Control method of a telemedicine robot and control system for controlling a telemedicine robot

Abstract

The disclosure provides a remote medical robot control method and a control system for controlling a remote medical robot, which can be applied to the technical field of medicine and the technical field of mechanical control. The remote medical robot control method comprises: in response to the master-slave mapping frequency of the communication signal between the master end and the slave end not satisfying the frequency threshold, extracting the real-time pose sequence of the slave end from the control information for controlling the slave end, the master end being used for responding to user operation and interacting with the slave end through the communication signal, and the slave end being used for controlling the remote medical robot to perform actions based on the control information; inputting the real-time pose sequence into a target prediction model to obtain a predicted pose sequence; performing trajectory interpolation on the predicted pose sequence according to the trajectory path of the predicted pose sequence to obtain a target pose sequence, the target master-slave mapping frequency satisfying the frequency threshold; inputting the target pose sequence into the controller of the slave end to generate a control instruction; and controlling the remote medical robot based on the control instruction.

Description

Technical Field

[0001] This disclosure relates to the fields of medical technology and mechanical control technology, and more specifically to a control method for a telemedicine robot and a control system for controlling the telemedicine robot. Background Technology

[0002] Remote surgery is a paradigm of minimally invasive surgery, dividing the surgical robot into a master end operated remotely by the surgeon and a slave end executed on-site during the operation. Due to communication network limitations, the communication frequency in remote surgery is much lower than the master-slave mapping frequency of the surgical robot. For example, the master-slave mapping frequency between the master and slave ends can reach over 1000 Hz. However, due to network limitations in remote surgery, the master-slave mapping frequency corresponding to the control data transmitted from the master end to the slave end is often less than 100 Hz, reducing the accuracy of the control data.

[0003] In realizing the concept disclosed herein, the inventors discovered at least the following problems in the related technology: the low master-slave mapping frequency in remote surgery reduces the accuracy of control data, resulting in reduced real-time performance and security of remote surgery. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a control method for a telemedicine robot and a control system for controlling the telemedicine robot.

[0005] According to a first aspect of this disclosure, a control method for a remote medical robot is provided, comprising: responding to a master-slave mapping frequency of a communication signal between a master and a slave that does not meet a frequency threshold; extracting a real-time pose sequence of the slave from control information used to control the slave, wherein the control information is received by the slave from the master via a communication signal, the master is used to interact with the slave via a communication signal in response to a user operation, and the slave is used to control the remote medical robot to perform actions based on the control information; inputting the real-time pose sequence into a target prediction model to obtain a predicted pose sequence for a future time; performing trajectory interpolation on the predicted pose sequence according to the trajectory path of the predicted pose sequence to obtain a target pose sequence corresponding to the target master-slave mapping frequency, wherein the target master-slave mapping frequency meets the frequency threshold; inputting the target pose sequence into the controller of the slave to generate control commands; and controlling the remote medical robot based on the control commands.

[0006] According to embodiments of this disclosure, extracting the real-time pose sequence of the slave end from control information for controlling the slave end includes: determining the joint angles of the slave end at multiple times from the control information; inputting the joint angles at multiple times into a kinematic model to obtain the real-time pose sequence of the slave end.

[0007] According to embodiments of this disclosure, the process of interpolating the predicted pose sequence based on its trajectory path to obtain a target pose sequence corresponding to the target master-slave mapping frequency includes: determining key points from the trajectory path that satisfy a preset target curvature threshold, where the key points represent the trajectory direction of the trajectory path; segmenting the trajectory path based on the key points to obtain multiple trajectory sub-paths; and interpolating the multiple trajectory sub-paths based on the pose change rate of the trajectory path to obtain a target pose sequence corresponding to the target master-slave mapping frequency.

[0008] According to embodiments of this disclosure, interpolating multiple trajectory sub-paths based on the pose change rate of the trajectory path to obtain a target pose sequence corresponding to the target master-slave mapping frequency includes: interpolating the trajectory sub-paths based on the pose change rate of the trajectory sub-paths to obtain target trajectory sub-paths; and splicing the multiple target trajectory sub-paths together based on their respective time information to obtain a target pose sequence corresponding to the target master-slave mapping frequency.

[0009] According to embodiments of this disclosure, interpolating a trajectory sub-path based on its pose change rate to obtain a target trajectory sub-path includes: determining a path segment to be interpolated from the trajectory sub-path based on a constraint condition between the pose change rate of the trajectory sub-path and a preset pose change rate, wherein the constraint condition indicates that the pose change rate is less than the preset pose change rate; determining a target interpolator algorithm based on the pose change rate of the path segment to be interpolated; and performing trajectory interpolation on the path segment to be interpolated using the target interpolator algorithm to obtain the target trajectory sub-path.

[0010] According to embodiments of this disclosure, determining the target interpolator algorithm based on the pose change rate of the path segment to be interpolated includes: determining the target interpolator algorithm as a linear interpolation algorithm in response to a linear change in the pose change rate of the path segment to be interpolated; and determining the target interpolator algorithm as a curve interpolation algorithm in response to a non-linear change in the pose change rate of the path segment to be interpolated.

[0011] According to embodiments of this disclosure, the target prediction model is trained in the following manner: acquiring historical master pose sequences and historical slave pose sequences at historical moments, wherein the historical control information generated by the master includes the historical master pose sequence, and the historical control commands generated by the slave at historical moments include the historical slave pose sequence; inputting the historical master pose sequence into the prediction model to obtain the predicted pose sequence; and training the prediction model based on the error value between the predicted pose sequence and the historical slave pose sequence to obtain the target prediction model.

