A method for detecting a collision state of a gap joint of an industrial robot

Abstract

The application discloses a kind of industrial robot gap hinge collision state detection methods, this method includes: the driving torque required by the end of SCARA type robot moves according to preset trajectory;Establish the nonlinear dynamics model of robot;Processing dynamics behavior obtains deviation sequence;ARIMA model is used to fit position deviation time series, and the linear approximation time series of position deviation is obtained;The detection threshold of the difference time series after the difference of position deviation sequence and its linear approximation sequence is calculated, with pulse factor as detection index, the collision state of robot gap hinge is detected online.By using the application, the collision state of gap hinge can be indirectly detected to prevent frequent collision of robot shaft and bearing, which can prevent the wear of hinge and cause the failure of hinge pair.The application can be widely applied to the field of robot system state monitoring as a kind of industrial robot gap hinge collision state detection method.

Description

Technical Field

[0001] This invention relates to the field of robot system condition monitoring technology, and in particular to a method for detecting collision conditions of industrial robot hinges with gaps. Background Technology

[0002] Robots, with their advantages of high adaptability, high precision, and high efficiency, are widely used in industries such as microelectronics manufacturing and automotive. Due to the requirements of dynamic fit between kinematic pairs and the errors generated during manufacturing, gaps between hinges are unavoidable. Excessive hinge gaps can lead to frequent separation and collisions of hinge elements, generating noise and causing severe vibrations in the robot, affecting its precision. Furthermore, repeated collisions of hinge elements can produce complex micromechanical phenomena, accelerating hinge wear. It is necessary to monitor the condition of hinges with gaps to prevent frequent collisions of shafts and bearings from exacerbating hinge wear and causing hinge pair failure. However, in practice, the gap value at the hinge is small, and oil and debris are often present at the hinge location, making it difficult to directly detect the collision state of shafts and bearings during robot operation. Therefore, an indirect method for detecting the collision state of hinges with gaps is needed. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide a method for detecting the collision state of hinges with gaps in industrial robots. This method can indirectly detect the collision state of hinges with gaps, preventing frequent collisions between robot axes and bearings from exacerbating hinge wear and causing hinge failure.

[0004] The first technical solution adopted in this invention is: a method for detecting the collision state of a hinge with gaps in an industrial robot, comprising the following steps:

[0005] Without considering hinges with gaps, the first and second link driving torques are obtained during the robot's movement according to a preset trajectory. The robot is a SCARA type robot.

[0006] Considering the hinge clearance at the connection between the robot's first and second links, a nonlinear dynamic model of the robot is constructed based on the driving torques of the first and second links.

[0007] Numerical simulation experiments were conducted based on the nonlinear dynamics model of the robot to obtain the actual end-effector position of the robot.

[0008] The robot's position deviation time series is obtained by calculating the difference between the robot's actual end position and the preset end position.

[0009] By fitting the robot's position deviation time series using a differential integrated moving average autoregressive model, a linear approximate time series of the robot's position deviation is obtained.

[0010] The robot's position deviation time series is subtracted from the robot's position deviation linear approximation time series, and the collision state of the robot's hinge with gaps is detected based on the subtraction calculation results.

[0011] Furthermore, the step of obtaining the first and second link driving torques during the robot's movement according to the preset trajectory, without considering hinges with gaps, specifically includes:

[0012] Without considering hinges with gaps, obtain the displacement and time of the robot's end position relative to the origin of the coordinate system after the robot moves according to the preset trajectory, and construct the relationship between the robot's end position and the angle of the components.

[0013] By differentiating and solving the relationship between the robot's end-effector position and the component's angle, the robot's component's angular velocity and angular acceleration can be obtained.

[0014] Based on the angular velocities and angular accelerations of the robot's components, the formulas for calculating the first and second link driving torques of the robot are constructed and solved to obtain the first and second link driving torques of the robot.

[0015] Furthermore, the calculation formulas for the first and second link driving torques of the robot are as follows:

[0016]

[0017] In the above formula, M1 represents the driving torque of the robot's first link, M2 represents the driving torque of the robot's second link, J0 represents the moment of inertia of the first link about the origin of the coordinate system, and J s2 The moment of inertia of the second link about the mass of the second driving link is represented by m. s2 L represents the mass of the mechanism, L1 represents the length of the robot's first drive link, L s2 θ1 and θ2 represent the difference between the length of the robot's second drive link and the length from the mass of the second drive link to the robot's end effector. This represents the angular velocity of the robot's first and second drive links. This represents the angular acceleration of the robot's first and second drive links.

