Method, device and system for eliminating jitter at tail end of mechanical arm and storage medium

Abstract

The invention provides a method, device and system for eliminating jitter at the tail end of a mechanical arm and a storage medium, and relates to the technical field of robot control, and the method for eliminating jitter at the tail end of the mechanical arm comprises the steps that historical motion data of the tail end of the mechanical arm to be subjected to jitter elimination is obtained and converted, and a sampling spectrogram is obtained; judging the sampling spectrogram according to a preset threshold value to obtain band-pass data; filtering the real-time motion data of the tail end of the mechanical arm to be subjected to shake elimination according to the band-pass data so as to eliminate shake of the tail end of the mechanical arm to be subjected to shake elimination in the motion process; through the jitter eliminating process, the filtered data can more accurately reflect the real motion state of the tail end of the mechanical arm, and therefore the operation precision of the mechanical arm is improved. Particularly, under the condition of fine operation or high-load working, the accuracy is crucial.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a method, device, system and storage medium for eliminating jitter at the end of a robot arm. Background Art

[0002] After decades of development, laparoscopic surgical robots have gradually been used in clinical practice. Compared with traditional manual laparoscopic surgery, the use of surgical robots can reduce the pain of patients, reduce the labor intensity of doctors, and improve the success rate of surgery. However, although laparoscopic surgical robots have shown significant advantages in clinical applications, in actual operations, the jitter problem of the end of the robotic arm and the end of its instrument is still a difficult problem that needs to be solved. At present, many existing technologies mainly rely on simple filtering or subtracting jitter errors to deal with this problem, but these methods have obvious defects.

[0003] First, the limitations of simple filtering methods. Many existing filtering technologies, such as low-pass filters, can suppress signals above a certain frequency, but they often cannot effectively distinguish between useful signals and noise. For example, when suppressing signals above 2Hz, the Butterworth low-pass filter may have a certain impact on useful low-frequency signals, resulting in signal distortion. In addition, when the resonant frequency of the end of the robot arm and the end of the instrument exceeds 2Hz, the suppression effect of these filters is significantly weakened, so that the filtered signal still contains higher frequency components, and thus cannot effectively eliminate jitter.

[0004] Secondly, there are deficiencies in the method of subtracting jitter errors. This method usually relies on the analysis of historical data, attempting to calculate the jitter error and subtract it from the real-time signal. However, due to the complexity and variability of jitter, especially during surgery, the characteristics of jitter may change as the operating conditions change, resulting in inaccurate error calculations. In addition, this method often cannot adapt to new jitter patterns in real time and may fail in dynamic environments, thus failing to meet the high-precision requirements of surgery. Summary of the invention

[0005] The problem solved by the present invention is how to eliminate the shaking of the end of the robot arm to improve the stability of the end of the robot arm during movement, thereby improving its operating accuracy.

[0006] In order to solve the above problems, the present invention provides a method, device, system and storage medium for eliminating jitter at the end of a robotic arm.

[0007] In a first aspect, the present invention provides a method for eliminating jitter at the end of a robotic arm, comprising:

[0008] Obtain historical motion data of the end of the robot arm to be debounced, and convert the historical motion data to obtain a sampling frequency spectrum;

[0009] Judging the sampled spectrum diagram according to a preset threshold value to obtain bandpass data;

[0010] The real-time motion data of the end of the mechanical arm to be de-jittered is filtered according to the passband data to eliminate the jitter of the end of the mechanical arm to be de-jittered during the movement.

[0011] Optionally, the preset threshold includes a first preset threshold and a second preset threshold, the bandpass data includes first temporary bandpass data and second bandpass data, and judging the sampled spectrum diagram according to the preset threshold to obtain the bandpass data includes:

[0012] Judging the sampled frequency spectrum according to the first preset threshold value to obtain the first bandpass data;

[0013] Filtering the sampled spectrum diagram according to the first bandpass data to obtain temporary sampled data;

[0014] The temporary sampling data is judged according to the second preset threshold to obtain the second bandpass data.

[0015] Optionally, filtering the real-time motion data of the end of the manipulator arm to be debounced according to the bandpass data includes:

[0016] Determining a corresponding Butterworth second-order low-pass filter according to the first band-pass data, and filtering the real-time motion data based on the Butterworth second-order low-pass filter to obtain temporary data;

[0017] Determine the corresponding notch filter according to the second bandpass data, and perform notch filtering on the temporary data based on the notch filter to obtain the processed final signal data, and the final signal data is the data obtained after eliminating the jitter of the end of the robotic arm to be de-jittered during the movement.

[0018] Optionally, the sampled spectrum graph includes a time domain spectrum and a frequency domain spectrum; the converting the historical motion data to obtain the sampled spectrum graph includes:

[0019] Based on the Fourier algorithm, the historical motion data is transformed to obtain time domain data and frequency domain data;

[0020] The corresponding time domain spectrum is constructed according to the time domain data, and the corresponding frequency domain spectrum is constructed according to the frequency domain data.

