CN116619422A - Motion capture and wrist joint angle estimation mechanical arm mapping method based on human body characteristic signals - Google Patents
Motion capture and wrist joint angle estimation mechanical arm mapping method based on human body characteristic signals Download PDFInfo
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- 210000003857 wrist joint Anatomy 0.000 title claims abstract description 39
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
- B25J13/088—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices with position, velocity or acceleration sensors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J17/00—Joints
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J18/00—Arms
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
- B25J9/163—Programme controls characterised by the control loop learning, adaptive, model based, rule based expert control
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract
The application discloses a mechanical arm mapping method based on motion capture of human body characteristic signals and wrist joint angle estimation, and belongs to the technical field of mechanical arm control. Comprising the following steps: firstly, designing a global frame, a local frame and a gForce arm ring frame by adopting an X-Y-Z fixed angle method, and obtaining joint angles of a shoulder and an elbow by an Euler angle calculation method of an arm joint; then, sEMG signals and IMU signals at the forearm and the wrist joint are collected, and the sEMG signals are processed to obtain a smooth envelope curve reflecting the change characteristics of the electromyographic signals; then, predicting the angle of the wrist joint by establishing a PSO-GRNN angle model based on the electromyographic signals; and finally, designing a joint action mapping relation from the arm to the mechanical arm according to the motion characteristics and the structural difference of the arm and the mechanical arm. According to the method, the electromyographic signals are integrated into the human-computer interaction process, so that the cooperative control of the human arms and the mechanical arms is effectively realized, the flexibility and the adaptability of the mechanical arms are improved, and the human-computer interaction experience is improved.
Description
Technical Field
The application belongs to the technical field of human-computer interaction of UR mechanical arms, and particularly relates to a mechanical arm mapping method based on motion capture and wrist joint angle estimation of human body characteristic signals.
Background
A robot arm is a mechanical device that mimics the structure and function of a human arm and that can perform various operational tasks, such as grasping, moving, assembling, welding, etc., within a certain space. The control modes of the mechanical arm mainly comprise two modes: the motion trail and parameters of the mechanical arm are preset in a programming mode, so that the mechanical arm can execute according to a program; the other is to use a remote controller or glove and other devices to control the action of the mechanical arm in real time by the operator in a manual mode. The former can ensure the accuracy and stability of the mechanical arm, but lacks flexibility and adaptability, and cannot cope with complex and varied environments; the latter, while improving the flexibility and adaptability of the robotic arm, requires high skill and experience from the operator and is prone to fatigue and mishandling from the operator.
In order to overcome the problems, the application provides a mechanical arm mapping method based on motion capture of human body characteristic signals and wrist joint angle estimation, which uses myoelectric signals and angle signals as motion information of human arms, realizes cooperative control of the human arms and the mechanical arms through technologies such as kinematic modeling, angle prediction, joint mapping and the like, and enables the mechanical arms to flexibly and naturally act according to intention and gestures of operators.
Disclosure of Invention
Aiming at the defects of the prior art, the application aims to provide the mechanical arm mapping method based on motion capture and wrist joint angle estimation of human body characteristic signals, which integrates electromyographic signals into the human-computer interaction process, effectively realizes cooperative control of human arms and mechanical arms, improves flexibility and adaptability of the mechanical arms and increases human-computer interaction experience.
The application provides a mechanical arm mapping method based on motion capture and wrist joint angle estimation of human body characteristic signals, which mainly comprises the following steps:
step 1: through carrying out kinematic modeling on the arm joints of a human body, designing a global frame, a local frame and a gForce arm ring frame according to Euler angle signals by utilizing an X-Y-Z fixed angle method, and obtaining joint angles of the shoulder and the elbow at the current moment by an Euler angle solving method of the arm joints of the upper limbs;
step 2: when the gForce arm band is worn on the forearm and the wrist joint of a subject, sEMG signals and IMU signals are respectively acquired, and the acquired electromyographic signals are subjected to pretreatment, feature extraction and envelope treatment, so that a smooth envelope curve can be obtained, and the change features of the electromyographic signals can be reflected; then, predicting the angle of the wrist joint by establishing a PSO-GRNN angle model based on the electromyographic signals;
step 3: the joint action mapping relation from the arm to the mechanical arm is designed on the basis of analyzing the movement characteristics and the structural differences of the arm and the mechanical arm.
