A robot time-domain vibration detection method and device for physical human-machine interaction

Abstract

This invention proposes a robot time-domain vibration detection method and device for physical human-machine interaction, relating to the field of robot-physical human-machine interaction technology. Addressing the challenges of existing technologies, this invention addresses the shortcomings of existing technologies in terms of accuracy, robustness, real-time performance, and user-friendliness by acquiring interaction force signal data blocks, identifying and registering extreme points within the data blocks, performing validity checks on these extreme points, and calculating vibration indices and smoothed vibration indices. This method effectively quantifies the degree of instability in physical human-machine interaction systems, providing reliable quantitative indicators for vibration suppression strategies and ensuring the system's stable and safe completion of collaborative tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of robot physical human-machine interaction, and in particular to a robot time-domain vibration detection method and device oriented to physical human-machine interaction. Background Art

[0002] In the field of physical human-robot interaction, humans and robots share a workspace and maintain physical contact, forming a complex coupled dynamic system. This system is widely used in tasks such as human-robot collaborative operations and robot-assisted medical care. It aims to combine human perception and decision-making capabilities with the high-load, high-precision capabilities of robots to achieve complementary advantages and improve operational performance. However, due to the rigid interaction between humans and robots, the system often suffers from vibration and instability, leading to interruption or failure of collaborative tasks and even posing a threat to the safety of both humans and robots.

[0003] Existing physical human-computer interaction instability detection technologies are mainly divided into the following categories:

[0004] First, methods based on additional equipment use devices such as electromyography (EMG) and force / torque sensors to measure or estimate human arm stiffness in real time as a marker for instability detection. However, these methods are significantly affected by signal noise, and the use of additional equipment reduces user-friendliness.

[0005] Second, energy analysis-based methods indirectly indicate system instability through energy changes. However, energy analysis methods cannot detect instability in a timely manner and cannot accurately reflect the vibration intensity of the system in some cases.

[0006] Third, methods based on time-domain signal analysis calculate the vibration index by performing online oscillation waveform recognition on the interaction force signal or detecting extreme points on the robot's end-point velocity. However, existing methods are sensitive to signal noise, and the vibration index calculation is sensitive to the number of extreme points within the time window, which can easily lead to false positives and sudden changes in the vibration index.

[0007] Fourth, frequency-domain signal analysis methods use discrete Fourier transforms (DFTs) or fast Fourier transforms (FFTs) to perform frequency analysis on the robot's end-point position or interaction force signals, calculating the proportion of unstable frequency components to provide an indication of instability. However, frequency-domain analysis methods suffer from a trade-off between sampling time and frequency resolution, making it difficult to effectively distinguish between intentional low-frequency motion and unintentional high-frequency oscillations.

[0008] Fifth, a heuristic approach based on the admittance interaction model calculates deviations from the nominal behavior of the admittance-controlled robot based on the current state and provides an instability detection flag. However, this approach relies heavily on the admittance parameters and the robot's velocity and acceleration boundaries, making the detection threshold difficult to adjust and changing over time.

[0009] In summary, existing technologies have problems with accuracy, robustness, real-timeness and user-friendliness, and are unable to meet the vibration detection needs of complex physical human-computer interaction systems. Summary of the Invention

[0010] In view of the above problems in the prior art, the first aspect of the present invention proposes a robot time-domain vibration detection method for physical human-machine interaction, which specifically includes the following steps:

[0011] Step S1: Obtain the interactive force signal data block, and encapsulate the interactive force signal to be detected online through a preset fixed time window to form a data block for subsequent processing. The width of the fixed time window is set according to the signal noise characteristics, specifically including: in each sampling period, the latest detected interactive force signal point is encapsulated in the fixed time window. Fill one by one to the rightmost side of a fixed time window with a constant width of N sampling periods, and delete the detected signal points on the leftmost side of the fixed time window , and finally get the data block within the fixed time window , where t k Indicates the time point of the kth sampling period, y k represents the interaction force signal value of the kth sampling period, N is the preset number of sampling periods, T s is the sampling period, T N is the width of the fixed time window, ;

[0012] Step S2: Identify and register extreme points in the data block, which include maximum points and minimum points. Specifically, in each sampling period, search for the data block The maximum value in and minimum value , determine the center point of the fixed time window Is it a maximum point or a minimum point? If so, register it and save it to the preset extreme point container. beginning;

[0013] Step S3: performing validity check on the extreme value points registered and saved in step S2, eliminating invalid extreme value points, and obtaining valid extreme value points;

[0014] Step S4: Based on the variable time window, the vibration index is calculated using the effective extreme value points, and the width of the variable time window is adjusted in real time according to the distribution of the effective extreme value points;

[0015] Step S5: Smoothing the vibration index by using a second-order low-pass filter to filter the vibration index to obtain a smoothed vibration index.