[0021] Optionally, the method for eliminating jitter at the end of the robotic arm further includes:

[0022] During the operation of the robot arm to be de-shaked, judging whether the real-time motion data exceeds a preset range based on machine learning or a preset rule engine;

[0023] If it exceeds the preset range, a feedback signal is generated, and a control strategy is triggered according to the feedback signal to adjust the robot arm to be de-shaked.

[0024] Optionally, the feedback signal includes an abnormality type and a corresponding deviation amount. If the abnormality exceeds the preset range, a feedback signal is generated, and a control strategy is triggered according to the feedback signal to adjust the robot arm to be debounced, including:

[0025] Triggering a corresponding preset control strategy according to the abnormality type and the corresponding deviation amount to automatically adjust the robot arm to be debounced;

[0026] The preset control strategy includes speed adjustment, path correction, gain adjustment and mode switching.

[0027] Optionally, the method for eliminating jitter at the end of the robotic arm further includes:

[0028] The final signal data is evaluated to obtain an evaluation result, and the final signal data is stored.

[0029] In a second aspect, the present invention provides a device for eliminating jitter at the end of a robotic arm, comprising:

[0030] An acquisition unit, used for acquiring historical motion data of the end of the robot arm to be debounced, and converting the historical motion data to obtain a sampling frequency spectrum diagram;

[0031] A processing unit, used to judge the sampled spectrum diagram according to a preset threshold value to obtain bandpass data;

[0032] The processing unit is also used to filter the real-time motion data of the end of the robotic arm to be de-jittered according to the bandpass data, so as to eliminate the jitter of the end of the robotic arm to be de-jittered during the movement.

[0033] In a third aspect, the present invention provides a system for eliminating jitter at the end of a robotic arm, comprising a memory and a processor;

[0034] The memory is used to store computer programs;

[0035] The processor is used to implement the method for eliminating jitter at the end of a robotic arm as described in the first aspect when executing the computer program.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for eliminating jitter at the end of a robotic arm as described in the first aspect is implemented.

[0037] The method, device, system and storage medium for eliminating jitter at the end of a mechanical arm of the present invention have the following beneficial effects: for the end of the mechanical arm to be eliminated, the historical motion data of the end can be collected through the sensor system. These data include key parameters such as position, speed, acceleration, etc., and the data accumulated in a certain period of time can usually reflect the motion characteristics of the end of the mechanical arm. The collected historical motion data is converted to generate a corresponding sampling spectrum. The spectrum is a conversion of the original signal into the frequency domain, which can clearly display the amplitude information of different frequency components. This process usually involves Fourier transform or other signal processing techniques to convert the time domain signal into a frequency domain signal for subsequent analysis. The spectrum is judged according to a preset threshold, and the components (such as low-frequency signals) within the specified frequency range are screened out, that is, bandpass data. The preset threshold is designed according to the specific performance and working environment of the end of the mechanical arm to ensure that only necessary motion information is retained during operation, while eliminating high-frequency noise and unnecessary fluctuations. Finally, using the previously obtained bandpass data, a suitable bandpass filter can be designed and constructed. The design goal of the filter is to distinguish the actual motion signal from jitter (noise) according to the frequency characteristics. The bandpass filter allows the signal within the preset frequency range to pass through, and the high-frequency components and low-frequency interference are effectively suppressed. After filtering, the part of the real-time motion data affected by jitter is reduced, making the remaining signal more stable and smooth, and more accurately representing the actual motion state of the end of the robot arm. That is, the high-frequency noise and jitter components in the real-time data are removed, so as to obtain smooth and accurate motion data.

[0038] Therefore, the present invention uses the above-mentioned jitter elimination process to make the filtered data more accurately reflect the actual motion state of the end of the robot arm, thereby improving its operating accuracy. Accuracy is particularly important in delicate operations or high-load working conditions. After eliminating jitter, the stability of the end of the robot arm during movement is improved, avoiding wear on the mechanical structure due to unnecessary vibration, and increasing the service life of the equipment. The smooth movement enables the end of the robot arm to complete tasks faster and more effectively, reducing invalid repetitive actions, thereby improving overall work efficiency and productivity. At the same time, when the end of the robot arm runs more smoothly and accurately, possible collisions and misoperations are reduced, the probability of accidents is reduced, and safety is improved. In summary, this technical solution eliminates the jitter of the end of the robot arm during movement by analyzing historical motion data and filtering real-time data, bringing a series of positive effects with clear intentions, which not only improves the accuracy and stability of the operation, but also enhances the overall work efficiency and safety of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic flow chart of a method for eliminating jitter at the end of a robotic arm according to an embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of a time domain spectrum of an embodiment of the present invention;

[0041] Figure 3 is a schematic diagram of a frequency domain spectrum of an embodiment of the present invention;

[0042] Figure 4 Schematic diagram of the amplitude-frequency response of a Butterworth low-pass filter according to an embodiment of the present invention;