Compared with the prior art, the technical scheme of the application has the following beneficial effects:
1. according to the application, the electromyographic signals and the angle signals are used as the motion information of the human arms, and the cooperative control of the human arms and the mechanical arms is realized through the technologies of kinematic modeling, angle prediction, joint mapping and the like, so that the mechanical arms can flexibly and naturally act according to the intention and gestures of operators, the flexibility and the adaptability of the mechanical arms are improved, and the mechanical arm is suitable for complex industrial environments and man-machine interaction scenes.
2. The gForce arm band is adopted as the data acquisition equipment, so that the gForce arm band can be conveniently worn on the forearm and wrist joint of a subject, equipment such as electrode plates and gloves are not required to be pasted, the burden and discomfort of an operator are reduced, and the data acquisition efficiency and comfort are improved.
3. According to the application, the PSO-GRNN angle predictor is used as a wrist joint angle estimation model, so that the characteristics of electromyographic signals can be effectively utilized, the accuracy and the instantaneity of wrist joint angle estimation are improved, meanwhile, the self-adaptive adjustment can be carried out according to different motion modes, and the generalization capability and the robustness of the model are enhanced.
Drawings
FIG. 1 is a schematic diagram of an overall arm map for implementing the method of the present application.
FIG. 2 is a schematic diagram of a global framework, a local framework, and a gForce arm ring framework in accordance with the present application.
FIG. 3 is a flow chart of the method of the present application.
Fig. 4 is a schematic diagram of a joint mapping relationship between an arm and a mechanical arm in the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent.
The application provides a mechanical arm mapping method based on motion capture of human body characteristic signals and wrist joint angle estimation, which utilizes a gForce bracelet developed by Shanghai's information technology limited company as data acquisition equipment, wherein the equipment can measure human body surface electromyographic signals (sEMG) and Inertial Measurement Unit (IMU) signals, including acceleration, angular velocity, magnetic field and the like. The device can be in wireless connection with a development board or a computer through Bluetooth, realizes real-time transmission and processing of data, can be conveniently worn on the forearm and wrist joint of a subject, does not need to be pasted with equipment such as electrode plates and wearing gloves, reduces the burden and uncomfortable feeling of operators, and improves the efficiency and the comfort of data acquisition. The whole arm mapping diagram is shown in fig. 1, and the specific operation steps of this embodiment are as follows:
step 1: the method comprises the steps of performing kinematic modeling on arm joints of a human body, designing a global frame, a local frame and a gForce arm ring frame according to Euler angle signals by using an X-Y-Z fixed angle method, and obtaining joint angles of shoulders and elbows at the current moment by using an Euler angle solving method of the arm joints of the upper limbs. FIG. 2 is a schematic diagram of a global framework, a local framework, and a gForce arm ring framework in the present application, where a global coordinate system (x G ,y G ,z G ) The x-axis points to the left; the y-axis is directed forward and the z-axis is directed upward. Two gForce arm rings are respectively worn at the middle parts of the upper arm and the forearm, and the gForce arm band frames are respectively (X) 1 ,Y 1 ,Z 1 ) And (X) 2 ,Y 2 ,Z 2 ) The upper arm partial frame (x H ,y H ,z H ) And forearm partial frameRack (x) F ,y F ,z F ) Coinciding with the global framework. Under the global framework, the directional rotation matrixes of the upper arm framework and the forearm framework can be obtained respectively as followsAnd->Wherein R is R 3×3 For the rotation matrix, 0 is the initial position, "G", "H" and "F" represent the global frame, upper arm frame and forearm frame, respectively. The partial frames of the upper arm and forearm are not stationary, the direction of the upper arm frame relative to the first gForce arm band frame and the direction of the forearm frame relative to the second gForce arm band frame are a constant matrix of +.>Andwherein, subscript "1" indicates a first gForce arm ring worn by the upper arm and subscript "2" indicates a second gForce arm ring worn by the forearm. When the operator moves the arm to a new posture, the directions of the upper arm and the forearm in the global frame are changed, and the rotation matrix of the upper arm and the forearm is +.>To describe. Where f represents the current arm position. From the first gForce arm ring at the upper arm the quaternion +.>Where (x, y, z) is a vector and w is a scalar. Because the quaternion and the Euler angle can be mutually converted, the azimuth of the lower arm and the upper arm of the global framework can be obtained through the quaternion>Similarly, the global can be derived from the second gForce arm ring at the forearmOrientation of the lower forearm of the frame->
According toThree angles of the shoulder joint, denoted q1, q2, q3, respectively, are obtained. Wherein q1 is adduction/abduction, q2 is shoulder anteversion/postextension, and q3 is internal rotation/external rotation; according to->An angle of the elbow joint and an angle of the radioulnar joint, denoted as q4, q5, respectively, can be obtained. Wherein q4 is elbow joint flexion/extension, and q5 is radioulnar joint supination front/back.