[0016] In conjunction with the first aspect, in some implementations of the first aspect, in step S1, the width of the fixed time window is set according to signal noise characteristics, specifically including:

[0017] If the intensity of the signal noise exceeds the preset noise threshold, the width of the fixed time window is increased to reduce the impact of noise on extreme point detection;

[0018] If the intensity of the signal noise does not exceed the preset noise threshold, the width of the fixed time window is reduced to improve the detection sensitivity of high-frequency vibration.

[0019] In conjunction with the first aspect, in some implementations of the first aspect, in step S2, identifying and registering extreme points in the data block includes:

[0020] Search for the maximum and minimum values in the data block, which are taken as the maximum and minimum values of the data block respectively;

[0021] Determine the center point of a fixed time window Is it a maximum point or a minimum point? If so, register it and save it to the preset extreme point container. At the beginning, if the center point is neither a maximum point nor a minimum point, no operation is performed. The calculation formula is , c is the center point index of the fixed time window.

[0022] In combination with the first aspect, in some implementations of the first aspect, if a new extreme point is registered and saved to a preset extreme point container in step S2 At the beginning, the new extreme point is recorded as the preset extreme point container In step S3, the validity of the extreme point registered and saved in step S2 is checked, including:

[0023] If the new extreme point is the only extreme point in the extreme point container, the new extreme point is determined to be a valid extreme point;

[0024] If the extreme point container contains more than one extreme point, the new extreme point is compared with the extreme point container. The validity of the new extreme point is judged by whether the absolute value of the difference between the second extreme point in the extreme point container exceeds the preset detection threshold, where the detection threshold is a preset multiple of the average absolute deviation of the interaction force signal values of all valid extreme points in the extreme point container, and the value range of the preset multiple is greater than or equal to 1.2 and less than or equal to 2.5.

[0025] In conjunction with the first aspect, in some implementations of the first aspect, if the extreme point container contains more than one extreme point, the validity of the new extreme point is determined by comparing the absolute value of the difference between the new extreme point and the second extreme point in the extreme point container to see whether it exceeds a preset detection threshold, including:

[0026] When the absolute value of the difference between the new extreme point and the second extreme point in the extreme point container exceeds the detection threshold, the new extreme point is marked as a valid extreme point;

[0027] When the absolute value of the difference does not exceed the detection threshold, the new extreme point is removed from the extreme point container.

[0028] In combination with the first aspect, in some implementations of the first aspect, in step S4, the width of the variable time window is adjusted in real time according to the distribution of valid extreme value points, including:

[0029] If the distribution span of the effective extreme value points involved in the vibration index calculation increases, the width of the variable time window increases to avoid sudden changes in the vibration index;

[0030] If the distribution span of the effective extreme value points involved in the vibration index calculation is reduced, the width of the variable time window is reduced to improve the real-time performance of vibration detection.

[0031] In conjunction with the first aspect, in some implementations of the first aspect, in step S4, calculating the vibration index using the effective extreme point includes:

[0032] In each sampling period, the index variable is determined according to the number of valid extreme points. The calculation formula of the index variable m is: ,in, It is an extreme point container The number of valid extreme value points included in , Count is the maximum number of valid extreme value points that are preset to participate in the vibration index calculation;

[0033] Based on the time interval between the index variable and the effective extreme point, the vibration index of the current sampling period is calculated. The vibration index V k The calculation formula is: , where Δt is the preset maximum time interval threshold between effective extreme points, is the signal value of the i-th effective extreme point in the extreme point container, is the signal value of the i+1th effective extreme point in the extreme point container, is the time point of the i-th effective extreme point in the extreme point container, is the time point of the i+1th valid extreme point in the extreme point container, The center point of the fixed time window time point, The time point of the first valid extreme point in the extreme point container.

