Quantitative testing method and system for peak locked-rotor torque of robot joint module

Abstract

The invention discloses a quantitative test method and system for a peak locked-rotor torque of a robot joint module, and relates to the technical field of robot joint performance evaluation, and the method comprises the steps: extracting a torque time-calendar curve of the joint module, defining the peak locked-rotor torque as a controlled low-frequency fatigue loading process, and building a mapping model of a torque amplitude and a failure cycle index; constructing a test system, and performing torque-current static calibration; embedding a specific excitation function in a current loop of the servo driver; the bus voltage and rotating speed change rate is monitored in real time, and the voltage and current impact at the unloading moment is inhibited by adopting a back electromotive force compensation algorithm and combining an energy consumption discharge loop; and repeatedly loading until the structure is damaged, recording the number of cycles, and drawing a peak torque-life curve as a basis for predicting the life of the whole machine. According to the method, quantitative association of the peak locked-rotor torque and the fatigue life is realized, quantitative evaluation from a static index to a dynamic life is realized, a basis is provided for life prediction of the whole robot, and the test precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot joint performance evaluation, and particularly relates to a method and system for quantitatively testing peak stall torque of a robot joint module. BACKGROUND

[0002] High dynamic response capability is a core technical index for a humanoid robot to realize complex actions such as somersaults, running and jumping. Peak stall torque, as a cornerstone for evaluating the instantaneous power output capability of a joint module, directly determines the limit motion performance of the robot.

[0003] Existing standards (such as GB / T 30549-2014) only define the peak stall torque as "the maximum torque allowed to be output for a short time", and lack a deep analysis of the physical failure mechanism. In actual working conditions, the internal mechanical structure (such as the reducer gear and output shaft) of the joint module bears a cyclic limit load when it bears the peak torque. Based on the first principle of material mechanics, this failure is essentially a low-frequency fatigue failure related to the material S-N curve (stress-life curve) and specific working conditions.

[0004] Existing technologies focus on torque control algorithms and system implementation, but there is no quantitative testing scheme for the correlation between "working conditions-life-peak torque". This results in the inability to accurately evaluate the service life and structural boundaries of the joint module under specific impact working conditions during the development of the robot, and there is a risk of overdesign or insufficient strength.

[0005] Based on this, a quantitative testing method and system for the peak stall torque of a robot joint module are now provided, which can eliminate the drawbacks of existing technical solutions. SUMMARY

[0006] The purpose of the present application is to provide a quantitative testing method and system for the peak stall torque of a robot joint module to solve the problem that the existing standards only define the peak torque without correlating the working conditions and life, and lack a quantitative testing method, resulting in design risks.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A quantitative testing method for the peak stall torque of a robot joint module, specifically comprising the following steps: Step S1, by sampling and analyzing the whole machine data of a humanoid robot under typical working conditions, extracting the torque time history curve of the joint module, based on material mechanics analysis, defining the peak stall torque as a controlled low-frequency fatigue loading process, and establishing a mapping relationship model between the torque amplitude and the failure cycle number; Step S2, a test system including a mechanical stall tool, a joint module, a servo driver and an upper computer is established to perform static calibration of the torque-current mapping relationship to ensure that the servo driver current loop control can reflect the output torque; Step S3, a specific excitation function is embedded in the servo driver current loop interruption, the total test period is set as T, the specific excitation function includes a preloading stage and a peak loading stage in a single loading, and symmetric reverse loading is performed at the T / 2 time point; Step S4, the bus voltage and the rotation rate change are monitored in real time, the reverse electromotive force compensation algorithm is used at the torque unloading moment, the duty cycle is actively adjusted and the energy consumption discharge loop is started to counter the instantaneous back pressure and prevent the servo driver from overvoltage protection and hardware damage; Step S5, step S3 is repeatedly performed, the cycle number when the joint module is structurally damaged is recorded, and the peak torque-life number curve of the joint module is drawn as the input of the whole robot life prediction.