[0012] According to embodiments of this disclosure, training a prediction model based on the error value between the predicted pose sequence and the historical slave pose sequence to obtain a target prediction model includes: updating the model parameters of the prediction model in response to the error value between the predicted pose sequence and the historical slave pose sequence being greater than an error threshold, thereby obtaining a prediction model to be determined; processing sample master pose sequences in a continuous preset period in response to the prediction model to be determined, thereby obtaining sample slave pose sequences; and determining the prediction model to be determined as the target prediction model in response to the periodic error value between the sample slave pose sequence and the label slave pose sequence in a continuous preset period being less than or equal to an error threshold.

[0013] A second aspect of this disclosure provides a control system for controlling a remote medical robot. The control system includes: a master terminal for generating control information for controlling a slave terminal in response to a user's control operation; and a slave terminal for receiving the control information sent by the master terminal and executing the aforementioned control method for the remote medical robot.

[0014] According to embodiments of this disclosure, the system further includes: a mapping medical robot, which is communicatively connected to a master terminal; the master terminal is further configured to: generate control information to be mapped in response to a user performing a control operation on the mapping medical robot; and map the control information to be mapped to obtain control information.

[0015] A third aspect of this disclosure provides a control device for a remote medical robot, comprising: an extraction module, configured to extract a real-time pose sequence of the slave end from control information used to control the slave end in response to a master-slave mapping frequency of a communication signal between a master end and a slave end not meeting a frequency threshold; the control information is received by the slave end from the master end via a communication signal; the master end is used to interact with the slave end via communication signals in response to user operation; and the slave end is used to control the remote medical robot to perform actions based on the control information; a first input module, configured to input the real-time pose sequence into a target prediction model to obtain a predicted pose sequence for a future time; a trajectory interpolation module, configured to perform trajectory interpolation on the predicted pose sequence according to the trajectory path of the predicted pose sequence to obtain a target pose sequence corresponding to the target master-slave mapping frequency, the target master-slave mapping frequency meeting a frequency threshold; a second input module, configured to input the target pose sequence into the controller of the slave end to generate control commands; and a control module, configured to control the remote medical robot based on the control commands.

[0016] A fourth aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0017] The fifth aspect of this disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0018] A sixth aspect of this disclosure also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0019] According to embodiments of this disclosure, in response to a master-slave mapping frequency of the communication signal between the master and slave ends not meeting a frequency threshold, the real-time pose sequence of the slave end can be predicted to obtain a predicted pose sequence. Trajectory interpolation is then performed on the predicted pose sequence based on its trajectory path to obtain a target pose sequence that meets the frequency threshold. This effectively converts a low-frequency predicted pose sequence into a high-frequency target pose sequence, improving the accuracy of the pose sequence. The target pose sequence is input to the controller at the slave end to generate control commands. Based on these commands, the remote medical robot is controlled, thereby reducing the impact of latency on the robot's movement, achieving synchronous pose tracking, improving the real-time performance and safety of remote surgery, and ensuring the accuracy of remote surgical operations. Attached Figure Description

[0020] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0021] Figure 1 This diagram schematically illustrates an application scenario of a control method for a remote medical robot according to an embodiment of the present disclosure.

[0022] Figure 2 A flowchart illustrating a control method for a telemedicine robot according to an embodiment of the present disclosure is shown schematically.

[0023] Figure 3 This schematically illustrates a flowchart of a process for interpolating a predicted pose sequence based on the trajectory path of the predicted pose sequence, according to an embodiment of the present disclosure, to obtain a target pose sequence corresponding to the target master-slave mapping frequency.

[0024] Figure 4 The flowchart illustrating the training method of the target prediction model is shown in the diagram.

[0025] Figure 5 A schematic diagram illustrating the structure of a control system for controlling a telemedicine robot is shown.

[0026] Figure 6 A schematic block diagram of a control device for a telemedicine robot according to an embodiment of the present disclosure is shown; and

[0027] Figure 7 A block diagram of an electronic device suitable for implementing a control method for a telemedicine robot according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0028] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0031] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0032] In the technical solution disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0033] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this disclosure all offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0034] Embodiments of this disclosure provide a control method for a remote medical robot, comprising: responding to a situation where the master-slave mapping frequency of the communication signal between the master and slave ends does not meet a frequency threshold; extracting a real-time pose sequence of the slave end from control information used to control the slave end, wherein the control information is received by the slave end from the master end via communication signals, the master end is used to interact with the slave end via communication signals in response to user operations, and the slave end is used to control the remote medical robot to perform actions based on the control information; inputting the real-time pose sequence into a target prediction model to obtain a predicted pose sequence for future time moments; performing trajectory interpolation on the predicted pose sequence according to the trajectory path of the predicted pose sequence to obtain a target pose sequence corresponding to the target master-slave mapping frequency, wherein the target master-slave mapping frequency meets the frequency threshold; inputting the target pose sequence into the controller of the slave end to generate control commands; and controlling the remote medical robot based on the control commands.

[0035] Figure 1 The diagram illustrates an application scenario of a control method for a remote medical robot according to an embodiment of the present disclosure.