[0043] Figure 5 is a schematic diagram of a time domain result after low-pass filtering according to an embodiment of the present invention;

[0044] Figure 6 is a schematic diagram of a frequency domain result after low-pass filtering according to an embodiment of the present invention;

[0045] Figure 7 is a schematic diagram of a Bode diagram of a notch filter according to an embodiment of the present invention;

[0046] Figure 8 is a schematic diagram of a time domain result after combined filtering according to an embodiment of the present invention;

[0047] Fig. 9 is a schematic diagram of a frequency domain result after combined filtering according to an embodiment of the present invention;

[0048] Fig.10 A schematic diagram of the structure of a device for eliminating jitter at the end of a robotic arm according to an embodiment of the present invention;

[0049] Fig.11 The present invention is a schematic diagram of the structure of a system for eliminating jitter at the end of a robotic arm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0051] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0052] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0053] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0054] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0055] The present embodiment provides a method, device, system and storage medium for eliminating jitter at the end of a robotic arm.

[0056] like Figure 1 As shown, a method for eliminating jitter at the end of a robotic arm provided by an embodiment of the present invention includes:

[0057] S100, obtaining historical motion data of the end of the robot arm to be debounced, and converting the historical motion data to obtain a sampling frequency spectrum;

[0058] Specifically, during the operation, the motion state of the end of the robotic arm is monitored in real time through sensors (such as accelerometers, gyroscopes, etc.), and its position, speed, acceleration and other parameters are recorded. And a suitable sampling frequency (for example, 100Hz or higher) is set to ensure that high-frequency jitter signals in the movement of the robotic arm can be captured.

[0059] The collected historical motion data is denoised to eliminate the impact of environmental noise on the signal. Common methods include smoothing filtering, detrending analysis, etc. The data is normalized so that different signals are within the same order of magnitude. Fast Fourier Transform (FFT) can be used to convert time domain data into frequency domain data. This step can help us analyze the different frequency components in the motion and identify the jitter frequency. Using the output of FFT, a spectrum graph is drawn with the horizontal axis as the frequency and the vertical axis as the corresponding amplitude. The spectrum graph can intuitively display the intensity of different frequency components. Identify the main frequency components, such as high-frequency noise, resonant frequency, and the location of the dominant low-frequency signal, for further analysis. The sampling spectrum graph includes a time domain spectrum and a frequency domain spectrum. For example, the original signal (historical motion data) consists of sinusoidal signals of 1Hz, 3Hz, 5Hz, and 7Hz, with a sampling frequency of 1000Hz and 1000 sampling points. Figure 2 and Figure 3 As shown, Figure 2 is the time domain spectrum, Figure 3 It is a frequency domain spectrum.

[0060] Through the spectrum analysis of historical motion data, the frequency components that cause the robot arm to shake can be accurately identified, thus providing a basis for the subsequent jitter suppression strategy. The obtained spectrum diagram provides important information for the design and optimization of the control algorithm, such as setting the cutoff frequency and gain of the controller, which can effectively improve the performance of the filter and reduce jitter.

[0061] S200, judging the sampled spectrum diagram according to a preset threshold value to obtain bandpass data;

[0062] Specifically, in the generated spectrum graph, the frequency range representing the useful signal is first determined. Generally, the effective signal frequency during surgery may be concentrated in a specific low frequency range (eg, 0-2 Hz).

[0063] The preset thresholds may include: a lower frequency limit (such as 0.5 Hz) to identify the effective low frequency range of the signal; an upper frequency limit (such as 2 Hz) to establish the effective high frequency range of the signal; and the upper and lower frequency limits are used to define the "bandpass permitted area" or "bandpass filtering", i.e., bandpass data.

[0064] Or, you can observe the bandpass value where the energy is concentrated in the frequency domain spectrum. For example, if more than 90% of the energy of the sampled spectrum is concentrated within 2Hz, the cutoff frequency (bandpass) of the bottom filter is set to 2Hz. Traverse each frequency component in the spectrum and make a judgment against the preset upper and lower limits. Keep the amplitude data within the lower and upper limits, and set the data of other frequency components to zero or delete. The bandpass data can include the bandpass data of low-pass filtering and the bandpass data of defect filtering.

[0065] By judging the spectrum diagram and obtaining the bandpass data, not only can the effectiveness of the signal be improved, but it can also effectively help improve the safety and efficiency of the procedure.

[0066] S300, filtering the real-time motion data of the end of the robotic arm to be de-jittered according to the bandpass data, so as to eliminate the jitter of the end of the robotic arm to be de-jittered during the movement.

[0067] Specifically, real-time motion data is continuously collected on the sensor at the end of the robotic arm. The data may include parameters such as position, velocity, and acceleration to facilitate the detection of jitter during motion. Ensure that the collected data has a sufficient sampling rate to capture subtle jitters in high-speed motion. The collected real-time motion data can be processed using a bandpass filter. The bandpass filter will filter out frequency components below and above the previously set frequency range (for example, 0.5Hz–2Hz). Common filters include Butterworth filters, Chebyshev filters, etc., and the appropriate filter design can be selected according to actual needs.

