Mechanical arm continuous control method based on bioelectricity signal recognition

Abstract

The invention relates to the technical field of mechanical arm control, and discloses a mechanical arm continuous control method based on bioelectricity signal recognition, and the method comprises the steps: collecting electromyographic signals of an arm muscle group, and carrying out filtering and denoising processing on the electromyographic signals; and the filtered and denoised electromyographic signals are converted into mechanical arm control parameters through the correlation mapping relation model, interpolation optimization based on control continuity is conducted on the mechanical arm control parameters, the mechanical arm control parameters are converted into joint torque parameters and tail end torque parameters, and mechanical arm control is conducted. According to the method, feature extraction is conducted on the filtered and denoised electromyographic signals through a correlation mapping relation model, time domain features and frequency domain features are extracted respectively, features representing electromyographic signal modes and features representing signal amplitudes are mapped into mechanical arm control parameters, and the mechanical arm control parameters are calculated based on an error value sequence after interpolation optimization. And nonlinear optimization is carried out on mechanical arm control parameters, a more continuous and smooth optimization result is obtained, and mechanical arm continuous control based on bioelectricity signal recognition is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot arm control, and in particular to a robot arm continuous control method based on bioelectric signal recognition. Background Art

[0002] With the development of robotics, artificial intelligence and bioelectric signal processing technology, controlling robotic arms based on human bioelectric signals (such as EEG signals, EMG signals, etc.) has become a research hotspot in the field of robotic control in recent years. In particular, electromyographic signals (EMG), which are electrical signals generated by muscle activity, can reflect the human body's movement intention. The detection of EMG signals does not rely on traditional hardware interfaces and can be easily obtained through surface electrodes. For patients with limb dysfunction, a more natural and intuitive control method can be achieved. As an important part of human assistive devices, robotic arms have broad application prospects in medical rehabilitation, disability assistance technology, industrial automation and other fields. However, traditional EMG signal control methods often ignore the need for force control. Different tasks often require different force control, especially in some fine operations, the user's action force needs to be accurately identified and adjusted. Therefore, how to achieve force adaptive control based on EMG signals so that the robotic arm can automatically adjust the control force according to different user needs and operation scenarios is an urgent problem to be solved in current research. Summary of the invention

[0003] In view of this, the present invention provides a method for continuous control of a robotic arm based on bioelectric signal recognition, which accurately identifies the user's force requirements in specific operations by sensing the user's muscle activity in real time, thereby achieving continuous control of the robotic arm.

[0004] To achieve the above object, the present invention provides a method for continuous control of a robotic arm based on bioelectric signal recognition, comprising the following steps: S1: Deploy surface electromyography sensors on the arm muscle groups connected to the robotic arm to collect electromyography signals of the arm muscle groups and perform filtering and denoising on the electromyography signals; S2: Construct the correlation mapping relationship model between the filtered and denoised electromyographic signals and the robot arm control parameters; S3: The filtered and denoised electromyographic signals are converted into robot arm control parameters using the association mapping relationship model, and the robot arm control parameters are interpolated and optimized based on control continuity; S4: Convert the interpolation optimized robot control parameters into joint torque parameters and end torque parameters, and send them to the robot control system to adjust the torque of the robot joints and the end effector, and control the rotation of the robot joints and the end effector.

[0005] As a further improvement method of the present invention: Optionally, the arm muscle group includes three muscle groups, the first to third muscle groups are upper arm muscle group, forearm muscle group and shoulder muscle group, respectively, and surface electromyography sensors are deployed on the three muscle groups respectively to collect electromyography signals of the three muscle groups, and the electromyography signals are filtered and denoised to remove noise information in the electromyography signals; The electromyographic signal is expressed as: ; in: represents the electromyographic signal of the i-th muscle group, Represents myoelectric signal The signal value at the nth signal moment in , , Represents the number of signal moments of the electromyographic signal. The signal acquisition time interval of the electromyographic signal is .

