CN117814927A

CN117814927A - Data processing method and device of flexible surgical robot, robot and medium

Info

Publication number: CN117814927A
Application number: CN202311829204.1A
Authority: CN
Inventors: 请求不公布姓名; 汪振
Original assignee: Shanghai Shiwei Medical Technology Co ltd
Current assignee: Shanghai Shiwei Medical Technology Co ltd
Priority date: 2023-12-27
Filing date: 2023-12-27
Publication date: 2024-04-05

Abstract

The application relates to a data processing method, a device, a robot and a medium of a flexible surgical robot, wherein the flexible surgical robot comprises at least one flexible robot arm, and the method comprises the following steps: acquiring a command position of the instrument end in response to a control command for the instrument end of the target flexible robotic arm; obtaining a trained target error observation model; the target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction; processing the instruction position based on the target error observation model to obtain an observation position of the tail end of the instrument; and obtaining the motion error of the target flexible robot arm based on the command position of the instrument tail end and the observation position of the instrument tail end. Therefore, the movement error of the target flexible robot arm is accurately observed through the trained target error observation model, the operation difficulty and the operation risk of a doctor in executing master-slave operation are reduced, and the safety of a patient is ensured.

Description

Data processing method and device of flexible surgical robot, robot and medium

Technical Field

The application relates to the technical field of medical instruments, in particular to a data processing method and device of a flexible surgical robot, the robot and a medium.

Background

Different from the traditional rigid surgical robot, because the flexible joint or the flexible transmission mechanism in the flexible surgical robot is provided with a flexible body, the rotation, the expansion or the bending and the like of the flexible body often have larger return difference (namely a gap of mechanical transmission) and flexible difference (namely an error of elastic deformation of the flexible body), so that the tail end position and the instruction position of the flexible surgical robot based on Cartesian positioning are easily caused to have larger deviation in the track following process of the flexible surgical robot, the operation difficulty of doctors in executing master-slave operation is increased, and surgical risks are easily generated.

Disclosure of Invention

In order to solve the technical problems, the application provides a data processing method, a device, a robot and a medium of a flexible surgical robot, so as to solve at least one technical problem in the related art.

In one aspect, the present application provides a data processing method of a flexible surgical robot, the flexible surgical robot including at least one flexible robotic arm, the method comprising:

responsive to a control command for an instrument tip of a target flexible robotic arm, obtaining a commanded position of the instrument tip; the target flexible robotic arm is any one of the at least one flexible robotic arm;

Obtaining a trained target error observation model; the target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction; the error observation model is used for observing the position of the instrument tail end of the target flexible robot arm;

processing the instruction position based on the target error observation model to obtain an observation position of the tail end of the instrument;

and obtaining the motion error of the target flexible robot arm based on the instruction position of the tail end of the instrument and the observation position of the tail end of the instrument.

In another aspect, the present application provides a data processing apparatus of a flexible surgical robot, the flexible surgical robot including at least one flexible robotic arm, the apparatus comprising:

the first processing module is used for responding to a control instruction aiming at the instrument tail end of the target flexible mechanical arm and acquiring the instruction position of the instrument tail end; the target flexible robotic arm is any one of the at least one flexible robotic arm;

the model acquisition module is used for acquiring a trained target error observation model under the condition that the position identification module does not exist at the tail end of the instrument; the target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction; the error observation model is used for observing the position of the instrument tail end of the target flexible robot arm;

The second processing module is used for processing the instruction position based on the target error observation model to obtain an observation position of the tail end of the instrument;

and the third processing module is used for obtaining the motion error of the target flexible robot arm based on the instruction position of the tail end of the instrument and the observation position of the tail end of the instrument.

the third processing module is used for obtaining the motion error of the target flexible robot arm based on the instruction position of the tail end of the instrument and the observation position of the tail end of the instrument;

and the compensation processing module is used for compensating and adjusting the instruction position of the tail end of the instrument based on the motion error under the condition that the motion error meets the error compensation condition so as to obtain the corrected position of the tail end of the instrument.

In another aspect, the present application provides a flexible surgical robot comprising:

a surgical robot comprising at least one flexible robotic arm for performing a surgical action;

control means for controlling the surgical robot motion;

a processor and a memory having at least one instruction, at least one program, code set, or instruction set stored therein, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by the processor to implement a data processing method of a flexible surgical robot as in any of the embodiments.

In another aspect, the present application provides a computer readable storage medium having stored therein at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by a processor to implement the data processing method of the flexible surgical robot of any of the embodiments.

The embodiment of the application provides a data processing method, a device, a robot and a medium of a flexible surgical robot, wherein the flexible surgical robot comprises at least one flexible robot arm, and the method comprises the following steps: acquiring a command position of the instrument end in response to a control command for the instrument end of the target flexible robotic arm; the target flexible robot is any one of at least one flexible robot; obtaining a trained target error observation model; the target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction; the error observation model is used for observing the position of the instrument tail end of the target flexible robot arm; processing the instruction position based on the target error observation model to obtain an observation position of the tail end of the instrument; and obtaining the motion error of the target flexible robot arm based on the command position of the instrument tail end and the observation position of the instrument tail end. Therefore, by means of real-time observation of the motion error of the target flexible robot arm by means of the trained target error observation model, return difference and flexible difference in the motion process can be rapidly detected. In addition, through accurately observing the motion error of the target flexible mechanical arm through the trained target error observation model, the operation position of the surgical tool or equipment can be controlled more accurately, the surgical tool is prevented from deviating from the preset position due to return difference or flexible difference, and the surgical tool is ensured to move according to the preset track and position, so that the deviation and error between the tail end position and the instruction position of the flexible surgical robot in the surgical operation are reduced, the operation difficulty and the operation risk of doctors in executing master-slave operation are further reduced, and the safety of patients is ensured.

