Method and apparatus for eliminating instability in human-computer interaction

Abstract

The present invention provides a method and device for eliminating instability in human-machine interaction. The method includes the following steps: constructing a stability observation model for human-machine interaction; determining a variable admittance control model of the robot according to the stability observation model; and adjusting the admittance parameters of the robot according to the variable admittance control model to eliminate instability in human-machine interaction. The present invention can eliminate the influence of stable misjudgment caused by the phase lead and delay of the high-pass and low-pass filters in the HRCO stability observer, thereby improving the detection accuracy and avoiding misjudgment. In addition, it can ensure that the robot quickly recovers when the external impedance suddenly increases and becomes unstable, and can ensure that the robot returns to the initial admittance parameters after stabilization to ensure the ease and smoothness of the human-machine interaction process.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control, and particularly to a method for eliminating instability in human-robot interaction and a device for eliminating instability in human-robot interaction. Background Art

[0002] The admittance control strategy is a commonly used technology in the field of robot control and is widely applied to the compliant control of robots. Since it can associate the external force / torque measured by the sensor with the position of the robot end effector, that is, force control can be achieved by controlling the position.

[0003] However, there is a problem with the existing admittance control strategy: when interacting with a human, if the human's stiffness suddenly increases, the robot is prone to unstable oscillations, which can cause harm to the robot and the human. To ensure safe and stable interaction with the robot, various techniques have been proposed to detect and prevent instability during interaction with the robot under admittance control. However, the current techniques for detecting and preventing instability during interaction with the robot under admittance control still have problems affecting the stability judgment due to the lead of the high-pass filter and the delay of the low-pass filter in the stability observer. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method for eliminating instability in human-robot interaction, which can eliminate the influence of the phase lead and delay of the high- and low-pass filters in the HRCO stability observer on the stable misjudgment, thereby improving the detection accuracy and avoiding misjudgment. In addition, it can ensure that the robot quickly recovers when it becomes unstable due to a sudden increase in external impedance, and can ensure that the robot returns to the initial admittance parameters after stabilization to ensure the smoothness of the human-robot interaction process.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for eliminating instability in human-robot interaction, comprising the following steps: constructing a stability observation model for human-robot interaction; determining a variable admittance control model of the robot according to the stability observation model; and adjusting the admittance parameters of the robot according to the variable admittance control model to eliminate instability in human-robot interaction.

[0007] According to an embodiment of the present invention, the stability observation model is used to reflect the change in force amplitude during human-robot interaction.

[0008] According to an embodiment of the present invention, the construction of the stability observation model for human - machine interaction specifically includes the following steps: constructing a Butterworth filter with infinite impulse response; constructing a first - type stability observer according to the Butterworth filter with infinite impulse response; constructing a second - type stability observer according to the first - type stability observer; constructing a stability observation model for human - machine interaction according to the second - type stability observer.

[0009] According to an embodiment of the present invention, a first - type stability observer is constructed based on the HPF and LPF amplitude response characteristics of the Butterworth filter with infinite impulse response.

[0010] According to an embodiment of the present invention, the stability observation model for human - machine interaction is:

[0011]

[0012] where, I std represents the numerical change in force amplitude, η represents the smoothing coefficient, I std represents the ratio of the windowed standard deviation of the time - domain signal F to the maximum allowable force F max .

[0013] According to an embodiment of the present invention, the variable admittance control model is:

[0014]

[0015]

[0016] where, m0 and d0 represent the initial values of the inertia m and the damping d, ε represents the stability threshold, and α represents the weight coefficient.

[0017] According to an embodiment of the present invention, adjusting the admittance parameters of the robot according to the variable admittance control model to eliminate the instability in human - machine interaction specifically includes the following steps: determining whether the stable value in human - machine interaction is less than the stability threshold; if not, adjusting the admittance parameters of the robot according to the stable value in human - machine interaction, the stability threshold, and the weight coefficient to eliminate the instability in human - machine interaction.

[0018] A device for eliminating the instability in human - machine interaction includes: a first modeling module for constructing a stability observation model for human - machine interaction; a second modeling module for determining a variable admittance control model of the robot according to the stability observation model; a control module for adjusting the admittance parameters of the robot according to the variable admittance control model to eliminate the instability in human - machine interaction.