[0006] Optionally, the filtering and denoising process is as follows: Decomposing the electromyographic signal into L layers of low-frequency wavelet coefficients and high-frequency wavelet coefficients by using a discrete wavelet transform method, wherein the discrete wavelet transform method uses a low-pass filter and a high-pass filter to filter the electromyographic signal, and sequentially obtains the low-frequency wavelet coefficients and high-frequency wavelet coefficients of the first layer, and uses a low-pass filter and a high-pass filter to filter the low-frequency wavelet coefficients of each layer, and obtains the low-frequency wavelet coefficients and high-frequency wavelet coefficients of the next layer; The low-frequency wavelet coefficients of the Lth layer are retained, and the high-frequency wavelet coefficients are filtered and denoised. The filtering and denoising formula is: ; ; in: Represents high-frequency wavelet coefficients The filtering and denoising results; Represents high-frequency wavelet coefficients The mean of Represents high-frequency wavelet coefficients The standard deviation of Represents high-frequency wavelet coefficients The number of layers; L represents the preset number of wavelet decomposition layers; Represents high-frequency wavelet coefficients Adaptive threshold of Reconstructing the L-th layer of low-frequency wavelet coefficients and the L-th layer of high-frequency wavelet coefficients after filtering and denoising to obtain a filtered and denoised electromyographic signal; The electromyographic signal The corresponding EMG signal after filtering and denoising is: , , The electromyographic signal after filtering and denoising The signal value at the nth signal moment in .

[0007] Optionally, the feature extraction module is used to extract biological features from the electromyographic signal after filtering and denoising, and convert the biological features into a biological feature matrix, wherein the biological features include time domain features and frequency domain features of the electromyographic signal after filtering and denoising, and the feature mapping module is a support vector machine structure, which is used to extract a feature vector representing the movement direction of the robotic arm as a movement parameter vector, and a feature vector representing the force at the end of the robotic arm as a force parameter vector from the biological feature matrix, and perform high-dimensional mapping on the movement parameter vector and the force parameter vector to obtain the robotic arm control parameters corresponding to the electromyographic signal after filtering and denoising.

[0008] Optionally, the time domain characteristics of the electromyographic signal after filtering and denoising include the root mean square value, average absolute value and zero crossing rate of the electromyographic signal after filtering and denoising, wherein the root mean square value is the root mean square of the electromyographic signal after filtering and denoising, the average absolute value is the average of the absolute values ​​of the signal values ​​in the electromyographic signal after filtering and denoising, and the zero crossing rate is the number of times the electromyographic signal after filtering and denoising crosses zero points; The frequency domain features of the electromyographic signal after filtering and denoising include the mean frequency, median frequency index and total spectrum energy of the electromyographic signal after filtering and denoising. The frequency domain feature extraction process of the electromyographic signal after filtering and denoising is as follows: Perform Fourier transform on the filtered and denoised electromyographic signal to obtain the spectrum of the filtered and denoised electromyographic signal under N continuous frequency indexes, calculate the center frequency of the N spectrum as the mean frequency of the filtered and denoised electromyographic signal, calculate the total energy of the N spectrum as the total spectrum energy of the filtered and denoised electromyographic signal, and divide the N spectrum into two parts with continuous frequency indexes based on the median frequency index so that the spectrum energy of the two parts is equal.