[0036] like Figure 1 As shown, application scenario 100 according to this embodiment may include a master terminal 101, a slave terminal 102, a remote medical robot 103, and a network 104. The network 104 serves as a medium for providing a communication link between the master terminal 101 and the slave terminal 102, and a medium for providing a communication link between the slave terminal 102 and the remote medical robot 103. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0037] The master terminal 101 is the user's operating terminal. The master terminal 101 has operation buttons, which the user can use to remotely control the remote medical robot 103. The master terminal 101 is used to generate control information based on the user's operation. The control information includes the real-time pose sequence of the slave terminal 102.

[0038] The slave device 102 has a server and a controller. The server can extract the real-time pose sequence of the slave device 102 from the control information used to control the slave device 102 when the master-slave mapping frequency of the communication signal between the master device 101 and the slave device 102 does not meet a frequency threshold. The real-time pose sequence is processed using a target prediction model to obtain a predicted pose sequence for future time periods. Based on the trajectory path of the predicted pose sequence, trajectory interpolation is performed on the predicted pose sequence to obtain the target pose sequence. The target pose sequence is then input into the controller of the slave device to generate control commands. The remote medical robot is controlled based on these control commands.

[0039] The telemedicine robot 103 is used to execute control commands to perform actions corresponding to those commands. The telemedicine robot can be a three-degree-of-freedom or six-degree-of-freedom robot. The telemedicine robot 103 can be equipped with various sensors to provide real-time surgical feedback to the user. For example, the sensors can be image acquisition devices, accelerometers, etc.

[0040] It should be noted that the control method for the remote medical robot provided in this embodiment can generally be executed by the slave end 102. Accordingly, the control device for the remote medical robot provided in this embodiment can generally be located in the slave end 102.

[0041] It should be understood that Figure 1 The number of master terminal 101, slave terminal 102, remote medical robot 103, and network 104 shown is merely illustrative. Depending on implementation needs, any number of master terminals, slave terminals, remote medical robots, and networks can be included.

[0042] It should be noted that the user operating the main terminal can be a professional with relevant operating qualifications and certifications, such as a medical worker with medical practice qualifications and certifications.

[0043] In one embodiment of this disclosure, a user can perform operations on the master end, and the slave end can control the remote medical robot to perform other actions based on the methods provided in the embodiments of this disclosure. The embodiments of this disclosure do not limit the specific type of action corresponding to the control command.

[0044] Figure 2 A flowchart illustrating a control method for a telemedicine robot according to an embodiment of the present disclosure is shown schematically.

[0045] like Figure 2 As shown, the control method of the remote medical robot in this embodiment includes operations S210 to S250.

[0046] In operation S210, in response to the fact that the master-slave mapping frequency of the communication signal between the master and slave ends does not meet the frequency threshold, the real-time pose sequence of the slave end is extracted from the control information used to control the slave end.

[0047] According to embodiments of this disclosure, in a remotely controlled surgical robot, the signal transmission frequency and data mapping relationship between the user (master) and the remote medical robot (communicating with the slave) are described. The time synchronization and feedback mechanism between the master control input and the slave actuator output is also described.

[0048] According to embodiments of this disclosure, the frequency threshold can be 100 Hz. For example, a master-slave mapping frequency often less than 100 Hz would make it difficult to guarantee the real-time performance of remote surgical control and the safety of surgical procedures.

[0049] According to embodiments of this disclosure, the control information is received from the master end by the slave end through communication signals. The master end is used to interact with the slave end in response to user operations, and the slave end is used to control the remote medical robot to perform actions based on the control information.

[0050] According to embodiments of this disclosure, the real-time pose sequence may include information such as the sequence from the starting pose point to the ending pose point, the pose change rate, and the pose acceleration. The real-time pose sequence is obtained by sorting the sequence from the starting pose point to the ending pose point, the pose change rate, and the pose acceleration based on timestamps.

[0051] For example, in response to user input, control information for the "grasping" action is generated. From this control information, the sequence of joint angle changes (real-time pose sequence) of the remote medical robot at the slave end during the "grasping" action is extracted.

[0052] In operation S220, the real-time pose sequence is input into the target prediction model to obtain the predicted pose sequence for future time moments.

[0053] According to embodiments of this disclosure, the target prediction model can be a machine learning algorithm. For example, the target prediction model can be a neural network algorithm, a random forest algorithm, a regression algorithm, etc.

[0054] For example, neural network algorithms can use the surgical motion change patterns learned during training to predict real-time pose sequences and obtain predicted pose sequences.

[0055] For example, the random forest algorithm can predict real-time pose sequences by ensemble of multiple decision trees.

[0056] In operation S230, trajectory interpolation is performed on the predicted pose sequence according to the trajectory path of the predicted pose sequence to obtain the target pose sequence corresponding to the target master-slave mapping frequency.

[0057] According to embodiments of this disclosure, the target master-slave mapping frequency satisfies a frequency threshold.

[0058] According to embodiments of this disclosure, the trajectory path of the predicted pose sequence is a mathematical model describing the continuous movement of a telemedicine robot in space. For example, the trajectory path describes the change in the position of each pose point in the predicted pose sequence over time.

[0059] For example, trajectory interpolation can be performed on the predicted pose sequence without changing the trajectory path direction.

[0060] For example, based on the requirements of trajectory interpolation for real-time performance and curvature accuracy, specific technical indicators are defined, including bow height accuracy, required inference time, and smoothness. By designing short trajectory interpolation algorithms based on these indicators, data continuity, real-time performance, and interpolation accuracy can be guaranteed. Among these, the interpolation accuracy requirement is greater than the real-time requirement.

[0061] According to embodiments of this disclosure, the target pose sequence may be cached on the slave device. The joint angles of the remote medical robot in the target pose sequence are then resampled according to the target master-slave mapping frequency.