[0019] A robot device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for eliminating instability in human - machine interaction described in the above - mentioned embodiments is implemented.

[0020] A non - transitory computer - readable storage medium stores a computer program, which, when executed by a processor, implements the method for eliminating instability in human - machine interaction described in the above - mentioned embodiments.

[0021] Advantages of the present invention:

[0022] 1), based on the HRCO stability observer, the present invention introduces I that reflects the change in reaction force amplitude std to construct a stability observation model for human - machine interaction OS , thereby being able to eliminate the influence of stable misjudgment caused by the phase lead and delay of the high - pass and low - pass filters in the HRCO stability observer. Thus, the detection accuracy can be improved and misjudgment can be avoided.

[0023] 2), by constructing a variable admittance control model based on the stability observation model of human - machine interaction, the present invention can ensure that the robot quickly recovers when it becomes unstable due to a sudden increase in external impedance, and can ensure that the robot returns to the initial admittance parameters after stabilization to ensure the ease and smoothness of the human - machine interaction process. Description of the Drawings

[0024] Figure 1 is a flowchart of the method for eliminating instability in human - machine interaction according to an embodiment of the present invention;

[0025] Figure 2 is a control process diagram of an admittance control model in the prior art;

[0026] Fig. 3(a) is a simulation result diagram of the frequency change of the simulated interaction force according to an embodiment of the present invention;

[0027] Fig. 3(b) is a simulation result diagram of the numerical change of the simulated interaction force according to an embodiment of the present invention;

[0028] Fig. 3(c) is a simulation comparison result diagram of I HRCO and I0 of the simulated interaction force according to an embodiment of the present invention;

[0029] Fig. 3(d) is a simulation result diagram of I os of the simulated interaction force according to an embodiment of the present invention

[0030] Figure 4 is a block diagram of the device for eliminating instability in human - machine interaction according to an embodiment of the present invention. Detailed Embodiments

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Figure 1 It is a flowchart of the method for eliminating instability in human - machine interaction in the embodiments of the present invention.

[0033] As Figure 1 shown, the method for eliminating instability in human - machine interaction in the embodiments of the present invention includes the following steps:

[0034] S1, construct a stability observation model for human - machine interaction.

[0035] Specifically, an infinite - impulse - response Butterworth filter can be constructed first, and then a first - type stability observer can be constructed based on the infinite - impulse - response Butterworth filter. Furthermore, a second - type stability observer can be constructed based on the first - type stability observer, and finally, a stability observation model for human - machine interaction can be constructed based on the second - type stability observer. Among them, the stability observation model can be used to reflect the change in force amplitude in human - machine interaction.

[0036] More specifically, the infinite - impulse - response Butterworth filter is specifically:

[0037]

[0038] where u represents the input signal, y represents the output signal, P and Q respectively represent the filtering orders of the feed - forward filter and the feedback filter, and a and b respectively represent the coefficients of the feedback filter and the feed - forward filter.

[0039] For example, the cut - off frequencies ω c of the high - pass filter (HPF) and the low - pass filter (LPF) of the infinite - impulse - response Butterworth filter can be set to 5 Hz (when the robot interacts with people, the frequency components of the upper - limb movement of the human body are within 5 Hz, and the frequency components of the autonomous movement are within 2 Hz), and the sampling period of the infinite - impulse - response Butterworth filter can be set to 5 ms (consistent with the robot sampling period). The configuration parameters of the high - pass filter (HPF) and the low - pass filter (LPF) of the infinite - impulse - response Butterworth filter are shown in Table 1 below.

[0040] Table 1

[0041] Filter type <![CDATA[a1]]> <![CDATA[a2]]> <![CDATA[b0]]> <![CDATA[b1]]> <![CDATA[b2]]> HPF -1.7786 0.8008 0.8949 -1.7897 0.8949 LPF -1.7786 0.8008 0.005542 0.011085 0.005542

[0042] Furthermore, a first - type stability observer can be constructed according to the HPF and LPF amplitude response characteristics of an infinite impulse response Butterworth filter, where the first - type stability observer is specifically:

[0043]

[0044] where I o represents a dimensionless value between 0 and 1, ||F h n || and ||F l n || respectively represent the Euclidean two - norm of the N - degree - of - freedom interaction force signal after passing through the HPF and LPF of the infinite impulse response Butterworth filter. It should be noted that to prevent the output value I o from mutating, when ||F l n || is less than 0.01 N, the value of I o can be set to zero.