[0018] Furthermore, the expression for the nonlinear dynamics model of the robot is as follows:

[0019]

[0020] In the above formula, and R represents the x- and y-direction components of the acceleration of the second driving rod mass. j Indicates the axis radius, e, F represents the eccentricity and azimuth angle of the center of the hinge shaft and the center of the bearing, respectively. x and F y These represent the x- and y-direction components of the normal contact force between hinges with gaps, respectively.

[0021] Furthermore, the specific formula for calculating the robot's position deviation time series is as follows:

[0022]

[0023] In the above formula, x t y t These represent the actual displacements of the robot's end effector relative to the origin of the coordinate system in the horizontal and vertical directions, respectively. Let R represent the preset displacements of the robot's end effector relative to the origin of the coordinate system in the horizontal and vertical directions, respectively, and t represent the robot's motion time. t} represents the time series of robot position deviations.

[0024] Furthermore, the differential integrated moving average autoregressive model includes an autoregressive model (AR), a moving average model (MA), and differencing operations. The step of fitting the robot's position deviation time series using the differential integrated moving average autoregressive model to obtain a linear approximate time series of the robot's position deviation specifically includes:

[0025] The stationarity of the robot's position deviation time series is tested by the ADF test to determine whether there is a unit root in the test series. If the unit root is found, the series is non-stationary; if it is not found, the series is stationary.

[0026] If the position deviation time series is a non-stationary series, perform a difference operation on the robot's position deviation time series to obtain the robot's position deviation time series after the difference operation;

[0027] The robot's position deviation time series after the difference operation is subjected to the ADF test again. If it is a non-stationary series, the difference operation is performed again until the series is stationary. The number of differences performed on the robot's position deviation time series is determined to be the order d of the difference operation.

[0028] The robot's position deviation time series after d-fold difference operations was fitted with different autoregressive moving average model parameters to obtain fitting results for different parameters;

[0029] The fitting results of different autoregressive moving average model parameters are evaluated using the AIC and BIC criteria respectively. The fitting results of the autoregressive moving average model parameters with the best performance in the evaluation results and the difference order d are selected as the order of the differential integrated moving average autoregressive model, and the optimal differential integrated moving average autoregressive model is output.

[0030] The robot's position deviation time series is input into the optimal difference integrated moving average autoregressive model for fitting, resulting in a linear approximate time series of the robot's position deviation.

[0031] Furthermore, the step of calculating the difference between the robot's position deviation time series and the robot's position deviation linear approximation time series, and detecting the collision state of the robot's hinge with gaps based on the difference calculation result, specifically includes:

[0032] The time dynamic response obtained from numerical simulation experiments based on the nonlinear dynamics model of the robot is calculated, and the ratio of the 3σ range of the difference sequence to the rectified average value is calculated to set the detection threshold.

[0033] The difference between the robot's position deviation time series and the robot's position deviation linear approximation time series is calculated, and the pulse factor is determined based on the calculation result. The pulse factor is the ratio of the peak-to-peak value of the vibration signal to the rectified average value.

[0034] Determine the pulse factor and detection threshold;

[0035] If the pulse factor is determined to be greater than the detection threshold, a collision occurs within the robot's gap hinge during that time period.

[0036] If the pulse factor is determined to be less than the detection threshold, the robot's hinge with gaps did not collide during that time period.

[0037] Furthermore, the specific formulas for calculating the detection threshold and the pulse factor are as follows:

[0038] The formula for calculating the detection threshold is:

[0039] D t =3σ / a v

[0040] In the above formula, D t The threshold value is represented by σ, which represents the standard deviation of the signal. v This represents the average of the absolute values ​​of the signal;

[0041] The formula for calculating the pulse factor is:

[0042] I = p k / a v

[0043] In the above formula, I represents the impulse factor, p k This indicates the peak-to-peak value of the signal.

