Teleoperation robot-assisted system and control method based on myo bracelet and virtual clamp

Abstract

The application discloses a teleoperation robot auxiliary system and control method based on a MYO bracelet and a virtual clamp, and the system comprises a teleoperation module, a trajectory learning module, an arm rigidity extraction module and a virtual clamp module; the arm rigidity extraction module acquires forearm electromyogram signals and muscle rigidity information based on the MYO bracelet; the teleoperation module realizes robot teleoperation of master-slave end space matching based on a TouchX device; the trajectory learning module acquires muscle rigidity information, robot state information and task information, learns an expert trajectory data set, and generates an optimal reference trajectory; the virtual clamp module generates auxiliary virtual clamp guide force based on the optimal reference trajectory and the robot state, realizes operation tremor suppression and environmental interference compensation through adaptive adjustment of trajectory guide force weight and muscle rigidity value, and feeds back to the operator through the TouchX device. The application can effectively improve the precision and efficiency of teleoperation.

Description

Technical Field

[0001] This invention relates to the field of teleoperated robot technology, specifically to a teleoperated robot auxiliary system and control method based on a MYO wristband and a virtual gripper. Background Technology

[0002] With the development of industrial automation, precision assembly, and hazardous operations, robots have become an important tool for replacing manual labor. Especially in complex and high-risk work environments, teleoperated robots can help operators remotely complete high-precision tasks. However, traditional teleoperation systems usually rely on physical input devices such as joysticks or buttons. This approach suffers from problems such as large operational errors, steep learning curves, low accuracy, susceptibility to hand tremors and environmental interference, lack of real-time feedback, and difficulty adapting to changes in dynamic environments.

[0003] Currently, issues such as environmental interference, operator unfamiliarity, and hand tremors exist during robot teleoperation, making it difficult for teleoperated robots to complete tasks requiring high precision. Furthermore, when obstacles are present in the environment, collisions are easily caused by disturbances.

[0004] For example, the patent publication number CN113386142A describes a remote-operated robot grinding and cutting integrated processing system and method based on a virtual fixture. This system includes acquiring the residual features of the casting and the relative pose of the end effector of the driven robot through a vision system, generating a virtual fixture suitable for processing the residual features, and constraining and guiding the end motion of the actively controlled robot through an artificial repulsive force field applied by the virtual fixture, thereby coordinating the motion of the driven processing robot to complete the processing task. However, it relies on a relatively complex vision system and virtual fixture control, requires high computing resources to handle the adjustment of the virtual fixture and force field control, and does not provide feedback to the operator to guide the operator's remote operation.

[0005] For example, patent publication number CN115284317A describes a remote-operated robot grinding method based on force-sensing virtual fixture assistance. This method uses a virtual environment display module and CHAI3D software to create virtual fixtures adapted to the grinding needs of different castings. After the virtual fixture is created, its posture is adjusted to ensure that the virtual fixture does not collide with the end effector of the master hand, and the operator's operation is guided by calculated feedback force. However, this method does not recognize the operator's intentions, nor does it handle sudden external interference. Summary of the Invention

[0006] To overcome the shortcomings and deficiencies of existing technologies, this invention provides a teleoperation robot assistance system and control method based on a MYO wristband and a virtual gripper. This invention collects expert trajectories, establishes a trajectory library using these trajectories, and performs teleoperation on the robot using a TouchX device. At the start of teleoperation, the MYO wristband acquires the operator's muscle activity and stiffness, representing human intent. Muscle stiffness information, robot state information, and task information are input into the trajectory learning module. The dynamic system in the trajectory learning module generates an optimal reference trajectory. During teleoperation, the virtual gripper module combines the optimal reference trajectory and robot state to generate an auxiliary virtual gripper guiding force, which is fed back to the operator via TouchX, guiding the operator's hand for teleoperation. This invention effectively improves the accuracy and efficiency of teleoperation.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] This invention provides a teleoperation robot assistance system based on a MYO wristband and a virtual gripper, comprising: a teleoperation module, a trajectory learning module, an arm stiffness extraction module, and a virtual gripper module;

[0009] The arm stiffness extraction module acquires forearm electromyography signals and muscle stiffness information based on the MYO wristband;

[0010] The teleoperation module enables robot teleoperation based on TouchX devices with master-slave spatial matching.

