A robot joint friction model parameter identification method, device and equipment

Abstract

The application provides a robot joint friction model parameter identification method, device and equipment. The method provided by the application comprises the following steps: a speed-related friction model, a temperature-related friction model and a load-related friction model are established to constitute a robot joint friction model; a plurality of groups of friction characteristic data of a robot joint in different working states are acquired, and initial value ranges of various parameters in the model are set based on expert knowledge and prior physical knowledge to constitute an initial parameter search space; an error square sum of model prediction is taken as a fitness function, the initial parameter search space is optimized based on the plurality of groups of friction characteristic data by using a genetic algorithm to obtain a candidate parameter search space; and an optimization function with the optimization objectives of minimizing a prediction error and minimizing a parameter physical deviation is constructed for the robot joint friction model.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and in particular to a method, apparatus and device for identifying parameters of a robot joint friction model. Background Technology

[0002] With the rapid development of robotics technology, industrial robots, service robots, and collaborative robots are increasingly widely used in manufacturing, healthcare, logistics, and other fields. In performing precision operations, force control tasks, or human-robot collaboration, robots face higher demands on the motion accuracy, dynamic response performance, and control stability of their joints. As a crucial factor affecting the dynamic characteristics of robots, joint friction torque has a significant impact on the stability, trajectory tracking accuracy, and energy consumption performance of motion control systems. Therefore, accurate modeling and identification of robot joint friction characteristics has become a key research focus in the field of robot control.

[0003] Traditional friction models are typically based on simplified Coulomb friction, viscous friction, or Stribeck models. While computationally simple, these models generally assume that the frictional torque depends only on the joint velocity, leading to significant prediction errors in practical applications and failing to meet the demands of high-precision control. Furthermore, traditional parameter identification methods often rely on local optimization algorithms such as linear least squares, which are prone to getting trapped in local optima and thus compromise accuracy. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus and device for identifying parameters of robot joint friction models, which can improve the accuracy of robot joint friction modeling and parameter identification.

[0005] Specifically, this application is implemented through the following technical solution:

[0006] The first aspect of this application provides a method for identifying parameters of a robot joint friction model, the method comprising:

[0007] Establish a velocity-dependent friction model to characterize the relationship between robot joint friction torque and joint velocity, a temperature-dependent friction model to characterize the characteristics of robot joint friction torque as a function of temperature, and a load-dependent friction model to characterize the dependence between robot joint friction torque and output load torque.

[0008] The robot joint friction model is determined based on the velocity-related friction model, the temperature-related friction model, and the load-related friction model.

[0009] Acquire multiple sets of friction characteristic data for robot joints under different working conditions; each set of friction characteristic data includes output load torque, joint velocity, joint temperature, and measured friction torque;

[0010] Based on expert knowledge and prior physical knowledge, initial value ranges are set for each parameter in the robot joint friction model, forming an initial parameter search space;

[0011] Using the sum of squared errors predicted by the model as the fitness function, and based on the multiple sets of friction characteristic data, a genetic algorithm is used to optimize the initial parameter search space to obtain a candidate parameter search space; the candidate parameter search space includes the optimized value range of each parameter in the robot joint friction model;

[0012] For any one of the velocity-related friction model, the temperature-related friction model, and the load-related model, the parameter identification problem of the sub-model is transformed into a multi-objective optimization problem, and an optimization function is constructed with the optimization objectives of minimizing prediction error and minimizing physical deviation of parameters.

[0013] Using the search space of the alternative parameters as constraints, the optimization function of each sub-model is solved based on the multiple sets of friction characteristic data to obtain the optimal values ​​of each parameter in each sub-model.

[0014] A second aspect of this application provides a device for identifying parameters of a robot joint friction model, the device comprising a construction module, a determination module, an acquisition module, and a calculation module; wherein...

[0015] The construction module is used to establish a velocity-dependent friction model to characterize the relationship between robot joint friction torque and joint speed, a temperature-dependent friction model to characterize the characteristics of robot joint friction torque as a function of temperature, and a load-dependent friction model to characterize the dependence between robot joint friction torque and output load torque.

[0016] The determining module is used to determine the robot joint friction model based on the velocity-related friction model, the temperature-related friction model, and the load-related friction model.

[0017] The acquisition module is used to acquire multiple sets of friction characteristic data of the robot joint under different working conditions; wherein, each set of friction characteristic data includes output load torque, joint speed, joint temperature and measured friction torque;

[0018] The determining module is used to set an initial value range for each parameter in the robot joint friction model based on expert knowledge and prior physical knowledge, thus forming an initial parameter search space.

[0019] The determining module is used to optimize the initial parameter search space based on the multiple sets of friction characteristic data using a genetic algorithm, with the sum of squared errors predicted by the model as the fitness function, to obtain a candidate parameter search space; the candidate parameter search space includes the optimized value range of each parameter in the robot joint friction model;

[0020] The construction module is used to transform the parameter identification problem of the robot joint friction model into a multi-objective optimization problem, and to construct an optimization function with the optimization objectives of minimizing prediction error and minimizing parameter physical deviation.

[0021] The calculation module is also used to solve the optimization function based on the multiple sets of friction characteristic data, using the alternative parameter search space as a constraint, to obtain the optimal values ​​of each parameter in the robot joint friction model.

[0022] A third aspect of this application provides a robot joint friction model parameter identification device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the methods provided in the first aspect of this application.

[0023] The robot joint friction model parameter identification method, apparatus, and equipment provided in this embodiment firstly comprehensively consider the multi-factor influence of joint friction torque variations with speed, temperature, and load, establishing a robot joint friction model that better reflects actual working conditions. Furthermore, by introducing expert knowledge and prior physical knowledge to construct an initial parameter search space and employing a genetic algorithm for global optimization, the accuracy and robustness of parameter identification are improved. Simultaneously, by constructing a multi-objective optimization function that minimizes prediction error and parameter physical deviation, the identified parameters are ensured to possess good physical rationality and interpretability while meeting high-precision prediction requirements. In summary, this method can significantly improve the accuracy and adaptability of robot joint friction modeling, providing reliable support for applications such as high-precision motion control and force control strategy design. Attached Figure Description

[0024] Figure 1 A flowchart of an embodiment of the robot joint friction model parameter identification method provided in this application;

[0025] Figure 2 A schematic diagram of the structure of a robot joint friction model provided in an exemplary embodiment of this application;

[0026] Figure 3 A schematic diagram of the structure of an embodiment of the robot joint friction model parameter identification device provided in this application;

[0027] Figure 4This is a hardware structure diagram of the robot joint friction model parameter identification device, which is the robot joint friction model parameter identification device of this application. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0029] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0030] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0031] The following specific embodiments are given to illustrate the technical solution of this application in detail.