[0068] Use the filtered data for practical applications. It can replace the original data or be used as new data for subsequent analysis. Feed the filtered signal back to the control system to adjust the robot arm movement in real time to correct the jitter.

[0069] Monitor the performance of the robot arm's motion in real time to see if there is still jitter in the filtered signal. If it is found that the jitter cannot be completely eliminated, the filter parameters can be optimized or the preset thresholds can be reset. The control strategy can be adjusted based on real-time feedback, such as refining the motion trajectory, increasing the stabilization time, or adjusting parameters such as speed and acceleration.

[0070] The above process can effectively eliminate most of the jitter of the end of the robot arm during movement through bandpass filtering, thereby improving movement stability. And by filtering out unnecessary high-frequency noise, the robot arm can more accurately execute the specified motion trajectory and reduce task errors. And after eliminating jitter, the operator can focus more on the control task without worrying about the unexpected consequences caused by jitter, thereby improving work efficiency. And by reducing unnecessary vibrations during movement, it can help reduce wear and damage to the robot arm and extend the service life of the equipment. And after filtering real-time data, the control strategy of the robot arm can be continuously optimized, making the control system more responsive and more adaptable.

[0071] In summary, filtering the real-time motion data of the robot arm to be de-jittered by bandpass data not only effectively eliminates the jitter, but also improves the overall operation accuracy and system stability, providing support for the further development of robot applications.

[0072] In this embodiment, for the end of the mechanical arm to be debounced, the historical motion data of the end can be collected through the sensor system. These data include key parameters such as position, speed, acceleration, etc., and the data accumulated within a certain period of time can usually reflect the motion characteristics of the end of the mechanical arm. The collected historical motion data is converted to generate a corresponding sampling spectrum. The spectrum is a conversion of the original signal into the frequency domain, which can clearly display the amplitude information of different frequency components. This process usually involves Fourier transform or other signal processing techniques to convert the time domain signal into a frequency domain signal for subsequent analysis. The spectrum is judged according to a preset threshold, and the components (such as low-frequency signals) within the specified frequency range are screened out, that is, bandpass data. The preset threshold is designed according to the specific performance and working environment of the end of the mechanical arm to ensure that only necessary motion information is retained during operation, while eliminating high-frequency noise and unnecessary fluctuations. Finally, using the previously obtained bandpass data, a suitable bandpass filter can be designed and constructed. The design goal of the filter is to distinguish the actual motion signal from the jitter (noise) based on the frequency characteristics. The bandpass filter allows the signal within the preset frequency range to be effectively suppressed by high-frequency components and low-frequency interference. After filtering, the part of the real-time motion data affected by jitter is reduced, making the remaining signal more stable and smooth, and more accurately representing the actual motion state of the end of the robot arm. That is, the high-frequency noise and jitter components in the real-time data are removed to obtain smooth and accurate motion data.

[0073] Therefore, this embodiment uses the above-mentioned jitter elimination process to make the filtered data more accurately reflect the actual motion state of the end of the robot arm, thereby improving its operating accuracy. Accuracy is particularly important in delicate operations or high-load working conditions. After eliminating jitter, the stability of the end of the robot arm during movement is improved, avoiding wear on the mechanical structure due to unnecessary vibration, and increasing the service life of the equipment. The smooth movement enables the end of the robot arm to complete tasks faster and more effectively, reducing invalid repetitive movements, thereby improving overall work efficiency and productivity. At the same time, when the end of the robot arm runs more smoothly and accurately, possible collisions and misoperations are reduced, the probability of accidents is reduced, and safety is improved. In summary, this technical solution eliminates the jitter of the end of the robot arm during movement by analyzing historical motion data and filtering real-time data, bringing a series of positive effects with clear intentions, which not only improves the accuracy and stability of the operation, but also enhances the overall work efficiency and safety of the equipment.

[0074] Optionally, the preset threshold includes a first preset threshold and a second preset threshold, the bandpass data includes first temporary bandpass data and second bandpass data, and judging the sampled spectrum diagram according to the preset threshold to obtain the bandpass data includes:

[0075] Judging the sampled frequency spectrum according to the first preset threshold value to obtain the first bandpass data;

[0076] Filtering the sampled spectrum diagram according to the first bandpass data to obtain temporary sampled data;

[0077] The temporary sampling data is judged according to the second preset threshold to obtain the second bandpass data.

[0078] Optionally, filtering the real-time motion data of the end of the manipulator arm to be debounced according to the bandpass data includes:

[0079] Determining a corresponding Butterworth second-order low-pass filter according to the first band-pass data, and filtering the real-time motion data based on the Butterworth second-order low-pass filter to obtain temporary data;

[0080] Determine the corresponding notch filter according to the second bandpass data, and perform notch filtering on the temporary data based on the notch filter to obtain the processed final signal data, and the final signal data is the data obtained after eliminating the jitter of the end of the robotic arm to be de-jittered during the movement.