Drawings

In order to more clearly illustrate the technical solutions and advantages of embodiments of the present application or of the prior art, the following description will briefly introduce the drawings that are needed in the embodiments or the prior art descriptions, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art;

FIG. 1 is a schematic illustration of an application scenario of a surgical robotic system, according to an exemplary embodiment;

FIG. 2 is a schematic illustration of the structure of a flexible robotic arm in a surgical robotic system, shown in accordance with an exemplary embodiment;

FIG. 3 is a schematic diagram illustrating bending control of a flexible robotic arm according to an exemplary embodiment;

FIG. 4 is a schematic structural view of a transmission mechanism in a flexible surgical robot, according to an exemplary embodiment;

FIG. 5 is a flow diagram illustrating a method of data processing for a flexible surgical robot, according to an exemplary embodiment;

FIG. 6 is a schematic diagram illustrating a modified configuration of a transmission mechanism in a flexible surgical robot, according to an exemplary embodiment;

FIG. 7 is a schematic diagram of the structure of an initial error observation model a, according to an exemplary embodiment;

FIG. 8 is a schematic diagram of the structure of an initial error observation model b, according to an exemplary embodiment;

FIG. 9 is a flow diagram illustrating another method of data processing for a flexible surgical robot, according to an exemplary embodiment;

FIG. 10 is a flow diagram illustrating another method of data processing for a flexible surgical robot, according to an exemplary embodiment;

FIG. 11 is a process diagram illustrating a data processing method of a flexible surgical robot, according to an example embodiment;

FIG. 12 is a block diagram of a data processing device of a flexible surgical robot, according to an exemplary embodiment;

FIG. 13 is a block diagram of a data processing device of another flexible surgical robot shown according to an exemplary embodiment;

fig. 14 is a block diagram of a hardware configuration of an electronic device of a data processing method of a flexible surgical robot according to an exemplary embodiment.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.

It should be noted that the terms "first," "second," and the like in the description and the claims of the embodiments of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In order to make the objects, technical solutions and advantages disclosed in the embodiments of the present application more apparent, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present application embodiments and are not intended to limit the present application embodiments.

The terms "first" and "second" are used below for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

Fig. 1 shows a schematic view of an application scenario of a surgical robotic system. The surgical robot system of the present embodiment includes a master operation device 100 (i.e., a control means), and a slave operation device 200 (i.e., a surgical robot) controlled by the master operation device 100.

The master operation device 100 has a control input device capable of transmitting a control command to the slave operation device 200 according to the action of the operator's hand and/or foot to drive and adjust the posture of the robot arm assembly of the slave operation device 200 and drive the execution instrument of the robot arm assembly to perform a corresponding operation.

As shown in fig. 1, the slave manipulator 200 has a robot arm assembly for performing surgical actions, a driving device for driving the robot arm assembly according to a control command, and a base for supporting the driving device, wherein the robot arm assembly includes at least one flexible robot arm, and the distal end of each flexible robot arm may be loaded with a performing instrument for performing different or the same surgical operations, including, but not limited to, clamping, cutting, shearing, stapling, electro-cutting, or electro-coagulation, etc. For example, the implement may be any of a variety of implements including, but not limited to, needle-holding forceps, scissors, graspers, and clip appliers. Needle-holding forceps instruments are generally used for realizing operations such as clamping, suturing, knotting and the like, shearing instruments are generally used for realizing operations such as thread shearing, dissection, cutting and the like, grasping forceps instruments are generally used for realizing operations such as grasping, pulling and the like, and clip applier instruments are generally used for ligating in cooperation with ligature clips.

As shown in fig. 2, a schematic structural diagram of a flexible mechanical arm 210 is shown, where the flexible mechanical arm 210 includes an inner flexible body 211, an outer rigid body 212, and a base 213, where the outer rigid body 212 has two degrees of freedom of extension and retraction, and the inner flexible body 211 has two degrees of freedom of extension and retraction, and the flexible body ends in an implement. The inner flexible body 211 can adjust the expansion and contraction amount of the inner flexible body relative to the outer layer through the expansion and contraction joint, the inner flexible body 211 rotates along with the outer rigid body, and the expansion and contraction joint of the outer rigid body 212 can adjust the expansion and contraction movement of the outer rigid body.

The bending control principle of the inner flexible body 211 is further described with reference to fig. 3, and as shown in fig. 3, the left and right sides of the inner flexible body 211 are respectively provided with a steel wire rope, the tension force of the left and right sides of the flexible body is controlled by controlling the elongation of the steel wire rope, and then the inner flexible body 211 is controlled to bend, in fig. 3, the left steel wire rope is contracted, the right steel wire rope is extended, the tension force of the left side of the inner flexible body 211 is greater than the tension force of the right side, and the inner flexible body 211 is bent to the left side.

Optionally, as further shown in fig. 1, the surgical robot system further includes an image device 400, where the image device 400 is configured to acquire an image of the surgical field in the cavity (referred to as the body cavity of the patient) captured by the endoscope, and further perform imaging processing on the image of the surgical field, and transmit the image to a first display device of the image device 400 and/or a second display device (not shown in the figure) of the main operating device 100 for displaying, so that the operator can observe the image of the surgical field. The surgical field images include, but are not limited to, the type, number, position and pose of the implement within the body cavity, the morphology of the target organ tissue or target brain tissue or surrounding vessels that need to be manipulated, and the like. Further, an endoscope for assisting in capturing images of the surgical field may be loaded from one of the flexible robotic arms 210 in the robotic arm assembly of the operation device 200, and may be displayed by the first display device and/or the second display device. It is to be understood that the image displayed by the image device 400 may be a two-dimensional or three-dimensional image. Endoscopes can include a variety of endoscopes used in surgery, such as thoracoscopes, arthroscopes, nasoscopes, and the like.

Optionally, the surgical robot system further includes a support device 300 (e.g., an operating table) for supporting the surgical object for surgery, and the support device 300 may be replaced with another surgical platform according to the type of surgery, which is not limited in this embodiment.