[0032] Figure 1 This is a flowchart of an embodiment of the robot joint friction model parameter identification method provided in this application. Figure 2 This is a schematic diagram of the structure of a robot joint friction model provided for an exemplary embodiment of this application. Please refer to... Figure 1 and Figure 2 The method provided in this embodiment may include:

[0033] S101. Establish a velocity-dependent friction model to characterize the relationship between robot joint friction torque and joint speed, a temperature-dependent friction model to characterize the characteristics of robot joint friction torque as a function of temperature, and a load-dependent friction model to characterize the dependence between robot joint friction torque and output load torque.

[0034] It should be noted that, in order to effectively address the complexity of friction modeling and parameter identification under multi-physics coupling in harmonic reducers, this application proposes a structural decoupling method for the friction model based on the separability assumption of the multi-factor action mechanism. Furthermore, considering the typical characteristics of harmonic reducer friction characteristics being subject to triple coupling of kinematic parameters (velocity), thermodynamic state (temperature), and dynamic load (output load torque), the traditional single-coupled model is decoupled, constructing three sub-models: a velocity-dependent friction model, a temperature-dependent friction model, and a load-dependent friction model. In this way, by separating different parameters for targeted modeling, the identification dimensionality in the high-dimensional parameter space can be effectively reduced, while preserving the physical interpretability of each sub-model.

[0035] The three sub-models will be introduced in detail below:

[0036] Specifically, the velocity-dependent friction model is used to characterize the relationship between the frictional torque of a robot joint and its joint velocity. The velocity-dependent friction model can be expressed by the following formula:

[0037] ;

[0038] in, This refers to the frictional torque of the robot joints. This refers to the joint rotation speed of the robot. Coulomb friction, For static friction, For Stribeck speed, Stribeck index, For the first speed-related parameters, For the second speed-related parameters, For the third velocity-related parameters; wherein, the The above The above The above The above The above The above These are the parameters to be identified. Specifically, the first term in the model (i.e. The first three terms represent the Stribeck effect, which characterizes the nonlinear transition from static to kinetic friction at low speeds in a joint; the last three terms are velocity-dependent polynomial terms. This is the viscous friction term, describing the effect of linear velocity; This is the turbulent drag term, which is a quadratic function and is direction-dependent. The high-order nonlinear term considers the non-Newtonian characteristics of lubricating oil and the high-order velocity effects caused by flexible deformation. This model takes the joint angular velocity as input and outputs the joint friction torque, which can be used for friction compensation control, or further converted into an equivalent control current via the motor torque constant.

[0039] It should be noted that the velocity-dependent friction model is used to characterize the dynamic relationship between the frictional torque of a robot joint and the change in joint angular velocity. This velocity-dependent friction model takes real-time joint velocity as input and performs sign separation on the original joint velocity signal. To distinguish the forward and reverse directions of robot joints, the absolute value of joint velocity is also processed. Then, the Stribeck effect term was used. The nonlinear variation process of static-dynamic friction force at low joint velocities is calculated; furthermore, higher-order polynomial terms are used. Further fitting of the effect of joint velocity on frictional torque, including linear velocity terms. Direction-dependent quadratic velocity term and the third nonlinear velocity term Finally, the two terms are summed, and the result is output as the joint speed-related control current. Subsequently, it is converted into the robot joint friction torque by the motor torque constant of the robot joint. Here, abs() is the absolute value function, sqrt() is the square root function, and sign() is the sign function.

[0040] Specifically, the first velocity-related parameter is related to the viscous friction of the robot joints. In the harmonic reducer, the total friction exhibits a nonlinear velocity dependence. As can be seen from the velocity-related model above, at low joint speeds, viscous friction dominates, and the total friction is proportional to the joint speed. When the joint speed approaches zero, the total friction approaches the non-zero value of Coulomb friction. As the speed increases, the Coulomb friction effect becomes more significant, and Coulomb friction dominates the total friction. At this point, the total friction remains essentially constant and has a weak relationship with speed changes. However, as the joint speed increases further, the Stribeck effect begins to appear. At this point, the total friction decreases slightly with increasing speed. Subsequently, with further increases in joint speed, driven by the linear growth of viscous friction, the total friction also increases further with increasing joint speed.

[0041] Furthermore, the second velocity-related parameter is related to the turbulent resistance of the robot joint, which is the nonlinear resistance caused by the change in lubricating oil under high-speed flow conditions. The second velocity-related parameter reflects the nonlinear influence of the lubricating oil flow state on the friction force when it changes from laminar to turbulent. When the joint velocity exceeds the critical threshold, the flow Reynolds number of the lubricating oil in the joint tooth surface gap (the Reynolds number is a dimensionless number used as an indicator to predict the flow state) breaks through the laminar limit, and the flow state of the lubricating oil changes to turbulent. Its energy dissipation rate is proportional to the square of the velocity. At this time, the joint friction force exhibits a quadratic function growth.

[0042] Furthermore, the third velocity-related parameter is related to the flexible deformation effect of the robot joint. It should be noted that changes in joint speed affect the viscosity of the lubricating oil in the robot joint, thus altering its frictional characteristics. At higher joint speeds, due to the extremely high shear rate experienced within the gaps between the joint teeth, the non-Newtonian fluid characteristics of the lubricating oil become prominent, and the viscosity of the lubricating oil gradually decreases with increasing shear rate (this decrease in viscosity with increasing shear rate is a shear thinning effect). In addition, due to the shear thinning effect of the lubricating oil, the oil film thickness falls below a critical value, allowing protrusions on the joint teeth to penetrate the oil film, increasing direct contact friction between the teeth. At this point, the coefficient of friction of the joint friction force will increase, thereby increasing the joint friction force.

[0043] As described above, the velocity-dependent friction model, through further decoupling, decomposes velocity-dependent friction into three independent terms related to viscous friction, turbulent drag, and flexible deformation effects. This helps to more accurately model the velocity-dependent friction of robot joints. The first, second, and third velocity-dependent parameters in the velocity-dependent friction model correspond to different physical mechanisms, making it easier to analyze and identify the influence of each parameter on velocity-dependent friction in a targeted manner. In this way, the prediction accuracy of the model can be effectively improved.