[0008] Further, the step S1 specifically includes: training an agent of the humanoid robot under a typical working condition through a reinforcement learning model, and deploying the agent to the whole robot to collect data for analysis, storing torque time history data based on a light communication middleware and generating a torque time history curve, defining a peak stall torque as a controlled low-frequency fatigue loading process based on material mechanics analysis, establishing a uniaxial strain amplitude-life relationship using the Manson-Coffin equation, and constructing a mapping relationship model of torque amplitude and failure cycle number, with the load spectrum as the input and the mapping graph of torque amplitude and failure cycle number as the output.

[0009] Further, the reinforcement learning model is any one of a MIMIC model and an AMP model, and the typical working condition includes jumping and falling recovery.

[0010] Further, the specific operation of the static calibration in the step S2 includes: The measured joint module is installed on the test table of the mechanical stall tool, the output end of the measured joint module is connected to the input end of the torque sensor, the output end of the torque sensor is locked, and the current value of the joint module and the torque value of the torque sensor are sampled at N points from 0 to the peak torque, where N is a positive integer greater than or equal to 5.

[0011] Further, the specific excitation function in the step S3 is used to generate a precise torque command in the servo driver current loop to test the steady-state torque output and electrical characteristics of the joint module under the pure stall state, and to provide pure static load data, and the specific logic of the specific excitation function includes: Let the peak torque be , and the total test period be , is 10s, the torque instruction is ; The torque instruction expression of the pre-loading stage is: , , wherein, is the test time, is 50ms, the pre-loading stage slowly increases the torque from 0 to through a linear ramp of time, to gently engage the gears and eliminate mechanical backlash in the transmission chain; The torque instruction expression of the peak loading stage is: , , wherein, is 100ms, at , the instruction jumps from to and remains for 100ms, the motor quickly enters and remains in the locked-rotor state, and the current and voltage data collected at this time are used to reflect the pure steady-state locked-rotor torque characteristics; In the symmetric reverse loading process, at , repeat the above stage process, the torque instruction is to obtain reverse locked-rotor data and form a complete bidirectional load cycle.

[0012] Further, the reverse electromotive force compensation algorithm in the step S4 is used to predict the speed based on the change of the encoder, and the calculation and compensation of the reverse electromotive force are realized through the reverse electromotive force coefficient. The specific operation of the reverse electromotive force compensation algorithm includes: The sampling resistance and the encoder are used to monitor the bus voltage and the speed change rate in real time. The encoder signal is collected at a high frequency, the mechanical angular velocity of the motor is estimated in real time after filtering processing, and the instantaneous reverse electromotive force estimate value is calculated by using the motor reverse electromotive force constant. The expression is: , wherein, is the instantaneous reverse electromotive force estimate value, is the motor reverse electromotive force constant, is the mechanical angular velocity of the motor, the instantaneous reverse electromotive force estimate value is directly superimposed on the voltage instruction output by the current loop as a feedforward quantity, and the expression is: , wherein, is the voltage instruction output to the motor by the servo driver, is the original voltage instruction generated by the servo driver according to the torque demand, the reverse electromotive force compensation algorithm is used to actively offset the disturbance of the reverse electromotive force, suppress the voltage and current impact at the unloading moment, protect the servo driver, and improve the dynamic stability.

[0013] Further, the energy-consuming relief circuit in step S4 converts the potential energy generated by the instantaneous counter-pressure into heat energy by connecting the bypass resistance, and the instantaneous counter-pressure is generated by the permanent magnet on the rotor cutting the magnetic field when the instantaneous reverse micro-motion occurs.

[0014] Further, the determination criterion of structural damage in step S5 is at least one of tooth breakage and deformation exceeding the preset tolerance of the joint module.

[0015] A quantitative test system for peak stall torque of a robot joint module is used to perform a quantitative test method for peak stall torque of a robot joint module, comprising: A mechanical stall tool is used as a test device to fix the joint module and realize the stall function, and the mechanical stall tool is a double-flange structure; A joint module is used as a measured device to perform a mechanism at an active degree of freedom of a humanoid robot; A servo driver is used as an electric control board card of the joint module, and the input is a direct current power supply and a communication instruction, and the servo program in the electric control board card is used to control the servo operation of the joint module; An upper computer is connected to the joint module through CAN communication, used to store torque time history data, send control instructions to the joint module, receive current feedback signals, and complete life quantitative evaluation.

[0016] Further, one set of flanges in the double-flange structure is used to lock the fixing flange of the joint module, and the other set of flanges is used to lock the output flange of the joint module.