It should be noted that fig. 1 is only an example. The surgical robot system is not limited to the device structure or number shown in the above figures, and in other application scenarios, corresponding adjustments may be made, such as adding or subtracting devices in fig. 1, adjusting the number of devices or components, or adjusting the structure of devices or components, etc.

Different from the traditional rigid surgical robot, because the flexible joint or the flexible transmission mechanism in the flexible surgical robot is provided with a flexible body, the rotation, the expansion or the bending and the like of the flexible body often have larger return difference (namely a gap of mechanical transmission) and flexible difference (namely an error of elastic deformation of the flexible body), so that the tail end position and the instruction position of the flexible surgical robot based on Cartesian positioning are easily caused to have larger deviation in the track following process of the flexible surgical robot, and the operation difficulty of doctors for executing master-slave operation is increased. And because of transmission return difference and flexible difference, the position of the tail end based on Cartesian positioning is abnormal, surgical risks are easy to generate, and irreversible risks are caused for patients.

Fig. 4 shows a schematic structural view of a transmission mechanism in a flexible surgical robot. The flexible surgical robot includes a motor 41, a decelerator 42, a flexible body 43, and an instrument tip 44. As shown in fig. 4, the right side of the motor 41 is connected with a speed reducer 42, and the tail end of the speed reducer 42 is connected with a flexible body 43, wherein the friction force and the inertia force existing in the speed reducer, as well as the manufacturing precision and the assembly precision of the speed reducer, can influence a transmission system, namely, the speed reducer 42 can generate return difference, wherein the return difference refers to position deviation or angle error generated when the rotation direction of the speed reducer is changed; the first base 45 restrains the left end face 43a of the flexible body 43 (i.e., near the proximal end face of the motor) so that the flexible body 43 can only rotate, and the second base 46 restrains the right end face 43b of the flexible body 43 (i.e., far from the distal end face of the motor, i.e., the end of the flexible body) so that the flexible body 43 can only rotate, and because the flexible body 43 has a certain flexibility, the flexible body can be bent to a certain extent, and thus errors in driving, i.e., flexible differences, can also be generated due to elastic deformation.

When the left end surface 43a of the flexible body 43 is controlled to twist by an angle α, the right end surface 43b of the flexible body 43 rotates by an angle θ. Because the flexible mechanical arm has return difference or has both the return difference and the return difference, the movement angles of the left end face 43a and the right end face 43b of the flexible body 43 are different, namely alpha is not equal to theta, so that the end position and the instruction position of the flexible robot have larger deviation, and the operation difficulty and the operation risk of doctors for executing master-slave operation are increased.

In view of this, embodiments of the present application provide a data processing method, apparatus, robot, and medium of a flexible surgical robot including at least one flexible robot arm, the method including: acquiring a command position of the instrument end in response to a control command for the instrument end of the target flexible robotic arm; the target flexible robot is any one of at least one flexible robot; obtaining a trained target error observation model; the target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction; the error observation model is used for observing the position of the instrument tail end of the target flexible robot arm; processing the instruction position based on the target error observation model to obtain an observation position of the tail end of the instrument; and obtaining the motion error of the target flexible robot arm based on the command position of the instrument tail end and the observation position of the instrument tail end. Therefore, by means of real-time observation of the motion error of the target flexible robot arm by means of the trained target error observation model, return difference and flexible difference in the motion process can be rapidly detected. In addition, through accurately observing the motion error of the target flexible mechanical arm through the trained target error observation model, the operation position of the surgical tool or equipment can be controlled more accurately, the surgical tool is prevented from deviating from the preset position due to return difference or flexible difference, and the surgical tool is ensured to move according to the preset track and position, so that the deviation and error between the tail end position and the instruction position of the flexible surgical robot in the surgical operation are reduced, the operation difficulty and the operation risk of doctors in executing master-slave operation are further reduced, and the safety of patients is ensured.

The embodiment of the application provides a data processing method of a flexible surgical robot. A specific workflow of a data processing method applied to one of the flexible surgical robots of fig. 5 is further described below.

Fig. 5 is a flow chart illustrating a data processing method of a flexible surgical robot according to an exemplary embodiment. The flexible surgical robot includes at least one flexible robotic arm, as shown in fig. 5, the method comprising:

s501: acquiring a command position of the instrument end in response to a control command for the instrument end of the target flexible robotic arm; the target flexible robotic arm is any one of the at least one flexible robotic arm.

The instrument end refers to the end of a flexible mechanical arm, and the flexible mechanical arm can be carried with an instrument for performing clamping, cutting, shearing, stitching, electrotome or electrocoagulation.

The control instructions may be instructions sent by the primary operating device for controlling movement of the instrument tip of the target flexible robotic arm to perform a corresponding surgical operation.

Optionally, the flexible surgical robot acquires a control instruction sent by the main operation device, and analyzes the control instruction to obtain an instruction position of the instrument end of the target flexible robot arm. The commanded position is a target operating position of the instrument end after performing the surgical operation.

S503: obtaining a trained target error observation model; the target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction; the error observation model is used for observing the position of the instrument end of the target flexible robot arm.

The current error state is an error state of the flexible surgical robot in the movement process of executing the control instruction. For example, the current error state may include any one of a first error state including a return error and a second error state for representing a return error and a compliance error. That is, the current error state includes two cases: (1) a first error state comprising only return error; (2) And a second error state including both the return error and the compliance error.

The target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction. The target error observation models corresponding to different current error states may be different. In an embodiment, the target error observation model corresponding to the first error state and the target error observation model corresponding to the second error state are different.

Optionally, a current error state of the flexible surgical robot in a motion process of executing the control instruction may be determined first, and then a trained target error observation model matched with the current error state is obtained based on the current error state, so as to observe the position of the instrument end of the target flexible robot arm through the error observation model. The training method for the target error observation model is described in detail later.

S505: and processing the instruction position based on the target error observation model to obtain the observation position of the tail end of the instrument.

Optionally, the command position of the instrument end can be input into the target error observation model for observation processing, and the observation position of the instrument end is obtained through output, namely the actual operation position of the instrument end of the target flexible robot arm after the operation is performed.