[0034] In combination with the first aspect, in some implementations of the first aspect, in step S5, filtering the vibration index using a second-order low-pass filter includes:

[0035] In each sampling period, the vibration index is input into a second-order low-pass filter to obtain a smoothed vibration index. The transfer function G(s) of the second-order low-pass filter is: ,in, s is the complex frequency of the Laplace transform.

[0036] In a second aspect, the present invention provides a robot time-domain vibration detection device for physical human-machine interaction, wherein the detection device adopts the detection method provided in any of the above embodiments, including:

[0037] The signal acquisition unit includes a six-dimensional force sensor deployed on the robot's end effector, which is used to collect interactive force signals in real time;

[0038] A data cache unit, consisting of a dual-port memory, is used to store the interaction force signal and form a data block with a fixed time window;

[0039] An extreme point processing unit, comprising a digital signal processor, is configured to perform the following operations: identifying maximum and minimum points in a data block, storing the extreme point corresponding to the center point of a fixed time window into an extreme point container, and performing validity verification on the extreme point;

[0040] a vibration index calculation unit, consisting of a variable time window controller and an arithmetic logic unit, for calculating the vibration index based on the effective extreme value points;

[0041] The filter output unit includes a second-order low-pass filter for smoothing and outputting the vibration index.

[0042] In a third aspect, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the detection method provided in any of the above embodiments.

[0043] In a fourth aspect, the present invention provides an electronic device, the electronic device comprising:

[0044] processor;

[0045] a memory for storing processor-executable instructions;

[0046] The processor is used to execute the detection method provided in any of the above embodiments.

[0047] Compared with the existing technology, the present invention introduces a combination of constant-width time windows and variable-width time windows to detect the extreme points of the interaction force signal in real time and calculate the vibration index, which has the following significant technical effects:

[0048] First, the accuracy of vibration detection is improved: Online detection of the extreme points of the interaction force signal using a constant-width time window effectively captures the signal's local characteristics and avoids misjudgments caused by signal noise. Before calculating the vibration index, the extreme points are validated and invalid ones are eliminated, further improving the accuracy of the vibration index. A variable-width time window adjusts the vibration index calculation range in real time to avoid sudden changes and distortion in the vibration index caused by fluctuations in the number of extreme points.

[0049] Second, robustness to signal noise is enhanced: the width of the constant-width time window can be dynamically adjusted based on the signal noise characteristics, ensuring effective detection of extreme points in different noise environments. Invalid extreme points are eliminated through validity testing, reducing noise interference with vibration index calculations.

[0050] Third, real-time performance is improved: By encapsulating interactive force signals online, data blocks are generated in real time for extreme point detection and vibration index calculation, ensuring the system can quickly respond to vibration changes. The variable-width time window design allows the vibration index calculation to be adjusted in real time based on the distribution of extreme points, avoiding the response delays caused by fixed time windows.

[0051] Fourth, the smoothness and practicality of the vibration index have been enhanced: a second-order low-pass filter is used to smooth the vibration index, eliminating high-frequency fluctuations and making it more continuous and smooth. The smoothed vibration index can more intuitively reflect the degree of system instability, providing reliable quantitative guidance for vibration suppression strategies.

[0052] Fifth, user-friendliness is improved: Vibration detection directly utilizes interactive force signals without the need for additional equipment, simplifying the system structure and reducing costs. By monitoring and evaluating system instability in real time, vibration suppression measures can be implemented promptly, ensuring the stability and safety of the physical human-machine interaction system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1FIG2 is a flow chart of a method for detecting robot time-domain vibration for physical human-machine interaction according to an embodiment of the present invention.

[0055] Figure 2 FIG2 is a schematic structural diagram of a robot time-domain vibration detection device for physical human-machine interaction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0057] The specific embodiments of the present invention are described below.

[0058] In response to the above-mentioned problems in the existing technology, the present invention proposes a robot time-domain vibration detection method for physical human-computer interaction, which solves the shortcomings of the existing technology in terms of accuracy, robustness, real-timeness and user-friendliness. It can effectively quantify the instability of the physical human-computer interaction system, provide reliable quantitative indications for vibration suppression strategies, and ensure that the system can complete collaborative tasks stably and safely.