[0011] The trajectory learning module acquires muscle stiffness information, robot state information, and task information, learns from expert trajectory datasets, and generates the optimal reference trajectory.

[0012] The virtual gripper module generates an auxiliary virtual gripper guiding force based on the optimal reference trajectory and robot status, and feeds it back to the operator through the TouchX device.

[0013] The present invention also provides a control method for a teleoperated robot-assisted system based on a MYO wristband and a virtual gripper, comprising the following steps:

[0014] Collect expert trajectory data and environmental obstacle information, complete robot tasks based on the teleoperation module, and generate a demonstration dataset;

[0015] A dynamic system model for trajectory learning is constructed in the trajectory learning module and trained based on an expert trajectory dataset;

[0016] Remote control of the robot using TouchX devices, and spatial matching between master and slave ends;

[0017] Forearm electromyography signals and muscle stiffness information are acquired using the MYO wristband;

[0018] By inputting muscle stiffness information, robot state information, and task information into the trained trajectory learning dynamic system model, the optimal reference trajectory is obtained.

[0019] The virtual gripper guiding force is calculated based on the robot's current real-time position, optimal reference trajectory, and muscle stiffness information.

[0020] As a preferred technical solution, the demonstration dataset is represented as follows:

[0021] Where, x t,n Let represent the robot's position and state at time t during the nth demonstration. For velocity state, x obj For obstacle information in the environment, A t,n This refers to the stiffness value of the expert's arm.

[0022] As a preferred technical solution, the steps for generating the demonstration dataset include:

[0023] Robot's position state x t,n and speed state Obtained through remote operation of the slave robot via the master-end tactile device;

[0024] The coordinate system of the master and slave robots is calibrated, and the initial pose X of the master haptic device is defined. l0 =[x l0 ,y l0 ,z l0 [and the initial pose X of the slave robotic arm] f0 =[x f0 ,y f0 ,z f0 ], by constructing a scaling matrix S = diag(S x ,S y ,S z ), so that the master-slave displacement satisfies X f =X f0 +S·(X l -X l0 Real-time pose mapping is performed on the master and slave robots, and the real-time pose X of the tactile device is collected. l (t)=[x l (t),y l (t),z l [(t)], calculate the pose of the end target and send it to the robot for execution;

[0025]

[0026] The joint angles are calculated using inverse kinematics and sent to the slave robotic arm to obtain the robot's position state x for performing the task. t,nThe velocity state is obtained by taking the time derivative with respect to the position.

[0027] Acquire the electromyography (EMG) signal from the i-th channel of the MYO wristband. i The sum of the electromyographic signal amplitudes is calculated as follows:

[0028]

[0029] The envelope E is obtained by performing a sliding window filter on the sum of the electromyographic signal amplitudes.

[0030]

[0031] Among them, w win The length of the sliding window;

[0032] The muscle stiffness value of an expert's arm is expressed as:

[0033]

[0034] Where α is a nonlinear coefficient.

[0035] As a preferred technical solution, a dynamic system model for trajectory learning in the trajectory learning module is constructed, specifically represented as follows:

[0036]

[0037] in, Represents the energy function. Let x represent the modulation matrix, z represent the robot state input, and z represent the vector composed of the robot state input, obstacle information, energy value at state x, and muscle stiffness A.

[0038] As a preferred technical solution, the energy function Specifically, it is expressed as follows:

[0039]

[0040] The energy function V(x) consists of a learnable neural network P1(x) and a quadratic form P2(x), where the P1(x) term is the weight parameter w and the feature extraction function f. k The weighted sum of (x), g(x) maps the input from dx-dimensional to dh-dimensional, c(x) performs a nonlinear feature expansion on the state x, and the parameter a k b k The feature parameters are and the activation function is . It is the tanh function, and the parameter κ is the coefficient of the quadratic term;

[0041] Vector z is represented as:

[0042]

[0043] in, Represents a trajectory learning dynamic system model, x obj Indicates obstacle information. Let x represent the energy value at state x, and A represent muscle stiffness.