[0044] The beneficial effects of the method of the present invention are as follows: Taking a SCARA-type robot system with a gap hinge as an example, the present invention establishes a dynamic model of the robot system, obtains the dynamic response of the robot system after numerical simulation of the model, and analyzes the difference between the dynamic response of the robot system and the dynamic response of the corresponding linear approximation system by using the impulse factor, thereby realizing the collision state detection of the gap hinge. That is, using the impulse factor as the detection index, the difference between the dynamic behavior of the robot system and the dynamic behavior of the corresponding linear approximation system is analyzed, thereby indirectly detecting the collision state of the gap hinge, reducing the error generated by the robot in the processing and manufacturing process, preventing frequent collisions of shafts and bearings from aggravating hinge wear and causing hinge pair failure. Attached Figure Description

[0045] Figure 1 This is a flowchart of the steps of a method for detecting collision state of an industrial robot with a gap in the hinge according to the present invention;

[0046] Figure 2 This is a schematic diagram of the acceleration time series obtained from numerical simulation of the robot in a specific embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the deviation sequence between the actual end position and the preset end position of the robot in a specific embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the robot differential time series according to a specific embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of the pulse factor of the differential sequence in a specific embodiment of the present invention;

[0050] Figure 6 This is a schematic diagram of the SCARA robot structure of the present invention. Detailed Implementation

[0051] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0052] Reference Figure 1 and Figure 6 This invention provides a method for detecting the collision state of a hinge with gaps in an industrial robot, the method comprising the following steps:

[0053] S1. Without considering hinges with gaps, calculate the torque that drives the first and second drive rods when the end effector of the SCARA robot moves along a preset trajectory.

[0054] Specifically, the method for calculating the torque driving the first and second drive rods is as follows:

[0055] The predetermined end effector trajectory of the robot relative to the origin of the coordinate system has displacements of x and y in the x and y directions. b (t), y b (t), where t is the motion time, and the relationship between the robot's end-effector position and the angle of the component is:

[0056]

[0057] In the above formula, L1 represents the length of the robot's first drive link, and L2 represents the length of the robot's second drive link;

[0058] Solving for the angles θ1 and θ2 of the robot's first and second drive links yields the component's angular velocity. and angular acceleration and The formula for calculating the torque driving the first and second drive rods is:

[0059]

[0060] In the above formula, M1 represents the driving torque of the robot's first link, M2 represents the driving torque of the robot's second link, J0 represents the moment of inertia of the first link about the origin of the coordinate system, and J s2 The moment of inertia of the second link about the mass of the second driving link is represented by m. s2 L represents the mass of the mechanism, L1 represents the length of the robot's first drive link, L s2 θ1 and θ2 represent the difference between the length of the robot's second drive link and the length from the mass of the second drive link to the robot's end effector. This represents the angular velocity of the robot's first and second drive links. This represents the angular acceleration of the robot's first and second drive links;

[0061] The parameters used in the calculation for the robot system are as follows: m s2 =16kg, J0=0.64kg·m 2 J s2 =0.22kg·m 2 L1 = 0.3m, L s2=0.1089m, the predetermined end-effector trajectory of the robot is:

[0062]

[0063] S2. Consider a hinge with a gap, establish a nonlinear dynamic model of the SCARA robot system, perform numerical simulation of the dynamic model, and obtain the dynamic behavior of the robot system.

[0064] Specifically, the established dynamic model of the SCARA robot system is as follows:

[0065]

[0066] In the above formula, and R represents the x- and y-direction components of the acceleration of the second driving rod mass. j Indicates the axis radius, e, F represents the eccentricity and azimuth angle of the center of the hinge shaft and the center of the bearing, respectively. x and F y These represent the normal contact forces F between hinges with gaps. n The components of the force in the x and y directions;

[0067] F n The normal contact force between hinges with gaps is expressed as follows:

[0068]

[0069] In the above formula, C r δ represents the material restitution coefficient, n represents the metallicity coefficient, and δ represents the contact depth. Indicates the contact normal velocity, The contact normal velocity at the start of the collision is represented by K, and the equivalent contact stiffness is represented by the following expression:

[0070]

[0071] In the above formula, R i and R j σ represents the radius of curvature of the bearing and shaft at the contact point, respectively. i and σ j The expression is as follows:

[0072]

[0073] In the above formula, E k Represents Young's modulus, v k Indicates Poisson's ratio;

[0074] The parameters used by the robot system in the simulation process of this invention are as follows: C r =0.9, n=1.5, R i =60.5mm, R j =60mm, E k =207 GPa, v k =0.3.

[0075] S3. Process the dynamic behavior of the robot system to obtain the time series of the deviation between the actual end position and the preset end position of the robot.

[0076] Specifically, the formula for calculating the robot's position deviation time series is as follows:

[0077]

[0078] In the above formula, x t y t These represent the actual displacements of the robot's end effector relative to the origin of the coordinate system in the horizontal and vertical directions, respectively. Let R represent the preset displacements of the robot's end effector relative to the origin of the coordinate system in the horizontal and vertical directions, respectively, and t represent the robot's motion time. t} represents the time series of the robot's position deviation;

[0079] The formulas for calculating the preset displacements of the robot's end effector relative to the origin of the coordinate system in the horizontal and vertical directions are as follows:

[0080]

[0081] In the above formula, L1 and L2 are the lengths of the first and second driving rods, respectively, and θ1 and θ2 are the angular displacements of the first and second driving rods, respectively.