[0009] Optionally, the feature extraction module is used to extract the biological features of the electromyographic signal after filtering and denoising, and convert it into a biological feature matrix. The representation form of the biological feature matrix is : ; in: T stands for transpose, Represents the time domain characteristics of the EMG signals after filtering and denoising of the three muscle groups. The frequency domain characteristics of the EMG signals after filtering and denoising of the three muscle groups are shown; , , The electromyographic signals after filtering and denoising are The root mean square value, mean absolute value, and zero crossing rate of The electromyographic signals after filtering and denoising are The mean frequency, median frequency index and total spectrum energy of ; The feature mapping module is used to extract the feature vector representing the movement direction of the robot arm from the biological feature matrix F as the movement parameter vector : ; The feature mapping module is used to extract the feature vector representing the force of the end of the robot arm from the biological feature matrix F as the force parameter vector : ; For the movement parameter vector and the velocity parameter vector Perform high-dimensional mapping to obtain the robot arm control parameter G corresponding to the electromyographic signal after filtering and denoising: ; ; ; in: is the control parameter of the robot arm's moving direction, is the force control parameter of the end of the robot arm; is the first weight matrix, is the second weight matrix, represents the first offset, represents the second offset, Represents the convolution operation; Get the control parameter sequence of the robot arm control parameter G, the control parameter sequence is : ; in: Indicates that at the signal acquisition time time- The interpolation optimization amount of the robot arm control parameters is the change of the robot arm control parameters before and after the interpolation optimization, and time represents the electromyographic signal The signal acquisition time is Indicates that at the signal acquisition time time- The interpolation optimization amount of the robot arm control parameters, , M represents the length of the control parameter sequence.

[0010] Optionally, based on the control parameter sequence, the robot control parameter G is interpolated and optimized based on control continuity, wherein the interpolation optimization formula is: ; in: represents the interpolation optimization result of the robot control parameter G, is the mean of the control parameter sequence E, represents the standard deviation of the control parameter sequence E; To optimize the amplitude control parameters, represents the optimization sensitivity control parameter, Indicates the preset adjustment parameters.

[0011] Optionally, the end torque parameter includes the position of the end effector of the robot arm and the speed in each direction; the conversion process of the joint torque parameter is: Get the end torque parameter, where the end torque parameter is the robot control parameter after interpolation optimization , is the interpolation optimized robot arm movement direction control parameter, The force control parameters of the end of the robot arm after interpolation optimization; The number of joints of the robot arm is R, and the joint angle of the rth joint is , , based on the joint angles and Based on the dynamic principle of the robot arm, the Jacobian matrix J for controlling the torque of the robot arm is constructed. The Jacobian matrix J is a matrix in the form of 1 row and R columns. The rth column element value in the Jacobian matrix J is the position of the robot end effector to the joint angle The partial derivative of The Jacobian matrix J is used to map the end torque parameter to the joint torque to obtain the joint torque parameter S: ; The joint torque parameter S is the speed of the R joints of the robot arm in each direction.

[0012] In order to solve the above problem, the present invention provides an electronic device, the electronic device comprising: A memory storing at least one instruction; Communication interface, enabling electronic equipment to communicate; and The processor executes the instructions stored in the memory to implement the above-mentioned method for continuous control of the robotic arm based on bioelectric signal recognition.

[0013] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for continuous control of a robotic arm based on bioelectric signal recognition.

[0014] Compared with the prior art, the present invention proposes a method for continuous control of a robotic arm based on bioelectric signal recognition, which has the following advantages: First of all, this scheme proposes a method for generating control parameters of a robotic arm, extracting the electromyographic signals of muscle groups in different parts of the arm, characterizing multi-directional bioelectric signals, and filtering and denoising the electromyographic signals using a filtering processing method combined with an adaptive threshold. This method can adapt to the noise levels and signal characteristics of different muscle groups, effectively remove noise without losing the main components of the signal, and provide a more accurate denoising effect without manually setting the threshold. The associated mapping relationship model is used to extract features of the electromyographic signals after filtering and denoising, and the time domain features and frequency domain features are extracted respectively. The features characterizing the electromyographic signal pattern are used to map to obtain the control parameters of the robotic arm's movement direction, and the features characterizing the signal amplitude are mapped to obtain the force control parameters of the robotic arm's end, thereby realizing the mapping of robotic arm control parameters based on bioelectric signal recognition.