[0044] Based on the distance between the current end-effector and the obstacle, and the degree of arm muscle tension, the vector z is multiplied by a degree factor:

[0045]

[0046]

[0047] Among them, parameters α1 and α2 determine the smoothness of the tracking and obstacle avoidance switching process, β represents the safety margin, and d represents the distance from the current robot position to the obstacle;

[0048] When a vector z is input into a three-layer fully connected neural network, the output is a six-dimensional vector:

[0049] n = Net(z; Θ) N )

[0050] Based on a six-dimensional vector n, a lower triangular matrix is ​​constructed, which is represented as:

[0051]

[0052] Constructing a positive definite matrix based on Cholesky's formula

[0053] As a preferred technical solution, remote control of the slave robot is performed using TouchX devices, enabling master-slave spatial matching:

[0054]

[0055] Among them, X l0 X f0 Let X be the initial Cartesian coordinates of Touch X and the end effector position of the slave robot. l (t) represents the real-time position of the main robot, X f (t) represents the real-time position of the slave robot;

[0056] The position of the master robot is obtained in real time, and the desired position of the slave robot's end effector is obtained through matching calculation. The result is then sent to the robot for execution to achieve teleoperation.

[0057] As a preferred technical solution, the virtual gripper guiding force value is calculated based on the robot's current real-time end position, optimal reference trajectory, and muscle stiffness information, specifically expressed as follows:

[0058]

[0059] Among them, A max A min The set muscle stiffness amplitude, k is a manually adjusted distance weighting factor, and x c,best This is the optimal reference trajectory. Mid-range current robot end-effector real-time position X B The nearest point.

[0060] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0061] (1) The present invention designs a virtual gripper module, which can provide different degrees of guiding force assistance to the operator based on information such as robot status, task objectives, and operator muscle status, so as to improve the operator's accuracy.

[0062] (2) The present invention designs a trajectory learning module based on an autonomous dynamic system that can take into account multiple information such as the operator's arm stiffness information, task information and environmental information to generate the optimal guidance trajectory, providing assistance to inexperienced teleoperators or providing more accurate guidance to skilled operators.

[0063] (3) This invention collects robot information and human intentions through MYO wristband and TouchX device, and adjusts the parameters of virtual gripper module in real time to provide different levels of assistance to operators in different remote operation tasks. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the implementation architecture of the teleoperated robot assistance system based on MYO wristband and virtual gripper of the present invention;

[0065] Figure 2 This is a flowchart illustrating the control method of the teleoperated robot assistance system based on the MYO wristband and virtual gripper of the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0067] Example

[0068] like Figure 1 As shown, this embodiment provides a teleoperation robot assistance system based on MYO wristband and virtual gripper, including: teleoperation module, trajectory learning module, arm stiffness extraction module and virtual gripper module;

[0069] The purpose of the teleoperation module is to enable remote control of the robot via external devices, allowing the robot to complete tasks in dangerous or inaccessible environments. In this embodiment, the teleoperation module is built using a TouchX device. First, the operation spaces of the robot and the teleoperation device are paired through master-slave space matching to ensure that the posture of the teleoperation device matches the robot's end effector and that the robot can move within a reasonable range when the operator remotely controls it. Then, the smoothness and flexibility of the operator's control over the robot are adjusted through a proportional adjustment factor to ensure the sensitivity and responsiveness of the teleoperation. The TouchX device can remotely control the robot using a handheld joystick, and the built-in gyroscope can map and match the end effector of the joystick with the robot's end effector posture.

[0070] The trajectory learning module is used to model and generalize the collected and constructed expert trajectories. In the teleoperation system, the trajectory learning module extracts features, learns and models the trajectories of some expert teleoperations to complete tasks. Then, when an untrained operator performs a teleoperation task, the optimal reference trajectory is automatically generated based on the task information. Based on the optimal reference trajectory and the robot's state, guidance is generated at the teleoperation device to guide the operator to complete the task more stably and accurately. The trajectory learning module is implemented through an autonomous dynamic system and has several dynamically adjustable parameters, including the number of layers in the neural network, the number of neurons in the hidden layer, and the feature parameters in the energy function.

[0071] The arm stiffness extraction module extracts the operator's arm stiffness and muscle activity information. In the teleoperation system, the operator wears a MYO wristband to acquire the operator's forearm electromyography (EMG) signals. Muscle stiffness values ​​are obtained through sliding window filtering and nonlinear transformation. By processing this information, the operator's intentions can be recognized. Based on the tension of the operator's arm muscles, the trajectory learning module's focus and the feedback of the virtual gripper's guiding force are adjusted, thereby improving the intelligence and flexibility of the teleoperation system and enhancing the operator's experience. The MYO wristband collects the operator's surface EMG signals through eight sensor channels and transmits the data wirelessly to the computer at the operating end in real time via Bluetooth. The received EMG signals are filtered and finally converted into the operator's muscle activity data through an algorithm.