[0062] In operation S240, the target pose sequence is input into the slave controller to generate control commands.

[0063] According to embodiments of this disclosure, a filter is used to denoise the target pose sequence, and then the target pose sequence is updated at a prediction frequency of 10 Hz to replace the past prediction values, establishing a control closed loop to monitor the accuracy of the prediction values ​​in real time. The filter can be a Kalman filter, a mean filter, a median filter, etc.

[0064] The S250 is used to control a remote medical robot based on control commands.

[0065] According to embodiments of this disclosure, in response to a master-slave mapping frequency of the communication signal between the master and slave ends not meeting a frequency threshold, the real-time pose sequence of the slave end can be predicted to obtain a predicted pose sequence. Trajectory interpolation is then performed on the predicted pose sequence based on its trajectory path to obtain a target pose sequence that meets the frequency threshold. This effectively converts a low-frequency predicted pose sequence into a high-frequency target pose sequence, improving the accuracy of the pose sequence. The target pose sequence is input to the controller at the slave end to generate control commands. Based on these commands, the remote medical robot is controlled, thereby reducing the impact of latency on the robot's movement, achieving synchronous pose tracking, improving the real-time performance and safety of remote surgery, and ensuring the accuracy of remote surgical operations.

[0066] According to embodiments of this disclosure, extracting the real-time pose sequence of the slave end from control information for controlling the slave end includes: determining the joint angles of the slave end at multiple times from the control information; inputting the joint angles at multiple times into a kinematic model to obtain the real-time pose sequence of the slave end.

[0067] According to embodiments of this disclosure, control information may include user operation commands, the direction of the end effector of the remote medical robot, spatial position, etc.

[0068] According to embodiments of this disclosure, the kinematic model may include forward kinematics and inverse kinematics. The kinematic model is used to solve for joint angles at multiple time points to obtain the real-time joint angles of each joint of the remote medical robot connected to the slave end.

[0069] According to embodiments of this disclosure, the real-time pose sequence may include the real-time joint angles, pose change rate, pose change acceleration, pose change frequency, etc. of each joint of the remote medical robot.

[0070] According to embodiments of this disclosure, the process of interpolating the predicted pose sequence based on its trajectory path to obtain a target pose sequence corresponding to the target master-slave mapping frequency includes: determining key points from the trajectory path that satisfy a preset target curvature threshold, where the key points represent the trajectory direction of the trajectory path; segmenting the trajectory path based on the key points to obtain multiple trajectory sub-paths; and interpolating the multiple trajectory sub-paths based on the pose change rate of the trajectory path to obtain a target pose sequence corresponding to the target master-slave mapping frequency.

[0071] According to the disclosed embodiments, the C3 continuous corner smoothing algorithm is used to process the predicted pose sequence and obtain the curvature of each point in the trajectory path of the predicted pose sequence.

[0072] According to embodiments of this disclosure, the preset target curvature threshold can be the maximum curvature in the trajectory path.

[0073] The smoothed trajectory path is segmented using key points to obtain multiple trajectory sub-paths. The length of each trajectory sub-path is then calculated.

[0074] According to embodiments of this disclosure, interpolating multiple trajectory sub-paths based on the pose change rate of the trajectory path to obtain a target pose sequence corresponding to the target master-slave mapping frequency includes: interpolating the trajectory sub-paths based on the pose change rate of the trajectory sub-paths to obtain target trajectory sub-paths; and splicing the multiple target trajectory sub-paths together based on their respective time information to obtain a target pose sequence corresponding to the target master-slave mapping frequency.

[0075] According to embodiments of this disclosure, multiple trajectory sub-paths with different trajectory directions are interpolated separately, achieving accurate data filling without affecting the overall path direction. The multiple target trajectory sub-paths are then concatenated according to time information to obtain a high-quality target pose sequence. This target pose sequence can be cached at the slave end, and the cached data can be resampled to convert the transmitted low-frequency data into high-frequency data before sending it to the signal receiver, enabling rapid response and precise remote surgical control.

[0076] According to embodiments of this disclosure, interpolating a trajectory sub-path based on its pose change rate to obtain a target trajectory sub-path includes: determining a path segment to be interpolated from the trajectory sub-path based on a constraint condition between the pose change rate of the trajectory sub-path and a preset pose change rate, wherein the constraint condition indicates that the pose change rate is less than the preset pose change rate; determining a target interpolator algorithm based on the pose change rate of the path segment to be interpolated; and performing trajectory interpolation on the path segment to be interpolated using the target interpolator algorithm to obtain the target trajectory sub-path.

[0077] According to embodiments of this disclosure, the preset pose change rate can be a pose change rate limit value determined based on constraints such as chord error, feed rate command, pose acceleration, and jerk.

[0078] Based on the constraint of the preset pose change rate, the trajectory sub-path is first optimized using the cubic acceleration curve; the trajectory sub-path is then divided into multiple path segments to be interpolated.

[0079] Simultaneously, the displacement within the path segment to be interpolated can be determined by the pose change rate of each point in the trajectory sub-path.

[0080] According to embodiments of this disclosure, determining the target interpolator algorithm based on the pose change rate of the path segment to be interpolated includes: determining the target interpolator algorithm as a linear interpolation algorithm in response to a linear change in the pose change rate of the path segment to be interpolated; and determining the target interpolator algorithm as a curve interpolation algorithm in response to a non-linear change in the pose change rate of the path segment to be interpolated.

