regression matrix and the new split friction torque; and perform cyclic iterative optimization on the nonlinear friction parameter based on the second linear friction parameter, the friction regression matrix and the new split friction torque until the second convergence condition is met.

[0038] Optionally, the torque splitting module is further configured to redetermine the split inertia moment from the total dynamic torque according to the optimized nonlinear friction parameter.

[0039] Optionally, the device further comprises:

[0040] The matrix design module is used to construct a mechanical arm friction model based on nonlinear friction parameters, joint motion information and linear friction parameters; and determine the friction regression matrix according to the mechanical arm friction model.

[0041] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a processor, a storage medium and a bus, wherein the storage medium stores program instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium through the bus, and the processor executes the program instructions to perform the steps of the parameter identification method of the robotic arm as described in any one of the first aspects.

[0042] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the parameter identification method of the robotic arm as described in any one of the first aspects are executed.

[0043] The beneficial effects of this application are:

[0044] The parameter identification method, device, electronic device and storage medium of the robotic arm provided in this application adopt a two-stage coupling optimization framework to perform joint alternating iterative optimization of the dynamic inertia parameters and nonlinear friction parameters. Each time the dynamic inertia parameters are optimized, the nonlinear friction parameters are immediately optimized to form a dynamic decoupling between the parameters, avoid the error accumulation problem, ensure the accurate identification of the dynamic inertia parameters and nonlinear friction parameters, and improve the accuracy of the dynamic inertia parameters and nonlinear friction parameters; the optimized dynamic inertia parameters and nonlinear friction parameters are full parameters, which are more convenient for application in dynamic models. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 Schematic diagram of the process of the parameter identification method of the robotic arm provided in the embodiment of the present application Figure 1 ;

[0047] Figure 2 Schematic diagram of the process of the parameter identification method of the robotic arm provided in the embodiment of the present application Figure 2 ;

[0048] Figure 3 Schematic diagram of the process of the parameter identification method of the robotic arm provided in the embodiment of the present application Figure 3 ;

[0049] Figure 4 Schematic diagram of the process of the parameter identification method of the robotic arm provided in the embodiment of the present application Figure 4 ;

[0050] Figure 5 A flowchart of a parameter identification method for a robotic arm provided in an embodiment of the present application;

[0051] Figure 6 A schematic diagram of the structure of a parameter identification device for a robotic arm provided in an embodiment of the present application;

[0052] Figure 7 A schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0054] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0055] In addition, the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0056] It should be noted that, in the absence of conflict, the features in the embodiments of this application can be combined with each other.

[0057] Figure 1 Schematic diagram of the process of the parameter identification method of the robotic arm provided in the embodiment of the present application Figure 1 ,like Figure 1 As shown, the method may include:

[0058] S101 , obtaining the dynamic inertia parameters of the robotic arm, the nonlinear friction parameters of the robotic arm, the joint motion information of the robotic arm, and the total dynamic torque.

[0059] In this embodiment, the robotic arm is composed of multiple joints, and the multiple joints are rigidly connected by connecting rods. The dynamic inertia parameters of the robotic arm are the parameters of the multiple connecting rods. The obtained dynamic inertia parameters and nonlinear friction parameters are the initial dynamic inertia parameters. and the initial nonlinear friction parameter .

[0060] Among them, the initial dynamic inertia parameters The initial mass of the i-th link can be included , the initial center of mass coordinates of the i-th link and the initial inertia tensor matrix of the i-th link , initial dynamic inertia parameters .

[0061] In some embodiments, the initial mass of each link can be obtained from the Unified Robot Description Format (URDF) file obtained from the 3D software. , initial center of mass coordinates and the initial inertia tensor matrix , to obtain the initial dynamic inertia parameters The initial dynamic inertia parameters obtained from the URDF file can largely restore the physical properties of the robotic arm, thereby solving the problem of insufficient physical consistency in parameter optimization from the root.

[0062] Furthermore, according to the initial dynamic inertia parameters , set the optimization boundary of the dynamic inertia parameters to , by constraining the search space, it is ensured that the optimization of the dynamic inertia parameters varies within a reasonable range.