[0081] Specifically, two different preset thresholds are defined: a first preset threshold and a second preset threshold. The two thresholds are used to distinguish the importance of different frequency components. The corresponding bandpass data is obtained by using different preset thresholds. For example, the first bandpass data has a cutoff frequency of 2 Hz and a bandwidth of 3 Hz, and the filtering process can be implemented by designing a corresponding Butterworth second-order low-pass filter, such as Figure 4-Figure 6 As shown, Figure 4 This is a schematic diagram of the amplitude-frequency response of the Butterworth low-pass filter. Usually, there is an obvious -3dB drop near the cutoff frequency of 2Hz, indicating that the filter effectively cuts off signal components above this frequency. Figure 5 It is a schematic diagram of the time domain result after low-pass filtering (temporary time domain data in temporary data). Figure 6 It is a schematic diagram of the frequency domain result after low-pass filtering (temporary time domain data in temporary data).

[0082] Through the above steps, we have successfully designed a second-order Butterworth low-pass filter to effectively filter the original signal. This process illustrates how to use modern signal processing tools to solve practical problems and ensure the clarity and reliability of the signal.

[0083] After the signal is low-pass filtered through the first bandpass data, there may be residual signals with larger amplitudes, such as Figure 6As shown, we noticed that at 3Hz, the amplitude-frequency response still has a large amplitude. This shows that the 3Hz frequency component has not been effectively reduced, possibly due to some periodic interference or noise. Therefore, it is necessary to set the second bandpass data to further clean up the remaining large amplitude signals, that is, to further reduce the influence of this frequency by setting the corresponding notch filter. A notch filter is a filter specially designed to suppress signals of a specific frequency. Its main feature is that it significantly attenuates the signal near a specific frequency, while having little effect on signals of other frequencies. For the 3Hz interference signal, designing a notch filter can effectively reduce its impact on the overall signal, such as Figure 7 As shown in the figure, the Bode diagram of the notch filter is a graphical tool used to represent the frequency response of a linear time-invariant system (LTI system), usually including an amplitude diagram and a phase diagram. For a notch filter, the Bode diagram can help us understand its behavior at different frequencies.

[0084] The above process combines the low-pass filter and the notch filter: The low-pass filter, i.e. the first bandpass data, initially eliminates high-frequency noise and smoothes the signal. The notch filter, i.e. the second bandpass data, further reduces the influence of specific frequencies (such as 3 Hz) to remove potential periodic interference.

[0085] After running the entire processing flow, a new graph should be generated to show the effect of the final signal, such as Figure 8-9 As shown, Figure 8 It is a schematic diagram of the time domain result after combined filtering (time domain data of the final signal data). Fig. 9 The schematic diagram of the frequency domain results after combined filtering (frequency domain data of the final signal data): Signals before and after filtering: It shows that the amplitude has dropped at 3Hz, and the signals of other frequencies are not significantly affected. The amplitude-frequency response of the notch filter: Confirm the obvious attenuation near 3Hz, verifying the effectiveness of the design. Through the notch filter, we can effectively suppress the 3Hz interference signal while retaining the integrity of the signal, which is a common practice in modern signal processing to ensure data quality and reliability.

[0086] After the above processing, the final signal data will be the data obtained after eliminating the jitter of the end of the robot arm to be de-jittered during the movement. This data can be used to control the transmission of the signal so that the robot arm can achieve a more stable movement.

[0087] Through two-stage filtering, both low-frequency and high-frequency noise in the signal are effectively eliminated, which significantly improves the quality of the final motion data. The processed signal data (final signal data) can provide more accurate input for the control algorithm, thereby achieving more delicate and sensitive control, making the robot arm more stable when performing tasks. After eliminating jitter, the operation path of the robot arm is more controllable, avoiding potential human injury or equipment damage, especially in scenarios with high medical or industrial standards. At the same time, for operators, clear and stable real-time data means fewer misoperations and higher work efficiency, which improves the overall operating experience. And the final signal data after filtering provides a reliable data basis for subsequent machine learning and data analysis, which can effectively support scientific research or optimize the working mode of the robot arm.

[0088] In summary, this process can effectively eliminate the jitter of the robotic arm during movement by setting preset thresholds, extracting bandpass data, and performing multi-level signal filtering, thereby improving control accuracy and system stability and creating conditions for the diversified application of the robotic arm.

[0089] Optionally, the sampled spectrum graph includes a time domain spectrum and a frequency domain spectrum; the converting the historical motion data to obtain the sampled spectrum graph includes:

[0090] Based on the Fourier algorithm, the historical motion data is transformed to obtain time domain data and frequency domain data;

[0091] The corresponding time domain spectrum is constructed according to the time domain data, and the corresponding frequency domain spectrum is constructed according to the frequency domain data.