[0017] Compared with the prior art, the beneficial effects of the present application are as follows: 1. The present application defines the peak stall torque as a controlled low-frequency fatigue loading process, and establishes a mapping model of torque amplitude and failure cycle number, realizes quantitative evaluation from static indicators to dynamic life, realizes quantitative correlation between peak stall torque and fatigue life, and provides a scientific basis for robot whole machine life prediction; 2. The present application embeds a segmented excitation function and a reverse electromotive force compensation algorithm to strip dynamic impact interference in the test, provides pure static load data, and protects the servo driver from counter-pressure damage. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The present application is a method step schematic diagram.

[0019] Figure 2 The present application is a system structure schematic diagram.

[0020] Figure 3 The present application is a sampling analysis diagram of the whole data of the humanoid robot.

[0021] Figure 4 A schematic diagram of the torque-current mapping relationship of the present application.

[0022] Figure 5 A sampling data schematic diagram of the excitation function of the present application.

[0023] Figure 6 A schematic diagram of the peak torque-life number curve of the present application.

[0024] Figure legend annotation: mechanical stall tool 10, joint module 20, servo driver 30, upper computer 40. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application is further described in detail below in combination with the drawings and examples.

[0026] Example 1 In this example, as shown in Figure 1 - Figure 6 A quantitative test method of the peak stall torque of a robot joint module, specifically comprising the following steps: Step S1, by sampling and analyzing the whole machine data of a humanoid robot under typical working conditions (as shown in Figure 3 ), the torque time history curve of the joint module 20 is extracted, based on the analysis of material mechanics, the peak stall torque is defined as a controlled low-frequency fatigue loading process, and a mapping relationship model of torque amplitude and failure cycle number is established; Step S2, a test system (as shown in Figure 2 ) including a mechanical stall tool 10, a joint module 20, a servo driver 30 and an upper computer 40 is established, a static calibration of the torque-current (T-I) mapping relationship (as shown in Figure 4 ) is carried out to ensure that the current loop control of the servo driver can reflect the output torque; Step S3, a specific excitation function (as shown in Figure 5 ) is embedded in the servo driver current loop, the total test period is set to T, the specific excitation function single loading includes a pre-loading stage and a peak loading stage, and a symmetrical reverse loading is performed at the T / 2 time point; Step S4, the bus voltage and the speed change rate are monitored in real time, the reverse electromotive force compensation algorithm is used at the moment of torque unloading, the duty cycle is actively adjusted and the energy consumption discharge loop is started, the instantaneous counter pressure is rushed, and the overvoltage protection and hardware damage of the servo driver 30 are prevented; Step S5, step S3 is repeatedly executed, after multiple cycles, structural damage (such as tooth breakage, deformation exceeding tolerance) will occur to the joint module 20, the cycle number when the structural damage of the joint module 20 occurs is recorded, and the peak torque-life number curve of the joint module 20 is drawn (asFigure 6 as an input of the whole life prediction of the humanoid robot; In the embodiment, the generating process of the peak torque-life cycle curve (as shown in Figure 6 The generating process of the peak torque-life cycle curve (as shown in In the embodiment, the application realizes the scientific transformation from the static performance index to the dynamic life evaluation by constructing the mapping relationship model of the torque amplitude and the failure cycle number, and fills the technical gap of the life prediction of the humanoid robot joint module under the limit load.

[0027] As shown in Figure 1 The step S1 specifically comprises: training the AGENT of the humanoid robot under the typical working condition through the reinforcement learning model, and deploying it to the whole humanoid robot to collect and analyze the data, storing the torque time history data and generating the torque time history curve based on the light communication middleware (data layer communication protocol LCM), defining the peak locked-rotor torque as the controlled low-frequency fatigue loading process based on the material mechanics analysis, establishing the uniaxial strain amplitude-life relationship by using the Manson-Coffin equation, constructing the mapping relationship model of the torque amplitude and the failure cycle number (Cycles), the input being the load spectrum, and the output being the mapping graph of the torque amplitude and the failure cycle number, and the Manson-Coffin equation being a classical fatigue model for describing the strain-life relationship of the material under the cyclic load. Specifically, the reinforcement learning model (RL) is any one of the MIMIC model and the AMP model, the typical working condition includes jumping and falling recovery, the MIMIC model is a mimic learning model for robot motion simulation, and the AMP model is an antagonistic motion prior model for generating natural motion trajectories.