[0081] According to embodiments of this disclosure, the curve interpolation algorithm may be a PH spline curve interpolation algorithm.

[0082] By analyzing the rate of change of pose of the path segment to be interpolated, either a linear interpolation algorithm or a PH spline curve interpolation algorithm is selected to obtain a more accurate target trajectory sub-path with curvature changes. Then, based on the time information of each of the multiple target trajectory sub-paths, they are concatenated to obtain a target pose sequence with smooth trajectory and accurate data.

[0083] Finally, the interpolated target pose sequence is output at a frequency consistent with the master-slave mapping of remote surgery to reproduce the master-end motion pattern, thereby enhancing the master-slave mapping and ensuring the real-time requirements of the control system.

[0084] The prediction and trajectory interpolation steps should ensure a certain level of real-time performance, such as completing prediction and trajectory interpolation inference in a sufficiently short time and outputting the inference results to the slave controller at a specific frequency in real time, so as to ensure the stability, transparency and smoother real-time synchronous tracking effect of the control system.

[0085] Figure 3 The flowchart illustrates a process of interpolating the predicted pose sequence according to the trajectory path of the predicted pose sequence to obtain a target pose sequence corresponding to the target master-slave mapping frequency, according to an embodiment of the present disclosure.

[0086] like Figure 3 As shown, this embodiment performs trajectory interpolation on the predicted pose sequence based on the trajectory path of the predicted pose sequence to obtain the target pose sequence corresponding to the target master-slave mapping frequency, including operations S301 to S309.

[0087] In operation S301, key points that meet the preset target curvature threshold are determined from the trajectory path.

[0088] In operation S302, the trajectory path is segmented according to key points to obtain multiple trajectory sub-paths.

[0089] In operation S303, based on the preset pose change rate constraint, the trajectory sub-path is optimized using the cubic acceleration curve to obtain the optimized trajectory sub-path.

[0090] In operation S304, the optimized trajectory sub-path is divided into multiple path segments to be interpolated.

[0091] In operation S305, determine whether the pose change rate of the path segment to be interpolated is linear. If yes, execute operation S306; otherwise, execute operation S307.

[0092] In operation S306, the linear interpolation algorithm is used as the target interpolator algorithm.

[0093] In operation S307, the curve interpolation algorithm is used as the target interpolator algorithm.

[0094] In operation S308, the target interpolator algorithm is used to perform trajectory interpolation on the path segment to be interpolated, and the target trajectory sub-path is obtained.

[0095] In operation S309, the multiple target trajectory sub-paths are spliced ​​together based on their respective time information to obtain the target pose sequence.

[0096] According to embodiments of this disclosure, the target prediction model is trained in the following manner: acquiring historical master pose sequences and historical slave pose sequences at historical moments, wherein the historical control information generated by the master includes the historical master pose sequence, and the historical control commands generated by the slave at historical moments include the historical slave pose sequence; inputting the historical master pose sequence into the prediction model to obtain the predicted pose sequence; and training the prediction model based on the error value between the predicted pose sequence and the historical slave pose sequence to obtain the target prediction model.

[0097] According to embodiments of this disclosure, both the historical master-end pose sequence and the historical slave-end pose sequence are obtained by cleaning and preprocessing the original dataset. For example, duplicate records in the original dataset are removed, and data uniqueness is ensured by checking and deleting; missing items in the original dataset are filled using Newton interpolation. Outliers are checked and processed using statistical methods (z-score), and noisy data in the original dataset is removed by wavelet filtering (the threshold of the wavelet filter is determined by a soft threshold function), thereby obtaining high-quality historical master-end pose sequences and historical slave-end pose sequences.

[0098] According to embodiments of this disclosure, the historical master pose sequence includes pose in Cartesian coordinates, pose change frequency, pose change rate, pose change acceleration, pose information of other joints in the remote medical robot, etc.

[0099] According to embodiments of this disclosure, the prediction model can be a neural network model. For example, the neural network can be based on time series for prediction, and the neural network framework can be a multilayer perceptron model, TimeMixer.

[0100] According to embodiments of this disclosure, the historical master pose sequence and the historical slave pose sequence are decomposed into multiple scales over time, that is, the information of the historical master pose sequence and the historical slave pose sequence is refined. For example, the Discrete Fourier Transform (DFT) is used to decompose the historical master pose sequence and the historical slave pose sequence of a multi-period time series to obtain seasonal and trend variation patterns. Prediction is then performed by aggregating the information of the multi-scale sequences combined with pose features.

[0101] To prevent overfitting and improve generalization ability, the exponential decay algorithm is used to update the learning rate.

[0102]

[0103] in, The initial learning rate, λ represents the learning rate at training step t, where t is the current training step number, and λ is the decay rate, controlling the speed at which the learning rate decreases. A certain amount of dropout is set to ensure the model's generalization ability, and the loss function is set to soft-DTW (Dynamic Time Warping), which improves the output to be closer to the true value than MSE (Mean Squared Error). A certain amount of dataset is used as a test set to evaluate the training results, and the model output values ​​are evaluated based on the evaluation results, including DTW, MSE, and RMSE, to optimize the model and fine-tune the parameters.

[0104] According to embodiments of this disclosure, the error value can be the average error between the predicted pose sequence (predicted value) and the historical slave pose sequence (actual value). Once the error value is less than a certain threshold, the target prediction model is deployed on the slave host computer for real-time prediction.