S507: and obtaining the motion error of the target flexible robot arm based on the command position of the instrument tail end and the observation position of the instrument tail end.

Wherein the motion error is used to reflect the degree of positional discrepancy between the commanded position after the control command is executed by the instrument tip and the actual position of the motion.

Optionally, after the commanded position at the end of the instrument and the observed position at the end of the instrument are obtained, the motion error of the target flexible robotic arm is determined by comparing the difference between the two. Through the motion error of the target flexible robot arm, the position correction adjustment or the position compensation is conveniently carried out according to the motion error, so that the actual operation position of the tail end of the instrument meets the control instruction requirement, and the operation risk is reduced. Illustratively, the motion error may include, but is not limited to, one or more of a motion angle error, a motion distance error, and the like.

In the embodiment, the return difference and the flexible difference in the movement process can be rapidly detected by real-time observation of the movement error of the target flexible robot arm by the trained target error observation model. In addition, through accurately observing the motion error of the target flexible mechanical arm through the trained target error observation model, the operation position of the surgical tool or equipment can be controlled more accurately, the surgical tool is prevented from deviating from the preset position due to return difference or flexible difference, and the surgical tool is ensured to move according to the preset track and position, so that the deviation and error between the tail end position and the instruction position of the flexible surgical robot in the surgical operation are reduced, the operation difficulty and the operation risk of doctors in executing master-slave operation are further reduced, and the safety of patients is ensured.

Alternatively, to account for the compliance and return differences of the transmission shown in fig. 4, the transmission may be modified prior to training the target error observation model. Fig. 6 shows a schematic structural diagram of a modified transmission mechanism in a flexible surgical robot. As shown in fig. 6, an end sensor 61 is added to the right end surface 43b of the flexible body 31, and actual movement position data of the flexible body, such as a rotation angle and a movement distance, is acquired by the end sensor 61. In this embodiment, the sensor selection encoder acts as an end sensor for end data acquisition, as the end motion of the transmission is rotational. In some embodiments, if the end motion of the actuator is movement, the end sensor may employ a sensor such as a grating, magnetic grating, vision, etc. for measuring the distance of movement of the end of the flexible body, etc.

It is noted that the end sensor 61 is not required during actual operation of the transmission itself in this embodiment. If the return difference flexible difference parameter identification in the trained target error observation model is completed, the end sensor 61 can be unloaded, and the target error observation model is directly utilized for position observation. However, if a sensor exists at the tail end of the transmission mechanism, the data acquisition can be directly performed by adopting the tail end sensor without adding an additional sensor.

Continuing with the example of fig. 6, in the case where the observation position includes an observation angle, after the transmission mechanism is modified, an initial error observation model may be constructed to observe the actual rotation angle of the right end face of the flexible body by the initial error observation model.

If the transmission mechanism includes both return difference and soft difference during the movement, the initial error observation model a constructed is shown in fig. 7, and the initial error observation model a may include a deviation determination module a 71, a joint return difference sub-model a 72 and an observation determination module a 73. Wherein q is the command position of the motor output shaft, epsilon is the position deviation, K2 is the torsional rigidity parameter of the flexible body, F_ext is the external force applied to the flexible body, M is the equivalent mass, C is the equivalent coulomb coefficient, For observing the acceleration of the right end of the flexible body,/-, is observed>For observing the velocity of the right end of the flexible body,/->An observed angle is an observed angle of the right end of the observed flexible body; />Is the initial bias of the observed value.

Alternatively, the observation determination module a may include a flexible body stress determination module, a friction sub-model, and an observation determination sub-module. Illustratively, the initial error observation model a may be expressed as:

the deviation determination module a may be expressed as:

the joint return difference model a can be expressed as:

the flexible body stress determination module may be expressed as:

the friction submodel may be expressed as:

the observation determination submodule may be represented as:

wherein ε ₀ For the deviation threshold, f ₀ For the friction threshold, f is friction, K2 ε (0, 1)]，

If the transmission mechanism only includes return difference in the motion process, the initial error observation model a in fig. 7 can be simplified, and an initial error observation model b shown in fig. 8 can be constructed. The initial observation model b may include a bias determination module b 81, a joint return difference model b 82, and an observation determination module b 83. Wherein q is the instruction position of the motor output shaft, epsilon is the position deviation, K2 is the torsional rigidity parameter of the flexible body, For observing the acceleration of the right end of the flexible body,/-, is observed>For observing the velocity of the right end of the flexible body,/->The observation angle is the observation angle of the right end of the obtained flexible body; />Is the initial bias of the observed value.

Illustratively, the initial error observation model b may be expressed as:

the bias determination module b can be expressed as:

the joint return difference model b can be expressed as:

the observation determination module b may be expressed as:

wherein ε ₀ As a result of the deviation threshold value,K2∈(0,1]。

the above parameter ε ₀ 、f ₀ 、K2、C、The parameters to be identified in the initial error observation model, namely the model parameters to be trained.

It should be noted that, the initial error observation model is specific to the case that the end motion of the transmission mechanism is rotation, and if the end motion of the transmission mechanism is movement, the initial error observation model may be adjusted accordingly.

After the transmission mechanism is improved and the initial error observation model is constructed, the initial error observation model can be trained. In some implementations, acquiring the trained target error observation model includes:

responding to a control instruction aiming at the instrument end of the sample flexible mechanical arm, and acquiring the actual rotation angle of the instrument end of the sample flexible mechanical arm to obtain acquisition tracks used for representing a plurality of acquisition moments;

Acquiring an initial error observation model, and processing an instruction angle corresponding to a control instruction through the initial error observation model to obtain a sample observation angle of the instrument tail end of the sample flexible robot arm;

constructing an instruction observation track corresponding to the acquisition track;

constructing an objective function based on the instruction observation track and the acquisition track;

and taking the minimized objective function as a training target, and optimizing model parameters of the initial error observation model to obtain a trained target error observation model.