[0044] The temperature-dependent friction model is used to characterize the temperature-dependent variation of the frictional torque of robot joints. The temperature-dependent friction model can be expressed by the following formula:

[0045] ;

[0046] This refers to the frictional torque of the robot joints. To represent the joint temperature of the robot, This refers to the joint rotation speed of the robot. The first temperature-related parameter; This is the second temperature-related parameter. This is the third temperature-related parameter; wherein, the The above The above The parameters to be identified.

[0047] It should be noted that during the operation of a harmonic reducer, friction between the joints generates heat. This accumulated heat raises the internal temperature of the reducer, and the viscosity of the lubricating oil between the joints is affected by temperature, thus influencing the frictional force. Specifically, as the temperature gradually increases, the viscosity of the lubricating oil decreases, resulting in poorer lubrication and increased friction. Conversely, as the temperature gradually decreases, the viscosity of the lubricating oil gradually increases, leading to greater friction. Furthermore, changes in the internal temperature of the harmonic reducer also cause dimensional changes in the reducer components, altering the pressure on the contact surfaces between the joints and thus affecting their frictional characteristics.

[0048] Referring to the preceding description, this temperature-dependent friction model considers the following factors: the change of lubricating oil viscosity with temperature: as temperature increases, the viscosity of the lubricating oil decreases, resulting in a deterioration of the oil film performance and an increase in friction.

[0049] Structural thermal expansion: Temperature changes cause changes in the dimensions of joint components, affecting contact pressure and friction characteristics;

[0050] Extreme temperature effects: At low temperatures, the viscosity of lubricating oil increases significantly, leading to increased friction; at high temperatures, the oil film is prone to rupture, which also causes instability in friction.

[0051] In summary, the influence of the internal temperature of the harmonic reducer on the joints is complex, and the internal temperature changes continuously as heat accumulates during operation. This continuous change in internal temperature affects the friction between the robot joints. Therefore, the robot joint friction model parameter identification method provided in this application incorporates a temperature-dependent friction model that considers the dynamic changes in internal temperature. This provides temperature-adaptive friction compensation for the robot joints, enhances the robustness of joint control and stability under high-temperature conditions, and provides reliable support for the accuracy of friction prediction.

[0052] The load-dependent friction model is used to characterize the dependence between the joint friction torque and the output load torque of a robot. The load-dependent friction model can be expressed by the following formula:

[0053] ;

[0054] in, This refers to the frictional torque of the robot joints. For the robot's output load torque, This refers to the joint rotation speed of the robot. For the parameters related to the first load, For the second load-related parameters; wherein, the The above The parameters to be identified.

[0055] It should be noted that the load refers to the load applied to the harmonic reducer, which is the external force or torque acting on the reducer's output shaft. These loads are generated when the robot joints drive the load. These loads applied to the harmonic reducer directly affect the magnitude of friction. Under different load levels, the degree of deformation between the robot joint contact surfaces varies, causing the friction coefficient to change. Specifically, as the load increases from low to high, the pressure between the robot joint contact surfaces increases, and the friction increases accordingly. Later, under high load conditions, the gear protrusions on the contact surfaces are flattened, thereby increasing the contact area between the robot joints and further increasing friction. Furthermore, the direction of the load also affects the magnitude and distribution of friction. For example, radial loads have a greater impact on friction than axial loads. In other words, in the low load stage: the contact surface pressure increases with increasing load, friction increases, and the linear term... Plays a dominant role; during the high-load stage: the protrusions on the joint tooth surface are flattened, the contact area increases, resulting in a non-linear increase in frictional force. The model is dominated by the direction of friction; the sign(ω) function preserves the difference between forward and reverse friction in the model.

[0056] As described above, when the robot joint inside the harmonic reducer is actually in operation, the load applied to the harmonic reducer changes dynamically. Therefore, the robot joint friction model parameter identification method provided in this application includes a load-related friction model that considers the changes in joint load. This can provide real-time load adaptive friction compensation for the robot joint, enhance the prediction of friction force of the joint cavity robot under high load and dynamic working conditions, and thus provide reliable support for the control stability of the robot joint of the harmonic reducer.

[0057] S102. Determine the robot joint friction model based on the speed-related friction model, the temperature-related friction model, and the load-related friction model.

[0058] Specifically, the total frictional force of a robot joint is composed of three sub-models: a velocity-dependent friction model, a temperature-dependent friction model, and a load-dependent friction model. The resulting robot joint model can be expressed by the following formula:

[0059] ;

[0060] in, This represents the total frictional torque of the robot's joints. This refers to the frictional torque of the robot joints. This refers to the frictional torque of the robot joints. This refers to the frictional torque of the robot's joints.

[0061] It should be noted that the three sub-models independently output their respective frictional torques based on their inputs. According to the assumption of linear superposition of multiple physical effects, the total frictional torque is recorded as the sum of the frictional torques output by the three sub-models. The assumption of linear superposition of multiple physical effects means that the effects of different physical mechanisms, such as velocity, temperature, and load, on the frictional torque are independent of each other and there is no cross-coupling effect. Therefore, the total frictional torque can be obtained by the algebraic sum of each independent physical effect.

[0062] Furthermore, it is understandable that, in one possible implementation, the robot joint model outputs a control current, which can then be converted into total frictional torque according to the following formula:

[0063] ;

[0064] in, This represents the total frictional torque of the robot's joints. The torque constant of the motor; To control the current.

[0065] S103. Acquire multiple sets of friction characteristic data of robot joints under different working conditions; wherein, each set of friction characteristic data includes output load torque, joint speed, joint temperature and measured friction torque.

[0066] In this step, it is necessary to acquire multiple sets of friction characteristic data of the robot joint under different working conditions. Each set of friction characteristic data includes joint speed, joint temperature, and load torque. In addition, each set of friction characteristic data also includes the measured friction torque, which is used to analyze and model the friction characteristics of the robot joint under different working conditions.

[0067] Specifically, acquiring multiple sets of friction characteristic data of robot joints under different working states includes:

[0068] Step 1: Under the preset initial temperature conditions, for each of the preset multiple output load torques, control the robot joints to load the current output load torque in sequence, and perform cyclic control of the robot joints according to the preset joint speed sequence, so that the robot joints run at each joint speed for a specified time in sequence.

[0069] In this step, the user pre-sets the initial temperature conditions, multiple output load torques, and a preset joint speed sequence. Under these initial temperature conditions, each of the preset output load torques is applied to the robot joints one by one. Furthermore, for each output load torque, the robot joints are controlled to run at each speed for a specified duration according to the preset joint speed sequence.