[0063] Initial nonlinear friction parameters It can also be called shape parameter, which can include the shape parameter of the Stribeck curve of the j-th joint , used to control the friction decay rate of each joint Stribeck curve to adapt to the friction characteristics of different joints. The Stribeck curve is used to represent the speed-dependent friction of the joint at low speed. The initial nonlinear friction parameter .

[0064] In some embodiments, the initial nonlinear friction parameter Friction parameters of each joint in It can be a preset value, for example, it can be 1.

[0065] Control the robot arm to move according to the pre-designed excitation trajectory, and collect the joint motion information and total dynamic torque of the robot arm based on the excitation trajectory Among them, joint motion information may include: joint position ,speed and acceleration .

[0066] In some embodiments, the excitation trajectory is designed using a quintic Fourier series.

[0067] S102 : Split the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter, and the joint motion information to obtain split inertia torque and split friction torque.

[0068] In this embodiment, the total dynamic torque of the robotic arm is decomposed to determine that the total dynamic torque of the robotic arm consists of two parts: an inertia term and a friction term.

[0069] According to the dynamic inertia parameters and joint motion information, the inertia term is determined from the total dynamic torque The inertia term is split out and the remaining part is used as the split friction torque .

[0070] The friction force on the joints of the manipulator includes linear friction and nonlinear friction. According to the nonlinear friction parameters, joint motion information and split friction torque , from the total dynamic torque The friction term is split out and the remaining part is used as the split inertia moment .

[0071] S103. Perform cyclic iterative optimization on the dynamic inertia parameters and the nonlinear friction parameters according to the split inertia moment and the split friction moment until a preset convergence condition is met.

[0072] In this embodiment, the dynamic inertia parameters are optimized according to the split inertia moment to obtain the optimized dynamic inertia parameters. According to the optimized dynamic inertia parameters, the above S102 is re-executed to determine the new split friction torque. , according to the nonlinear friction parameters, joint motion information and the new split friction torque , optimize the nonlinear friction parameters until the optimized nonlinear friction parameters are obtained.

[0073] According to the optimized nonlinear friction parameters, re-execute the above S102 to determine the new split inertia moment , according to the new split inertia moment , S103 is executed cyclically until the optimized dynamic inertia parameters and the optimized nonlinear friction parameters meet the preset convergence conditions.

[0074] In some embodiments, the dynamic inertia parameter and the nonlinear friction parameter satisfy different convergence conditions.

[0075] In some embodiments, the dynamic inertia parameters are optimized using an outer loop, and the nonlinear friction parameters are optimized using an inner loop, that is, each time the dynamic inertia parameters are optimized, the nonlinear friction parameters are optimized in a cycle until the nonlinear friction parameters converge, and then the dynamic inertia parameters are optimized for the next time, and then the nonlinear friction parameters are optimized in a cycle again.

[0076] S104 , obtaining target dynamic inertia parameters and target nonlinear friction parameters when a preset convergence condition is satisfied, wherein the target dynamic inertia parameters and target nonlinear friction parameters are used to perform dynamic control on the robotic arm.

[0077] In this embodiment, the dynamic inertia parameter when the preset convergence condition is satisfied is determined as the target dynamic inertia parameter, and the nonlinear friction parameter when the preset convergence condition is satisfied is determined as the target nonlinear friction parameter.

[0078] In the process of controlling the robot arm, the dynamic model of the robot arm is used to control each joint of the robot arm according to the target dynamic inertia parameters and the target nonlinear friction parameters.

[0079] The parameter identification method of the robotic arm provided in the above embodiment adopts a two-stage coupled optimization framework to perform joint alternating iterative optimization of the dynamic inertia parameters and the nonlinear friction parameters. Each time the dynamic inertia parameters are optimized, the nonlinear friction parameters are immediately optimized, forming a dynamic decoupling between the parameters, avoiding the error accumulation problem, ensuring the accurate identification of the dynamic inertia parameters and the nonlinear friction parameters, and improving the accuracy of the dynamic inertia parameters and the nonlinear friction parameters; the optimized dynamic inertia parameters and nonlinear friction parameters are full parameters, which are more convenient for application in dynamic models.