[0028] Specifically, the specific operation of the static calibration in the step S2 comprises: The measured joint module 20 is installed on the test table of the mechanical locked-rotor tool 10, the output end of the measured joint module 20 is connected to the input end of the torque sensor, the output end of the torque sensor is locked, and the current value of the joint module 20 and the torque value of the torque sensor are sampled from 0 to the peak torque at N points, wherein N is a positive integer greater than or equal to 5.

[0029] Specifically, the specific excitation function in step S3 is used to generate a precise torque command in the servo driver current loop to test the steady-state torque output and electrical characteristics of the joint module 20 in a pure stall state, providing clean static load data. The specific logic of the specific excitation function includes: Let the peak torque be The total testing period is , The time is 10 seconds, and the torque command is... ; The torque command expression for the preloading phase is: , ,in, For the test time, The preloading phase lasted 50ms. A linear ramp of time gradually increases the torque from 0 to... Gentle force is used to mesh gears, eliminate mechanical backlash in the transmission chain, and establish initial contact stress, thereby avoiding impact caused by gaps during subsequent step loading. Backlash is the gap between components such as gears or couplings in the transmission chain, which can lead to nonlinear impacts in torque transmission. The torque command expression for the peak loading phase is: , ,in, For 100ms, in At that moment, the instruction came from Step to The system backlash has been eliminated in the first stage, and this step will not cause a significant kinetic energy impact. The motor quickly enters and remains in a stall state. The current and voltage data collected at this time are used to reflect the pure steady-state stall torque characteristics, eliminating the interference of dynamic inertia. During symmetrical reverse loading (simulating alternating stress), At that time, the above-mentioned phase process is repeated, and the torque command is... To obtain reverse stall data and form a complete bidirectional load cycle; The key to the above stages lies in decoupling dynamic impact and static response. The preloading stage is responsible for overcoming the nonlinear backlash of the system, bringing the mechanical system into a "tight" linear contact state. Subsequently, a step loading is applied on the basis of eliminated backlash. At this time, almost all the energy output by the motor is used to generate static torque (converted into contact stress between gears and bearings) rather than acceleration, thus ensuring... The purity of the stage data provides a reliable load input for accurate strain-life analysis; In this embodiment, the loading period of the excitation function can be adjusted according to the actual test requirements, and the typical value is 10 seconds. In actual tests, the time parameter can be appropriately adjusted according to the specifications and response characteristics of the joint module to ensure that the backlash is eliminated while avoiding overheating or overloading.

[0030] Specifically, considering the problem of harmful back electromotive force caused by the sudden change of speed at the unloading moment after maintaining the peak torque, the present application designs a back electromotive force compensation algorithm, which is used to predict the speed based on the change of the encoder, calculate and compensate the back electromotive force through the back electromotive force coefficient, and the specific operation of the back electromotive force compensation algorithm includes: The sampling resistance and the encoder are used to monitor the bus voltage and the speed change rate in real time. The encoder signal is collected at a high frequency, the mechanical angular velocity of the motor is estimated in real time after filtering processing, and the instantaneous back electromotive force estimate is calculated using the motor back electromotive force constant, and the expression is: wherein, is the instantaneous back electromotive force estimate, specifically refers to the estimated value of the back electromotive force generated by the sudden movement of the motor rotor cutting the magnetic field at the unloading moment of the joint module 20, and is the core calculation target of the compensation algorithm, is the motor back electromotive force constant, with units of V·s / rad or V / (rad / s), representing the proportional relationship between the motor speed and the back electromotive force, which is determined by the motor design specifications, is the mechanical angular velocity of the motor, specifically refers to the instantaneous mechanical rotation angular velocity of the motor rotor, with units of rad / s, and the instantaneous back electromotive force estimate is directly superimposed on the voltage command output by the current loop as a feedforward quantity, and the expression is: wherein, is the voltage command output by the servo driver 30 to the motor, which has superimposed the back electromotive force compensation quantity, and is used to offset the disturbance of the back electromotive force, is the original voltage command generated by the servo driver 30 according to the torque demand, which does not consider the influence of the back electromotive force, and the back electromotive force compensation algorithm is used to actively offset the disturbance of the back electromotive force, suppress the voltage and current impact at the unloading moment, protect the servo driver 30 and improve the dynamic stability.