[0070] It should be noted that the preset initial temperature condition is set according to actual needs. For example, in one possible implementation, the preset initial temperature can be 20℃. Furthermore, the preset multiple output load torques are also set according to actual needs; in this embodiment, they are not limited. For example, in one embodiment, the preset multiple output load torques include A, B, and C. Further, the preset joint speed sequence includes a slow constant speed segment, a medium constant speed segment, and a high constant speed segment. Each of these segments includes multiple speed values. For example, in one embodiment, the slow constant speed segment includes 0.1 rad / s, 0.2 rad / s, and 0.3 rad / s; the medium constant speed segment includes 0.4 rad / s, 0.5 rad / s, and 0.6 rad / s; and the high constant speed segment includes speed values ​​of 0.7 rad / s, 0.8 rad / s, 0.9 rad / s, and 1.0 rad / s. Finally, the specified duration is also set according to actual needs; in this embodiment, it is not limited. For example, in one embodiment, the specified duration is 5ms.

[0071] In this step, for example, at 20℃, the robot is controlled to load an output load torque A, and the robot joints are cyclically controlled at speeds of 0.1 rad / s, 0.2 rad / s, 0.3 rad / s, 0.4 rad / s, 0.5 rad / s, 0.6 rad / s, 0.7 rad / s, 0.8 rad / s, 0.9 rad / s, and 1.0 rad / s. That is, the robot is first controlled to run at 0.1 rad / s for 5 ms, then at 0.2 rad / s for 5 ms, and so on, until the robot runs at 1.0 rad / s for 5 ms. Then, the next cycle begins, and the robot is controlled to repeat the above speed sequence again, specifically as follows: the robot is controlled to run at 0.1 rad / s for 5 ms, at 0.2 rad / s for 5 ms, and so on, until it runs at 1.0 rad / s for 5 ms before starting the next cycle.

[0072] As described above, the data acquisition process is implemented multiple times through loop control. This ensures that the robot joints operate stably under different combinations of loads and speeds, simulating various working states and providing a foundation for subsequent data acquisition.

[0073] Step 2: During operation, continuously monitor joint temperature changes. When the joint temperature reaches the preset upper limit, terminate the current test process.

[0074] In this step, the joint temperature is continuously monitored during operation. When the joint temperature reaches the preset upper limit, the current test is immediately terminated to avoid overheating and damage to the joint or testing equipment. This ensures that the entire test process is carried out within a safe temperature range and provides a basis for temperature grouping in subsequent steps.

[0075] It should be noted that the preset upper temperature limit can be determined based on the temperature tolerance of the joint or testing equipment; however, this application does not impose such a limit. For example, the preset upper temperature limit is 50°C.

[0076] In this step, the joint temperature change is continuously monitored, and the current test process is terminated when the joint temperature reaches 50°C.

[0077] Step 3: During operation, raw friction characteristic data are collected according to the preset sampling period, and continuous data with temperature changes not exceeding the preset threshold are grouped into the same temperature group, which is regarded as a set of raw friction characteristic data obtained under relatively stable temperature conditions; wherein, the raw friction characteristic data includes output load torque, joint speed, joint temperature and joint friction torque.

[0078] It should be noted that during the operation of the robot joint, raw friction characteristic data is also collected according to a preset sampling period. This raw friction characteristic data includes output load torque, joint speed, joint temperature and joint friction torque.

[0079] It should be noted that the preset sampling period is set according to actual needs, and is not limited in this embodiment. For example, in one possible implementation, the preset sampling period is 1ms.

[0080] Referring to the preceding process, it can be understood that with a preset sampling period of 1ms (i.e., data is collected every 1ms), and a specified duration of 5ms, controlling the robot joint to run for 5ms at any speed will collect 5 sets of raw friction characteristic data. For example, controlling the robot joint to run for 5ms at 0.1 rad / s will collect raw friction characteristic data sets 11, 12, 13, 14, and 15; controlling the robot joint to run for 5ms at 0.2 rad / s will collect raw friction characteristic data sets 21, 22, 23, 24, and 25. Each set of data includes joint temperature (e.g., joint temperature of 20.3℃), joint speed (e.g., joint speed of 0.1 rad / s), output load torque (e.g., output load torque of A), and joint friction torque (e.g., joint friction torque of 0.5 Nm).

[0081] Continuing with the above example, in one embodiment, at 20°C, the robot is controlled to apply an output load torque A at the specified rates of 0.1 rad / s, 0.2 rad / s, 0.3 rad / s, 0.4 rad / s, 0.5 rad / s, 0.6 rad / s, 0.7 rad / s, 0.8 rad / s, and 0.9 rad / s. When the robot joint is cyclically controlled at a speed of 1.0 rad / s, specifically controlling the robot joint to run for 5 ms at a speed of 0.1 rad / s, the five sets of data collected can be recorded according to the structure of [output load torque, joint speed, joint temperature, joint friction torque]. These five sets of data include: [A, 0.1 rad / s, 20.2℃, 0.5 Nm], [A, 0.1 rad / s, 20.3℃, 0.51 Nm], [A, 0.1 rad / s, 20.3℃, 0.50 Nm], [A, 0.1 rad / s, 20.4℃, 0.52 Nm], [A, 0.1 rad / s, 20.4℃, 0.53 Nm]. Through the above process, multiple sets of similar raw friction characteristic data can be collected.

[0082] In this step, furthermore, continuous data with temperature variations not exceeding a preset threshold are grouped into the same temperature group, and are considered as a set of original friction characteristic data obtained under relatively stable temperature conditions. The preset threshold is set according to actual needs, and is not limited in this embodiment. The following explanation uses a preset threshold of 3℃ as an example. For example, if a robot joint runs at a speed sequence of 0.1 rad / s, 0.2 rad / s, 0.3 rad / s, 0.4 rad / s, 0.5 rad / s, 0.6 rad / s, 0.7 rad / s, 0.8 rad / s, 0.9 rad / s, and 1.0 rad / s for five cycles, and the joint temperature changes by 3°C, reaching 23°C, then the original friction characteristic data collected during these five cycles will be grouped into the same temperature group. This means that the robot's joint temperature is considered to be the same across these five cycles. Specifically, if the temperature before the five cycles starts is 20°C and the temperature at the end is 23°C, then the temperature corresponding to this temperature group could be 21.5°C.

[0083] Step 4: For each output load torque, each temperature group, and each joint speed, collect multiple joint friction torques, remove outliers from the multiple joint friction torques, and calculate their average values ​​to obtain the measured joint friction torques for the output load torque, the temperature group, and the joint speed.