[0059] Example 1

[0060] like Figure 1 As shown, the present invention proposes a robot time domain vibration detection method for physical human-machine interaction, which specifically includes the following steps:

[0061] Step S1: Obtain an interactive force signal data block, and encapsulate the interactive force signal to be detected online through a preset fixed time window to form a data block for subsequent processing. The width of the fixed time window is set according to the signal noise characteristics.

[0062] Specifically include: in each sampling period, the latest detected interaction force signal point Fill one by one to the rightmost side of a fixed time window with a constant width of N sampling periods, and delete the detected signal points on the leftmost side of the fixed time window , and finally obtain the data block within a fixed time window, where t k Indicates the time point of the kth sampling period, y k represents the interaction force signal value of the kth sampling period, N is the preset number of sampling periods, T s is the sampling period, T N is the width of the fixed time window, .

[0063] The core of this design concept is to ensure that the data block always contains the latest interaction force signal through dynamic updates of fixed time windows, providing real-time data support for subsequent extreme point detection.

[0064] Step S2: Identify and register extreme value points in the data block, which include maximum value points and minimum value points.

[0065] Specifically include: searching for data blocks in each sampling period The maximum value in and minimum value , determine the center point of the fixed time window Is it a maximum point or a minimum point? If so, register it and save it to the preset extreme point container. beginning.

[0066] The design concept of this step is to capture local features in the interaction force signal through extreme point detection, providing basic data for subsequent vibration index calculation. The identification of extreme points depends on the width of a fixed time window. A window width that is too small may introduce noise interference, while a window width that is too large may miss high-frequency vibration information. Therefore, a reasonable window width setting is key to ensuring the accuracy of extreme point detection.

[0067] Step S3: Perform validity check on the extreme value points registered and saved in step S2, eliminate invalid extreme value points, and obtain valid extreme value points.

[0068] The design concept of this step is to further screen out reliable extreme points through validity testing, avoid misjudgment caused by noise or signal fluctuations, and thus improve the accuracy of the vibration index.

[0069] Step S4: Based on the variable time window, the vibration index is calculated using the effective extreme value points, and the width of the variable time window is adjusted in real time according to the distribution of the effective extreme value points.

[0070] The design concept of this step is to avoid the sudden change and distortion of the vibration index caused by the fixed time window through the dynamic adjustment of the variable time window, thereby enhancing the robustness and real-time performance of the vibration index.

[0071] Step S5: Smoothing the vibration index by using a second-order low-pass filter to filter the vibration index to obtain a smoothed vibration index.

[0072] The design concept of this step is to eliminate high-frequency fluctuations in the vibration index through filtering, making it more continuous and smooth, thereby providing reliable quantitative indications for vibration suppression strategies.

[0073] The present invention solves the shortcomings of existing technologies in terms of accuracy, robustness, real-timeness and user-friendliness. It can effectively quantify the instability of physical human-computer interaction systems, provide reliable quantitative indications for vibration suppression strategies, and ensure that the system can complete collaborative tasks stably and safely.

[0074] In conjunction with the first aspect, in some implementations of the first aspect, in step S1, the width of the fixed time window is set according to signal noise characteristics, specifically including:

[0075] If the intensity of the signal noise exceeds the preset noise threshold, the width of the fixed time window is increased to reduce the impact of noise on extreme point detection;

[0076] If the intensity of the signal noise does not exceed the preset noise threshold, the width of the fixed time window is reduced to improve the detection sensitivity of high-frequency vibration.

[0077] This embodiment of the present invention further defines a method for setting the width of a fixed time window. The width of the fixed time window is dynamically adjusted based on the signal-to-noise characteristics to ensure robustness to signal noise. By dynamically adjusting the width of the fixed time window, the system can effectively detect extreme points in various noise environments, thereby improving the accuracy and robustness of vibration detection.

[0078] In conjunction with the first aspect, in some implementations of the first aspect, in step S2, identifying and registering extreme points in the data block includes:

[0079] Search for the maximum and minimum values in the data block, which are taken as the maximum and minimum values of the data block respectively;

[0080] Determine the center point of a fixed time window Is it a maximum point or a minimum point? If so, register it and save it to the preset extreme point container. At the beginning, if the center point is neither a maximum point nor a minimum point, no operation is performed. The calculation formula is , c is the center point index of the fixed time window.