[0015] At the same time, this scheme proposes a continuous control method for the robotic arm. Combined with the interpolation optimization results of the robotic arm control parameters corresponding to the past electromyographic signals, the robotic arm control parameters are nonlinearly optimized based on the error values ​​of the robotic arm control parameters before and after the interpolation optimization to obtain a more continuous and smooth optimization result. In the optimization process, the amplitude and sensitivity are controlled to avoid excessive control of the optimization result by the error value. When the input value is large, the output of the tanh function tends to be stable, which can naturally suppress the influence of large-amplitude noise and improve the robustness of the robotic arm control parameters after interpolation optimization. The interpolation optimized robotic arm control parameters are converted into joint torque parameters and end torque parameters, and sent to the control system of the robotic arm to control the movement speed of the robotic arm joints and the end effector in different directions, thereby realizing continuous control of the robotic arm based on bioelectric signal recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of a method for continuous control of a robotic arm based on bioelectric signal recognition provided by one embodiment of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] The embodiment of the present application provides a method for continuous control of a robotic arm based on bioelectric signal recognition. The execution subject of the method for continuous control of a robotic arm based on bioelectric signal recognition includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for continuous control of a robotic arm based on bioelectric signal recognition can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0020] Reference Figure 1 , Embodiment 1 of the present invention is: A method for continuous control of a robotic arm based on bioelectric signal recognition comprises the following steps: S1: Deploy surface electromyography sensors on the arm muscle groups connected to the robotic arm to collect electromyography signals of the arm muscle groups and perform filtering and denoising on the electromyography signals.

[0021] The arm muscle groups include three muscle groups, the first to third muscle groups are the upper arm muscle group, the forearm muscle group and the shoulder muscle group, and surface electromyography sensors are respectively deployed on the three muscle groups to collect electromyography signals of the three muscle groups, and the electromyography signals are filtered and denoised to remove noise information in the electromyography signals; The electromyographic signal is expressed as: ; in: represents the electromyographic signal of the i-th muscle group, Represents myoelectric signal The signal value at the nth signal moment in , , Represents the number of signal moments of the electromyographic signal. The signal acquisition time interval of the electromyographic signal is In one embodiment of the present invention, the collected electromyographic signal is used to control the robot arm, and After a certain period of time, the electromyographic signal is collected again.

[0022] The filtering and denoising process is as follows: Decomposing the electromyographic signal into L layers of low-frequency wavelet coefficients and high-frequency wavelet coefficients by using a discrete wavelet transform method, wherein the discrete wavelet transform method uses a low-pass filter and a high-pass filter to filter the electromyographic signal, and sequentially obtains the low-frequency wavelet coefficients and high-frequency wavelet coefficients of the first layer, and uses a low-pass filter and a high-pass filter to filter the low-frequency wavelet coefficients of each layer, and obtains the low-frequency wavelet coefficients and high-frequency wavelet coefficients of the next layer; The low-frequency wavelet coefficients of the Lth layer are retained, and the high-frequency wavelet coefficients are filtered and denoised. The filtering and denoising formula is: ; ; in: Represents high-frequency wavelet coefficients The filtering and denoising results; Represents high-frequency wavelet coefficients The mean of Represents high-frequency wavelet coefficients The standard deviation of Represents high-frequency wavelet coefficients The number of layers; L represents the preset number of wavelet decomposition layers; Represents high-frequency wavelet coefficients Adaptive threshold of Reconstructing the L-th layer of low-frequency wavelet coefficients and the L-th layer of high-frequency wavelet coefficients after filtering and denoising to obtain a filtered and denoised electromyographic signal; The electromyographic signal The corresponding EMG signal after filtering and denoising is: , , The electromyographic signal after filtering and denoising In a specific embodiment of the present invention, the signal reconstruction method is to superimpose the L-th layer of low-frequency wavelet coefficients and the L-th layer of high-frequency wavelet coefficients after filtering and denoising as the signal reconstruction result.