[0084] In this step, for the multiple joint friction torques collected in step 3, outlier values ​​are removed from the multiple joint friction torque data collected at each joint speed in each temperature group. For example, 80% of the data in the middle is dynamically extracted and retained, while the rest is removed to eliminate transient interference during the start-up and stop process. Then, the average value of the retained joint friction torques is calculated to obtain the output load torque, the measured joint friction torque at the temperature group and the joint speed.

[0085] For example, referring to the example in step 3, under the temperature group of 21.5℃, for 0.1 rad / s, a total of five cycles of data were collected, with each cycle corresponding to five sets of data, for a total of 25 sets of data. For each set of data, only the middle 80% is retained, and then the average value of the joint friction torque in the retained data is calculated, for example, denoted as c. In this way, a set of data is obtained, which is (output load torque A, 0.1 rad / s, 21.5℃, c). This set of data is the collected output load torque, the measured joint friction torque under the temperature group and the joint speed.

[0086] It should be noted that by sequentially applying multiple output load torques under preset initial temperature conditions and combining them with joint velocity sequences for system testing, comprehensive coverage of joint friction characteristics under various working conditions was achieved, ensuring data diversity and representativeness. By continuously monitoring the joint temperature during testing and terminating the test when the upper temperature limit was reached, the impact of high temperatures on system stability and data accuracy was effectively avoided, improving the safety and controllability of the testing process. Furthermore, by collecting raw friction characteristic data at preset sampling periods and grouping data with small temperature variations into the same temperature group, the friction characteristics of the joint at different temperatures can be extracted while ensuring temperature stability, enhancing the scientific rigor and discriminability of temperature-related modeling. Moreover, outlier removal and averaging of multiple sets of friction torque data under the same working condition significantly improved the noise resistance and statistical reliability of the sampled data, avoiding interference from occasional measurement errors on model identification accuracy.

[0087] In summary, the data collected by this method is reasonably distributed across multidimensional variables (temperature, load, velocity), exhibiting high data quality and strong physical consistency, thus laying a solid data foundation for the high-precision identification and generalization capabilities of subsequent friction models.

[0088] S104. Based on expert knowledge and prior physical knowledge, set initial value ranges for each parameter in the robot joint friction model to form an initial parameter search space.

[0089] It should be noted that the relevant parameters in the velocity-dependent friction model, temperature-dependent friction model, and load-dependent friction model that constitute the robot joint friction model need to be selected with appropriate values ​​to form a suitable robot joint friction model.

[0090] Specifically, in this step, we combine the mechanical characteristics of the robot joints, analyze the physical constraints of each parameter in the robot joint friction model, and consult experts in robot dynamics or tribology. By combining the experts' knowledge, we obtain experimental data related to robot joint friction, determine the initial value range of each parameter, and construct the initial parameter search space.

[0091] It should be noted that, for example, in conjunction with the above examples, in this embodiment, the parameters to be identified involved in the robot joint friction model include: , , , , , , , , , , , The initial parameter search space is [0.1, 5.0] N·m、 [0.1, 5.0] N·m、 [0.01, 1.0] rad / s [0.001, 0.1]、 [0.0,2.0]、 [0.0,2.0]、 [0.0,2.0]、 [0.0, 1.0]、 [0.0, 1.0]、 [0.0, 1.0]、 [0.0, 10.0]、 [0.0, 10.0].

[0092] S105. Using the sum of squared errors predicted by the model as the fitness function, and based on the multiple sets of friction characteristic data, the initial parameter search space is optimized using a genetic algorithm to obtain a candidate parameter search space; the candidate parameter search space includes the optimized value range of each parameter in the robot joint friction model.

[0093] In this step, based on the multiple sets of friction characteristic data collected in S103, the initial parameter search space constructed in S104 is optimized using a genetic algorithm to obtain a more accurate alternative parameter search space. This alternative parameter search space includes the optimized value range of each parameter in the robot joint friction model.

[0094] Optionally, in one possible implementation, the initial parameter search space is optimized using a genetic algorithm based on the multiple sets of friction characteristic data, using the sum of squared errors predicted by the model as the fitness function, to obtain a candidate parameter search space, including:

[0095] Step 1: Divide the initial value range of each parameter in the initial parameter search space into intervals to divide the initial parameter search space into a first sub-search space and a second sub-search space.

[0096] In practice, the initial value range of each parameter is divided into two parts according to the median value to obtain the first sub-interval and the second sub-interval corresponding to the parameter. Furthermore, the first sub-intervals of all parameters constitute the first sub-search space, and the second sub-intervals of all parameters constitute the second sub-search space.

[0097] In specific implementation, this step further refines the initial value range of each parameter in the initial parameter search space constructed in S104, dividing the initial parameter search space into a first sub-search space and a second sub-search space. For example, for the first velocity-related parameter with a value range of [0.01, 0.1], its parameter range is divided into two segments, where the first value range is [0.01, 0.055] and the second value range is [0.055, 0.1].

[0098] Step 2: Using the first sub-search space and the second sub-search space as the search ranges respectively, and the sum of squared model prediction errors as the fitness function, the genetic algorithm is used to perform optimization search in the first sub-search space and the second sub-search space respectively to obtain the first optimal solution set in the first sub-search space and the second optimal solution set in the second sub-search space.

[0099] In this step, random parameter combinations are initialized in each sub-search space. The evaluation fitness of the robot joint friction model is evaluated by calculating the sum of squared errors between the predicted friction torque and the measured joint friction torque predicted by the robot joint friction model. The population is iteratively optimized through selection, crossover, and mutation operations until convergence or the maximum number of iterations is reached. Finally, the first optimal solution set in the first sub-search space and the second optimal solution set in the second sub-search space are obtained. Each solution set includes a set of parameter combinations with the smallest error.

[0100] In practice, the fitness function is as follows:

[0101] ;

[0102] in, A set of friction model parameters; The total number of samples; For the first The actual observed value at time; This is the output estimated by the model based on the current parameters.

[0103] Furthermore, the genetic algorithm process executed in each sub-search space includes: First, a population of size N is initialized within each sub-search space, with each individual being a set of parameter vectors uniformly and randomly generated within the subspace; each individual in the population is substituted into the robot joint friction model to calculate the predicted value, and the fitness is evaluated based on the sum of squared prediction errors; next, based on the fitness function, superior individuals are selected for the next generation using methods such as roulette wheel selection, competitive selection, or elite strategies to ensure that the superior fitness of these individuals is passed on; subsequently, the selected superior individuals are paired and subjected to single-point crossover, uniform crossover, or arithmetic crossover to generate the next generation of individuals; to enhance population diversity and avoid premature convergence to local minima, small random perturbations are applied to the parameters of some individuals to introduce new parameter combinations; finally, the best individual is retained based on fitness, and the population is iteratively updated until the stopping condition is met (such as reaching the maximum number of iterations or the fitness change being below a threshold).