q4＝arccos(a 12 r 13 +a 22 r 23 +a 32 r 33 ) (10)
q5＝arccos(r 11 a 11 +r 21 a 21 +r 31 a 31 ) (11)
Step 2: myoelectric signals (sEMG) and Inertial Measurement Unit (IMU) signals at the forearm and wrist of the subject are acquired through the gForce arm strap, and the angle of the wrist is predicted using the myoelectric signals. For this reason, the IMU sensor that 8 passageway myoelectric sensor and 3 axle accelerometer, 3 axle gyroscopes, 3 axle magnetometers are constituteed is used, measures EMG signal and IMU signal respectively to develop the board through bluetooth wireless connection and give data to the computer, realize real-time transmission and the processing of data. And (3) sampling frequency of 250Hz is adopted for the acquired signals, each frame comprises 100 sampling points, 2.5 frames of data are acquired per second, a sliding time window method is adopted for resampling the upper limb sEMG signals and the wrist joint angle at the current moment, the two ends of the sliding time window correspond to the synchronization points of the sEMG signals and the joint angle signals, and the stepping length of the sliding time window ensures that the sEMG and the angle data in the sliding time window are mutually synchronized. For the collected electromyographic signals, the following three steps are performed: pretreatment: in order to eliminate direct current components and high-frequency noise in the electromyographic signals, the embodiment adopts a band-pass filter, the passband range of the band-pass filter is 20Hz-500Hz, the type of the filter is a Butterworth filter, the order of the filter is 4, and the amplitude range of the electromyographic signals after passing through the filter is-1.5V-1.5V; feature extraction: to extract the effective characteristics of the electromyographic signals, the present embodiment employs Root Mean Square (RMS) characteristics that reflect the strength of the electromyographic signalsDegree and trend of change, for the n-th frame electromyographic signal, its RMS l The characteristics of (n) are shown below, in which IEMG l (n) (l=1, 2 · · ·) 8) myoelectric sensor measuring a value of an n-th frame IEMG signal; envelope processing: in order to obtain a smooth envelope, the change characteristics of the electromyographic signals can be reflected. The envelope E (n) of the electromyographic signal is obtained by calculating the average value of the RMS characteristics, E (n) is the average value of L RMS characteristics of the nth frame, L is the number of sampling points contained in each frame, and in this embodiment, l=100.
Then, in order to predict the angle of the wrist joint by using the electromyographic signals, a PSO-GRNN angle prediction model is established, wherein the model is an angle prediction model combining a Particle Swarm Optimization (PSO) algorithm and a Generalized Regression Neural Network (GRNN), the model can effectively utilize the characteristics of the electromyographic signals, the accuracy and the instantaneity of the estimation of the angle of the wrist joint are improved, and meanwhile, the adaptive adjustment can be carried out according to different motion modes, so that the generalization capability and the robustness of the model are enhanced. The model comprises two PSO-GRNN angle predictors with the same network structure, and the PSO-GRNN angle predictors respectively correspond to two motion modes of palmar flexion/dorsiflexion and ulnar deviation/radial deviation. Putting the sEMG signal and the wrist joint angle at the current moment into a corresponding PSO-GRNN angle predictor for training according to the marked motion mode label, and taking the wrist joint angle vector with the same data length after the time stamp is shifted backwards for 30ms-300ms as a predictor training label; establishing two PSO-GRNN angle predictors with the same network structure and corresponding to the two motion modes; and (3) taking one sample at a time as a test set by using a k-fold cross validation method, taking the rest k-1 samples as training sets, and repeating the experiment for k times, so that each sample can be used as a test set number. Before each training, training data is randomly shuffled by taking a sample as a unit, and the fixed data sequence is reduced for a prediction modelOverfitting of parameters affects. According to the embodiment, parameters of the GRNN network are optimized by using a PSO algorithm according to sample data, wherein the parameters comprise smoothing parameters and weight parameters, and the smoothing parameters are used for controlling the shape of an activation function of a mode layer neuron to influence the fitting capacity and generalization capacity of the network; the weight parameter is used for