[0031] Specifically, the energy-consuming relief circuit in step S4 converts the potential energy generated by the instantaneous back pressure into heat energy by connecting the bypass resistance. The instantaneous back pressure is generated by the permanent magnet on the rotor cutting the magnetic field at the moment of instantaneous reverse micro-motion. The instantaneous reverse micro-motion refers to the small reverse rotation of the rotor due to mechanical elastic rebound at the unloading moment.

[0032] Specifically, the criteria for determining structural damage in step S5 are at least one of the following: broken teeth in the joint module 20 or deformation exceeding the preset tolerance. The preset tolerance refers to the allowable deformation range set in the design stage of the joint module 20, and the specific value is determined according to the application scenario, material properties and structural design parameters of the joint module.

[0033] like Figure 3 As shown in the figure, this is an overall data sampling analysis diagram of the humanoid robot joint module 20. By sampling and analyzing the humanoid robot under typical working conditions, the torque time-history curve is obtained. The horizontal axis of the figure is the sampling timestamp, and the vertical axis is the torque value (unit: Nm). The blue curve represents the preset command torque, and the red curve represents the actual torque fed back by the joint module 20. This figure intuitively presents the dynamic matching relationship between the command torque and the actual torque. By comparing the degree of fit between the two curves, the response accuracy and following stability of the joint module 20 to the torque command are verified, and it is determined whether there are problems such as torque output delay or excessive fluctuation in the joint module 20 during actual operation. Figure 4 The torque-current mapping relationship diagram of the joint module 20 is drawn based on the static calibration results of step S2. The horizontal axis of the diagram is the current (unit: A), and the vertical axis is the torque value of the joint module output terminal sampled by the torque sensor (unit: Nm). This diagram establishes a quantitative correspondence between current and torque. By presenting the variation law of the joint module output torque under different currents, the actual output torque of the joint module 20 can be calculated by collecting current signals and combining the mapping relationship of this diagram during the test. Figure 5 This is a test sampling data graph of the piecewise waveform excitation function. The horizontal axis of the graph is the timestamp (in seconds), and the vertical axis is the torque value (in Nm). The graph shows a complete cycle of the excitation function consisting of two positive and negative square waves. The positive and negative square waves correspond to the forward and reverse "preloading-peak loading" processes, respectively. It clearly shows the complete waveform of the torque linearly increasing from 0 to 10% of the peak torque (preloading stage), stepping to the peak torque and holding it (peak loading stage), and then repeating the process in reverse. This graph verifies that the excitation function outputs accurately according to the preset logic, confirms that the preloading stage can effectively eliminate backlash, and the peak loading stage can stably maintain the torque. It ensures that the piecewise loading method can eliminate inertial impulse interference and provide clean data for subsequent fatigue analysis. Figure 6This is a peak torque-life cycles graph. The horizontal axis represents life cycles (Cycles) on a logarithmic scale (1000-1000000), and the vertical axis represents the peak torque value (unit: Nm). This curve serves as the input for predicting the overall lifespan of the humanoid robot, realizing a closed-loop process from data acquisition to lifespan assessment. The data points in this graph represent "the number of cycles on the horizontal axis when the joint module experiences fatigue failure after repeatedly loading the corresponding vertical axis torque value," presenting the correlation between peak torque and joint module fatigue life. This facilitates the direct determination of the sustainable working number of the joint module under different peak torques. For example, the number of cycles corresponding to a certain peak torque can serve as a scientific basis for setting the robot maintenance cycle.

[0034] Therefore, according to Figures 3 to 6 It can be seen that the present invention can support a quantitative testing process from working condition analysis to life prediction through experimental data, system calibration, excitation function execution and result presentation, and has the feasibility of implementation.