[0081] The embodiments of the present invention detect extreme points to capture local features in the interaction force signal, providing basic data for subsequent vibration index calculation. The identification of extreme points relies on the width of a fixed time window. A window width that is too small may introduce noise interference, while a window width that is too large may miss high-frequency vibration information. Therefore, properly setting the window width is key to ensuring accurate extreme point detection.

[0082] In conjunction with the first aspect, in some implementations of the first aspect, if a new extreme value point is registered and saved at the beginning of a preset extreme value point container in step S2, the new extreme value point is recorded as the first extreme value point in the preset extreme value point container. In step S3, a validity check is performed on the extreme value point registered and saved in step S2, including:

[0083] If the new extreme point is the only extreme point in the extreme point container, the new extreme point is determined to be a valid extreme point;

[0084] If the extreme point container contains more than one extreme point, the new extreme point is compared with the extreme point container. The validity of the new extreme point is judged by whether the absolute value of the difference between the second extreme point in the extreme point container exceeds the preset detection threshold, where the detection threshold is a preset multiple of the average absolute deviation of the interaction force signal values of all valid extreme points in the extreme point container, and the value range of the preset multiple is greater than or equal to 1.2 and less than or equal to 2.5.

[0085] The embodiment of the present invention further screens out reliable extreme points through validity testing, avoids misjudgment caused by noise or signal fluctuation, and thus improves the accuracy of the vibration index.

[0086] In conjunction with the first aspect, in some implementations of the first aspect, if the extreme point container contains more than one extreme point, the validity of the new extreme point is determined by comparing the absolute value of the difference between the new extreme point and the second extreme point in the extreme point container to see whether it exceeds a preset detection threshold, including:

[0087] When the absolute value of the difference between the new extreme point and the second extreme point in the extreme point container exceeds the detection threshold, the new extreme point is marked as a valid extreme point;

[0088] When the absolute value of the difference does not exceed the detection threshold, the new extreme point is removed from the extreme point container.

[0089] This embodiment of the present invention further defines a validation process for new extreme points. In practical applications, the validity of a new extreme point is determined by comparing the absolute value of the difference between the new extreme point and the second extreme point in the extreme point container to see if it exceeds a detection threshold. This process ensures the selection of reliable extreme points, providing reliable data support for subsequent vibration index calculations.

[0090] In combination with the first aspect, in some implementations of the first aspect, in step S4, the width of the variable time window is adjusted in real time according to the distribution of valid extreme value points, including:

[0091] If the distribution span of the effective extreme value points involved in the vibration index calculation increases, the width of the variable time window increases to avoid sudden changes in the vibration index;

[0092] If the distribution span of the effective extreme value points involved in the vibration index calculation is reduced, the width of the variable time window is reduced to improve the real-time performance of vibration detection.

[0093] This embodiment of the present invention dynamically adjusts the variable time window to ensure that the vibration index calculation can adapt to changes in vibration frequency and amplitude, thereby avoiding the sudden changes and distortion of the vibration index caused by a fixed time window. The width adjustment process of the variable time window depends on the distribution of effective extreme points, ensuring that the system can respond to vibration changes in real time.

[0094] In conjunction with the first aspect, in some implementations of the first aspect, in step S4, calculating the vibration index using the effective extreme point includes:

[0095] In each sampling period, the index variable is determined according to the number of valid extreme points. The calculation formula of the index variable m is: ,in, It is an extreme point container The number of valid extreme value points included in , Count is the maximum number of valid extreme value points that are preset to participate in the vibration index calculation;

[0096] Based on the time interval between the index variable and the effective extreme point, the vibration index of the current sampling period is calculated. The vibration index V k The calculation formula is: , where Δt is the preset maximum time interval threshold between effective extreme points, is the signal value of the i-th effective extreme point in the extreme point container, is the signal value of the i+1th effective extreme point in the extreme point container, is the time point of the i-th effective extreme point in the extreme point container, is the time point of the i+1th valid extreme point in the extreme point container, The center point of the fixed time window time point, The time point of the first valid extreme point in the extreme point container.

[0097] This embodiment of the present invention dynamically adjusts the variable time window to ensure that the vibration index calculation can adapt to changes in vibration frequency and amplitude, thereby avoiding the sudden changes and distortion of the vibration index caused by a fixed time window. The vibration index calculation process relies on the number of valid extreme points and the time interval, ensuring that the system can respond to vibration changes in real time, thereby improving the accuracy and robustness of the vibration index.