Optionally, in response to a control instruction for the instrument end of the sample flexible robotic arm, an actual rotation angle of the instrument end of the sample flexible robotic arm is acquired, and an acquisition trajectory y1 for characterizing a plurality of acquisition instants is constructed based on the actual rotation angle. And processing the instruction angle corresponding to the control instruction through the constructed initial error observation model to obtain a sample observation angle of the instrument end of the sample flexible robot arm, and constructing an instruction observation track y2 corresponding to the acquisition track based on the sample observation angle. Then, an objective function G is constructed based on the commanded observation trajectory and the acquisition trajectory. The objective function G may be expressed, for example, as g= |y2-y1|, wherein I representing the norm. And taking the minimized objective function G as a training target, and optimizing model parameters of the initial error observation model to obtain a trained objective error observation model. Wherein the model parameters to be trained may include ε ₀ 、f ₀ 、K2、C、The specific model parameters to be trained may be adjusted based on the initial error observation model. Exemplary, for an initial error observation model a, its model parameters to be identified include ε ₀ 、f ₀ K2, C and->For the initial error observation model b, the model parameters which need to be identified comprise epsilon ₀ K2 and->

In some embodiments, where the current error state is the second error state, processing the commanded position based on the error observation model, the obtaining the observed position of the instrument tip includes:

s5071: and inputting the instruction position into a target error observation model to obtain an initial observation value of the target flexible robot arm.

Specifically, by inputting the instruction position of the instrument end into the trained target error observation model, the initial observation value of the target flexible robot arm is obtainedThe target error observation model is trained based on the initial error observation model in fig. 7.

S5073: an initial position deviation between the command value and the initial observed value of the command position is calculated.

Specifically, a deviation determining module in the target error observation model calculates an instruction value q and an initial observation value of an instruction positionThe initial position deviation epsilon between them, i.e. +. >

S5075: and processing the initial position deviation based on the joint return difference model in the target error observation model to obtain the target position deviation.

Specifically, the initial position deviation is processed through a joint return difference model in the target error observation model, so that the target position deviation is obtained. Illustratively, by comparing the initial position deviation ε with the initial position deviation ε ₀ And determining the target position deviation according to the sizes of the two and the expression of the joint return difference model.

The expression of the joint return difference model is as follows:in a specific example, if the initial position deviation epsilon is larger than the initial position deviation epsilon ₀ Calculating the target position deviation as the initial position deviation epsilon and the initialization position deviation epsilon ₀ Difference between them. If the initial position deviation epsilon is smaller than the initial position deviation epsilon ₀ Calculating the target position deviation as the initial position deviation epsilon and the initial position deviation epsilon ₀ And a sum of the values. If the initial position deviation epsilon is smaller than the initial position deviation epsilon ₀ Negative number and ε of (2) ₀ And determining that the target position deviation is zero.

S5077: and acquiring an external force acting on the target flexible robot arm and a friction force of the target flexible robot arm.

Alternatively, the external force f_ext acting on the target flexible robot arm and the frictional force F of the target flexible robot arm are acquired by sensors.

S5079: and obtaining the observation position of the tail end of the instrument based on the external force, the friction force, the target position deviation and the torsional rigidity parameter of the flexible body.

In some embodiments, deriving the observed position of the instrument tip based on the external force, the friction force, the target positional deviation, and the compliant body torsional stiffness parameter comprises:

obtaining acting force of the target flexible robot arm based on the external force, the friction force, the target position deviation and the torsional rigidity parameter of the flexible body;

determining an observation angular acceleration of the instrument end based on the acting force of the target flexible robot arm and the equivalent mass of the target flexible robot arm;

and obtaining the observation angle of the tail end of the instrument based on the observation angular acceleration and the observation bias.

Specifically, the product of the friction force F, the torsional rigidity parameter K2 of the flexible body and the target position deviation epsilon is calculated through the flexible body stress determining module a, the sum of the product result and the external force F_ext applied to the flexible body is calculated, and the sum and the observation speed are calculatedAnd (3) carrying out difference on the product of the equivalent coulomb coefficient C to obtain the acting force tau of the target flexible robot arm. Then, based on the acting force tau of the target flexible robot arm and the equivalent mass M, the terminal acceleration of the flexible body is obtained >I.e. the observed angular acceleration of the instrument tip. Then, based on the observation angular acceleration of the instrument tip +.>Obtaining the end angular velocity of the flexible body>And based on the angular velocity of the end of the flexible bodyAnd initial bias of observed values +.>And calculating to obtain the observation angle of the tail end of the instrument.

In some embodiments, where the current error state is the first error state, processing the commanded position based on the target error observation model to obtain the observed position of the instrument tip includes:

s5071': and inputting the instruction position into a target error observation model to obtain an initial observation value of the target flexible robot arm.

Specifically, by inputting the instruction position of the instrument end into the trained target error observation model, the initial observation value of the target flexible robot arm is obtainedThe target error observation model is trained based on the initial error observation model in fig. 8.

S5073': an initial position deviation between the command value and the initial observed value of the command position is calculated.

Specifically, a deviation determining module in the target error observation model calculates an instruction value q and an initial observation value of an instruction positionThe initial position deviation epsilon between them, i.e. +.>

S5075': and processing the initial position deviation based on the joint return difference model in the target error observation model to obtain the target position deviation.

The expression of the joint return difference model is as follows:in a specific example, if the initial position deviation ε is greater than the initial position deviation ε ₀ Calculating the target position deviation as the initial position deviation epsilon and the initialization position deviation epsilon ₀ Difference between them. If the initial position deviation epsilon is smaller than the initial position deviation epsilonInitiating positional deviation ε ₀ Calculating the target position deviation as the initial position deviation epsilon and the initial position deviation epsilon ₀ And a sum of the values. If the initial position deviation epsilon is smaller than the initial position deviation epsilon ₀ Negative number and ε of (2) ₀ And determining that the target position deviation is zero.