[0092] Specifically, in signal processing and data analysis, Fourier transform is used to convert historical motion data into time domain spectra and frequency domain spectra. Historical motion data usually refers to motion-related data recorded within a certain period of time, such as speed, acceleration, position, etc. Based on this data, we can analyze the characteristics, laws and trends of motion.

[0093] Fourier transform is a mathematical tool that analyzes the frequency components of a signal by converting it from the time domain to the frequency domain. It reveals the amplitude and phase information of each frequency component in the signal, which can help us better understand the nature of the signal.

[0094] Time domain data: refers to the original recorded motion data, which represents the signal that changes over time. These data are usually presented in the form of continuous or discrete time series.

[0095] Frequency domain data: Use the Fourier transform algorithm to convert time domain data into frequency domain data. Frequency domain data shows the contribution of different frequency components to motion data, including their amplitude and phase. Commonly used Fourier transform algorithms include fast Fourier transform (FFT).

[0096] Time domain spectrum: By plotting the time domain data with time as the horizontal axis and the signal amplitude as the vertical axis, a graph is obtained to visually show the change trend of the data over time. Frequency domain spectrum: The frequency domain data is plotted with frequency as the horizontal axis and the signal amplitude or intensity as the vertical axis to show the contribution of different frequency components.

[0097] Through time domain and frequency domain plots, important motion patterns and key features, such as the dominance and changes of frequency components, can be identified. Frequency domain plots can be used to identify noise and abnormal frequency components in the signal, which helps improve the quality of data acquisition and analysis. Time domain plots can clearly show the dynamic process of the action, making it easy to analyze key events, peaks and attenuation characteristics during the motion process. In motion control and system modeling, frequency domain analysis can help design appropriate controllers and optimize the response characteristics of the system.

[0098] By converting historical motion data into time domain and frequency domain spectra and using Fourier transform methods, important information in the signal can be effectively extracted and analyzed. This not only helps to understand motion characteristics, but also provides an important basis for dynamic monitoring, system design and control, and improves the application and analysis capabilities of motion-related engineering technologies.

[0099] Optionally, the method for eliminating jitter at the end of the robotic arm further includes:

[0100] During the operation of the robot arm to be de-shaked, judging whether the real-time motion data exceeds a preset range based on machine learning or a preset rule engine;

[0101] If it exceeds the preset range, a feedback signal is generated, and a control strategy is triggered according to the feedback signal to adjust the robot arm to be de-shaked.

[0102] Optionally, the feedback signal includes an abnormality type and a corresponding deviation amount. If the abnormality exceeds the preset range, a feedback signal is generated, and a control strategy is triggered according to the feedback signal to adjust the robot arm to be debounced, including:

[0103] Triggering a corresponding preset control strategy according to the abnormality type and the corresponding deviation amount to automatically adjust the robot arm to be debounced;

[0104] The preset control strategy includes speed adjustment, path correction, gain adjustment and mode switching.

[0105] Specifically, when the robot is working, the jitter phenomenon will affect its accuracy and operating efficiency. In the method of eliminating jitter, the introduction of a dynamic judgment mechanism based on machine learning or a preset rule engine will effectively improve the stability and responsiveness of the robot. This method aims to monitor the motion state of the robot in real time, and trigger the corresponding control strategy by judging whether the real-time motion data exceeds the preset range to ensure the smooth operation of the robot.

[0106] During the operation of the robotic arm, real-time motion data (such as position, speed, acceleration, etc.) will be acquired in real time through sensors and data acquisition systems.

[0107] Machine learning method: The system uses machine learning algorithms (such as anomaly detection algorithms) to analyze historical data, build models, and automatically determine the normal range of real-time motion data. This method can adapt to changes in the robot's operating environment through continuous learning and updating.

[0108] Preset rule engine: Based on preset logical rules (such as threshold detection), the system can quickly identify and make judgments when anomalies occur. These rules can be manually adjusted to meet different working environments and operating requirements.

[0109] When the real-time motion data exceeds the preset range, a feedback signal is generated. The feedback signal includes the abnormality type, which refers to the nature of the abnormality of the current motion data, such as: Static jitter: static jitter caused by mechanical structure or external interference. Dynamic jitter: jitter caused by improper control strategy during motion. Deviation refers to the degree of difference between the real-time motion data and the expected value. Quantifying these two values ​​helps the control system determine the magnitude of adjustment.