[0080] In one possible implementation, Figure 2 Schematic diagram of the process of the parameter identification method of the robotic arm provided in the embodiment of the present application Figure 2 ,like Figure 2 As shown, the above S102 splits the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter and the joint motion information to obtain the split inertia torque and the split friction torque, which may include:

[0081] S201. Determine the split friction torque from the total dynamic torque according to the dynamic inertia parameter and the joint motion information.

[0082] In this embodiment, the total dynamic torque of the robotic arm is decomposed into an inertia term and a friction term, which can be expressed as follows:

[0083]

[0084] in, is the kinematic regression matrix, that is, the observation matrix of the inertial term, is the dynamic inertia parameter. Information about the various parameters of the friction term will be introduced in the following sections.

[0085] According to joint position ,speed and acceleration , modeled and linearized using the Newton-Euler method, and the kinematic regression matrix was determined , according to the initial dynamic inertia parameters and the kinematic regression matrix , calculate and predict the moment of inertia, from the total dynamic moment The predicted inertia moment is split out, and the remaining part is the split friction moment .

[0086] Specifically, the friction torque is decomposed into The calculation formula is as follows:

[0087]

[0088] S202 , determining the split inertia moment from the total dynamic torque according to the split friction torque, the nonlinear friction parameter, and the friction regression matrix of the joint motion information.

[0089] In this embodiment, the pre-established mechanical arm friction model is decomposed to design the friction regression matrix of the nonlinear friction parameters and the velocity in the joint motion information. , to convert the nonlinear terms of the manipulator friction model into linear friction parameters Explicit expression of . Among them, the friction regression matrix It is about the nonlinear friction parameter and joint velocity function.

[0090] According to the split friction torque and friction regression matrix, the predicted friction torque is determined from the total dynamic torque The predicted friction torque is split out, and the remaining part is the split inertia torque .

[0091] The parameter identification method of the robotic arm provided in the above embodiment splits the total dynamic torque into split inertia torque and split friction torque based on dynamic inertia parameters, nonlinear friction parameters and joint motion information, so as to perform differentiated optimization on the dynamic inertia parameters and nonlinear friction parameters, dynamically decouple the dynamic inertia parameters and nonlinear friction parameters, and reduce parameter identification errors.

[0092] In some embodiments, the method may further include:

[0093] A friction model of the robotic arm is constructed based on nonlinear friction parameters, joint motion information and linear friction parameters; and a friction regression matrix is ​​determined according to the friction model of the robotic arm.

[0094] In this embodiment, the expression of the constructed mechanical arm friction model can be expressed as:

[0095]

[0096] Among them, the initial linear friction parameter , is the Coulomb friction coefficient of the jth joint, which is a constant friction coefficient independent of the velocity direction. is the Stribeck effect coefficient of the j-th joint, is the speed-dependent friction coefficient at low speed, is the viscous friction coefficient of the j-th joint, which is proportional to the velocity.

[0097] Decompose the above-mentioned robot arm friction model and design the friction regression matrix , converting the nonlinear terms of the manipulator friction model into linear friction parameters The friction term in the total dynamic torque of the manipulator can be expressed as .

[0098] In some embodiments, Figure 3 Schematic diagram of the process of the parameter identification method of the robotic arm provided in the embodiment of the present application Figure 3 ,like Figure 3 As shown, the process of determining the split inertia moment from the total dynamic torque in S202 according to the friction regression matrix of the split friction torque, nonlinear friction parameters and joint motion information may include:

[0099] S301 : Calculate a first linear friction parameter according to the split friction torque and the friction regression matrix.

[0100] In this embodiment, since the friction torque can be expressed based on the linear friction parameter, the nonlinear friction parameter and the joint velocity, the split friction torque is determined. , friction regression matrix of nonlinear friction parameters and joint motion information Afterwards, the first linear friction parameter can be calculated .