[0035] Example 2 The difference from Example 1 is that, as in Example 1, Figure 2 As shown, this invention provides a quantitative testing system for the peak stall torque of a robot joint module, used to perform a quantitative testing method for the peak stall torque of a robot joint module, including: Mechanical stall tooling 10 serves as a testing device for fixing joint module 20 and realizing stall function. Mechanical stall tooling 10 is a double flange structure. The joint module 20, as the device under test, is used to execute the active degree of freedom mechanism of the humanoid robot. The active degree of freedom mechanism refers to the key mechanism part of the humanoid robot that has the power drive capability and can actively realize posture adjustment or motion output. The joint module 20 provides power, rather than relying on external force or passively following the movement. The degree of freedom refers to the dimension in which the mechanism can move independently (such as rotation and translation). For example, the shoulder joint of the humanoid robot has 3 degrees of freedom (pitch, yaw, roll). Each degree of freedom corresponds to an active drive mechanism. The active degree of freedom mechanism includes, but is not limited to, the shoulder joint, elbow joint, wrist joint, hip joint, knee joint, ankle joint, waist rotation mechanism, etc. The servo driver 30 (device under test controller) serves as the electrical control board for the joint module 20. Its inputs are DC power and communication commands. It controls the servo movement of the joint module 20 through the servo program within the electrical control board. The host computer 40 (data processing program) is connected to the joint module 20 via CAN communication. It is used to store torque time history data, send control commands to the joint module 20 and receive current feedback signals to complete life quantification assessment. A torque sensor, connected to the output terminal of joint module 20, is used for torque-current calibration and to collect the output torque value of joint module 20. a sampling resistor for monitoring the bus voltage in real time; an encoder for monitoring the motor speed change rate in real time and participating in the back electromotive force compensation; Specifically, one set of flanges in the double-flange structure is used to lock the fixed flange of the joint module 20, and the other set of flanges is used to lock the output flange of the joint module 20. The double-flange structure is made of high-strength aluminum alloy material, and the flange spacing is adjustable to adapt to the installation of modules of different sizes. The mechanical stall tool 10 is fixed to the test bench by bolts to ensure no displacement or deformation during peak loading, ensuring the repeatability and accuracy of the test.

[0036] In summary, the quantitative test method and system realize scientific evaluation of the peak stall torque and fatigue life of the robot joint module, have high stability, high precision and strong practicality, and are suitable for life prediction and reliability design of high-performance joint modules such as humanoid robots.

[0037] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A quantitative testing method for the peak stall torque of a robot joint module, characterized in that, Specifically comprising the following steps: Step S1, by sampling and analyzing the whole machine data of the humanoid robot under typical working conditions, the torque time history curve of the joint module is extracted, based on the analysis of materials mechanics, the peak locked-rotor torque is defined as a controlled low-frequency fatigue loading process, and a mapping relationship model of torque amplitude and failure cycle number is established; Step S2, a test system including mechanical locked-rotor tooling, joint module, servo driver and upper computer is established, and the static calibration of torque-current mapping relationship is carried out to ensure that the current loop control of the servo driver can reflect the output torque; Step S3, a specific excitation function is embedded in the servo driver current loop, the total test period is set as T, the specific excitation function includes a preloading stage and a peak loading stage in a single loading, and symmetric reverse loading is performed at T / 2 time point; Step S4, real-time monitoring of bus voltage and speed change rate, reverse electromotive force compensation algorithm is used at the moment of torque unloading, duty cycle is actively adjusted and energy consumption discharge loop is started, which can prevent servo driver overvoltage protection and hardware damage; Step S5, repeat step S3, record the cycle number when the joint module structure is damaged, draw the peak torque-life number curve of the joint module, and use it as the input of the whole machine life prediction of the humanoid robot.

2. The method of claim 1, wherein the peak stall torque of the robot joint module is quantified by: The step S1 specifically comprises: training the agent of the humanoid robot under typical working conditions by a reinforcement learning model, and deploying it to the humanoid robot to collect data for analysis, storing torque time history data based on light communication middleware and generating torque time history curve, defining the peak locked-rotor torque as a controlled low-frequency fatigue loading process based on the analysis of materials mechanics, establishing the relationship between single-axis strain amplitude and life by using Manson-Coffin equation, and constructing the mapping relationship model of torque amplitude and failure cycle number, the input is load spectrum, and the output is the mapping graph of torque amplitude and failure cycle number.