[0023] S2: Construct a correlation mapping relationship model between the filtered and denoised electromyographic signals and the robot arm control parameters.

[0024] The feature extraction module is used to extract biological features from the electromyographic signal after filtering and denoising, and convert the biological features into a biological feature matrix. The biological features include time domain features and frequency domain features of the electromyographic signal after filtering and denoising. The feature mapping module is a support vector machine structure, which is used to extract a feature vector representing the movement direction of the robotic arm as a movement parameter vector, and a feature vector representing the force of the end of the robotic arm as a force parameter vector from the biological feature matrix, and perform high-dimensional mapping on the movement parameter vector and the force parameter vector to obtain the robotic arm control parameters corresponding to the electromyographic signal after filtering and denoising.

[0025] The time domain characteristics of the electromyographic signal after filtering and denoising include the root mean square value, average absolute value and zero crossing rate of the electromyographic signal after filtering and denoising, wherein the root mean square value is the root mean square of the electromyographic signal after filtering and denoising, the average absolute value is the mean of the absolute values ​​of the signal values ​​in the electromyographic signal after filtering and denoising, and the zero crossing rate is the number of times the electromyographic signal after filtering and denoising crosses zero points; The frequency domain features of the electromyographic signal after filtering and denoising include the mean frequency, median frequency index and total spectrum energy of the electromyographic signal after filtering and denoising. The frequency domain feature extraction process of the electromyographic signal after filtering and denoising is as follows: Perform Fourier transform on the filtered and denoised electromyographic signal to obtain the spectrum of the filtered and denoised electromyographic signal under N continuous frequency indexes, calculate the center frequency of the N spectrums as the mean frequency of the filtered and denoised electromyographic signal, calculate the total energy of the N spectrums as the total spectrum energy of the filtered and denoised electromyographic signal, and divide the N spectrums into two parts with continuous frequency indexes based on the median frequency index so that the spectrum energies of the two parts are equal, the N continuous frequency indexes are 0 to N-1, the median frequency index is between 0 and N-1, each frequency index corresponds to a spectrum and a frequency component, and the frequency component corresponding to the jth frequency index is : ; in: ; Represents the time interval between adjacent signal moments in the electromyographic signal after filtering and denoising; The filtered denoised electromyographic signal The mean frequency of : ; in: Represents the electromyographic signal after filtering and denoising The spectrum at the jth frequency index.

[0026] S3: The filtered and denoised electromyographic signals are converted into robot arm control parameters using the association mapping relationship model, and the robot arm control parameters are interpolated and optimized based on control continuity.

[0027] The feature extraction module is used to extract the biological features of the electromyographic signal after filtering and denoising, and convert it into a biological feature matrix. The representation form of the biological feature matrix is : ; in: T stands for transpose, Represents the time domain characteristics of the EMG signals after filtering and denoising of the three muscle groups. The frequency domain characteristics of the EMG signals after filtering and denoising of the three muscle groups are shown; , , The electromyographic signals after filtering and denoising are The root mean square value, mean absolute value, and zero crossing rate of The electromyographic signals after filtering and denoising are The mean frequency, median frequency index and total spectrum energy of ; The feature mapping module is used to extract the feature vector representing the movement direction of the robot arm from the biological feature matrix F as the movement parameter vector : ; The feature mapping module is used to extract the feature vector representing the force of the end of the robot arm from the biological feature matrix F as the force parameter vector : ; For the movement parameter vector and the velocity parameter vector Perform high-dimensional mapping to obtain the robot arm control parameter G corresponding to the electromyographic signal after filtering and denoising: ; ; ; in: is the control parameter of the robot arm's moving direction, is the force control parameter of the end of the robot arm; is the first weight matrix, is the second weight matrix, represents the first offset, represents the second offset, Represents the convolution operation; Get the control parameter sequence of the robot arm control parameter G, the control parameter sequence is : ; in: Indicates that at the signal acquisition time time- The interpolation optimization amount of the robot arm control parameters is the change of the robot arm control parameters before and after the interpolation optimization, and time represents the electromyographic signal The signal acquisition time is Indicates that at the signal acquisition time time- The interpolation optimization amount of the robot arm control parameters, , M represents the length of the control parameter sequence. In one embodiment of the present invention, the feature mapping module can be trained using one or more combinations of the ADAM optimization algorithm, the Newton iteration method, and the gradient descent method.