[0101] In some embodiments, the least square method and the initial nonlinear friction parameters are used. Split friction torque Perform fitting to obtain the first linear friction parameter , the first linear friction parameter is the initial linear friction parameter. After the dynamic inertia parameters are optimized, the new split friction torque is calculated based on the optimized dynamic inertia parameters. , based on the new split friction torque For the first linear friction parameter to update.

[0102] For example, the first linear friction parameter The calculation formula can be expressed as:

[0103]

[0104] S302 : Determine the split inertia moment from the total dynamic torque according to the first linear friction parameter and the friction regression matrix.

[0105] In this embodiment, according to the first linear friction parameter and the friction regression matrix , calculate the predicted friction matrix, split the predicted friction torque from the total dynamic torque, and the remaining part is the split inertia moment .

[0106] Example, splitting the moment of inertia The calculation formula can be expressed as:

[0107]

[0108] The parameter identification method of the robotic arm provided in the above embodiment adopts the robotic arm friction model to determine the friction regression matrix so as to convert the nonlinear friction parameters into explicit expressions of linear friction parameters, and significantly reduces the error in modeling the friction mutation phenomenon in the low-speed zone based on the nonlinear friction parameters.

[0109] In one possible implementation, Figure 4 Schematic diagram of the process of the parameter identification method of the robotic arm provided in the embodiment of the present application Figure 4 ,like Figure 4 As shown, the above S103 performs cyclic iterative optimization on the dynamic inertia parameters and the nonlinear friction parameters according to the split inertia moment and the split friction moment until the preset convergence condition is satisfied, which may include:

[0110] S401 , performing outer loop iterative optimization on the dynamic inertia parameters according to the dynamic inertia parameters, joint motion information, and split inertia moments until a first convergence condition is met.

[0111] In this embodiment, the outer circle cycle optimization dynamic inertia parameters are first defined, and the joint position ,speed and acceleration , modeled and linearized using the Newton-Euler method, and the kinematic regression matrix was determined , according to the current dynamic inertia parameters and the kinematic regression matrix , determine the predicted moment of inertia, based on the predicted moment of inertia and the split moment of inertia The difference between the dynamic inertia parameters is calculated by the interior point method. Optimize and obtain the optimized dynamic inertia parameters .

[0112] For example, the objective function for optimizing the kinetic parameters can be expressed as:

[0113]

[0114] in, m is the number of links of the robot arm, and the first convergence condition of the dynamic inertia parameters is the optimized dynamic inertia parameters and the dynamic inertia parameters before optimization The absolute value of the difference is greater than the first preset threshold. For example, the first convergence condition may be: .

[0115] After optimizing the dynamic inertia parameters, based on the optimized dynamic inertia parameters Optimize the nonlinear friction parameters. After optimizing the nonlinear friction parameters once, calculate the new split inertia moment based on the optimized nonlinear friction parameters. , and perform the next optimization on the dynamic inertia parameters.

[0116] S402 : Re-split the total dynamic torque according to the dynamic inertia parameters and joint motion information after each optimization to obtain a new split friction torque.

[0117] In this embodiment, after the dynamic inertia parameters are optimized, the split friction torque split from the dynamic total torque will change. Specifically, according to the optimized dynamic inertia parameters and the kinematic regression matrix , calculate the new predicted moment of inertia from the total dynamic moment The new predicted inertia moment is split out, and the remaining part is the new split friction moment .

[0118] Example, new split friction torque The calculation formula can be expressed as:

[0119]

[0120] S403. Each time the dynamic inertia parameters are optimized, the nonlinear friction parameters are iteratively optimized in an inner loop according to the nonlinear friction parameters, the joint motion information and the new split friction torque until the second convergence condition is met.

[0121] In this embodiment, the inner circle cycle optimization nonlinear friction parameters are defined, and the nonlinear friction parameters are And joint motion information, calculate the predicted friction torque, according to the predicted friction torque and the new split friction torque The difference between the nonlinear friction parameters is calculated using the interior point method. Optimize and obtain the optimized nonlinear friction parameters .