[0105] According to embodiments of this disclosure, training a prediction model based on the error value between the predicted pose sequence and the historical slave pose sequence to obtain a target prediction model includes: updating the model parameters of the prediction model in response to the error value between the predicted pose sequence and the historical slave pose sequence being greater than an error threshold, thereby obtaining a prediction model to be determined; processing sample master pose sequences in a continuous preset period in response to the prediction model to be determined, thereby obtaining sample slave pose sequences; and determining the prediction model to be determined as the target prediction model in response to the periodic error value between the sample slave pose sequence and the label slave pose sequence in a continuous preset period being less than or equal to an error threshold.

[0106] If the error between the predicted pose sequence and the historical slave pose sequence is within the tolerance range, the predicted value is continuously output and updated. If the error between the predicted value and the true value is greater than the tolerance range (greater than the error threshold), incremental training begins to update the weights of the neural network model in the surgical intent modeling module in real time, allowing the neural network model to flexibly adapt to the current surgical environment. If the periodic error between the sample slave pose sequence and the labeled slave pose sequence in a continuous preset period is within the tolerance range (less than or equal to the error threshold), incremental training stops and the prediction model is saved. If the periodic error between the sample slave pose sequence and the labeled slave pose sequence in a continuous preset period exceeds the tolerance range, incremental training continues and the weights are updated.

[0107] According to embodiments of this disclosure, the control method for remote medical robots is suitable for open-loop remote surgical control and also for remote surgical control with force feedback. This control method represents a novel predictive control paradigm for telecontrol systems and a novel frequency amplification approach. It intelligently intervenes in the pose signals received from the operator at future moments, reducing the impact of latency on motion, achieving synchronous pose tracking while improving the mapping frequency of remote surgery, thus ensuring the real-time performance and accuracy of remote surgical control.

[0108] Figure 4 The flowchart illustrating the training method of the target prediction model is shown in the diagram.

[0109] like Figure 4 As shown, the training method of the target prediction model in this embodiment includes operations S401 to S407.

[0110] In operation S401, the historical master pose sequence and historical slave pose sequence at a historical moment are obtained. The historical control information generated by the master includes the historical master pose sequence, and the historical control instructions generated by the slave at a historical moment include the historical slave pose sequence.

[0111] In operation S402, the historical master pose sequence is input into the prediction model to obtain the predicted pose sequence.

[0112] According to embodiments of this disclosure, a historical master-slave pose sequence is input into a prediction model to obtain a pose sequence to be interpolated. Trajectory interpolation is performed on the pose sequence to be interpolated to obtain a predicted pose sequence with target master-slave mapping frequency, wherein the target master-slave mapping frequency satisfies a frequency threshold.

[0113] The predicted pose sequence and the historical slave pose sequence have the same master-slave mapping frequency, thereby reducing data errors caused by hysteresis.

[0114] In operation S403, it is determined whether the error value between the predicted pose sequence and the historical slave pose sequence is greater than the error threshold. If yes, operation S404 is executed; otherwise, operation S407 is executed. In operation S404, the model parameters of the prediction model are updated to obtain the prediction model to be determined.

[0115] In operation S405, in response to the prediction model to be determined processing the sample master pose sequence in a continuous preset period, the sample slave pose sequence is obtained.

[0116] In operation S406, determine whether the periodic error value between the sample end pose sequence and the label end pose sequence in the continuous preset period is less than or equal to the error threshold. If not, execute operation S401; if yes, execute operation S407.

[0117] In operation S407, the prediction model to be determined is identified as the target prediction model.

[0118] Figure 5 A schematic diagram of the control system used to control a telemedicine robot is shown.

[0119] like Figure 5 As shown, the control system 500 for controlling a remote medical robot in this embodiment includes a master end 101 and a slave end 102.

[0120] The master terminal 101 is used to generate control information for controlling the slave terminal 102 in response to the user's control operation.

[0121] The slave terminal 102 is used to receive control information sent by the master terminal 101 and execute the control method of the remote medical robot described above.

[0122] According to an embodiment of this disclosure, the master terminal 101 can be a user's operating terminal. The master terminal 101 has operation buttons, and the user can use the operation buttons to remotely control the remote medical robot 103.

[0123] According to embodiments of this disclosure, slave terminal 102 can be communicatively connected to master terminal 101. Control information is received from master terminal 101.

[0124] In response to a situation where the master-slave mapping frequency of the communication signal between the master and slave terminals 101 does not meet a frequency threshold, the slave terminal 102 extracts its real-time pose sequence from the control information used to control the slave terminal 102. The real-time pose sequence is then processed using a target prediction model to obtain a predicted pose sequence for future timeframes. Based on the trajectory path of the predicted pose sequence, trajectory interpolation is performed to obtain the target pose sequence. This target pose sequence is then input into the controller of the slave terminal 102 to generate control commands. These control commands are then used to control the remote medical robot.

[0125] Slave 102 may include a server and a controller.

[0126] The server can be used to execute the control methods described above for the remote medical robot.

[0127] The controller can be used to generate control commands based on the target pose sequence.

[0128] According to embodiments of this disclosure, the system further includes: a mapping medical robot, which is communicatively connected to a master terminal; the master terminal is further configured to: generate control information to be mapped in response to a user performing a control operation on the mapping medical robot; and map the control information to be mapped to obtain control information.

[0129] According to embodiments of this disclosure, the mapping medical robot and the telemedicine robot are robots with the same degrees of freedom. The user indirectly controls the telemedicine robot by controlling the mapping medical robot, thereby maintaining the doctor's intuitiveness and precision in operation.