[0028] Based on the control parameter sequence, the robot control parameter G is interpolated and optimized based on control continuity, wherein the interpolation optimization formula is: ; in: represents the interpolation optimization result of the robot control parameter G, is the mean of the control parameter sequence E, represents the standard deviation of the control parameter sequence E; To optimize the amplitude control parameters, represents the optimization sensitivity control parameter, Indicates the preset adjustment parameters.

[0029] S4: Convert the interpolation optimized robot control parameters into joint torque parameters and end torque parameters, and send them to the robot control system to adjust the torque of the robot joints and the end effector, and control the rotation of the robot joints and the end effector.

[0030] The end torque parameters include the position of the end effector of the robot arm and the speed in each direction; the conversion process of the joint torque parameters is: Get the end torque parameter, where the end torque parameter is the robot control parameter after interpolation optimization , is the interpolation optimized robot arm movement direction control parameter, The force control parameters of the end of the robot arm after interpolation optimization; The number of joints of the robot arm is R, and the joint angle of the rth joint is , , based on the joint angles and Based on the dynamic principle of the robot arm, the Jacobian matrix J for controlling the torque of the robot arm is constructed. The Jacobian matrix J is a matrix in the form of 1 row and R columns. The rth column element value in the Jacobian matrix J is the position of the robot end effector to the joint angle The partial derivative of The Jacobian matrix J is used to map the end torque parameter to the joint torque to obtain the joint torque parameter S: ; The joint torque parameter S is the speed of the R joints of the robot arm in each direction.

[0031] Embodiment 2: This scheme conducts comparative experiments on the robotic arm continuous control method based on bioelectric signal recognition, PID control method, fuzzy control method and reinforcement learning method. By setting target tasks for the robotic arm, the control performance of the robotic arm under different control methods is evaluated. The comparative experimental results are shown in Table 1: Table 1 ; As shown in Table 1, the robotic arm continuous control method based on bioelectric signal recognition is significantly superior to other methods in terms of response time, error, control accuracy and task completion rate. The interpolation optimization method based on bioelectric signals and continuous control parameters can significantly reduce the response time and error and improve the robotic arm control accuracy.

[0032] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0033] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0034] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0035] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for continuous control of a robotic arm based on bioelectric signal recognition, characterized in that: The method comprises: S1: Deploy surface electromyography sensors on the arm muscle groups connected to the robotic arm to collect electromyography signals of the arm muscle groups and perform filtering and denoising on the electromyography signals; The robot arm is composed of connecting rods, joints, end effectors and control systems. The connecting rods are used to support the joints and the end effectors. The joints are used to realize the rotation and movement between the connecting rods. The end effectors are used to complete the components of the refined operation tasks. The types of end effectors include grippers, welding heads and cutting tools. S2: constructing a correlation mapping relationship model between the filtered and denoised electromyographic signal and the robotic arm control parameters, wherein the robotic arm control parameters include the robotic arm movement direction control parameters and the robotic arm end force control parameters, and the correlation mapping relationship model includes a feature extraction module and a feature mapping module; The robot arm movement direction control parameter is the position of the robot arm end effector, and the robot arm end force control parameter is the speed of the robot arm end effector in each direction; S3: The filtered and denoised electromyographic signals are converted into robot arm control parameters using the association mapping relationship model, and the robot arm control parameters are interpolated and optimized based on control continuity; S4: Convert the interpolation optimized robot control parameters into joint torque parameters and end torque parameters, and send them to the robot control system to adjust the torque of the robot joints and the end effector, and control the rotation of the robot joints and the end effector.