[0045] Furthermore, a second - type stability observer, that is, an HRCO stability observer, can be constructed based on the first - type stability observer obtained above, where the second - type stability observer, that is, the HRCO stability observer is specifically:

[0046]

[0047] where η represents a smoothing coefficient, which can be specifically set to 0.02.

[0048] Furthermore, based on the second - type stability observer obtained above, that is, the HRCO stability observer, I std reflecting the force amplitude ratio in human - machine interaction can be introduced to construct a stability observation model for human - machine interaction, where the stability observation model for human - machine interaction is specifically:

[0049]

[0050] where I std represents the force amplitude change value, I std represents the ratio of the windowed standard deviation of the time - domain signal F to the maximum allowable force F max , and I std The specific expression is:

[0051]

[0052] where F max represents the maximum value of the force signal (normalized between 0 and 1 for I std ), p represents the window size for calculating the standard deviation (its value is equal to 0.1 / T s, for calculating within 0.1 second in the sampling period T s the change of I std ).

[0053] S2. Determine the variable admittance control model of the robot according to the stability observation model.

[0054] Specifically, the variable admittance control model of the robot can be determined according to the characteristics of the stability observation model, that is, the characteristics that can reflect the change of the force amplitude in the human-robot interaction. Among them, the variable admittance control model is specifically:[[]]

[0055]

[0056]

[0057] where m0 and d0 represent the initial values of the inertia m and the damping d, ε represents the stability threshold, and α represents the weight coefficient.

[0058] It should be noted that the initial values m0 and d0 of the inertia m and the damping d are the lowest admittance parameters to ensure the stable operation of the robot; the stability threshold ε is the stable output value of the stability observation model under a 2Hz input signal, used to judge whether the current human-robot interaction is stable; the weight coefficient α is the weight coefficient for adjusting the admittance parameters, which can be adjusted according to the response degree of the robot to the admittance parameters to ensure that the robot can recover stability in time when it is unstable.

[0059] In addition, it should be noted that the variable admittance control model of the present invention is obtained on the basis of the admittance control model in the prior art. For example, the admittance control model represented by the following expression:[[]]

[0060]

[0061] F ext = F d - F s

[0062] where x r , x c respectively represent the reference position and the issued position, M, D, and K respectively represent the virtual inertia, damping, and stiffness in the admittance parameters, F ext represents the input of the admittance control model, F d represents the virtual force in the interaction, and F s represents the interaction force between the human or the environment and the robot. Specifically, as Figure 2 shown, F ext can be input into the admittance control model, and then the admittance control model outputs X d , and the output X d of the admittance control model can be input into the robot to control the robot to drag X and match the expected drag Xe Compare to determine the desired interaction force F in the interaction environment e .

[0063] Furthermore, to ensure the compliance of the robot's dragging during human-robot interaction, F d 、 x r and K can both be set to zero. Additionally, since each Cartesian space variable is independent, without loss of generality, therefore, taking one dimension as an example, the admittance control model can be rewritten as:

[0064]

[0065]

[0066] From this, the acceleration f ext can be measured by the force sensor, and the input of the inner-loop position controller at the bottom layer of the robot x can be obtained by integrating the acceleration .

[0067] S3, adjust the admittance parameters of the robot according to the variable admittance control model to eliminate the instability in human-robot interaction.

[0068] Specifically, first determine whether the stable value in human-robot interaction is less than the stable threshold. If not, then adjust the admittance parameters of the robot according to the stable value, stable threshold, and weight coefficient in human-robot interaction to eliminate the instability in human-robot interaction.

[0069] More specifically, referring to the above variable admittance control model, it can be known that it is possible to determine whether the current human-robot interaction is stable according to the output value of the current stability observation model I O and the stable threshold ε. If so, then m(t) = m0; if not, then m(t) = m0 + α(I os (t) - ε) to adjust the admittance parameters of the robot, thereby eliminating the instability in human-robot interaction, that is, the unstable jitter phenomenon of the robot.