[0130] In order to reproduce the user's operation on the mapped medical robot, in response to the user's control operation on the mapped medical robot, control information to be mapped is generated; the control information to be mapped is then mapped to obtain the control information for controlling the remote medical robot.

[0131] Figure 6 A schematic block diagram of a control device for a telemedicine robot according to an embodiment of the present disclosure is shown.

[0132] like Figure 6 As shown, the control device 600 of the remote medical robot in this embodiment includes an extraction module 610, a first input module 620, a trajectory interpolation module 630, a second input module 640, and a control module 650.

[0133] The extraction module 610 is used to extract the real-time pose sequence of the slave end from the control information used to control the slave end in response to the master-slave mapping frequency of the communication signal between the master and slave ends not meeting the frequency threshold. The control information is received by the slave end from the master end through communication signals. The master end is used to interact with the slave end in response to user operations, and the slave end is used to control the remote medical robot to perform actions based on the control information. In one embodiment, the extraction module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0134] The first input module 620 is used to input the real-time pose sequence into the target prediction model to obtain the predicted pose sequence at future time points. In one embodiment, the first input module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0135] The trajectory interpolation module 630 is used to perform trajectory interpolation on the predicted pose sequence according to the trajectory path of the predicted pose sequence, to obtain a target pose sequence corresponding to the target master-slave mapping frequency, wherein the target master-slave mapping frequency satisfies a frequency threshold. In one embodiment, the trajectory interpolation module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0136] The second input module 640 is used to input the target pose sequence into the controller at the slave end to generate control commands. In one embodiment, the second input module 640 can be used to perform the operation S240 described above, which will not be repeated here.

[0137] The control module 650 is used to control the remote medical robot based on control commands. In one embodiment, the control module 650 can be used to perform the operation S250 described above, which will not be repeated here.

[0138] According to embodiments of this disclosure, the extraction module 610 includes a first determining submodule and a first input submodule. The first determining submodule is used to determine the joint angles of the slave end at multiple times from the control information; the first input submodule is used to input the joint angles at multiple times into the kinematic model to obtain the real-time pose sequence of the slave end.

[0139] According to embodiments of this disclosure, the trajectory interpolation module 630 includes a second determining submodule, a segmentation submodule, and a trajectory interpolation submodule. The second determining submodule is used to determine key points in the trajectory path that satisfy a preset target curvature threshold, where the key points represent the trajectory direction of the trajectory path; the segmentation submodule is used to segment the trajectory path according to the key points to obtain multiple trajectory subpaths; the trajectory interpolation submodule is used to perform trajectory interpolation on the multiple trajectory subpaths according to the pose change rate of the trajectory path to obtain a target pose sequence corresponding to the target master-slave mapping frequency.

[0140] According to embodiments of this disclosure, the trajectory interpolation submodule includes a trajectory interpolation unit and a stitching unit. The trajectory interpolation unit is used to perform trajectory interpolation on the trajectory subpath according to the pose change rate of the trajectory subpath to obtain the target trajectory subpath; the stitching unit is used to stitch together the multiple target trajectory subpaths according to their respective time information to obtain a target pose sequence corresponding to the target master-slave mapping frequency.

[0141] According to embodiments of this disclosure, the trajectory interpolation unit further includes a first determining subunit, a second determining subunit, and a trajectory interpolation subunit. The first determining subunit is used to determine a path segment to be interpolated from the trajectory subpath based on a constraint condition between the pose change rate of the trajectory subpath and a preset pose change rate, where the constraint condition indicates that the pose change rate is less than the preset pose change rate. The second determining subunit is used to determine a target interpolator algorithm based on the pose change rate of the path segment to be interpolated. The trajectory interpolation subunit is used to perform trajectory interpolation on the path segment to be interpolated using the target interpolator algorithm to obtain the target trajectory subpath.

[0142] According to an embodiment of the present disclosure, the second determining subunit includes: determining the target interpolator algorithm as a linear interpolation algorithm in response to a linear change in the pose change rate of the path segment to be interpolated; and determining the target interpolator algorithm as a curve interpolation algorithm in response to a non-linear change in the pose change rate of the path segment to be interpolated.

[0143] According to embodiments of this disclosure, the target prediction model is trained in the following manner: acquiring historical master pose sequences and historical slave pose sequences at historical moments, wherein the historical control information generated by the master includes the historical master pose sequence, and the historical control commands generated by the slave at historical moments include the historical slave pose sequence; inputting the historical master pose sequence into the prediction model to obtain the predicted pose sequence; and training the prediction model based on the error value between the predicted pose sequence and the historical slave pose sequence to obtain the target prediction model.

[0144] According to embodiments of this disclosure, training a prediction model based on the error value between the predicted pose sequence and the historical slave pose sequence to obtain a target prediction model includes: updating the model parameters of the prediction model in response to the error value between the predicted pose sequence and the historical slave pose sequence being greater than an error threshold, thereby obtaining a prediction model to be determined; processing sample master pose sequences in a continuous preset period in response to the prediction model to be determined, thereby obtaining sample slave pose sequences; and determining the prediction model to be determined as the target prediction model in response to the periodic error value between the sample slave pose sequence and the label slave pose sequence in a continuous preset period being less than or equal to an error threshold.

[0145] According to embodiments of this disclosure, any plurality of modules among the extraction module 610, the first input module 620, the trajectory interpolation module 630, the second input module 640, and the control module 650 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the extraction module 610, the first input module 620, the trajectory interpolation module 630, the second input module 640, and the control module 650 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the extraction module 610, the first input module 620, the trajectory interpolation module 630, the second input module 640, and the control module 650 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0146] Figure 7 A block diagram of an electronic device suitable for implementing a control method for a telemedicine robot according to an embodiment of the present disclosure is shown schematically.