[0104] In this way, by iteratively optimizing the parameter vectors in the population through this process, we can ensure efficient searching for the global optimal solution within the sub-search space, and finally obtain the first optimal solution set within the first sub-search space and the second optimal solution set within the second sub-search space.

[0105] Step 3: For each parameter, the interval formed by the two optimal solutions of the first optimal solution set and the second optimal solution set corresponding to that parameter is taken as the new search interval for that parameter; wherein, the new search intervals of all parameters together constitute the candidate parameter search space.

[0106] Specifically, taking the first velocity-related parameter as an example, if the optimal solution for the first velocity-related parameter in the first optimal solution set is 0.12, and the optimal solution for the first velocity-related parameter in the second optimal solution set is 0.20, then the new search interval for the first velocity-related parameter is [0.12, 0.20]. Similarly, the same operation is performed on all other parameters to obtain new search intervals for each parameter. Then, the new search intervals for all parameters are combined to form the candidate parameter search space. In this way, the candidate parameter search space is smaller and more concentrated than the initial parameter search space, and includes a better range of parameters.

[0107] S106. The parameter identification problem of the robot joint friction model is transformed into a multi-objective optimization problem, and an optimization function is constructed with the optimization objectives of minimizing the prediction error and minimizing the physical deviation of the parameters.

[0108] Specifically, the optimization function is:

[0109] ;

[0110] in, The predicted friction torque is the output of the key friction model of the robot corresponding to the i-th set of friction characteristic data. The measured friction torque corresponds to the i-th set of friction characteristic data; N is the number of the multiple sets of friction characteristic data; M is the number of parameters included in the robot joint friction model; Let j be the current value of the j-th parameter in the robot joint friction model. Let be the prior value of the j-th parameter.

[0111] In this step, by transforming parameter identification into a multi-objective optimization problem, an optimization function is constructed with the goal of minimizing prediction error and physical deviation of parameters. This ensures that while maintaining the accuracy of model prediction, each parameter conforms to physical laws and prior knowledge. In this way, the accuracy and physical rationality of the robot joint friction model can be improved, providing reliable parameter support for the precise control, motion planning and performance optimization of robot joints.

[0112] S107. Using the alternative parameter search space as a constraint, solve the optimization function based on the multiple sets of friction characteristic data to obtain the optimal values ​​of each parameter in the robot joint friction model.

[0113] In this step, the search space of alternative parameters obtained in S105 is used as a constraint. Based on the multiple sets of friction characteristic data collected in S103 (including output load torque, joint velocity, joint temperature and measured friction torque), the optimization function constructed in S106 is solved to obtain the optimal values ​​of each parameter in the robot joint friction model.

[0114] In practice, a global search can be performed based on multi-objective optimization algorithms (such as NSGA-II, MOEA / D, etc.) to obtain the optimal values ​​of each parameter.

[0115] It should be noted that in the parameter identification of the robot joint friction model, the parameter value range (i.e., the candidate parameter search space) is first determined based on the genetic algorithm. Then, using the value range of each parameter as a constraint, a multi-objective optimization method is combined to solve for the specific optimal value. The genetic algorithm has global search capabilities and is suitable for discovering potential optimal solution regions over a large range, avoiding the local optimum trap caused by inaccurate initial estimation. The multi-objective optimization algorithm performs a fine search within the narrowed parameter range, further improving the accuracy and convergence efficiency of parameter identification. This strategy, starting coarse and then fine, can effectively improve the overall identification quality. Initially, the genetic algorithm filters out a large number of unreasonable or invalid regions, forming a candidate parameter search space. Multi-objective optimization is performed within this candidate search space, greatly reducing the search dimensionality and computational complexity, improving the convergence speed, and avoiding blindly searching with high overhead in a large and complex initial parameter search space. The genetic algorithm integrates expert experience and physical prior knowledge to generate candidate search ranges, ensuring that the model parameters are always within a physically reasonable range. Multi-objective optimization considers both minimum prediction error and minimum physical deviation, resulting in optimization results with stronger engineering interpretability. By leveraging global information explored through genetic algorithms, a reasonable range of possible changes in friction characteristics under different working conditions can be covered; the optimal value selected by multi-objective optimization can take into account the accuracy under different working conditions, making the final model more robust and adaptable in practical applications.

[0116] In summary, the two-stage identification method combining global search with multi-objective optimization and local refinement improves identification accuracy while ensuring the physical rationality of parameters and optimization efficiency.

[0117] As described above, the core innovation of the robot joint friction model parameter identification method provided in this application lies in dividing the joint friction modeling process into three factors: speed, temperature, and load. It also introduces genetic algorithms and multi-objective optimization strategies to improve identification accuracy and model physical consistency. Firstly, this application comprehensively considers three friction influencing factors—speed-related, temperature-related, and load-related—when modeling robot joint friction, overcoming the limitations of traditional models that only focus on a single factor (such as speed dependence). This significantly improves the comprehensiveness, accuracy, and adaptability of friction modeling under complex working conditions. Specifically, by introducing a temperature-related friction model, the problem of friction torque drift during long-term operation or in high-temperature environments is effectively solved. The load-related friction model accurately reflects the response of friction torque to changes in end-effector load, enhancing the model's time-varying characteristics and load response capability, which is particularly beneficial for precision force control or assembly robot tasks. Secondly, in the parameter optimization and identification process, this application first introduces expert knowledge and prior physical knowledge to construct an initial parameter search space, avoiding the problem of physical inconsistencies in the optimal solution caused by blind searching, and enhancing the physical interpretability and practical usability of the identification results. Then, a genetic algorithm is used for global optimization, leveraging its strong global search capability and resistance to getting trapped in local optima to effectively address the optimization of nonlinear, multi-peak, and multi-variable friction model parameters, improving global convergence capability. Simultaneously, a multi-objective optimization function that minimizes prediction error and parameter physical deviation is constructed, pursuing high-precision prediction while considering parameter physical rationality, achieving a balance between identification accuracy and robustness. Thirdly, the robot joint friction model parameter identification method provided in this application significantly improves the accuracy, robustness, and versatility of joint friction modeling, applicable to various types of industrial robot joints, laying a reliable foundation for downstream applications such as force control, friction compensation, and joint health assessment, and contributing to improving the trajectory tracking accuracy, stability, and energy efficiency of robot motion control.