S5077': and obtaining the observation position of the tail end of the instrument based on the target position deviation and the torsional rigidity parameter of the flexible body.

Optionally, the observed position of the instrument tip is calculated based on the target positional deviation and the flexible body torsional stiffness parameter K2.

In some embodiments, the observation angle is included at the observation location; based on the target position deviation and the flexible body torsional stiffness parameter, obtaining the observed position of the instrument end comprises: obtaining the observation angular velocity of the tail end of the instrument based on the target position deviation and the torsional rigidity parameter of the flexible body; and obtaining the observation angle of the instrument end based on the observation angular velocity and the observation bias of the instrument end.

Specifically, calculating the product of the target position deviation and the torsional rigidity parameter K2 of the flexible body to obtain the end observation angular velocity of the flexible bodyBased on the end observation angular velocity of the flexible body>And initial bias of observed values +.>And calculating to obtain the observation angle of the tail end of the instrument.

It should be noted that, the above-mentioned motion error of the target flexible robot arm is applicable to a case where a position recognition module (for example, an end sensor) is present at the end of the instrument, and is also applicable to a case where the position recognition module is not present at the end of the instrument.

In some embodiments, as shown in fig. 9, the method further comprises:

s901: and under the condition that the motion error meets the error compensation condition, compensating and adjusting the command position of the tail end of the instrument based on the motion error to obtain the corrected position of the tail end of the instrument.

Optionally, under the condition that the motion error meets the error compensation condition, the command position of the tail end of the instrument is compensated and adjusted based on the motion error and return difference flexible difference compensation algorithm, so as to obtain the corrected position of the tail end of the instrument. Illustratively, the error replenishment condition may include, but is not limited to, the motion error being greater than an error threshold, or the like.

Taking the transmission mechanism as an example of rotation, the corrected position of the tail end of the instrument is the corrected joint angle position. Illustratively, the return difference soft difference compensation algorithm may be expressed as:wherein (1)>The motion error of the flexible robot arm is the target; q is the commanded position of the instrument tip; k3 is a gain parameter, is an ultra parameter, and the specific numerical value of the ultra parameter is debugged and confirmed according to actual conditions.

According to the embodiment, on the basis of real-time observation of the motion error of the flexible robot arm and rapid detection of return difference and flexible difference in the motion process, the motion error is compensated and adjusted in real time under the condition that the motion error meets the error compensation condition, so that inaccuracy in the operation process is remarkably reduced, and the accuracy and reliability of the operation are improved.

In addition, in the control process of the flexible robot arm, the real-time observation and compensation of the motion error can greatly lighten the workload of doctors. The doctor does not need to worry about small deviation of the surgical tool, but can concentrate on other important aspects of the operation, so that the operation difficulty of the doctor is further reduced. Meanwhile, higher-level operation consistency among different doctors can be ensured, namely, a relatively consistent movement track and position can be maintained in the operation process no matter what the experience level of the doctor is, so that more reliable operation results are provided, and the safety of a patient is further ensured.

In some embodiments, as shown in fig. 10, after obtaining the commanded position of the instrument tip in response to the control command for the instrument tip of the target flexible robotic arm, the method further comprises:

s1001: in the case where a position recognition module is present at the instrument tip, the position recognition module obtains the recognized position of the instrument tip.

Alternatively, in the case where a position identification module (e.g., an end sensor) is present at the instrument end, the identified position of the instrument end may be obtained directly based on the position identification module.

Correspondingly, the obtaining the motion error of the target flexible robot arm based on the command position of the instrument tail end and the observation position of the instrument tail end comprises the following steps:

s1003: and obtaining the motion error of the target flexible robot arm based on the command position of the tail end of the instrument and the identification position of the tail end of the instrument.

Optionally, after the identification position of the distal end of the instrument acquired by the position identification module is acquired, the identification position is taken as an actual movement position, and the movement error of the target flexible robot arm is determined by comparing the difference between the command position of the distal end of the instrument and the identification position. Through the motion error of the target flexible robot arm, the position correction adjustment or the position compensation is conveniently carried out according to the motion error, so that the actual operation position of the tail end of the instrument meets the control instruction requirement, and the operation risk is further reduced. And a position recognition module is arranged at the tail end of the instrument, so that the recognition position of the tail end of the instrument is directly obtained based on the position recognition module, the data interaction quantity is reduced, and the data processing efficiency can be improved.

For ease of understanding, the data processing method of the flexible surgical robot provided in the present application will be described in detail with reference to fig. 11.

As shown in fig. 11, before identifying model parameters of the target error observation model, it is detected whether a position identification module (e.g., an end sensor) exists at the end of the flexible body of the flexible surgical robot. If no position recognition module is present, a position recognition module (e.g., an end sensor) needs to be installed to detect movement of the instrument end. After detecting the movement of the tail end of the instrument, the near-end motor can be driven to follow the excitation track, meanwhile, the far-end sensor collects the movement of the tail end of the instrument, and according to the movement of the tail end of the flexible body and the near end of the flexible body, the model parameters of the initial error observation model are identified by adopting an optimization algorithm, so that the deviation between the tail end and the near end movement is minimum, and the target error observation model is obtained. After the parameter identification of the target error observation model is completed, the observation position of the instrument end can be determined based on the target error observation model. In addition, for the transmission mechanism without a position recognition module at the tail end of the instrument, after the parameter recognition of the target error observation model is completed, the position recognition module can be unloaded, and the subsequent compensation algorithm and the actual observation can not be used.

If the position recognition module is detected to exist, the distal movement of the flexible body, namely the recognition position of the tail end of the instrument, is recognized directly based on the position recognition module.

After the obtained observation position of the instrument end or the identification position of the instrument end, the command position can be compensated and adjusted by combining the command position of the instrument end to obtain the corrected position of the instrument end. In the return difference flexible difference compensation process, the current command position and the motion condition of the transmission tail end (such as the observation position of the tail end of the instrument) observed by the target error observation model or the motion condition fed back by the tail end sensor (such as the identification position of the tail end of the instrument) need to be acquired, and the command position is corrected through a return difference flexible difference compensation algorithm to obtain the corrected position of the tail end of the instrument. The proximal motor then performs the corresponding work following the corrected position of the instrument tip.