[0082] S4. Fit the position deviation time series using the ARIMA model to obtain a linear approximate time series of the position deviation;

[0083] Specifically, the location deviation time series is fitted using the ARIMA model, which is a differential autoregressive moving average model ARIMA(p,d,q) obtained by combining the autoregressive model (AR), the moving average model (MA), and the difference operation.

[0084] First, the ADF test and KPSS test are used to test the stationarity of the positional deviation time series. If the time series fails the stationarity test, the series is differencing and then the stationarity test is performed again. This process continues until the series passes the test. At this point, the number of differencing operations on the time series is the order d of the differencing.

[0085] Secondly, the time series after d-fold difference operations are fitted with different AR and MA orders. The ARIMA models fitted with different parameters are evaluated using the AIC and BIC criteria. The best AR and MA orders in the evaluation results are selected as the order of the ARIMA model.

[0086] Finally, the optimal AR and MA order fitting is used to fit the position deviation time series after d-fold difference operations, and the fitted series is a linear approximate time series of position deviation.

[0087] S5. The difference sequence is obtained by subtracting the position deviation time series from its linear approximation time series. The detection threshold is calculated, and the pulse factor of the difference sequence is used as the detection index to detect the collision state of the robot with gap hinges online.

[0088] Specifically, the detection threshold is calculated as follows: after simulating the robot model to obtain a sufficiently long dynamic response time, the ratio of the 3σ range of the difference sequence during this time period to the rectified average value is calculated. The calculation formula is:

[0089] D t =3σ / a v

[0090] In the above formula, D t The threshold value is represented by σ, which represents the standard deviation of the signal. v This represents the average of the absolute values ​​of the signal;

[0091] The pulse factor of a differential sequence is calculated as follows: the pulse factor can be used to detect whether there is an impulse component in a signal. Its value is the ratio of the peak-to-peak value of the vibration signal to the rectified average value. The calculation formula is:

[0092] I = p k / a v

[0093] In the above formula, I represents the impulse factor, p k Indicates the peak-to-peak value of the signal;

[0094] When the pulse factor of the differential sequence exceeds a set threshold, the gap hinge element will collide violently during that time period.

[0095] After performing numerical simulation on the robot model, the acceleration curves of the first and second drive links were obtained, as shown below. Figure 2 As shown, the collision between the shaft and bearing in a hinge with clearance causes a sudden change in the acceleration curve. The difference sequence obtained by subtracting the deviation sequence between the robot's actual end position and the preset end position from its linear approximation time sequence is as follows. Figure 3 and Figure 4As shown, the areas of sharp fluctuation in the differential sequence correspond to the abrupt changes in the acceleration curve. Based on the aforementioned method for calculating the detection threshold, the calculated threshold value is 13.18. Using the pulse factor of the differential sequence as the detection index, the pulse factor of the differential time series is calculated every 0.5 seconds. Figure 5 As shown, the pulse factor detection revealed violent collisions between the hinges during the time periods of 0s-0.5s, 1.5s-2.0s, 2.5s-3.0s, and 4.5s-5.0s, which is consistent with the results of the acceleration curve obtained from the simulation. Therefore, the effectiveness of the method of the present invention is proved.