[0070] The beneficial effects of the present invention are as follows:

[0071] 1), Based on the HRCO stability observer, the present invention introduces I std reflecting the change in the reaction force amplitude to construct the stability observation model I OS of human-robot interaction, thereby being able to eliminate the influence of the phase lead and delay of the high-pass and low-pass filters in the HRCO stability observer on the stable misjudgment, and thus being able to improve the detection accuracy and avoid misjudgment;

[0072] 2) Based on the stability observation model of human - machine interaction, the present invention constructs a variable admittance control model, which can ensure that the robot quickly recovers when the external impedance suddenly increases and becomes unstable, and can ensure that the robot returns to the initial admittance parameters after stabilization to ensure the ease and smoothness of the human - machine interaction process.

[0073] Next, the effectiveness of the method for eliminating instability in human - machine interaction of the present invention will be further elaborated through the simulation result diagrams shown in FIGS. 3(a), 3(b), 3(c) and 3(d).

[0074] Among them, FIG. 3(a) shows the simulation result diagram of the change of the simulated interaction force signal frequency between 0 and 10 Hz, and FIG. 3(b) shows the simulation result diagram of the change of the magnitude of the simulated interaction force from 5 N to 10 N. Further, the light - colored curve I0 in FIG. 3(c) can be obtained by processing the simulated signal through the first - type stability observer, and the dark - colored curve I in FIG. 3(c) can be obtained by processing the simulated signal through the second - type stability observer, that is, the HRCO stability observer. HRCO 。

[0075] By comparing the light - colored curve I0 and the dark - colored curve I HRCO It can be seen that the dark - colored curve I processed by the second - type stability observer, that is, the HRCO stability observer HRCO is significantly smoother than the light - colored curve I0 processed by the first - type stability observer. However, when the simulated interaction force changes from - 5 N to 10 N at 19 s to 21 s of the simulated interaction force signal, and due to the phase lead and delay of the high - pass and low - pass filters in the second - type stability observer, that is, the HRCO stability observer, the value at 20 s is amplified to exceed the stability threshold. Moreover, at 0 s at the beginning and 40 s at the end, the output values of the second - type stability observer, that is, the HRCO stability observer, both have obvious protrusions, which may lead to misjudgment.

[0076] Among them, FIG. 3(d) shows the simulation result diagram of the stability observation model of human - machine interaction of the present invention processing the simulated signal. From this, it can be seen that the simulation curve I obtained by the stability observation model of human - machine interaction of the present invention processing the simulated signal os , at 0 s, 20 s and 40 s, there are no prominent values that affect misjudgment, and the output value is close to 0. Thus, the influence of the phase lead and delay of the high - pass and low - pass filters can be eliminated.

[0077] Corresponding to the method for eliminating instability in human - machine interaction in the above - mentioned embodiment, the present invention also proposes a device for eliminating instability in human - machine interaction.

[0078] Such as Figure 4As shown in the figure, the device for eliminating instability in human - machine interaction according to an embodiment of the present invention includes a first modeling module 10, a second modeling module 20, and a control module 30. Among them, the first modeling module 10 can be used to construct a stability observation model of human - machine interaction; the second modeling module 20 can be used to determine a variable admittance control model of the robot according to the stability observation model; the control module 30 can be used to adjust the admittance parameters of the robot according to the variable admittance control model to eliminate instability in human - machine interaction.

[0079] In an embodiment of the present invention, the first modeling module 10 can specifically be used to construct a Butterworth filter with infinite impulse response, and then can construct a first - type stability observer according to the Butterworth filter with infinite impulse response, and further can construct a second - type stability observer according to the first - type stability observer, and finally can construct a stability observation model of human - machine interaction. Among them, the stability observation model can be used to reflect the change in force amplitude in human - machine interaction.

[0080] More specifically, the Butterworth filter with infinite impulse response is specifically:

[0081]

[0082] Among them, u represents the input signal, y represents the output signal, P and Q respectively represent the filtering orders of the feed - forward filter and the feedback filter, and a and b respectively represent the coefficients of the feedback filter and the feed - forward filter.

[0083] For example, the cut - off frequencies ω of the high - pass filter (HPF) and the low - pass filter (LPF) of the Butterworth filter with infinite impulse response c can be set to 5Hz (when the robot interacts with a human, the frequency components of the human upper - limb movement are within 5Hz, and the frequency components of the autonomous movement are within 2Hz), and the sampling period of the Butterworth filter with infinite impulse response can be set to 5ms (consistent with the robot sampling period). The configuration parameters of the high - pass filter (HPF) and the low - pass filter (LPF) of the Butterworth filter with infinite impulse response are shown in Table 1 below.