The embodiment of the application also provides a data processing device of the flexible surgical robot. Fig. 12 is a block diagram of a data processing device of a flexible surgical robot, according to an example embodiment. As shown in fig. 12, the flexible surgical robot includes at least one flexible robot arm, and the data processing apparatus 1200 of the flexible surgical robot may include at least:

A first processing module 1201, configured to obtain a command position of an instrument tip in response to a control command for the instrument tip of the target flexible robotic arm; the target flexible robot is any one of at least one flexible robot;

a model acquisition module 1203 configured to acquire a trained target error observation model in the case where the position recognition module is not present at the distal end of the instrument; the target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction; the error observation model is used for observing the position of the instrument tail end of the target flexible robot arm;

the second processing module 1205 is configured to process the instruction position based on the target error observation model to obtain an observation position of the instrument end;

a third processing module 1207, configured to obtain a motion error of the target flexible robot based on the commanded position of the distal end of the instrument and the observed position of the distal end of the instrument.

In some embodiments, the current error state includes any one of a first error state for representing including a return error, and a second error state for representing including a return error and a compliance error.

In some embodiments, where the current error state is the first error state, the second processing module includes:

The first acquisition sub-module is used for inputting the instruction position into the target error observation model to obtain an initial observation value of the target flexible robot arm;

the first deviation determining sub-module is used for calculating initial position deviation between the instruction value of the instruction position and the initial observed value;

the second deviation determining submodule is used for processing the initial position deviation based on the joint return difference model in the target error observation model to obtain target position deviation;

and the first processing submodule is used for obtaining the observation position of the tail end of the instrument based on the target position deviation and the torsional rigidity parameter of the flexible body.

In some embodiments, the observation angle is included at the observation location; the first processing sub-module is further configured to:

obtaining the observation angular velocity of the tail end of the instrument based on the target position deviation and the torsional rigidity parameter of the flexible body;

and obtaining the observation angle of the instrument end based on the observation angular velocity and the observation bias of the instrument end.

In some embodiments, in the case where the current error state is the second error state, the second processing module includes:

the second acquisition sub-module is used for inputting the instruction position into the target error observation model to obtain an initial observation value of the target flexible robot arm;

The third deviation determining sub-module is used for calculating initial position deviation between the instruction value of the instruction position and the initial observed value;

the fourth deviation determining submodule is used for processing the initial position deviation based on the joint return difference model in the target error observation model to obtain target position deviation;

the third acquisition sub-module is used for acquiring external force acting on the target flexible robot arm and friction force of the target flexible robot arm;

and the second processing sub-module is used for obtaining the observation position of the tail end of the instrument based on the external force, the friction force, the target position deviation and the torsional rigidity parameter of the flexible body.

In some embodiments, the second processing sub-module is further configured to:

In some implementations, the model acquisition module is further to:

In some embodiments, the apparatus further comprises:

and the identification module is used for acquiring the identification position of the tail end of the instrument based on the position identification module under the condition that the position identification module exists at the tail end of the instrument.

Correspondingly, the third processing module is further configured to:

and obtaining the motion error of the target flexible robot arm based on the command position of the tail end of the instrument and the identification position of the tail end of the instrument.

In some embodiments, the apparatus further comprises:

and the compensation module is used for compensating and adjusting the command position of the tail end of the instrument based on the motion error under the condition that the motion error meets the error compensation condition, so as to obtain the correction position of the tail end of the instrument.

It should be noted that, the data processing device embodiment of the flexible surgical robot provided in the embodiment of the present application and the data processing method embodiment of the flexible surgical robot are based on the same inventive concept.

The embodiment of the application also provides a data processing device of the flexible surgical robot. Fig. 13 is a block diagram of a data processing device of a flexible surgical robot, according to an exemplary embodiment. As shown in fig. 13, the flexible surgical robot includes at least one flexible robotic arm, and the data processing device 1300 of the flexible surgical robot may include at least:

a first processing module 1301, configured to obtain a command position of an instrument end in response to a control command for the instrument end of the target flexible robot arm; the target flexible robot is any one of at least one flexible robot;

a model obtaining module 1303, configured to obtain a trained target error observation model in a case where the position identifying module does not exist at the end of the instrument; the target error observation model is matched with the current error state of the flexible surgical robot in the motion process of executing the control instruction; the error observation model is used for observing the position of the instrument tail end of the target flexible robot arm;

a second processing module 1305, configured to process the instruction position based on the target error observation model, to obtain an observation position of the instrument end;

a third processing module 1307, configured to obtain a motion error of the target flexible robot based on the command position of the instrument end and the observation position of the instrument end;

And the compensation processing module 1309 is used for compensating and adjusting the command position of the tail end of the instrument based on the motion error to obtain the corrected position of the tail end of the instrument under the condition that the motion error meets the error compensation condition.

Embodiments of the present application also provide a flexible surgical robot comprising:

control means for controlling the surgical robot motion;

the flexible surgical robot comprises a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the data processing method of the flexible surgical robot according to any embodiment.

Embodiments of the present application also provide a computer readable storage medium storing at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by a processor to implement a data processing method of a flexible surgical robot as provided by the method embodiments described above.

Alternatively, in the present description embodiment, the storage medium may be located in at least one network server among a plurality of network servers of the computer network. Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The memory of the embodiments of the present specification may be used for storing software programs and modules, and the processor executes various functional applications and data processing by executing the software programs and modules stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for functions, and the like; the storage data area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide access to the memory by the processor.

Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the data processing method of the flexible surgical robot provided by the method embodiment.