[0072] The virtual gripper module acquires the robot's status and optimal reference trajectory in real time, searches for the robot's current optimal target point on the optimal reference trajectory, constructs the gravitational resultant force between the optimal target point and the task completion point on the robot's end effector, and feeds it back to the teleoperation device to guide the operator in remotely controlling the robot. The virtual gripper module contains multiple dynamically adjustable parameters, including the gravitational weight of the task completion point on the robot, the gravitational weight of the optimal trajectory point on the robot, and attention weights that are dynamically adjusted according to the operator's arm muscle tension.

[0073] like Figure 2 As shown, this embodiment also provides a control method for a teleoperated robot assistance system based on a MYO wristband and a virtual gripper. The operator is a user holding a Touch X end effector and a MYO wristband. The Touch X end effector is used to remotely control the robot, and the MYO wristband is used to collect the operator's muscle activity to obtain arm stiffness values ​​and acquire the operator's intention. The operator and the robot are connected via a network. The specific steps include:

[0074] S1: Collect expert trajectory data and environmental obstacle information. Human experts complete robot tasks through teleoperation teaching, generating a demonstration dataset. Where x t,n Let represent the robot's position and state at time t during the nth demonstration. For velocity state, x obj For obstacle information in the environment (manually entered), A t,n This refers to the stiffness value of the expert's arm.

[0075] Robot state data x t,n , The coordinate system of the master and slave robots is first calibrated by teleoperating the slave robot using the master haptic device, and the initial pose X of the master haptic device is defined. l0 =[x l0 ,y l0 ,z l0 [and the initial pose X of the slave robotic arm] f0 =[x f0 ,y f0 ,z f0 By designing a suitable scaling matrix S = diag(S x ,S y ,S z ), so that the master-slave displacement satisfies X f =X f0 +S·(X l -X l0 Then, real-time pose mapping is performed on the master and slave robots to collect the real-time pose X of the tactile device. l (t)=[x l (t),y l (t),z l [(t)], calculate the pose of the end target and send it to the robot for execution;

[0076]

[0077] The joint angles are solved using inverse kinematics and sent to the slave robotic arm, thus obtaining the position data x for the robot to perform the task. t,nBy taking the time derivative with respect to the position, we can obtain...

[0078] Calculating arm stiffness requires first obtaining the raw electromyography (EMG) signals from the 8 channels acquired by the MYO wristband. i EMG i Let represent the electromyographic signal acquired by the i-th channel of the MYO wristband, and k be the current sampling time. Then, the sum of the amplitudes S of the eight raw electromyographic signals acquired by the MYO wristband at one sampling time can be expressed as:

[0079]

[0080] E is defined as the envelope of the sum of the amplitudes of the eight raw electromyographic signals, w win Let S be the length of the sliding window. The envelope E can be obtained by performing sliding window filtering on the sum S of the amplitudes of the eight raw electromyographic signals:

[0081]

[0082] Let A be the muscle stiffness and α be the nonlinear coefficient. By transforming the envelope E of the sum of the amplitudes of the eight electromyographic signals, the muscle stiffness A can be obtained:

[0083]

[0084] S2: After obtaining the expert dataset, the trajectory learning dynamic system model in the trajectory learning module is constructed as follows:

[0085]

[0086] Where the energy function Calculated using the following formula:

[0087]

[0088] The energy function V(x) mainly consists of a learnable neural network P1(x) and a quadratic form P2(x), where the P1(x) term is the weight parameter w and the feature extraction function f. k The weighted sum of (x), g(x) maps the input from dx-dimensional to dh-dimensional, c(x) performs a nonlinear feature expansion on x, enabling the system to capture complex nonlinear relationships, and the parameter a k b k The feature parameters are and the activation function is . The function is tanh, and the quadratic form P2(x) guarantees the radial unboundedness of the energy function V(x), with parameter κ being the coefficient of the quadratic term;

[0089] Modulation matrix The calculation method is as follows: first, input the robot state x and... Obstacle information x obj Energy value at state x Combined with muscle stiffness A, they form a vector z:

[0090]

[0091] The vector z needs to be multiplied by a degree factor based on the distance between the current end position and the obstacle, and the degree of arm muscle tension.