[0084] Table 1

[0085] Filter type <![CDATA[a1]]> <![CDATA[a2]]> <![CDATA[b0]]> <![CDATA[b1]]> <![CDATA[b2]]> HPF -1.7786 0.8008 0.8949 -1.7897 0.8949 LPF -1.7786 0.8008 0.005542 0.011085 0.005542

[0086] Furthermore, a first - type stability observer can be constructed according to the amplitude response characteristics of the HPF and LPF of the Butterworth filter with infinite impulse response. Among them, the first - type stability observer is specifically:

[0087]

[0088] Among them, I oRepresents a dimensionless value between 0 and 1, ||F h n || and ||F l n || respectively represent the Euclidean two-norm after the N-degree-of-freedom interaction force signal passes through the HPF and LPF of an infinite impulse response Butterworth filter. It should be noted that to prevent the output value I o from mutating, when ||F l n is less than 0.01 N, I o can be set to zero.

[0089] Furthermore, a second type of stability observer, namely the HRCO stability observer, can be constructed based on the first type of stability observer obtained above. Among them, the second type of stability observer, namely the HRCO stability observer, is specifically:

[0090]

[0091] Among them, η represents the smoothing coefficient, which can be specifically set to 0.02.

[0092] Furthermore, based on the second type of stability observer obtained above, namely the HRCO stability observer, I std reflecting the force amplitude ratio in human-computer interaction can be introduced to construct a stability observation model for human-computer interaction. Among them, the stability observation model for human-computer interaction is specifically:

[0093]

[0094] Among them, I std represents the force amplitude change value, and I std represents the ratio of the window standard deviation of the time-domain signal F to the maximum allowable force F max , and I std The specific expression is:

[0095]

[0096] Among them, F max represents the maximum value of the force signal (normalizing I std between 0 and 1), p represents the window size for calculating the standard deviation (its value is equal to 0.1 / T s , used to calculate the change of I s within 0.1 seconds in the sampling period T std ).

[0097] In an embodiment of the present invention, the second modeling module 20 may be specifically configured to determine the variable admittance control model of the robot according to the characteristics of the stability observation model, that is, the characteristics that can reflect the change of the force amplitude in the human-robot interaction. Among them, the variable admittance control model is specifically:

[0098]

[0099]

[0100] Among them, m0 and d0 represent the initial values of the inertia m and the damping d, ε represents the stability threshold, and α represents the weight coefficient.

[0101] It should be noted that the initial values m0 and d0 of the inertia m and the damping d are the lowest admittance parameters to ensure the stable operation of the robot; the stability threshold ε is the stable output value of the stability observation model under a 2Hz input signal, which is used to judge whether the current human-robot interaction is stable; the weight coefficient α is the weight coefficient for adjusting the admittance parameters, which can be adjusted according to the response degree of the robot to the admittance parameters to ensure that the robot can recover stability in a timely and rapid manner when it is unstable.

[0102] In addition, it should be noted that the variable admittance control model of the present invention is obtained on the basis of the admittance control model in the prior art. For example, the admittance control model represented by the following expression:

[0103]

[0104] F ext =F d -F s

[0105] Among them, x r 、x c respectively represent the reference position and the issued position, M, D, and K respectively represent the virtual inertia, damping, and stiffness in the admittance parameters, F ext represents the input of the admittance control model, F d represents the virtual force in the interaction, and F s represents the interaction force between the human or the environment and the robot. Specifically, as Figure 2 shown, F ext can be input into the admittance control model, and then the admittance control model outputs X d , and the output X d of the admittance control model can be input into the robot to control the robot to drag X and compare it with the expected drag X e to determine the expected interaction force F e in the expected interaction environment.

[0106] Furthermore, to ensure the compliance of the robot's drag in the human-robot interaction, F d 、 x r Both \(x\) and \(K\) can be set to zero. Additionally, since each Cartesian space variable is independent, without loss of generality, therefore, taking one dimension as an example, the admittance control model can be rewritten as:

[0107]

[0108]

[0109] From this, the acceleration can be calculated f ext can be measured by the force sensor, and the input \(x\) of the inner - loop position controller at the bottom layer of the robot can be obtained by integrating the acceleration obtained.

