A laparoscopic surgery robot slide table motion control method and device

Abstract

The application discloses a kind of laparoscopic surgery robot slide table motion control method and device, it is related to surgical robot technical field, including: based on focus CT image establishes focus three-dimensional model, obtains target area and its boundary coordinates, calculates coordinate conversion matrix and obtains unified coordinate system by space registration;According to user control instruction, select movement mode;Record the operation data of user, and the corresponding force speed curve, force displacement sequence and habitual force of user classification are obtained, establish comprehensive control model and error compensation model;When entering fine adjustment mode, real-time distance, user type and force displacement sequence are input into comprehensive control model, determine control scheme, according to real-time positioning error, correct output displacement in reverse, so that auxiliary surgical instrument reaches target position;Realize the high-precision motion control of laparoscopic surgery robot slide table (2), significantly shorten the operation time, accurately and quickly position the target position of auxiliary instrument, improve operation efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of surgical robots, in particular to a sliding table motion control method and device of a laparoscopic surgical robot. BACKGROUND

[0002] In laparoscopic surgery, a full-time medical support and control of surgical auxiliary instruments is often needed to assist the surgeon. In order to avoid fatigue and shaking of personnel, surgical robots are used for auxiliary operation. The surgical robot can control the auxiliary instrument in a passive control mode, and the surgeon sends control instructions to the surgical robot through foot pedals, voice, handle buttons, etc. The surgical robot passively executes the corresponding control instructions until the ideal surgical field of view or the ideal target position is reached.

[0003] Due to the stability and precision of transmission control, the surgeon generally controls the mechanical arm by manual button to implement auxiliary work, but the target position of the robot cannot be accurately and quickly positioned during the control process, resulting in increased regulation time and regulation error. The Chinese invention patent with application number 202110405332.8 provides a sliding table motion control method and device of a laparoscopic surgical robot, which obtains the force applied to the sliding table and determines the speed of the sliding table, and adjusts the speed limit to ensure the stability and accuracy of the surgeon using surgical instruments for surgical operation.

[0004] In the prior art, acceleration, constant speed and deceleration are adjusted by the force of the button, but different doctors have different habitual forces, which will result in different distances and times of deceleration sliding, and finally cause different degrees of deviation from the target position and increase the regulation time. SUMMARY

[0005] The present application provides a sliding table motion control method and device of a laparoscopic surgical robot, which solves the problem of long regulation time and large regulation error in the prior art, and achieves the technical effect of accurate and rapid regulation.

[0006] The present application provides a sliding table motion control method of a laparoscopic surgical robot, which is applied to a surgical robot including a master control arm 1, a sliding table 2, a slave control arm 3 and an operation button, comprising:

[0007] S1: a lesion three-dimensional model is established based on a lesion CT image, a target region and its boundary coordinates are obtained, a coordinate conversion matrix is calculated for spatial registration to obtain a unified coordinate system;

[0008] S2: Select a movement mode according to the user control instruction, the movement mode including a coarse adjustment mode and a fine adjustment mode; in the coarse adjustment mode, the slide table 2 is regulated and controlled according to the user control instruction, the real-time distance between the auxiliary control arm 3 and the target area is monitored in real time based on the unified coordinate system, and if the real-time distance is less than a safety threshold, the movement is stopped and it is prompted to enter the fine adjustment mode;

[0009] S21: Record the operation data of the user, classify the user to obtain a corresponding force-speed curve, a force-displacement sequence and a habitual force, establish a comprehensive control model and an error compensation model;

[0010] S22: When entering the fine adjustment mode, the real-time distance, the user type and the force-displacement sequence are input into the comprehensive control model to determine a regulation and control scheme, and the output displacement is corrected in reverse according to the real-time positioning error, so that the auxiliary surgical instrument reaches the target position.

[0011] Further, the operation data includes the pressing force, the duration, the moving distance of the slide table 2 and the error record of the button pressing; based on the operation data, first analysis data and second analysis data are obtained, the first analysis data refers to the average force, the pressing duration and the total moving distance in the coarse adjustment stage; and the second analysis data refers to a force distribution histogram and a final positioning error in the fine adjustment stage.

[0012] The control instruction refers to the operation button manipulated by the user and the corresponding pressing force applied, the moving direction of the slide table 2 is regulated and controlled based on the operation button, and the moving speed of the slide table 2 is controlled based on the pressing force.