[0092]

[0093] The parameters α1 and α2 are considered to determine the smoothness of the tracking and obstacle avoidance switching process, β represents the safety margin, and d represents the distance from the current robot position to the obstacle. Based on the input z, it can be divided into the following cases: when the distance to the obstacle is far, α1 is large and α2 is small, and the input focuses more on the position x, i.e., the task trajectory. When the distance is close, α1 is small and α2 is large, and the input focuses more on the obstacle avoidance part. The arm stiffness A is used as a complete input; a large value indicates arm tension, possibly due to encountering an obstacle, or the need to perform high-precision operations without encountering an obstacle; a small value indicates simple teleoperation and following tasks. z will be used as an input vector for a neural network. The parameters α1, α2, and the arm stiffness value A will affect the activation of different neurons in the neural network, representing different states encountered during teleoperation. z is input into a three-layer fully connected neural network with 80 hidden layer neurons, the activation function being the tanh function, and the output being a six-dimensional vector n.

[0094] n = Net(z; Θ) N )

[0095] Let a six-dimensional vector n form a lower triangular matrix N, where the values ​​of matrix N are:

[0096]

[0097] Then, the positive definite matrix M is constructed using the Cholesky formula:

[0098]

[0099] Combining M and V forms a trajectory learning system:

[0100]

[0101] The parameters of the model can be obtained by optimizing and training the parameters using an expert dataset and solving the optimization problem using the following loss function:

[0102]

[0103] Once the optimal model parameters are obtained, the model can be constructed and expert trajectories can be generalized:

[0104] S3: Start using the master robot's TouchX haptic device to remotely control the slave robot and perform master-slave spatial matching;

[0105]

[0106] Among them, X l0 X f0 Let X be the initial Cartesian coordinates of Touch X and the end effector position of the slave robot. l (t) represents the real-time position of the main robot, X f (t) represents the real-time position of the slave robot. The position of the master robot is obtained in real time, and then the desired position of the slave robot's end is obtained through matching calculation. Then, the position is sent to the robot for execution to realize teleoperation.

[0107] S4: Obtain the electromyographic signal of the operator's arm through the MYO device on the operator's arm to obtain the stiffness value A of the operator's arm;

[0108] S5: Based on the task requirements, input the task start point information and the operator arm stiffness A into the trained trajectory learning dynamic system model to obtain the optimal reference trajectory.

[0109] S6: Based on the robot's current real-time end position X B Optimal reference trajectory The virtual gripper guiding force value is calculated using the following formula, based on the operator arm stiffness value A:

[0110]

[0111] Among them, A max A min The set muscle stiffness amplitude, k is a manually adjusted distance weighting factor, and x c,best This is the optimal reference trajectory. Mid-range current robot end-effector real-time position X B The nearest point. The larger the value of the operator's arm stiffness A, the more it indicates that the task requires precise alignment or that there is an emergency situation where obstacles are encountered. In this case, the value of the feedback force F will be increased, allowing the virtual fixture to better guide the operator to perform teleoperation and effectively reducing the operator's hand tremor.