[0122] Among them, the outer circle loop optimization and the inner circle loop optimization are to perform a round of iterative optimization on the nonlinear friction parameters until convergence, and then perform the next iterative optimization on the dynamic inertia parameters. That is, each time the dynamic inertia parameters are optimized, the nonlinear friction parameters are iteratively optimized once until convergence. Finally, when the dynamic inertia parameters converge, the optimization of the dynamic inertia parameters and the nonlinear friction parameters is completed.

[0123] In some embodiments, the process of performing inner loop iterative optimization on the nonlinear friction parameters according to the nonlinear friction parameters, the joint motion information, and the new split friction torque until the second convergence condition is satisfied in S403 may include:

[0124] The second linear friction parameter is calculated according to the friction regression matrix and the new split friction torque; the nonlinear friction parameter is cyclically iteratively optimized according to the second linear friction parameter, the friction regression matrix and the new split friction torque until the second convergence condition is met.

[0125] In this embodiment, the new split friction torque is calculated based on the optimized dynamic inertia parameters. After that, you need to set the first linear friction parameter Updated to the second linear friction parameter , in order to re-determine the new split inertia moment from the total dynamic moment .

[0126] For example, the second linear friction parameter The calculation formula can be expressed as:

[0127]

[0128] According to the second linear friction parameter The new predicted friction matrix is ​​calculated based on the friction regression matrix, and the new split friction torque is calculated based on the new predicted friction matrix and the new split friction torque. The difference between the nonlinear friction parameters is calculated using the interior point method. Optimize and obtain the optimized nonlinear friction parameters .

[0129] For example, the objective function for optimizing nonlinear friction parameters can be expressed as:

[0130]

[0131] in, nis the number of joints of the robot arm, and the second convergence condition of the nonlinear friction parameter is the optimized nonlinear friction parameter and nonlinear friction parameters before optimization The absolute value of the difference is greater than the second preset threshold. For example, the first convergence condition may be: .

[0132] In some embodiments, the method may further include:

[0133] According to the optimized nonlinear friction parameters, the split inertia moment is re-determined from the total dynamic torque.

[0134] In this embodiment, according to the optimized nonlinear friction parameters The predicted friction torque is calculated from the joint velocity and the second linear friction parameter, and the total dynamic torque is obtained from The new predicted friction torque is split out, and the remaining part is the new split inertia torque .

[0135] Example, new split moment of inertia The calculation formula can be expressed as:

[0136]

[0137] The parameter identification method of the robotic arm provided in the above embodiment identifies the dynamic inertia parameters and nonlinear friction parameters separately through a double-loop method, ensuring that the dynamic inertia parameters and nonlinear friction parameters can be accurately identified, making it easy to apply the dynamic inertia parameters and nonlinear friction parameters separately to the scenarios where they are required.

[0138] Figure 5 The flow chart of the parameter identification method of the robot arm provided in the embodiment of the present application is as follows: Figure 5 As shown, the process of the parameter identification method may include:

[0139] S501, initializing parameters; wherein the initialization parameters include obtaining initial dynamic inertia parameters, initial nonlinear friction parameters, joint motion information and total dynamic torque.

[0140] S502: Calculate the initial split inertia moment and split friction moment.

[0141] S503. Optimize dynamic inertia parameters using the interior point method.

[0142] S504. Calculate new split friction torque and linear friction parameters using the optimized dynamic inertia parameters.

[0143] S505. Optimize nonlinear friction parameters using the interior point method.

[0144] S506: Determine whether the optimized nonlinear friction parameters have converged. If not, jump to S507; if converged, jump to S508.

[0145] S507 , calculating new linear friction parameters according to the optimized nonlinear friction parameters, and then jumping to S505 .

[0146] S508 , judging whether the optimized dynamic inertia parameters have converged; if not, jumping to S509 ; if converged, jumping to S510 .

[0147] S509 , recalculate the split inertia moment according to the optimized nonlinear friction parameters and the new linear friction parameters, and then jump to S503 .

[0148] S510 , end, output target dynamic inertia parameters and target nonlinear friction parameters.