[0118] The robot joint friction model parameter identification method provided in this embodiment firstly comprehensively considers the multi-factor influence of joint friction torque variations with speed, temperature, and load, establishing a robot joint friction model that better reflects actual working conditions. Furthermore, by introducing expert knowledge and prior physical knowledge to construct an initial parameter search space and employing a genetic algorithm for global optimization, the accuracy and robustness of parameter identification are improved. Simultaneously, by constructing a multi-objective optimization function that minimizes prediction error and parameter physical deviation, the identified parameters are ensured to possess good physical rationality and interpretability while meeting high-precision prediction requirements. In summary, this method can significantly improve the accuracy and adaptability of robot joint friction modeling, providing reliable support for applications such as high-precision motion control and force control strategy design.

[0119] Optionally, in one possible implementation, after obtaining the optimal values ​​of each parameter in each sub-model, the method further includes: dynamically adjusting the parameters in the robot joint friction model using an online optimization algorithm based on the continuously collected output load torque, joint velocity, joint temperature, and friction torque data during the actual operation of the robot; wherein the online optimization algorithm includes any one of the recursive least squares algorithm, extended Kalman filter algorithm, and unscented Kalman filter algorithm.

[0120] In practice, during the actual operation of the robot, real-time data such as the output load torque of the joint, joint speed, joint temperature and friction torque are continuously collected. Based on this real-time data, the parameters in the robot joint friction model are recursively updated and adjusted through online optimization algorithms.

[0121] For example, when using the recursive least squares algorithm for updating, the parameters can be recursively estimated in real time using the newly collected real-time data at each time step.

[0122] For example, when using the extended Kalman filter algorithm to update parameters, the friction model parameters can be introduced as state variables into the system state-space model to estimate the dynamic changes of parameters online. This is suitable for approximately linear or weakly nonlinear systems.

[0123] For example, after updating parameters using the unscented Kalman filter algorithm, the unscented transformation can be used for parameter estimation, which is suitable for highly nonlinear friction modeling problems.

[0124] As described above, this online parameter update process is carried out without interrupting the robot's operation and is dynamically adjusted at certain time steps or data windows, thereby enhancing the adaptability and real-time accuracy of the friction model.

[0125] The above-mentioned online optimization algorithm significantly improves the adaptability and robustness of the robot joint friction model under dynamic conditions. Compared with traditional static parameter identification methods, this method has the following technical advantages:

[0126] By introducing real-time data for dynamic adjustment, the friction model parameters can reflect changes in friction characteristics caused by environmental temperature variations, joint aging, and lubrication conditions, enhancing the model's timeliness. The online-updated friction model can be fed back to the controller in real time to compensate for frictional disturbances during joint movement, thereby improving the accuracy and stability of robot trajectory tracking and force control. The system can automatically adapt to sudden or gradual changes in operating conditions (such as sudden load changes or lubrication failure), effectively reducing the impact of friction modeling errors on system control performance.

[0127] In summary, the method provided in this embodiment not only improves the static identification accuracy of robot joint friction modeling, but also realizes adaptive optimization of the model during dynamic operation, providing technical support for high-precision and robust robot control systems.

[0128] Corresponding to the aforementioned embodiment of a robot joint friction model parameter identification method, this application also provides an embodiment of a robot joint friction model parameter identification device.

[0129] An embodiment of the robot joint friction model parameter identification device disclosed in this application can be applied to a robot joint friction model parameter identification device. The device embodiment can be implemented through software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of the robot joint friction model parameter identification device loading the corresponding computer program instructions from the non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 4 The diagram shown is a hardware structure diagram of the robot joint friction model parameter identification device, which is the location of the robot joint friction model parameter identification device in this application. (Except for...) Figure 4 In addition to the processor, memory, network interface, and non-volatile memory shown, the robot joint friction model parameter identification device in the embodiment may also include other hardware depending on the actual function of the robot joint friction model parameter identification device, which will not be described in detail here.

[0130] Figure 3 This is a schematic diagram of the structure of Embodiment 1 of the robot joint friction model parameter identification device provided in this application. Please refer to... Figure 3 The apparatus provided in this embodiment includes a construction module 310, a determination module 320, an acquisition module 330, and a calculation module 340; wherein,

[0131] The construction module 310 is used to establish a velocity-dependent friction model to characterize the relationship between robot joint friction torque and joint speed, a temperature-dependent friction model to characterize the characteristics of robot joint friction torque as a function of temperature, and a load-dependent friction model to characterize the dependence between robot joint friction torque and output load torque.

[0132] The determining module 320 is used to determine the robot joint friction model based on the speed-related friction model, the temperature-related friction model, and the load-related friction model.

[0133] The acquisition module 330 is used to acquire multiple sets of friction characteristic data of the robot joint under different working states; wherein, each set of friction characteristic data includes output load torque, joint speed, joint temperature and measured friction torque;

[0134] The determining module 320 is used to set an initial value range for each parameter in the robot joint friction model based on expert knowledge and prior physical knowledge, thereby forming an initial parameter search space.

[0135] The determining module 320 is used to optimize the initial parameter search space based on the multiple sets of friction characteristic data using a genetic algorithm, with the sum of squared errors predicted by the model as the fitness function, to obtain a candidate parameter search space; the candidate parameter search space includes the optimized value range of each parameter in the robot joint friction model;

[0136] The construction module 310 is used to transform the parameter identification problem of the robot joint friction model into a multi-objective optimization problem, and construct an optimization function with the optimization objectives of minimizing prediction error and minimizing parameter physical deviation.

[0137] The calculation module 340 is further configured to use the alternative parameter search space as a constraint condition to solve the optimization function based on the multiple sets of friction characteristic data, so as to obtain the optimal values ​​of each parameter in the robot joint friction model.

[0138] The apparatus of this embodiment can be used to perform... Figure 1 The steps of the method embodiment shown are similar in principle and process, and will not be repeated here.

[0139] Please continue to refer to Figure 4 This application also provides a robot joint friction model parameter identification device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the methods provided in the first aspect of this application.

[0140] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods provided in this application.