[0098] In combination with the first aspect, in some implementations of the first aspect, in step S5, filtering the vibration index using a second-order low-pass filter includes:

[0099] In each sampling period, the vibration index is input into a second-order low-pass filter to obtain a smoothed vibration index. The transfer function G(s) of the second-order low-pass filter is: ,in, s is the complex frequency of the Laplace transform.

[0100] The present invention eliminates high-frequency fluctuations in the vibration index through filtering, making it more continuous and smooth, thereby providing reliable quantitative indicators for vibration suppression strategies. The parameter settings of the second-order low-pass filter directly affect the smoothness and practicality of the vibration index, ensuring that the system can respond to vibration changes in real time.

[0101] In practical applications, the system filters the vibration index by setting the parameters of a second-order low-pass filter. The filtered vibration index is more continuous and smooth, more intuitively reflecting the degree of system instability. This process ensures that the system can respond to vibration changes in real time, thereby improving the accuracy and robustness of the vibration index.

[0102] Example 2

[0103] like Figure 2 As shown, in a second aspect, the present invention provides a robot time-domain vibration detection device for physical human-machine interaction, the detection device adopts the detection method provided in any of the above embodiments, including:

[0104] The signal acquisition unit 10 includes a six-dimensional force sensor deployed on the end effector of the robot, which is used to collect interactive force signals in real time;

[0105] The data cache unit 20 is composed of a dual-port memory and is used to store the interaction force signal and form a data block with a fixed time window;

[0106] The extreme point processing unit 30 includes a digital signal processor and is configured to perform the following operations: identifying maximum and minimum points in the data block, storing the extreme point corresponding to the center point of the fixed time window into an extreme point container, and performing validity verification on the extreme point;

[0107] a vibration index calculation unit 40 , composed of a variable time window controller and an arithmetic logic unit, for calculating the vibration index based on the effective extreme value points;

[0108] The filtering output unit 50 includes a second-order low-pass filter, which is used to smooth the vibration index and output it.

[0109] The present invention solves the shortcomings of existing technologies in terms of accuracy, robustness, real-timeness and user-friendliness. It can effectively quantify the instability of physical human-computer interaction systems, provide reliable quantitative indications for vibration suppression strategies, and ensure that the system can complete collaborative tasks stably and safely.

[0110] The present invention further provides an electronic device, comprising:

[0111] processor;

[0112] a memory for storing processor-executable instructions;

[0113] The processor is used to execute the detection method provided in any of the above embodiments.

[0114] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is used to execute the detection method provided in any of the above embodiments.

[0115] Computer-readable storage media can be any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A robot time-domain vibration detection method for physical human-machine interaction, characterized in that: include: Step S1: Obtain the interactive force signal data block, and encapsulate the interactive force signal to be detected online through a preset fixed time window to form a data block for subsequent processing. The width of the fixed time window is set according to the signal noise characteristics, specifically including: in each sampling period, the latest detected interactive force signal point is encapsulated in the fixed time window. Fill one by one to the rightmost side of a fixed time window with a constant width of N sampling periods, and delete the detected signal points on the leftmost side of the fixed time window , and finally get the data block within the fixed time window , where t k Indicates the time point of the kth sampling period, y k represents the interaction force signal value of the kth sampling period, N is the preset number of sampling periods, T s is the sampling period, T N is the width of the fixed time window, ; Step S2: Identify and register the extreme value points in the data block, which include maximum value points and minimum value points. Specifically, in each sampling period, search for the data block The maximum value in and minimum value , determine the center point of the fixed time window Is it the maximum point or the minimum point? If so, register it and save it to the preset extreme point container. beginning; Step S3: performing validity check on the extreme value points registered and saved in step S2, eliminating invalid extreme value points, and obtaining valid extreme value points; Step S4: Calculating the vibration index using the effective extreme value points based on a variable time window, wherein the width of the variable time window is adjusted in real time according to the distribution of the effective extreme value points; Step S5: performing smoothing processing on the vibration index by filtering the vibration index with a second-order low-pass filter to obtain a smoothed vibration index.

2. The detection method according to claim 1, wherein In step S1, the width of the fixed time window is set according to the signal noise characteristics, specifically including: If the intensity of the signal noise exceeds a preset noise threshold, increasing the width of the fixed time window; If the intensity of the signal noise does not exceed a preset noise threshold, the width of the fixed time window is reduced.