controlling the linear combination mode of the neurons of the output layer, and influences the output value and the error value of the network. The method is mainly characterized in that in the prior art, the process of determining parameters of the generalized regression neural network has larger uncertainty and extremely low efficiency, and the accuracy of prediction calculation of the neural network model is seriously affected. The PSO algorithm is an optimization algorithm based on population intelligence, and finds an optimal solution by simulating the behaviors of biological populations such as shoals of birds or fish in nature, and has two important concepts: particles and fitness functions, the particles being the basic unit in the PSO algorithm, representing a feasible solution, i.e. a set of parameters of the GRNN network; the fitness function is an evaluation criterion in the PSO algorithm, and is used for measuring the advantages and disadvantages of particles, namely the prediction performance of the GRNN network. The basic idea of the PSO algorithm is: each particle flies in the search space while recording its own optimal position and global optimal position, and its own flight speed and direction are adjusted according to the two positions, so as to expect to find a better position. The PSO algorithm flow is as follows: 1. initializing a set of random particles, wherein each particle comprises two attributes of position and speed, the position represents a parameter of the GRNN network, and the speed represents the change rate of the parameter; 2. calculating the fitness value of each particle, namely the prediction error of the GRNN network; 3. comparing the fitness value of each particle with the historical optimal fitness value of each particle, and if the current fitness value is better, updating the historical optimal position of each particle; 4. comparing the historical optimal fitness value of each particle with the global optimal fitness value, and if the historical optimal fitness value of a certain particle is better, updating the global optimal position; 5. updating the speed and the position of each particle according to the historical optimal position and the global optimal position of each particle; 6. repeating the steps 2-5 until the preset iteration times or error threshold value is reached. Each PSO-GRNN angle predictor consists of four layers: input layer: this layer receives as input the envelope of the electromyographic signals, in this embodiment 8 neurons,respectively corresponding to 8 myoelectric sensors; mode layer: the layer calculates Euclidean distance between each sample and each sample in the training set according to the data of the input layer, and takes the distance as output. The mode layer has N neurons, wherein N is the number of samples in the training set; output layer: the layer calculates the predicted value of each sample according to the output of the mode layer, takes the predicted value as output, and the output layer is provided with 1 neuron corresponding to one angle of the wrist joint; bias layer: the layer calculates the error of each sample according to the output of the output layer, takes the error as the output, the offset layer is provided with 1 neuron corresponding to the error of one angle of the wrist joint, the time stamp of the output angle value of the predictor is integrally pushed forward for 30ms-300ms and is compared with the angle of the real-time wrist joint, the Root Mean Square Error (RMSE) of the evaluation standard is obtained, and when the RMSE of the evaluation standard is smaller than the set threshold value, the output angle of the predictor is accurate; and when the evaluation standard Root Mean Square Error (RMSE) is larger than a set threshold value, data are collected again for training. The evaluation criterion mean square error is expressed by the following formula. Wherein θ p For the prediction result of PSO-GRNN angle predictor, θ r Represents real-time knee angle, n=k.
In order to improve the prediction performance of the PSO-GRNN angle predictor, a motion mode classification algorithm is adopted, and the algorithm can judge whether the current motion mode is a palmar flexion/dorsiflexion motion mode or a ulnar deviation/radial deviation motion mode according to the envelope curve of the electromyographic signals, the characteristics of acceleration, angular velocity, magnetic field and the like of IMU signals, and input the current data into the corresponding PSO-GRNN predictor to conduct real-time angle prediction. After the model is trained, the trained PSO-GRNN angle predictor can be utilized to obtain two variable angle values q6 and q7 of palmar flexion/dorsiflexion and ulnar deviation/radial deviation of the wrist joint.