The data processing method of the flexible surgical robot provided by the embodiment of the application can be executed in a terminal, a computer terminal, a server or similar computing devices. Fig. 14 is a hardware block diagram of a server of a data processing method of a flexible surgical robot according to an exemplary embodiment. As shown in fig. 14, the server 400 may vary considerably in configuration or performance, and may include one or more central processing units (Central Processing Units, CPU) 410 (the central processing unit 410 may include, but is not limited to, a microprocessor MCU, a programmable logic device FPGA, etc.), a memory 340 for storing data, one or more storage mediums 420 (e.g., one or more mass storage devices) for storing applications 424 or data 422. Wherein memory 430 and storage medium 420 may be transitory or persistent. The program stored on the storage medium 420 may include one or more modules, each of which may include a series of instruction operations on a server. Still further, the central processor 410 may be configured to communicate with the storage medium 420 and execute a series of instruction operations in the storage medium 420 on the server 400. The server 400 may also include one or more power supplies 460, one or more wired or wireless network interfaces 450, one or more input/output interfaces 440, and/or one or more operating systems 421, such as Windows ServerTM, mac OS XTM, unixTM, linuxTM, freeBSDTM, etc.

The input-output interface 440 may be used to receive or transmit data via a network. The specific example of the network described above may include a wireless network provided by a communication provider of the server 400. In one example, the input-output interface 440 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the input/output interface 440 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 14 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the server 400 may also include more or fewer components than shown in fig. 14, or have a different configuration than shown in fig. 14.

It should be noted that: the foregoing sequence of the embodiments of the present application is only for describing, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device and server embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and references to the parts of the description of the method embodiments are only required.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, and the relevant program may be stored in a computer readable storage medium, where the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The foregoing description of the preferred embodiments of the present application is not intended to be limiting, but rather is intended to cover any and all modifications, equivalents, alternatives, and improvements within the spirit and principles of the present application.

Claims

1. A method of data processing for a flexible surgical robot, the flexible surgical robot comprising at least one flexible robotic arm, the method comprising:

2. The method of claim 1, wherein the current error state comprises any one of a first error state representing an error comprising a return error and a second error state representing an error comprising a return error and a flex error.

3. The method of claim 2, wherein, in the case where the current error state is the first error state, the processing the commanded position based on the target error observation model to obtain the observed position of the instrument tip comprises:

Inputting the instruction position into the target error observation model to obtain an initial observation value of the target flexible robot arm;

calculating an initial position deviation between the instruction value of the instruction position and the initial observed value;

processing the initial position deviation based on a joint return difference model in the target error observation model to obtain a target position deviation;

and obtaining the observation position of the tail end of the instrument based on the target position deviation and the torsional rigidity parameter of the flexible body.

4. A method according to claim 3, characterized in that the observation position comprises an observation angle; the obtaining the observation position of the instrument tail end based on the target position deviation and the flexible body torsional rigidity parameter comprises the following steps:

obtaining the observation angular velocity of the tail end of the instrument based on the target position deviation and the flexible body torsional rigidity parameter;

and obtaining the observation angle of the instrument tail end based on the observation angular speed and the observation bias of the instrument tail end.

5. The method of claim 2, wherein, in the case where the current error state is the second error state, the processing the commanded position based on the error observation model to obtain the observed position of the instrument tip comprises:

acquiring an external force acting on the target flexible robot arm and a friction force of the target flexible robot arm;

and obtaining the observation position of the tail end of the instrument based on the external force, the friction force, the target position deviation and the torsional rigidity parameter of the flexible body.

6. The method of claim 5, wherein the deriving the observed position of the instrument tip based on the external force, the friction force, the target positional deviation, and a compliant body torsional stiffness parameter comprises:

obtaining acting force of the target flexible robot arm based on the external force, the friction force, the target position deviation and the flexible body torsional rigidity parameter;

determining an observation angular acceleration of the instrument end based on the acting force of the target flexible robotic arm and the equivalent mass of the target flexible robotic arm;

7. The method of any of claims 1-6, wherein the acquiring a trained target error observation model comprises:

responding to a control instruction aiming at the instrument end of a sample flexible mechanical arm, and acquiring the actual rotation angle of the instrument end of the sample flexible mechanical arm to obtain acquisition tracks for representing a plurality of acquisition moments;

acquiring an initial error observation model, and processing an instruction angle corresponding to the control instruction through the initial error observation model to obtain a sample observation angle of the instrument tail end of the sample flexible robot arm;

and optimizing model parameters of the initial error observation model by taking the minimized objective function as a training target to obtain a trained target error observation model.

8. The method of claim 1, wherein after the obtaining the commanded position of the instrument tip in response to the control command for the instrument tip of the target flexible robotic arm, the method further comprises:

Acquiring the identification position of the instrument end based on the position identification module under the condition that the position identification module exists at the instrument end;

the obtaining the motion error of the target flexible robot arm based on the instruction position of the instrument tail end and the observation position of the instrument tail end comprises:

and obtaining the motion error of the target flexible robot arm based on the instruction position of the tail end of the instrument and the identification position of the tail end of the instrument.

9. The method according to any one of claims 1-6, 8, further comprising:

and under the condition that the motion error meets an error compensation condition, compensating and adjusting the command position of the tail end of the instrument based on the motion error to obtain a corrected position of the tail end of the instrument.

10. A data processing device for a flexible surgical robot, the flexible surgical robot comprising at least one flexible robotic arm, the device comprising:

11. A data processing device for a flexible surgical robot, the flexible surgical robot comprising at least one flexible robotic arm, the device comprising:

12. A flexible surgical robot, comprising:

Control means for controlling the surgical robot motion;

a processor and a memory in which at least one instruction, at least one program, code set, or instruction set is stored, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by the processor to implement a data processing method of a flexible surgical robot according to any one of claims 1-9.

13. A computer readable storage medium, characterized in that at least one instruction, at least one program, code set or instruction set is stored in the computer readable storage medium, the at least one instruction, at least one program, code set or instruction set being loaded and executed by a processor to implement a data processing method of a flexible surgical robot according to any one of claims 1-9.