3. The detection method according to claim 1, wherein In step S2, identifying and registering extreme points in the data block includes: Searching for the maximum value and the minimum value in the data block, and taking them as the maximum value and the minimum value of the data block respectively; Determine the center point of the fixed time window Is it the maximum point or the minimum point? If so, register it and save it to the preset extreme point container. At the beginning, if the center point is neither a maximum point nor a minimum point, no operation is performed, wherein the center point The calculation formula is , c is the center point index of the fixed time window.

4. The detection method according to claim 1, wherein If a new extreme point is registered and saved to the beginning of the preset extreme point container in step S2, the new extreme point is recorded as the first extreme point in the preset extreme point container. In step S3, the validity check of the extreme point registered and saved in step S2 is performed, including: If the new extreme value point is the only extreme value point in the extreme value point container, determining the new extreme value point as the valid extreme value point; If the extreme point container contains more than one extreme point, the validity of the new extreme point is judged by comparing whether the absolute value of the difference between the new extreme point and the second extreme point in the extreme point container exceeds a preset detection threshold, wherein the detection threshold is a preset multiple of the average absolute deviation of the interaction force signal values of all valid extreme points in the extreme point container, and the value range of the preset multiple is greater than or equal to 1.2 and less than or equal to 2.

5.

5. The detection method according to claim 4, characterized in that If the extreme point container contains more than one extreme point, the validity of the new extreme point is determined by comparing the absolute value of the difference between the new extreme point and the second extreme point in the extreme point container to see whether it exceeds a preset detection threshold, including: When the absolute value of the difference between the new extreme point and the second extreme point in the extreme point container exceeds the detection threshold, marking the new extreme point as a valid extreme point; When the absolute value of the difference does not exceed the detection threshold, the new extreme point is removed from the extreme point container.

6. The detection method according to claim 1, characterized in that In step S4, the width of the variable time window is adjusted in real time according to the distribution of the effective extreme value points, including: If the distribution span of the effective extreme value points participating in the vibration index calculation increases, the width of the variable time window increases; If the distribution span of the effective extreme value points involved in the calculation of the vibration index is reduced, the width of the variable time window is reduced.

7. The detection method according to claim 1, characterized in that In the step S4, the vibration index is calculated using the effective extreme point, including: In each sampling period, an index variable is determined according to the number of valid extreme value points. The calculation formula of the index variable m is: , where size is the extreme point container The number of the effective extreme value points included in , Count is the preset maximum number of the effective extreme value points participating in the calculation of the vibration index; Based on the time interval between the index variable and the effective extreme point, the vibration index of the current sampling period is calculated. The vibration index V k The calculation formula is: , where Δt is the preset maximum time interval threshold between the effective extreme points, is the signal value of the i-th effective extreme point in the extreme point container, is the signal value of the i+1th effective extreme point in the extreme point container, is the time point of the i-th valid extreme point in the extreme point container, is the time point of the i+1th valid extreme point in the extreme point container, The center point of the fixed time window time point, The time point of the first valid extreme point in the extreme point container.

8. The detection method according to claim 1, wherein In step S5, filtering the vibration index using a second-order low-pass filter includes: In each sampling period, the vibration index is input into the second-order low-pass filter to obtain the smoothed vibration index. The transfer function G(s) of the second-order low-pass filter is: ,in, s is the complex frequency of the Laplace transform.

9. A robot time-domain vibration detection device for physical human-machine interaction, characterized in that: The detection device adopts the detection method according to any one of claims 1 to 8, comprising: The signal acquisition unit includes a six-dimensional force sensor deployed on the robot's end effector, which is used to collect interactive force signals in real time; a data cache unit, composed of a dual-port memory, for storing the interaction force signal and forming a data block of a fixed time window; an extreme point processing unit, comprising a digital signal processor, configured to perform the following operations: identifying maximum and minimum points in the data block, storing the extreme point corresponding to the center point of the fixed time window into an extreme point container, and performing validity verification on the extreme point; a vibration index calculation unit, consisting of a variable time window controller and an arithmetic logic unit, for calculating the vibration index based on the effective extreme value points; The filtering output unit includes a second-order low-pass filter, which is used to smooth the vibration index and output it.

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