[0110] According to the generated feedback signal, the corresponding control strategy will be triggered to adjust the robot arm. The preset control strategy mainly includes speed adjustment. When jitter is detected, the movement speed of the robot arm can be reduced in real time to reduce the jitter caused by rapid movement. At the same time, the motion load of the robot arm is reduced through the smooth acceleration and deceleration mode. The preset control strategy also includes path correction. If the robot arm is detected to deviate from the preset path, the system will correct the path according to the real-time data and feedback signal to ensure that the robot arm moves along the precise trajectory. The preset control strategy also includes gain adjustment. The gain parameters of the control system are adjusted according to the jitter level to enhance the system's ability to suppress jitter. In the case of severe jitter, appropriately reducing the gain can avoid oscillation caused by the system being too sensitive. The preset control strategy also includes mode switching. When an abnormal situation occurs, the system can automatically switch to a backup operation mode (such as a low-precision mode) to first ensure the safe operation of the equipment, and then gradually return to the normal operation mode.

[0111] Through real-time monitoring and dynamic adjustment, the degree of jitter of the robot arm can be significantly reduced, thereby improving the accuracy of processing and operation. The introduction of machine learning technology enables the system to have self-learning and adaptability, reduce manual intervention, and improve the level of intelligent operation. Through real-time monitoring and feedback, abnormal conditions can be quickly discovered and corrected, reducing the risk of system failure and damage, and improving the reliability of equipment. And by quickly and automatically adjusting the strategy, it can reduce unnecessary downtime and production waste caused by jitter, and improve overall work efficiency. At the same time, it can also adapt to various complex working environments and external interference to adjust the strategy in real time, which helps to improve the performance of the robot arm in specific tasks.

[0112] That is, by combining real-time monitoring, feedback generation and control strategy adjustment, jitter can be effectively eliminated during the operation of the robot arm. This intelligent adjustment mechanism can not only improve the stability and reliability of the robot arm, but also help improve work efficiency, adapt to changing operating environments, and expand the potential of the robot arm in various application scenarios.

[0113] Optionally, the method for eliminating jitter at the end of a robotic arm further comprises:

[0114] The final signal data is evaluated to obtain an evaluation result, and the final signal data is stored.

[0115] Specifically, in the method of eliminating jitter at the end of the robot arm, the step of further evaluating and storing the final signal data is of great significance. The final signal data contains filtered motion data, which can more truly reflect the motion state of the end of the robot arm during operation and effectively eliminate most jitter and interference.

[0116] When evaluating the final signal data, a series of evaluation indicators can be set, such as stability, noise level, signal clarity, spectrum analysis, etc. The evaluation method can use statistical analysis (RMS value, standard deviation), spectrum analysis (Fourier transform) and other methods to analyze the processed signal data, quantify its characteristics, and generate evaluation results. The final signal data and its evaluation results should be stored in a database to facilitate subsequent data analysis, troubleshooting, and training of machine learning models.

[0117] Through layer-by-layer signal processing, we ensure that the final signal data can truly reflect the movement of the robot arm, reduce the impact of misleading data, and ensure the reliability of subsequent analysis. By eliminating unnecessary jitter and interference, the smoothness of the robot arm's movement can be effectively improved, thereby improving its overall operating performance and processing accuracy. The data evaluation results can provide a basis for the intelligent monitoring system. By analyzing the differences between historical data and real-time data, it can timely warn of abnormal conditions and predict faults. At the same time, the long-term preservation of signal data and evaluation results lays the foundation for subsequent algorithm optimization and improvement, helping to continuously improve the technical level and performance of the robot arm.

[0118] This method effectively eliminates the jitter of the end of the robot arm and improves the stability and accuracy of the data by bandpass filtering and notch filtering of real-time motion data. Combined with data evaluation and storage methods, it promotes the intelligent development of the system, lays a solid foundation for future optimization and maintenance, and helps to improve the performance and stability of the robot arm in the application.

[0119] like Fig.10 As shown, an embodiment of the present invention provides a device for eliminating jitter at the end of a robotic arm, comprising:

[0120] An acquisition unit, used for acquiring historical motion data of the end of the robot arm to be debounced, and converting the historical motion data to obtain a sampling frequency spectrum diagram;

[0121] A processing unit, used to judge the sampled spectrum diagram according to a preset threshold value to obtain bandpass data;

[0122] The processing unit is also used to filter the real-time motion data of the end of the robotic arm to be de-jittered according to the bandpass data, so as to eliminate the jitter of the end of the robotic arm to be de-jittered during the movement.

[0123] The device for eliminating jitter at the end of a robotic arm of this embodiment is used to implement the method for eliminating jitter at the end of a robotic arm as described above. Its advantages over the prior art are the same as the advantages of the method for eliminating jitter at the end of a robotic arm as described above over the prior art, and will not be repeated here.

[0124] like Fig.11 As shown, a system for eliminating jitter at the end of a robotic arm provided by an embodiment of the present invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the method for eliminating jitter at the end of a robotic arm as described above when executing the computer program.

[0125] In other words, a system for eliminating jitter at the end of a robotic arm includes a memory and a processor coupled to the memory; the memory is configured to store a computer program; and the processor is configured to perform the following operations when executing the computer program:

[0126] Obtain historical motion data of the end of the robot arm to be debounced, and convert the historical motion data to obtain a sampling frequency spectrum;

[0127] Judging the sampled spectrum diagram according to a preset threshold value to obtain bandpass data;

[0128] The real-time motion data of the end of the mechanical arm to be de-jittered is filtered according to the passband data to eliminate the jitter of the end of the mechanical arm to be de-jittered during the movement.