2. A method for continuous control of a robotic arm based on bioelectric signal recognition as claimed in claim 1, characterized in that: The arm muscle groups include three muscle groups, the first to third muscle groups are the upper arm muscle group, the forearm muscle group and the shoulder muscle group, and surface electromyography sensors are respectively deployed on the three muscle groups to collect electromyography signals of the three muscle groups, and the electromyography signals are filtered and denoised to remove noise information in the electromyography signals; The electromyographic signal is expressed as: ； in: represents the electromyographic signal of the i-th muscle group, Represents myoelectric signal The signal value at the nth signal moment in , , Represents the number of signal moments of the electromyographic signal. The signal acquisition time interval of the electromyographic signal is .

3. A method for continuous control of a robotic arm based on bioelectric signal recognition as claimed in claim 2, characterized in that: The filtering and denoising process is as follows: Decomposing the electromyographic signal into L layers of low-frequency wavelet coefficients and high-frequency wavelet coefficients by using a discrete wavelet transform method, wherein the discrete wavelet transform method uses a low-pass filter and a high-pass filter to filter the electromyographic signal, and sequentially obtains the low-frequency wavelet coefficients and high-frequency wavelet coefficients of the first layer, and uses a low-pass filter and a high-pass filter to filter the low-frequency wavelet coefficients of each layer, and obtains the low-frequency wavelet coefficients and high-frequency wavelet coefficients of the next layer; The low-frequency wavelet coefficients of the Lth layer are retained, and the high-frequency wavelet coefficients are filtered and denoised. The filtering and denoising formula is: ； ； in: Represents high-frequency wavelet coefficients The filtering and denoising results; Represents high-frequency wavelet coefficients The mean of Represents high-frequency wavelet coefficients The standard deviation of Represents high-frequency wavelet coefficients The number of layers; L represents the preset number of wavelet decomposition layers; Represents high-frequency wavelet coefficients Adaptive threshold of Reconstructing the L-th layer of low-frequency wavelet coefficients and the L-th layer of high-frequency wavelet coefficients after filtering and denoising to obtain a filtered and denoised electromyographic signal; The electromyographic signal The corresponding EMG signal after filtering and denoising is: , , The electromyographic signal after filtering and denoising The signal value at the nth signal moment in .

4. A method for continuous control of a robotic arm based on bioelectric signal recognition as claimed in claim 1, characterized in that: The feature extraction module is used to extract biological features from the electromyographic signal after filtering and denoising, and convert the biological features into a biological feature matrix. The biological features include time domain features and frequency domain features of the electromyographic signal after filtering and denoising. The feature mapping module is a support vector machine structure, which is used to extract a feature vector representing the movement direction of the robotic arm as a movement parameter vector, and a feature vector representing the force of the end of the robotic arm as a force parameter vector from the biological feature matrix, and perform high-dimensional mapping on the movement parameter vector and the force parameter vector to obtain the robotic arm control parameters corresponding to the electromyographic signal after filtering and denoising.

5. A method for continuous control of a robotic arm based on bioelectric signal recognition as claimed in claim 4, characterized in that: The time domain characteristics of the electromyographic signal after filtering and denoising include the root mean square value, average absolute value and zero crossing rate of the electromyographic signal after filtering and denoising, wherein the root mean square value is the root mean square of the electromyographic signal after filtering and denoising, the average absolute value is the mean of the absolute values ​​of the signal values ​​in the electromyographic signal after filtering and denoising, and the zero crossing rate is the number of times the electromyographic signal after filtering and denoising crosses zero points; The frequency domain features of the electromyographic signal after filtering and denoising include the mean frequency, median frequency index and total spectrum energy of the electromyographic signal after filtering and denoising. The frequency domain feature extraction process of the electromyographic signal after filtering and denoising is as follows: Perform Fourier transform on the filtered and denoised electromyographic signal to obtain the spectrum of the filtered and denoised electromyographic signal under N continuous frequency indexes, calculate the center frequency of the N spectrum as the mean frequency of the filtered and denoised electromyographic signal, calculate the total energy of the N spectrum as the total spectrum energy of the filtered and denoised electromyographic signal, and divide the N spectrum into two parts with continuous frequency indexes based on the median frequency index so that the spectrum energy of the two parts is equal.