[0112] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A control method for a teleoperated robot-assisted system based on a MYO wristband and a virtual gripper, characterized in that, The teleoperation robot assistance system based on MYO wristband and virtual gripper includes: teleoperation module, trajectory learning module, arm stiffness extraction module and virtual gripper module; The arm stiffness extraction module acquires forearm electromyography signals and muscle stiffness information based on the MYO wristband; The teleoperation module enables robot teleoperation based on TouchX devices with master-slave spatial matching. The trajectory learning module acquires muscle stiffness information, robot state information, and task information, learns from expert trajectory datasets, and generates the optimal reference trajectory. The virtual gripper module generates an auxiliary virtual gripper guiding force based on the optimal reference trajectory and robot status, and feeds it back to the operator through the TouchX device. Includes the following steps: Collect expert trajectory data and environmental obstacle information, complete robot tasks based on the teleoperation module, and generate a demonstration dataset; A dynamic system model for trajectory learning is constructed in the trajectory learning module and trained based on an expert trajectory dataset; The trajectory learning dynamic system model in the trajectory learning module is constructed as follows: ； in, Represents the energy function. Represents the modulation matrix, This indicates the robot's status input. This indicates the robot's state input, obstacle information, and state. The vector formed by the combination of the energy value at the point and the muscle stiffness A; Energy function Specifically, it is expressed as follows: ； The energy function V(x) consists of a learnable neural network P1(x) and a quadratic form P2(x), where the P1(x) term is the weight parameter w and the feature extraction function f. k The weighted sum of (x), g(x) maps the input from dx-dimensional to dh-dimensional, and c(x) represents the state. Perform nonlinear feature expansion, parameters , The feature parameters are ϱ(β), the activation function is ϱ(β), and the parameters are tanh. The coefficient of the quadratic term; Vector z is represented as: ； in, This represents a trajectory learning dynamic system model. Indicates obstacle information. Representing state The energy value at the point, where A represents muscle stiffness; Based on the distance between the current end-effector and the obstacle, and the degree of arm muscle tension, the vector z is multiplied by a degree factor: ； ； ； in, For nonlinear coefficients, parameters , This determines the smoothness of the tracking and obstacle avoidance switching process. The safety margin is indicated by d, which represents the distance from the current robot position to the obstacle. When a vector z is input into a three-layer fully connected neural network, the output is a six-dimensional vector: ； Based on a six-dimensional vector n, a lower triangular matrix is ​​constructed, which is represented as: ； Constructing a positive definite matrix based on Cholesky's formula ; Remote control of the robot using TouchX devices, and spatial matching between master and slave ends; Forearm electromyography signals and muscle stiffness information are acquired using the MYO wristband; By inputting muscle stiffness information, robot state information, and task information into the trained trajectory learning dynamic system model, the optimal reference trajectory is obtained. The virtual gripper guiding force is calculated based on the robot's current real-time position, optimal reference trajectory, and muscle stiffness information.

2. The control method for the teleoperated robot auxiliary system based on the MYO wristband and virtual gripper according to claim 1, characterized in that, The demonstration dataset is represented as follows: ; in, Let represent the robot's position and state at time t during the nth demonstration. In terms of speed state, Information about obstacles in the environment. This refers to the stiffness value of the expert's arm.

3. The control method for the teleoperated robot-assisted system based on the MYO wristband and virtual gripper according to claim 2, characterized in that, The steps for generating the demonstration dataset include: robot position status and speed state Obtained through remote operation of the slave robot via the master-end tactile device; The coordinate system of the master and slave robots is calibrated, and the initial pose of the master haptic device is defined. Initial pose of the slave robotic arm By constructing a scaling matrix So that the master-slave displacement satisfies Real-time pose mapping is performed on the master and slave robots to collect the real-time pose of the tactile devices. The target pose is calculated and sent to the robot for execution. ； The joint angles are calculated using inverse kinematics and sent to the slave robotic arm to obtain the robot's position state for performing the task. The velocity state is obtained by taking the time derivative with respect to the position. ; Acquire the electromyography signal from the i-th channel of the MYO wristband. The sum of the electromyographic signal amplitudes is calculated as follows: ； The envelope 𝐸 is obtained by performing a sliding window filter on the sum of the electromyographic signal amplitudes. ； in, The length of the sliding window; The muscle stiffness value of an expert's arm is expressed as: ； in, These are nonlinear coefficients.

4. The control method for the teleoperated robot-assisted system based on the MYO wristband and virtual gripper according to claim 1, characterized in that, Remote control of the robot using TouchX devices, and spatial matching between master and slave ends: ； in, , These are the initial Cartesian coordinates of Touch X and the end effector position of the slave robot. Real-time location of the main robot Real-time position of the slave robot; The position of the master robot is obtained in real time, and the desired position of the slave robot's end effector is obtained through matching calculation. The result is then sent to the robot for execution to achieve teleoperation.

5. The control method for the teleoperated robot auxiliary system based on the MYO wristband and virtual gripper according to claim 2, characterized in that, Based on the robot's current real-time end-effector position, optimal reference trajectory, and muscle stiffness information, the virtual gripper guiding force value is calculated, specifically expressed as: ； Among them, A max A min The set muscle stiffness amplitude, k is a manually adjusted distance weighting factor. This is the optimal reference trajectory. Mid-range current real-time position of the robot's end effector The nearest point.

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