Step 3: the joint action mapping relation from the arm to the mechanical arm is designed on the basis of analyzing the movement characteristics and the structural differences of the arm and the mechanical arm. FIG. 4 shows an arm and a robot according to the present applicationAnd the joint mapping relation is shown in a schematic diagram, and through analysis of the motion characteristics and structural differences of the arm and the mechanical arm, the body arm is found to have 3 joints and 7 degrees of freedom, and the mechanical arm is found to have 6 joints and 6 degrees of freedom. Meanwhile, on the spatial distribution of the joints, the arm shoulder joint and the 1 st joint and the 2 nd joint of the robot are positioned at the same position, the arm elbow joint and the 3 rd joint of the robot are positioned at the same position, and the arm radioulnar joint and the wrist joint are positioned at the same position as the 4 th joint, the 5 th joint and the 6 th joint of the robot. According to the difference of the joint degree of freedom comparison and the spatial distribution position of the arm and the mechanical arm, the joint 1 and the joint 2 of the robot are similar to the shoulder joint structure of the arm, wherein the shoulder joint has one more internal rotation-external rotation degree of freedom, and no corresponding joint is similar to the 6-free mechanical arm; the joint 3 of the mechanical arm is similar to the elbow joint of the arm; the joints 4, 5 and 6 of the robot are similar to the radioulnar joint, wrist joint of an arm. According to the structural analysis of the arm and the mechanical arm, a joint action mapping relation from the arm to the mechanical arm is designed, and the mapping relation can be expressed as: the adduction (+)/abduction (-) q1 of the shoulder joint corresponds to the joint angle theta of the mechanical arm 1 The forward flexion (+)/backward extension (-) q2 corresponds to the joint angle theta of the mechanical arm 2 The method comprises the steps of carrying out a first treatment on the surface of the The elbow joint flexion (+)/extension (-) q4 corresponds to the joint angle theta of the mechanical arm 3 The method comprises the steps of carrying out a first treatment on the surface of the The radioulnar joint (wrist joint) has a supination (+)/supination (-) q5 corresponding to the arm joint angle theta 5 The method comprises the steps of carrying out a first treatment on the surface of the The palmar flexion (+)/dorsal extension (-) q6 of the wrist joint corresponds to the joint angle theta of the mechanical arm 4 The ulnar deviation (+)/radial deviation (-) q7 corresponds to the joint angle theta of the mechanical arm 6 。
The application has been described above with respect to the disclosed embodiments so that those skilled in the art of research may make and use the application, and various modifications of the embodiments will be apparent to those skilled in the art, without departing from the scope of the application, any simple modifications, analogous changes and modifications of the above embodiments according to the techniques of the application will still fall within the scope of the application.
Claims (4)
1. The mechanical arm mapping method based on motion capture and wrist joint angle estimation of human body characteristic signals is characterized by comprising the following steps of:
step 1: performing kinematic modeling on the arm joints of a human body, designing a global frame, a local frame and a gForce arm ring frame by using an X-Y-Z fixed angle method, and obtaining joint angles of the shoulder and the elbow at the current moment by using an Euler angle solving method of the arm joints of the upper limbs;
step 2: when the gForce arm band is worn on the forearm and the wrist joint of a subject, sEMG signals and IMU signals are collected, and the electromyographic signals are preprocessed, extracted in characteristics and subjected to envelope processing, so that smooth envelope lines reflecting the change characteristics of the electromyographic signals can be obtained; then, predicting the angle of the wrist joint by establishing a PSO-GRNN angle model based on the electromyographic signals;
step 3: the joint action mapping relation from the arm to the mechanical arm is designed based on the motion characteristics and the structural differences of the arm and the mechanical arm.
2. The human upper limb joint solution according to step 1 of claim 1, wherein: by defining a global coordinate system (x G ,y G ,z G ) The x-axis points to the left; the y-axis is directed forward and the z-axis is directed upward. Two gForce arm rings are respectively worn at the middle parts of the upper arm and the forearm, and the gForce arm band frames are respectively (X) 1 ,Y 1 ,Z 1 ) And (X) 2 ,Y 2 ,Z 2 ) The upper arm partial frame (x H ,y H ,z H ) And forearm partial frame (x) F ,y F ,z F ) Coinciding with the global framework. Under the global framework, the directional rotation matrixes of the upper arm framework and the forearm framework can be obtained respectively as followsAnd->Wherein R is R 3×3 For the rotation matrix, 0 is the initial position, "G", "H" and "F" represent the global frame, upper arm frame and forearm frame, respectively. The partial frames of the upper arm and forearm being not stationary, the upper arm frameA matrix in which the direction relative to the first gForce arm band frame and the direction of the forearm frame relative to the second gForce arm band frame are constant, respectively +.>And->Wherein, subscript "1" indicates a first gForce arm ring worn by the upper arm and subscript "2" indicates a second gForce arm ring worn by the forearm.