6. A method for continuous control of a robotic arm based on bioelectric signal recognition as claimed in claim 1, characterized in that: The feature extraction module is used to extract the biological features of the electromyographic signal after filtering and denoising, and convert it into a biological feature matrix. The representation form of the biological feature matrix is : ； in: T stands for transpose, Represents the time domain characteristics of the EMG signals after filtering and denoising of the three muscle groups. The frequency domain characteristics of the EMG signals after filtering and denoising of the three muscle groups are shown; , , The electromyographic signals after filtering and denoising are The root mean square value, mean absolute value and zero crossing rate of The electromyographic signals after filtering and denoising are The mean frequency, median frequency index and total spectrum energy of ; The feature mapping module is used to extract the feature vector representing the movement direction of the robot arm from the biological feature matrix F as the movement parameter vector : ； The feature mapping module is used to extract the feature vector representing the force of the end of the robot arm from the biological feature matrix F as the force parameter vector : ； For the movement parameter vector and the velocity parameter vector Perform high-dimensional mapping to obtain the robot arm control parameter G corresponding to the electromyographic signal after filtering and denoising: ； ； ； in: is the control parameter of the robot arm's moving direction, is the force control parameter of the end of the robot arm; is the first weight matrix, is the second weight matrix, represents the first offset, represents the second offset, Represents the convolution operation; Get the control parameter sequence of the robot arm control parameter G, the control parameter sequence is : ； in: Indicates that at the signal acquisition time time- The interpolation optimization amount of the robot arm control parameters is the change of the robot arm control parameters before and after the interpolation optimization, and time represents the electromyographic signal The signal acquisition time is Indicates that at the signal acquisition time time- The interpolation optimization amount of the robot arm control parameters, , M represents the length of the control parameter sequence.

7. A method for continuous control of a robotic arm based on bioelectric signal recognition as claimed in claim 6, characterized in that: Based on the control parameter sequence, the robot control parameter G is interpolated and optimized based on control continuity, wherein the interpolation optimization formula is: ； in: represents the interpolation optimization result of the robot control parameter G, is the mean of the control parameter sequence E, represents the standard deviation of the control parameter sequence E; To optimize the amplitude control parameters, represents the optimization sensitivity control parameter, Indicates the preset adjustment parameters.

8. The method for continuous control of a robotic arm based on bioelectric signal recognition as claimed in claim 1, characterized in that: The end torque parameters include the position of the end effector of the robot arm and the speed in each direction; the conversion process of the joint torque parameters is: Get the end torque parameter, where the end torque parameter is the robot control parameter after interpolation optimization , is the interpolation optimized robot arm movement direction control parameter, The force control parameters of the end of the robot arm after interpolation optimization; The number of joints of the robot arm is R, and the joint angle of the rth joint is , , based on the joint angles and Based on the dynamic principle of the robot arm, the Jacobian matrix J for controlling the torque of the robot arm is constructed. The Jacobian matrix J is a matrix in the form of 1 row and R columns. The rth column element value in the Jacobian matrix J is the position of the robot end effector to the joint angle The partial derivative of The Jacobian matrix J is used to map the end torque parameter to the joint torque to obtain the joint torque parameter S: ； The joint torque parameter S is the speed of the R joints of the robot arm in each direction.

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