[0110] In an embodiment of the present invention, the control module 30 can be specifically configured to determine whether the stable value in the human - machine interaction is less than the stable threshold. If not, the admittance parameters of the robot are adjusted according to the stable value, the stable threshold, and the weight coefficient in the human - machine interaction to eliminate the instability in the human - machine interaction.

[0111] More specifically, referring to the above - mentioned variable admittance control model, it can be known that whether the current human - machine interaction is stable can be judged according to the output value of the current stability observation model \(I\) OS and the stable threshold \(\varepsilon\). If so, then \(m(t)=m_0\); if not, then \(m(t)=m_0+\alpha(I\) os (t)-\varepsilon)\) to adjust the admittance parameters of the robot, thereby eliminating the instability in the human - machine interaction, that is, the unstable jitter phenomenon of the robot.

[0112] The beneficial effects of the present invention are as follows:

[0113] 1), Based on the HRCO stability observer, the present invention introduces \(I\) std reflecting the change of the reaction force amplitude to construct the stability observation model \(I\) OS of the human - machine interaction, so as to be able to eliminate the influence of the phase lead and delay of the high - pass and low - pass filters in the HRCO stability observer on the stable misjudgment. Therefore, the detection accuracy can be improved and misjudgment can be avoided;

[0114] 2), By constructing a variable admittance control model based on the stability observation model of the human - machine interaction, the present invention can ensure that the robot can quickly recover when it becomes unstable due to a sudden increase in external impedance, and can ensure that the robot returns to the initial admittance parameters after stabilization to ensure the ease and smoothness of the human - machine interaction process.

[0115] Next, the effectiveness of the method for eliminating the instability in the human - machine interaction of the present invention will be further elaborated through the simulation result diagrams shown in FIGS. 3(a), 3(b), 3(c), and 3(d).

[0116] Among them, Fig. 3(a) shows the simulation result diagram of the change of the simulated interaction force signal frequency between 0 and 10 Hz, and Fig. 3(b) shows the simulation result diagram of the change of the magnitude of the simulated interaction force from 5 N to 10 N. Further, by processing the simulated signal through the first type of stability observer, the light curve I0 in Fig. 3(c) can be obtained, and by processing the simulated signal through the second type of stability observer, that is, the HRCO stability observer, the dark curve I in Fig. 3(c) can be obtained. HRCO .

[0117] By comparing the light curve I0 and the dark curve I HRCO It can be seen that the dark curve I processed by the second type of stability observer, that is, the HRCO stability observer HRCO is significantly smoother than the light curve I0 processed by the first type of stability observer; however, when the simulated interaction force signal is between 19 s and 21 s, the simulated interaction force changes from -5 N to 10 N, and because of the phase lead and delay of the high-pass and low-pass filters in the second type of stability observer, that is, the HRCO stability observer, the value at 20 s is amplified to exceed the stability threshold; moreover, at the beginning 0 s and the end 40 s, the output values of the second type of stability observer, that is, the HRCO stability observer, both have obvious protrusions, which may lead to misjudgment.

[0118] Among them, Fig. 3(d) shows the simulation result diagram of the stability observation model of the human-computer interaction of the present invention processing the simulated signal. From this, it can be seen that the simulation curve I obtained by the stability observation model of the human-computer interaction of the present invention processing the simulated signal os , at 0 s, 20 s, and 40 s, there are no prominent values that affect misjudgment, and the output value is close to 0. Therefore, it can eliminate the influence of the phase lead and delay of the high-pass and low-pass filters.

[0119] Corresponding to the above embodiments, the present invention also proposes a robot device.

[0120] The robot device of the embodiment of the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method for eliminating instability in human-computer interaction according to the above embodiments.

[0121] According to the robot device proposed by the embodiment of the present invention, it can eliminate the influence of the stability misjudgment caused by the phase lead and delay of the high-pass and low-pass filters in the HRCO stability observer, thereby improving the detection accuracy and avoiding misjudgment. In addition, it can also ensure that the robot can quickly recover when the external impedance suddenly increases and becomes unstable, and can ensure that the robot returns to the initial admittance parameters after stabilization to ensure the ease and smoothness of the human-computer interaction process.