[0141] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0142] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. 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 the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0143] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for identifying parameters of a robot joint friction model, characterized in that, The method includes: Establish a velocity-dependent friction model to characterize the relationship between robot joint friction torque and joint velocity, a temperature-dependent friction model to characterize the characteristics of robot joint friction torque as a function of temperature, and a load-dependent friction model to characterize the dependence between robot joint friction torque and output load torque. The robot joint friction model is determined based on the velocity-related friction model, the temperature-related friction model, and the load-related friction model. Acquire multiple sets of friction characteristic data for robot joints under different working conditions; each set of friction characteristic data includes output load torque, joint velocity, joint temperature, and measured friction torque; Based on expert knowledge and prior physical knowledge, initial value ranges are set for each parameter in the robot joint friction model, forming an initial parameter search space; Using the sum of squared errors predicted by the model as the fitness function, and based on the multiple sets of friction characteristic data, a genetic algorithm is used to optimize the initial parameter search space to obtain a candidate parameter search space; the candidate parameter search space includes the optimized value range of each parameter in the robot joint friction model; The parameter identification problem of the robot joint friction model is transformed into a multi-objective optimization problem, and an optimization function is constructed with the optimization objectives of minimizing prediction error and minimizing physical deviation of parameters. Using the alternative parameter search space as a constraint, the optimization function is solved based on the multiple sets of friction characteristic data to obtain the optimal values ​​of each parameter in the robot joint friction model.

2. The method according to claim 1, characterized in that, The acquisition of multiple sets of friction characteristic data of robot joints under different working states includes: Under the preset initial temperature conditions, for each of the preset multiple output load torques, the robot joints are sequentially controlled to load the current output load torque, and the robot joints are cyclically controlled according to the preset joint speed sequence, so that the robot joints run at each joint speed for a specified time in sequence. During operation, the joint temperature is continuously monitored. When the joint temperature reaches the preset upper limit, the current test process is terminated. During operation, raw friction characteristic data are collected according to a preset sampling period, and continuous data with temperature changes not exceeding a preset threshold are grouped into the same temperature group, which is regarded as a set of raw friction characteristic data obtained under relatively stable temperature conditions; wherein, the raw friction characteristic data includes output load torque, joint speed, joint temperature and joint friction torque; For each output load torque, each temperature group, and each joint speed, multiple joint friction torques are collected. Outliers are removed from the multiple joint friction torques and the average value is calculated to obtain the measured joint friction torques for the output load torque, the temperature group, and the joint speed.

3. The method according to claim 1, characterized in that, The fitness function is the sum of squared errors predicted by the model. Based on the multiple sets of friction characteristic data, a genetic algorithm is used to optimize the initial parameter search space to obtain a candidate parameter search space, including: The initial value range of each parameter in the initial parameter search space is divided into intervals to divide the initial parameter search space into a first sub-search space and a second sub-search space. The first sub-search space and the second sub-search space are respectively used as the search range. The sum of squared errors predicted by the model is used as the fitness function. The genetic algorithm is used to perform optimization search in the first sub-search space and the second sub-search space respectively to obtain the first optimal solution set in the first sub-search space and the second optimal solution set in the second sub-search space. For each parameter, the interval formed by the two optimal solutions of the first optimal solution set and the second optimal solution set corresponding to that parameter is taken as the new search interval for that parameter; wherein, the new search intervals of all parameters together constitute the search space of the alternative parameters.

4. The method according to claim 1, characterized in that, The optimization function is: ； in, The predicted friction torque is output by the robot joint friction model corresponding to the i-th set of friction characteristic data. The measured friction torque corresponds to the i-th set of friction characteristic data; N is the number of the multiple sets of friction characteristic data; M is the number of parameters included in the robot joint friction model; Let j be the current value of the j-th parameter in the robot joint friction model. Let be the prior value of the j-th parameter.

5. The method according to claim 1, characterized in that, The velocity-related friction model is as follows: ； in, This refers to the frictional torque of the robot joints. This refers to the joint rotation speed of the robot. For Coulomb friction, For static friction, For Stribeck speed, Stribeck index, For the first speed-related parameters, For the second speed-related parameters, For the third velocity-related parameter, abs() is the absolute value function; where, the The above The above The above The above The above The above The parameters to be identified.

6. The method according to claim 1 or 5, characterized in that, The temperature-dependent friction model is as follows: ； in, For the robot's frictional torque; For the robot's joint temperature, This refers to the joint rotation speed of the robot. This is the first temperature-related parameter; This is the second temperature-related parameter. Here, the third temperature-related parameter is defined, abs() is the absolute value function, sqrt() is the square root function, and sign() is the sign function; where, the... The above The above The parameters to be identified.

7. The method according to claim 4, characterized in that, The load-related friction model is as follows: ； in, For the robot's frictional torque; For the robot's output load torque, This refers to the joint rotation speed of the robot. For the parameters related to the first load, For the second load-related parameters, sign() is the sign function; where, the The above The parameters to be identified.

8. The method according to claim 1, characterized in that, After obtaining the optimal values ​​of each parameter in the robot joint friction model, the method further includes: Based on the continuously collected output load torque, joint velocity, joint temperature, and friction torque data during the actual operation of the robot, an online optimization algorithm is used to dynamically adjust the parameters in the robot joint friction model; wherein, the online optimization algorithm includes any one of the following: recursive least squares algorithm, extended Kalman filter algorithm, and unscented Kalman filter algorithm.

9. A device for identifying parameters of a robot joint friction model, characterized in that, The device includes a construction module, a determination module, an acquisition module, and a calculation module; wherein, The construction module is used to establish a velocity-dependent friction model to characterize the relationship between robot joint friction torque and joint speed, a temperature-dependent friction model to characterize the characteristics of robot joint friction torque as a function of temperature, and a load-dependent friction model to characterize the dependence between robot joint friction torque and output load torque. The determining module is used to determine the robot joint friction model based on the velocity-related friction model, the temperature-related friction model, and the load-related friction model. The acquisition module is used to acquire multiple sets of friction characteristic data of the robot joint under different working conditions; wherein, each set of friction characteristic data includes output load torque, joint speed, joint temperature and measured friction torque; The determining module is used to set an initial value range for each parameter in the robot joint friction model based on expert knowledge and prior physical knowledge, thus forming an initial parameter search space. The determining module is used to optimize the initial parameter search space based on the multiple sets of friction characteristic data using a genetic algorithm, with the sum of squared errors predicted by the model as the fitness function, to obtain a candidate parameter search space; the candidate parameter search space includes the optimized value range of each parameter in the robot joint friction model; The construction module is used to transform the parameter identification problem of the robot joint friction model into a multi-objective optimization problem, and to construct an optimization function with the optimization objectives of minimizing prediction error and minimizing parameter physical deviation. The calculation module is also used to solve the optimization function based on the multiple sets of friction characteristic data, using the alternative parameter search space as a constraint, to obtain the optimal values ​​of each parameter in the robot joint friction model.

10. A device for identifying parameters of a robot joint friction model, characterized in that, The device includes 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 steps of the method according to any one of claims 1-8.

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