[0013] Further, a coordinate conversion matrix is calculated for spatial registration, including:

[0014] A plurality of optical marker points are pasted on the surface of the patient, the positions of the marker points in the image coordinate system are obtained through CT scanning, and a marker point coordinate set is generated; the conversion matrix of the image coordinate and the operating table coordinate system is calculated based on the real-time tracking of the optical navigation of the marker points;

[0015] The spatial registration is to map the conversion matrix to the master control arm 1, dynamically align the image coordinate system and the operating table coordinate system, and calibrate the zero point of the master control arm 1 guide rail and the slide table 2.

[0016] Further, the force-speed curve is a curve corresponding to the pressing force and the moving speed of the slide table 2, the force input of different users in various operation scenarios and the corresponding speed output data of the slide table 2 are recorded, which are used to describe the mapping relationship between the user operation force and the speed of the slide table 2;

[0017] The habitual pressing force of the user is obtained based on the force-displacement sequence, and the habitual force refers to the force generated by the user habitually pressing;

[0018] The control model is configured to receive force-speed curve characteristics, force-displacement sequence characteristics and habitual force characteristics, and output ideal force value, ideal displacement of the slide 2 and speed control instruction.

[0019] The error compensation model is established based on overshoot or undershoot data records generated when the user operates the slide 2 in a historical surgery, and is used for in-surgery reverse correction of the output displacement.

[0020] Further, the method further comprises: S3: acquiring an ultrasound image and a fluorescence image, calculating a first centroid based on the ultrasound image; extracting a blood vessel region by threshold segmentation based on the fluorescence image, and calculating a second centroid; acquiring a target point set based on real-time target position monitoring, and calculating a third centroid and a standard deviation of the target point set;

[0021] The target centroid is determined according to the first centroid, the second centroid and the third centroid, and a new boundary of the region is obtained according to the target centroid and the standard deviation;

[0022] The dynamic change difference value is determined according to the new boundary of the region, and the force-speed curve is adjusted according to the dynamic change value;

[0023] The acquired fluorescence image is subjected to threshold segmentation to extract a blood vessel region, and centroid coordinates of the blood vessel region are calculated to obtain the second centroid by mapping the centroid coordinates to a unified coordinate system;

[0024] The target point set at time t is The centroid coordinates are obtained as follows: The standard deviations of the target point set on three coordinate axes are calculated according to the standard deviation formula; the target point set includes all feature points of the target region;

[0025] The first centroid, the second centroid and the third centroid are fused to obtain the target centroid; the semi-axis length of the target region is calculated based on the standard deviation; and the new boundary of the region is determined according to the target centroid and the semi-axis length;

[0026] The target blood vessel displacement speed is calculated according to the optical flow method, the norm value of the target blood vessel displacement speed is taken as the target speed, the average speed of the current slide 2 is obtained, and the ratio of the target speed to the average speed of the slide 2 is taken as the dynamic change difference value.

[0027] Further, the method further comprises:

[0028] S31: setting a state vector and an observation vector according to the new boundary of the region and the adjusted force-speed curve to obtain a prediction equation, predicting a displacement amount at the next time based on the actual displacement and the speed of the slide 2, and calculating a compensation amount based on the habitual force of the user;

[0029] The prediction time domain, the control time domain and the constraint condition are set, the optimal control sequence is obtained according to a prediction equation and a compensation amount, and the regulation and control scheme is determined based on a first-step control amount of the optimal control sequence.

[0030] A sliding table motion control device of a laparoscopic surgery robot, the surgery robot comprising a master arm 1 for performing surgery, a sliding table 2 and a slave arm 3; the master arm 1 is located above or at one side of a patient, the sliding table 2 is controlled to slide on the master arm 1 by a motor, and the slave arm 3 is movably connected to the sliding table 2 at one end and is provided with a fixed holder 4 at the other end for fixing and holding auxiliary instruments.

[0031] Further, the surgery robot further comprises an operator 5 and a display screen 6, and the operator 5 is provided with operation buttons;

[0032] The operation buttons comprise up buttons, down buttons and fine adjustment button groups;

[0033] The fine adjustment button groups comprise up buttons and down buttons; the user control instructions correspond to the operation buttons, the up buttons and the down buttons represent a coarse adjustment mode, and the fine adjustment button groups represent a fine adjustment mode; the user sends control instructions by operating the operation buttons, obtains the force applied to the operation buttons and determines the moving speed of the sliding table 2 according to the corresponding relationship;

[0034] The display screen 6 is used for receiving a lesion CT image, a target region and boundary coordinates thereof and displaying a distance heat map in real time.