When the operator moves the arm to a new posture, the directions of the upper arm and the forearm in the global frame are changed, and the rotation matrix of the upper arm and the forearm is usedTo describe. Where f represents the current arm position. From the first gForce arm ring at the upper arm the quaternion +.>Where (x, y, z) is a vector and w is a scalar. Because the quaternion and the Euler angle can be mutually converted, the azimuth of the lower arm and the upper arm of the global framework can be obtained through the quaternion>Similarly, the orientation of the forearm under the global framework can be derived from the second gForce arm ring at the forearm +.>
According toThree angles of the shoulder joint, denoted q1, q2, q3, respectively, are obtained. Wherein q1 is adduction/abduction, q2 is shoulder anteversion/postextension, and q3 is internal rotation/external rotation; according to->An angle of the elbow joint and an angle of the radioulnar joint, denoted as q4, q5, respectively, can be obtained. Wherein q4 is elbow joint flexion/extension, and q5 is radioulnar joint supination front/back.
q4＝arccos(a 12 r 13 +a 22 r 23 +a 32 r 33 ) (6)
q5＝arccos(r 11 a 11 +r 21 a 21 +r 31 a 31 ) (7)
3. The wrist angle estimation method according to step 2 of claim 1, wherein: by establishing two PSO-GRNN angle predictors with the same network structure, the two PSO-GRNN angle predictors correspond to two motion modes of palmar flexion/dorsiflexion, ulnar deviation/radial deviation; and (3) putting the sEMG signals and the wrist joint angle at the current moment into a corresponding PSO-GRNN angle predictor for training according to the marked motion mode label. PSO after training-a GRNN angle predictor determining a current motion pattern by a motion pattern classification algorithm and inputting data to a corresponding PSO-GRNN predictor for real-time angle prediction according to the current motion pattern. The magnitude of the electromyographic signal is represented by a root mean square RMS characteristic, in which IEMG l (n) (l=1, 2 · · ·) 8) myoelectric sensor the value of the n-th frame IEMG signal is measured, the average value of the L RMS features of the nth frame is E (n); the real-time wrist joint angle theta and the corresponding sEMG signals obtained through calculation are input into the corresponding PSO-GRNN angle predictor for angle prediction through the classified motion modes, and two angle predicted values q6 and q7 of the palmar flexion/dorsiflexion and ulnar deviation/radial deviation of the wrist joint can be obtained.
4. The arm to arm joint mapping of step 3 of claim 1, wherein: through analysis of the motion characteristics and structural differences of the arm and the mechanical arm, the arm is found to have 7 degrees of freedom of 3 joints, and the mechanical arm is found to have 6 degrees of freedom of 6 joints. Meanwhile, on the spatial distribution of the joints, the arm shoulder joint and the 1 st joint and the 2 nd joint of the mechanical arm are positioned at the same position, the arm elbow joint and the 3 rd joint of the mechanical arm are positioned at the same position, and the arm radioulnar joint and the wrist joint are positioned at the same position as the 4 th joint, the 5 th joint and the 6 th joint of the robot. According to the difference of the joint degree of freedom comparison and the spatial distribution position of the arm and the mechanical arm, the joint 1 and the joint 2 of the robot are similar to the shoulder joint structure of the arm, wherein the shoulder joint has one more internal rotation/external rotation degree of freedom, and no corresponding joint is similar to the 6-free mechanical arm; the joint 3 of the mechanical arm is similar to the elbow joint of the arm; the joints 4, 5 and 6 of the robot are similar to the radioulnar joint, wrist joint of an arm. The adduction (+)/abduction (-) q1 of the shoulder joint corresponds to the joint angle theta of the mechanical arm 1 The forward flexion (+)/backward extension (-) q2 corresponds to the joint angle theta of the mechanical arm 2 The method comprises the steps of carrying out a first treatment on the surface of the The elbow joint flexion (+)/extension (-) q4 corresponds to the joint angle theta of the mechanical arm 3 The method comprises the steps of carrying out a first treatment on the surface of the The radioulnar joint (wrist joint) has a supination (+)/supination (-) q5 corresponding to the arm joint angle theta 5 The method comprises the steps of carrying out a first treatment on the surface of the The palmar flexion (+)/dorsal extension (-) q6 of the wrist joint corresponds to the joint angle theta of the mechanical arm 4 The ulnar deviation (+)/radial deviation (-) q7 corresponds to the joint angle theta of the mechanical arm 6 。
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