[0149] Based on the above method embodiment, an embodiment of the present application also provides a parameter identification device for a robotic arm. Figure 6 A schematic diagram of the structure of a parameter identification device for a robotic arm provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the device may include:

[0150] The information acquisition module 601 is used to obtain the dynamic inertia parameters of the manipulator, the nonlinear friction parameters of the manipulator, the joint motion information of the manipulator, and the total dynamic torque;

[0151] The torque splitting module 602 is used to split the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter and the joint motion information to obtain the split inertia torque and the split friction torque;

[0152] Iterative optimization module 603, configured to perform cyclic iterative optimization on the dynamic inertia parameters and the nonlinear friction parameters according to the split inertia moment and the split friction moment until a preset convergence condition is met;

[0153] The parameter acquisition module 604 is used to acquire target dynamic inertia parameters and target nonlinear friction parameters when a preset convergence condition is met. The target dynamic inertia parameters and target nonlinear friction parameters are used to perform dynamic control on the robotic arm.

[0154] Optionally, the torque splitting module 602 is specifically used to determine the split friction torque from the total dynamic torque based on the dynamic inertia parameters and joint motion information; and determine the split inertia torque from the total dynamic torque based on the friction regression matrix of the split friction torque, nonlinear friction parameters and joint motion information.

[0155] Optionally, the torque splitting module 602 is further configured to calculate a first linear friction parameter based on the split friction torque and the friction regression matrix; and determine the split inertia moment from the total dynamic torque based on the first linear friction parameter and the friction regression matrix.

[0156] Optionally, the iterative optimization module 603 is specifically used to perform outer loop iterative optimization on the dynamic inertia parameters according to the dynamic inertia parameters, joint motion information and split inertia torque until the first convergence condition is met; re-split the total dynamic torque according to the optimized dynamic inertia parameters and joint motion information to obtain a new split friction torque; and perform inner loop iterative optimization on the nonlinear friction parameters according to the nonlinear friction parameters, joint motion information and the new split friction torque until the second convergence condition is met.

[0157] Optionally, the iterative optimization module 603 is also used to calculate the second linear friction parameter based on the friction regression matrix and the new split friction torque; and perform cyclic iterative optimization on the nonlinear friction parameter based on the second linear friction parameter, the friction regression matrix and the new split friction torque until the second convergence condition is met.

[0158] Optionally, the torque splitting module 602 is further configured to re-determine the split inertia torque from the total dynamic torque according to the optimized nonlinear friction parameter.

[0159] Optionally, the device may further include:

[0160] The matrix design module is used to construct a friction model of the manipulator based on nonlinear friction parameters, joint motion information and linear friction parameters; and to determine the friction regression matrix according to the friction model of the manipulator.

[0161] The above-mentioned device is used to execute the method provided in the above-mentioned embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0162] The above modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more microprocessors, or one or more field programmable gate arrays (FPGAs). For example, when a module is implemented by scheduling program code through a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0163] Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of the present application. The electronic device 700 includes a processor 701, a storage medium 702, and a bus. The storage medium 702 stores program instructions executable by the processor 701. When the electronic device 700 is running, the processor 701 and the storage medium 702 communicate via the bus, and the processor 701 executes the program instructions to perform the above-mentioned method embodiment. The specific implementation methods and technical effects are similar and will not be repeated here.

[0164] Optionally, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the above method embodiment is executed.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0166] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0167] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0168] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor (English: processor) to perform some steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard drives, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), magnetic disks or optical disks, and other media that can store program code.