3. The method of claim 2, wherein the peak stall torque of the robot joint module is quantified by: The reinforcement learning model is any one of MIMIC model and AMP model, and the typical working conditions include jumping and falling recovery.

4. The method of claim 1, wherein, The specific operation of static calibration in step S2 includes: The measured joint module is installed on the test table of the mechanical locked-rotor tooling, the output end of the measured joint module is connected to the input end of the torque sensor, the output end of the torque sensor is locked, and the current value of the joint module and the torque value of the torque sensor are sampled from 0 to peak torque at N points, wherein N is a positive integer greater than or equal to 5.

5. The method of claim 1, wherein, The specific excitation function in step S3 is used to generate precise torque instructions in the servo driver current loop to test the steady-state torque output and electrical characteristics of the joint module under pure locked-rotor state, and provide pure static load data, the specific logic of the specific excitation function includes: The peak torque is set to , the total test period is , 10s, and the torque command is ; The torque command expression for the preloading phase is: , ,in, For the test time, The preloading phase lasts 50ms. A linear ramp of time gradually increases the torque from 0 to... It uses gentle force to mesh gears and eliminate mechanical backlash in the transmission chain; The torque command expression for the peak loading stage is: , ,in, For 100ms, in At that moment, the instruction came from Step to The motor quickly enters and remains in a stall state for 100ms. The current and voltage data collected at this time are used to reflect the pure steady-state stall torque characteristics. In the symmetrical reverse loading process, at the above-mentioned stage process is repeated, the torque instruction is to obtain the reverse locked-rotor data, constituting a complete bidirectional load cycle.

6. The method of claim 1, wherein, The reverse electromotive force compensation algorithm in step S4 is used to predict the speed based on the change of the encoder, and the calculation and compensation of the reverse electromotive force are realized through the reverse electromotive force coefficient, and the specific operation of the reverse electromotive force compensation algorithm includes: The bus voltage and the speed change rate are monitored in real time by using a sampling resistor and an encoder. The encoder signal is collected at a high frequency, and the mechanical angular velocity of the motor is estimated in real time after filtering processing. The instantaneous back electromotive force estimate is calculated by using the motor back electromotive force constant, and the expression is as follows: wherein, is the instantaneous back electromotive force estimate, is the motor back electromotive force constant, is the motor mechanical angular velocity, the instantaneous back electromotive force estimate is directly superimposed on the voltage command output by the current loop as a feedforward quantity, and the expression is as follows: wherein, is the voltage command output by the servo driver to the motor, is the original voltage command generated by the servo driver according to the torque demand, the back electromotive force compensation algorithm is used to actively offset the disturbance of the back electromotive force, suppress the voltage and current impact at the unloading moment, protect the servo driver, and improve the dynamic stability.

7. The method of claim 6, wherein the peak stall torque of the robot joint module is quantified by, The energy-consuming relief circuit in the step S4 converts the potential energy generated by the transient counter-pressure into heat energy by connecting the bypass resistance, and the transient counter-pressure is generated by the permanent magnet on the rotor cutting the magnetic field when the transient reverse micro-motion.

8. The method of claim 1, wherein, The determination criterion of the structural damage in the step S5 is at least one of the tooth breakage and the deformation exceeding the preset tolerance of the joint module.

9. A system for quantitatively testing a peak stall torque of a robot joint module for performing the method for quantitatively testing a peak stall torque of a robot joint module according to any one of claims 1 to 8, characterized in that The application comprises: A mechanical stall tool as a test device for fixing the joint module and realizing the stall function, the mechanical stall tool being a double-flange structure; The joint module as a measured device for executing the mechanism at the active degree of freedom of the humanoid robot; The servo driver as an electric control board card of the joint module, the input being a direct current power supply and a communication instruction, and the servo program in the electric control board card being used to control the servo operation of the joint module; The upper computer being connected with the joint module through CAN communication, used for storing the torque time history data, sending the control instruction to the joint module, receiving the current feedback signal, and completing the life quantitative evaluation.

10. The system of claim 9, wherein the system is configured to: One set of flanges in the double-flange structure is used for locking the fixed flange of the joint module, and the other set of flanges is used for locking the output flange of the joint module.

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