[0096] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for detecting collision states of hinges with gaps in an industrial robot, characterized in that, Includes the following steps: Without considering hinges with gaps, the first and second link driving torques are obtained during the robot's movement according to a preset trajectory. The robot is a SCARA type robot. Considering the hinge clearance at the connection between the robot's first and second links, a nonlinear dynamic model of the robot is constructed based on the driving torques of the first and second links. Numerical simulation experiments were conducted based on the nonlinear dynamics model of the robot to obtain the actual end-effector position of the robot. The robot's position deviation time series is obtained by calculating the difference between the robot's actual end position and the preset end position. The stationarity of the robot's position deviation time series is tested by the ADF test to determine whether there is a unit root in the test series. If the unit root is found, the series is non-stationary; if it is not found, the series is stationary. If the position deviation time series is a non-stationary series, perform a difference operation on the robot's position deviation time series to obtain the robot's position deviation time series after the difference operation; The robot's position deviation time series after differencing is subjected to an ADF test again. If it is a non-stationary series, differencing is performed again until the series becomes stationary. The number of differencing operations performed on the robot's position deviation time series is determined as the order of differencing. ; right The robot's position deviation time series after the second difference operation was fitted with different autoregressive moving average model parameters to obtain fitting results with different parameters; The fitting results of different autoregressive moving average model parameters were evaluated using the AIC and BIC criteria, respectively. The fitting results and differencing order of the autoregressive moving average model parameters with the best performance in the evaluation results were selected. As the order of the differential integrated moving average autoregressive model, the optimal differential integrated moving average autoregressive model is output. The robot's position deviation time series is input into the optimal difference integrated moving average autoregressive model for fitting, and a linear approximate time series of the robot's position deviation is obtained. The time series of robot position deviations is subtracted from the linear approximation time series of robot position deviations. Based on the subtraction result, the collision state of the robot's hinges with gaps is detected, including: The time-dynamic response is obtained from numerical simulation experiments based on the nonlinear dynamic model of the robot, and the difference sequence of this time-dynamic response is calculated. The ratio of the range to the rectified average value is used to set the detection threshold; The difference between the robot's position deviation time series and the robot's position deviation linear approximation time series is calculated, and the pulse factor is determined based on the calculation result. The pulse factor is the ratio of the peak-to-peak value of the vibration signal to the rectified average value. Determine the pulse factor and detection threshold; If the pulse factor is determined to be greater than the detection threshold, a collision exists within the dynamic response of the robot's hinge with gaps at that time. If the pulse factor is determined to be less than the detection threshold, the robot's hinge with gaps did not collide within the dynamic response time.

2. The method for detecting collision state of a hinge with clearance in an industrial robot according to claim 1, characterized in that, The step of obtaining the first and second link driving torques during the robot's movement according to a preset trajectory, without considering hinges with gaps, specifically includes: Without considering hinges with gaps, obtain the displacement and time of the robot's end position relative to the origin of the coordinate system after the robot moves according to the preset trajectory, and construct the relationship between the robot's end position and the angle of the components. By differentiating and solving the relationship between the robot's end-effector position and the component's angle, the robot's component's angular velocity and angular acceleration can be obtained. Based on the angular velocities and angular accelerations of the robot's components, the formulas for calculating the first and second link driving torques of the robot are constructed and solved to obtain the first and second link driving torques of the robot.

3. The method for detecting collision state of a hinge with gap in an industrial robot according to claim 2, characterized in that, The specific formulas for calculating the first and second link driving torques of the robot are as follows: ； In the above formula, This represents the first lever driving torque of the robot. This represents the driving torque of the robot's second link. This represents the moment of inertia of the first rod about the origin of the coordinate system. This represents the moment of inertia of the second rod about the mass of the second driving rod. Indicates the quality of the organization. This indicates the length of the robot's first drive link. This represents the difference between the length of the robot's second drive link and the length from the mass point of the second drive link to the robot's end effector. , This indicates the angle between the robot's first and second drive links. , This represents the angular velocity of the robot's first and second drive links. , This represents the angular acceleration of the robot's first and second drive links.

4. The method for detecting collision state of a hinge with gap in an industrial robot according to claim 3, characterized in that, The specific expression for the nonlinear dynamics model of the robot is shown below: ； In the above formula, and The accelerations of the second driving rod mass point are respectively in direction and Components in direction, Indicates the axis radius. , These represent the eccentricity and azimuth angle of the hinge axis center and bearing center, respectively, including the clearance. and These represent the normal contact forces between hinges with gaps. direction and Component of force in direction.

5. The method for detecting collision state of a hinge with clearance in an industrial robot according to claim 4, characterized in that, The specific formula for calculating the robot's position deviation time series is as follows: ； In the above formula, , These represent the actual displacements of the robot's end effector relative to the origin of the coordinate system in the horizontal and vertical directions, respectively. , These represent the preset displacements of the robot's end effector relative to the origin of the coordinate system in the horizontal and vertical directions, respectively. Indicates the robot's movement time. This represents the time series of the robot's positional deviation.

6. The method for detecting collision state of a hinge with clearance in an industrial robot according to claim 5, characterized in that, The specific formulas for calculating the detection threshold and the pulse factor are as follows: The formula for calculating the detection threshold is: ； In the above formula, Indicates the detection threshold. The standard deviation of the signal is represented. This represents the average of the absolute values ​​of the signal; The formula for calculating the pulse factor is: ； In the above formula, Indicates the pulse factor. This indicates the peak-to-peak value of the signal.

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