[0169] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited to them. Any changes or substitutions that can be easily conceived by any person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A parameter identification method for a robotic arm, characterized in that: The method comprises: Acquiring dynamic inertia parameters of the robotic arm, nonlinear friction parameters of the robotic arm, joint motion information of the robotic arm, and total dynamic torque; Splitting the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter, and the joint motion information to obtain split inertia torque and split friction torque; According to the split inertia moment and the split friction moment, the dynamic inertia parameter and the nonlinear friction parameter are cyclically iteratively optimized until a preset convergence condition is satisfied; Obtaining target dynamic inertia parameters and target nonlinear friction parameters when the preset convergence condition is satisfied, wherein the target dynamic inertia parameters and the target nonlinear friction parameters are used to dynamically control the robotic arm; The step of performing cyclic iterative optimization on the dynamic inertia parameter and the nonlinear friction parameter according to the split inertia moment and the split friction moment until a preset convergence condition is satisfied includes: performing outer loop iterative optimization on the dynamic inertia parameters according to the dynamic inertia parameters, the joint motion information, and the split inertia moment until a first convergence condition is satisfied; Re-splitting the total dynamic torque according to the optimized dynamic inertia parameters and the joint motion information to obtain a new split friction torque; Each time the dynamic inertia parameter is optimized, the nonlinear friction parameter is iteratively optimized in an inner loop according to the nonlinear friction parameter, the joint motion information and the new split friction torque until a second convergence condition is met.

2. The method according to claim 1, characterized in that The step of splitting the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter, and the joint motion information to obtain split inertia torque and split friction torque includes: determining the split friction torque from the total dynamic torque according to the dynamic inertia parameter and the joint motion information; The split inertia moment is determined from the total dynamic torque according to the split friction torque, the nonlinear friction parameter and the friction regression matrix of the joint motion information.

3. The method according to claim 2, characterized in that The determining the split inertia moment from the total dynamic torque according to the split friction torque, the nonlinear friction parameter and the friction regression matrix of the joint motion information includes: Calculating a first linear friction parameter according to the split friction torque and the friction regression matrix; The split inertia moment is determined from the total dynamic torque according to the first linear friction parameter and the friction regression matrix.

4. The method according to claim 2, characterized in that The step of performing inner loop iterative optimization on the nonlinear friction parameter according to the nonlinear friction parameter, the joint motion information, and the new split friction torque until a second convergence condition is satisfied includes: Calculating a second linear friction parameter according to the friction regression matrix and the new split friction torque; The nonlinear friction parameter is optimized iteratively according to the second linear friction parameter, the friction regression matrix and the new split friction torque until the second convergence condition is met.

5. The method according to claim 1, wherein The method further comprises: The split inertia moment is re-determined from the total dynamic moment according to the optimized nonlinear friction parameter.

6. The method according to claim 2, characterized in that The method further comprises: Construct a friction model of the robotic arm based on nonlinear friction parameters, joint motion information and linear friction parameters; The friction regression matrix is ​​determined according to the mechanical arm friction model.

7. A parameter identification device for a robotic arm, characterized in that: The device comprises: An information acquisition module, configured to acquire the dynamic inertia parameters of the robotic arm, the nonlinear friction parameters of the robotic arm, the joint motion information of the robotic arm, and the total dynamic torque; a torque splitting module, configured to split the total dynamic torque according to the dynamic inertia parameter, the nonlinear friction parameter, and the joint motion information to obtain split inertia torque and split friction torque; an iterative optimization module, configured to perform cyclic iterative optimization on the dynamic inertia parameter and the nonlinear friction parameter according to the split inertia moment and the split friction moment, until a preset convergence condition is satisfied; a parameter acquisition module, configured to acquire target dynamic inertia parameters and target nonlinear friction parameters when the preset convergence condition is satisfied, wherein the target dynamic inertia parameters and the target nonlinear friction parameters are used to perform dynamic control on the robotic arm; The iterative optimization module is specifically used to perform outer loop iterative optimization on the dynamic inertia parameters according to the dynamic inertia parameters, the joint motion information and the split inertia torque until the first convergence condition is met; re-split the total dynamic torque according to the dynamic inertia parameters and the joint motion information after each optimization to obtain a new split friction torque; and each time the dynamic inertia parameters are optimized, perform inner loop iterative optimization on the nonlinear friction parameters according to the nonlinear friction parameters, the joint motion information and the new split friction torque until the second convergence condition is met.

8. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores program instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the program instructions to perform the steps of the parameter identification method of the robotic arm as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the parameter identification method of the robotic arm according to any one of claims 1 to 6 are executed.

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