[0147] likeFigure 7 As shown, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0148] RAM 703 stores various programs and data required for the operation of electronic device 700. Processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Processor 701 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0149] According to embodiments of this disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the input / output (I / O) interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0150] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0151] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 702 and / or RAM 703 and / or one or more memories other than ROM 702 and RAM 703 described above.

[0152] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to enable the computer system to implement the remote medical robot control method provided in the embodiments of this disclosure.

[0153] When the computer program is executed by the processor 701, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0154] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 709, and / or installed from a removable medium 711. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0155] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0156] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0158] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0159] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A control system for controlling a remote medical robot, characterized in that, The control system includes: The master terminal is used to generate control information for controlling the slave terminal in response to user control operations; The slave device is configured to receive the control information sent by the master device, and in response to the master-slave mapping frequency of the communication signal between the master and slave devices not meeting the frequency threshold, extract the real-time pose sequence of the slave device from the control information used to control the slave device. The control information is received by the slave device from the master device through the communication signal. The master device is configured to interact with the slave device through communication signals in response to user operation. The slave device is configured to control the remote medical robot to perform actions based on the control information. The real-time pose sequence is input into a target prediction model to obtain a predicted pose sequence for future time moments. Based on the trajectory path of the predicted pose sequence, trajectory interpolation is performed on the predicted pose sequence to obtain a target pose sequence corresponding to the target master-slave mapping frequency, which meets the frequency threshold. The target pose sequence is input into the controller of the slave device to generate control commands. The remote medical robot is then controlled based on the control commands.

2. The system according to claim 1, characterized in that, The step of extracting the real-time pose sequence of the slave device from the control information used to control the slave device includes: The joint angles of the slave end at multiple times are determined from the control information; The joint angles at the multiple moments are input into the kinematic model to obtain the real-time pose sequence of the slave end.

3. The system according to claim 1, characterized in that, The step of performing trajectory interpolation on the predicted pose sequence based on the trajectory path of the predicted pose sequence to obtain the target pose sequence corresponding to the target master-slave mapping frequency includes: From the trajectory path, key points that satisfy a preset target curvature threshold are determined, and the key points represent the trajectory direction of the trajectory path; The trajectory path is segmented based on the key points to obtain multiple trajectory sub-paths; Based on the pose change rate of the trajectory path, trajectory interpolation is performed on multiple trajectory sub-paths to obtain the target pose sequence corresponding to the target master-slave mapping frequency.

4. The system according to claim 3, characterized in that, The step of interpolating multiple trajectory sub-paths based on the pose change rate of the trajectory path to obtain the target pose sequence corresponding to the target master-slave mapping frequency includes: The trajectory sub-path is interpolated based on the pose change rate of the trajectory sub-path to obtain the target trajectory sub-path; Based on the time information of each of the multiple target trajectory sub-paths, the multiple target trajectory sub-paths are spliced ​​together to obtain the target pose sequence corresponding to the target master-slave mapping frequency.

5. The system according to claim 4, characterized in that, The step of interpolating the trajectory sub-path according to the pose change rate of the trajectory sub-path to obtain the target trajectory sub-path includes: Based on the constraint between the pose change rate of the trajectory sub-path and the preset pose change rate, the path segment to be interpolated is determined from the trajectory sub-path, wherein the constraint indicates that the pose change rate is less than the preset pose change rate. The target interpolator algorithm is determined based on the pose change rate of the path segment to be interpolated; The target interpolator algorithm is used to perform trajectory interpolation on the path segment to be interpolated, thereby obtaining the target trajectory sub-path.

6. The system according to claim 5, characterized in that, The algorithm for determining the target interpolator based on the pose change rate of the path segment to be interpolated includes: In response to the linear change rate of the pose of the path segment to be interpolated, the target interpolator algorithm is determined to be a linear interpolation algorithm; In response to the non-linear change in the pose change rate of the path segment to be interpolated, the target interpolator algorithm is determined to be a curve interpolation algorithm.

7. The system according to claim 1, characterized in that, The target prediction model was trained in the following way: The historical master pose sequence and historical slave pose sequence at a historical moment are obtained. The historical control information generated by the master includes the historical master pose sequence, and the historical control command generated by the slave at a historical moment includes the historical slave pose sequence. The historical master pose sequence is input into the prediction model to obtain the predicted pose sequence; The prediction model is trained based on the error value between the predicted pose sequence and the historical slave pose sequence to obtain the target prediction model.

8. The system according to claim 7, characterized in that, The step of training the prediction model based on the error value between the predicted pose sequence and the historical slave pose sequence to obtain the target prediction model includes: In response to the error value between the predicted pose sequence and the historical slave pose sequence being greater than the error threshold, the model parameters of the prediction model are updated to obtain the prediction model to be determined. In response to the prediction model to be determined processing the sample master pose sequence in a continuous preset period, the sample slave pose sequence is obtained. In response to the periodic error between the sample end pose sequence and the label end pose sequence in the continuous preset period being less than or equal to the error threshold, the prediction model to be determined is determined as the target prediction model.

9. The system according to claim 1, characterized in that, The system also includes: The medical robot is mapped and communicates with the main device. The main terminal is also used to: generate control information to be mapped in response to the user's control operation on the mapped medical robot; and map the control information to be mapped to obtain the control information.

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