[0129] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for eliminating jitter at the end of a robotic arm as described above is implemented.

[0130] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0131] Obtain historical motion data of the end of the robot arm to be debounced, and convert the historical motion data to obtain a sampling frequency spectrum;

[0132] Judging the sampled spectrum diagram according to a preset threshold value to obtain bandpass data;

[0133] The real-time motion data of the end of the mechanical arm to be de-jittered is filtered according to the passband data to eliminate the jitter of the end of the mechanical arm to be de-jittered during the movement.

[0134] A system for eliminating jitter at the end of a mechanical arm that can serve as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The system for eliminating jitter at the end of a mechanical arm is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The system for eliminating jitter at the end of a mechanical arm can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0135] The system for eliminating jitter at the end of the robot arm includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.

[0136] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0137] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for eliminating jitter at the end of a robotic arm, characterized in that: include: Obtain historical motion data of the end of the robot arm to be debounced, and convert the historical motion data to obtain a sampling frequency spectrum; Judging the sampled spectrum diagram according to a preset threshold value to obtain bandpass data; The real-time motion data of the end of the mechanical arm to be de-jittered is filtered according to the passband data to eliminate the jitter of the end of the mechanical arm to be de-jittered during the movement.

2. The method for eliminating jitter at the end of a robotic arm according to claim 1, characterized in that: The preset threshold includes a first preset threshold and a second preset threshold, the bandpass data includes first temporary bandpass data and second bandpass data, and judging the sampled spectrum diagram according to the preset threshold to obtain the bandpass data includes: Judging the sampled frequency spectrum according to the first preset threshold value to obtain the first bandpass data; Filtering the sampled spectrum diagram according to the first bandpass data to obtain temporary sampled data; The temporary sampling data is judged according to the second preset threshold to obtain the second bandpass data.

3. The method for eliminating jitter at the end of a robotic arm according to claim 2, characterized in that: The filtering of the real-time motion data of the end of the robotic arm to be debounced according to the passband data comprises: Determining a corresponding Butterworth second-order low-pass filter according to the first band-pass data, and filtering the real-time motion data based on the Butterworth second-order low-pass filter to obtain temporary data; Determine the corresponding notch filter according to the second bandpass data, and perform notch filtering on the temporary data based on the notch filter to obtain the processed final signal data, and the final signal data is the data obtained after eliminating the jitter of the end of the robotic arm to be de-jittered during the movement.

4. The method for eliminating jitter at the end of a robotic arm according to claim 3, characterized in that: The sampled spectrum graph includes a time domain spectrum and a frequency domain spectrum; the historical motion data is converted to obtain a sampled spectrum graph, including: Based on the Fourier algorithm, the historical motion data is transformed to obtain time domain data and frequency domain data; The corresponding time domain spectrum is constructed according to the time domain data, and the corresponding frequency domain spectrum is constructed according to the frequency domain data.

5. The method for eliminating jitter at the end of a robot arm according to claim 4, characterized in that: Also includes: During the operation of the robot arm to be de-shaked, judging whether the real-time motion data exceeds a preset range based on machine learning or a preset rule engine; If it exceeds the preset range, a feedback signal is generated, and a control strategy is triggered according to the feedback signal to adjust the robot arm to be de-shaked.

6. The method for eliminating jitter at the end of a robot arm according to claim 5, characterized in that: The feedback signal includes the abnormality type and the corresponding deviation amount. If the abnormality exceeds the preset range, a feedback signal is generated, and a control strategy is triggered according to the feedback signal to adjust the robot arm to be debounced, including: Triggering a corresponding preset control strategy according to the abnormality type and the corresponding deviation amount to automatically adjust the robot arm to be debounced; The preset control strategy includes speed adjustment, path correction, gain adjustment and mode switching.

7. The method for eliminating jitter at the end of a robotic arm according to claim 3, characterized in that: Also included: The final signal data is evaluated to obtain an evaluation result, and the final signal data is stored.

8. A device for eliminating jitter at the end of a robotic arm, characterized in that: include: An acquisition unit, used for acquiring historical motion data of the end of the robot arm to be debounced, and converting the historical motion data to obtain a sampling frequency spectrum diagram; A processing unit, used to judge the sampled spectrum diagram according to a preset threshold value to obtain bandpass data; The processing unit is also used to filter the real-time motion data of the end of the robotic arm to be de-jittered according to the bandpass data, so as to eliminate the jitter of the end of the robotic arm to be de-jittered during the movement.

9. A system for eliminating jitter at the end of a robotic arm, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for eliminating jitter at the end of a robotic arm according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for eliminating jitter at the end of a robotic arm as described in any one of claims 1 to 7 is implemented.

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