[0122] Corresponding to the above embodiments, the present invention also provides a non-transitory computer-readable storage medium.

[0123] The non-transitory computer-readable storage medium of the embodiments of the present invention stores a computer program, and when the program is executed by a processor, it implements the method for eliminating instability in human-computer interaction according to the above embodiments.

[0124] According to the non-transitory computer-readable storage medium provided by the embodiments of the present invention, a computer program is stored thereon, and when the program is executed by a processor, it implements the method for eliminating instability in human-computer interaction according to the above embodiments. Thus, it can eliminate the influence of stable misjudgment caused by the phase lead and delay of the high-pass and low-pass filters in the HRCO stability observer, thereby improving the detection accuracy and avoiding misjudgment. In addition, it can also ensure that the robot can quickly recover when the external impedance suddenly increases and becomes unstable, and can ensure that the robot returns to the initial admittance parameters after stabilization to ensure the smooth flow field of the human-computer interaction process.

[0125] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more unless otherwise specifically defined.

[0126] In the present invention, unless otherwise clearly defined and limited, the terms "mounted", "connected", "connected to", "fixed" and other terms should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0127] In the present invention, unless otherwise clearly defined and limited, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "below" and "beneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0128] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0129] Any process or method description represented in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a manner other than shown or discussed, including in a substantially simultaneous manner according to the functions involved or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0130] The logic and / or steps represented in a flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in connection with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0131] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0132] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0133] In addition, in each embodiment of the present invention, each functional unit can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0134] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for eliminating instability in human-computer interaction, characterized in that, Including the following steps: Construct a stability observation model for human-robot interaction; Determine the variable admittance control model of the robot according to the stability observation model; Adjust the admittance parameters of the robot according to the variable admittance control model to eliminate instability in human-robot interaction, The stability observation model of the human-robot interaction is: Among them, I std represents the value of the force amplitude change, η represents the smoothing coefficient, and I std represents the ratio of the windowed standard deviation of the time-domain signal F to the maximum allowable force F max ​ The variable admittance control model is: Wherein, m0 and d0 represent the initial values of the inertia m and the damping d, ε represents the stability threshold, and α represents the weight coefficient.

2. The method for eliminating instability in human-computer interaction according to claim 1, wherein The stability observation model is used to reflect the change of force amplitude in human-robot interaction.

3. The method for eliminating instability in human-computer interaction according to claim 1, wherein The construction of the stability observation model for human-robot interaction specifically includes the following steps: Construct a Butterworth filter with infinite impulse response: Construct a first type of stability observer according to the Butterworth filter with infinite impulse response; Construct a second type of stability observer according to the first type of stability observer; Construct a stability observation model for human-robot interaction according to the second type of stability observer.

4. The method for eliminating instability in human-computer interaction according to claim 3, characterized in that, Construct a first type of stability observer according to the HPF and LPF amplitude response characteristics of the Butterworth filter with infinite impulse response.

5. The method for eliminating instability in human-computer interaction according to claim 1, characterized in that The adjustment of the admittance parameters of the robot according to the variable admittance control model to eliminate instability in human-robot interaction specifically includes the following steps: Judge whether the stable value in human-robot interaction is less than the stability threshold; If not, adjust the admittance parameters of the robot according to the stable value in the human-robot interaction, the stability threshold and the weight coefficient to eliminate instability in the human-robot interaction.

6. A device for eliminating instability in human-computer interaction, characterized in that, Including: A first modeling module for constructing a stability observation model for human-robot interaction; A second modeling module for determining the variable admittance control model of the robot according to the stability observation model; A control module for adjusting the admittance parameters of the robot according to the variable admittance control model to eliminate instability in human-robot interaction, The stability observation model of the human-robot interaction is: Among them, I std represents the numerical value of the force amplitude change, η represents the smoothing coefficient, I std represents the ratio of the windowed standard deviation of the time-domain signal F to the maximum allowable force F max of the ratio, The variable admittance control model is: Wherein, m0 and d0 represent the initial values of the inertia m and the damping d, ε represents the stability threshold, and α represents the weight coefficient.

7. A robotic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it realizes the method for eliminating instability in human-robot interaction according to any one of claims 1-5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it realizes the method for eliminating instability in human-robot interaction according to any one of claims 1-5.

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