[0035] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0036] By adopting the coarse adjustment and fine adjustment modes, high-precision motion control of the sliding table of the laparoscopic surgery robot is realized; by reasonably controlling the movement of the sliding table, the auxiliary instruments are quickly moved to the vicinity of the target region, accurate fine adjustment is realized, the surgery time is significantly shortened, the auxiliary surgery instruments are accurately provided, the shaking caused by the medical staff holding the surgery instruments is avoided, the auxiliary instruments are more accurately positioned, and the surgery efficiency and quality are improved. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A flowchart of a sliding table motion control method of a laparoscopic surgery robot in an embodiment of the application;

[0038] Figure 2 A schematic diagram of a sliding table motion control device of a laparoscopic surgery robot in an embodiment of the application;

[0039] Fig. 1 is a schematic diagram of a sliding table motion control device of a laparoscopic surgery robot in an embodiment of the application; DETAILED DESCRIPTION

[0040] For the purpose of promoting the understanding of the present application, the application will be described in further detail below with reference to the attached drawings; in such drawings there show preferred embodiments of the application, however, the application can be realized in many different forms and should not be considered limited to the described embodiments; on the contrary, it is provided these embodiments with the purpose of making the present application's disclosure more thorough and complete.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; as used herein, the use of the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0042] Embodiment one: as shown, a sliding table motion control method of a laparoscopic surgical robot, applied to a surgical robot, including a master control arm 1, a sliding table 2, a slave control arm 3 and an operation button; the method comprises: Figure 1

[0043] S1: establishing a lesion three-dimensional model based on a lesion CT image, obtaining a target region and its boundary coordinates, calculating a coordinate conversion matrix for spatial registration to obtain a unified coordinate system;

[0044] The spatial registration includes: pasting a plurality of optical markers on the patient's body surface, obtaining the positions of the markers in the image coordinate system through CT scanning, and generating a marker coordinate set; real-time tracking the markers based on optical navigation, and calculating the conversion matrix of the image coordinate system and the operating table coordinate system;

[0045] The spatial registration is to map the conversion matrix to the master control arm 1, dynamically align the image coordinate system and the operating table coordinate system, and calibrate the zero point of the master control arm 1 guide rail and the sliding table 2.

[0046] Specifically, the lesion CT image is obtained by CT scanning, and DICOM format data is generated; DICOM (Digital Imaging and Communications in Medicine) format refers to medical digital imaging and communication, which solves the problem of incompatible image data formats generated by different medical devices (such as CT, MRI, X-ray machine), and realizes standardized storage, transmission and sharing of medical images. A lesion three-dimensional model is generated by an image processing system, and a surgical target region is labeled. The target region circled by the doctor on the display screen 6 is obtained, the boundary coordinates are extracted, and a virtual fence (safe operation boundary) is generated.

[0047] ​4-6 optical markers (such as NDI Polaris special reflective balls) are pasted on the patient's body surface, the selected positions include the open hole position, the rib, the iliac crest, etc., the positions of the markers in the image coordinate system are obtained through CT scanning, a marker coordinate set is generated, the optical navigation system (NDI Polaris) is used to track the marker in real time, the conversion matrix of the image coordinate system and the operating table coordinate system is calculated, the conversion matrix is mapped to the main control arm 1 controller, and then the spatial registration is performed; the spatial registration refers to dynamically aligning the image coordinate system and the operating table coordinate system, and then zero-point calibrating the main control arm 1 guide rail and the sliding table 2 to ensure the three-axis motion accuracy.

[0048] S2: selecting a movement mode according to a user control instruction, the movement mode including a coarse adjustment mode and a fine adjustment mode;

[0049] In the coarse adjustment mode, the sliding table 2 is controlled according to the user control instruction, specifically, the user control instruction refers to the operation button controlled by the user and the corresponding pressing force, the movement direction of the sliding table 2 is controlled based on the operation button, and the movement speed of the sliding table 2 is controlled based on the pressing force, that is, the movement direction and the movement speed of the sliding table 2 are controlled according to the operation button and the corresponding pressing force controlled by the user; the real-time distance between the auxiliary control arm 3 and the target area is monitored in real time based on the unified coordinate system, if the real-time distance is less than a safety threshold, the movement is stopped and it is prompted to enter the fine adjustment mode, and then the auxiliary control arm 3 is further controlled to move in the fine adjustment mode so that the auxiliary surgical instrument reaches the target position.

[0050] In some embodiments, the main control arm 1 is located above or on one side of the patient, the main control arm 1 can move left and right and forward and backward, the sliding table 2 slides on the main control arm 1, one end of the auxiliary control arm 3 is fixedly connected with the sliding table 2, and the other end is used for fixing and clamping the auxiliary instrument. The operation button includes an up button, a down button and a fine adjustment button group, the fine adjustment button group includes an up button and a down button; the user control instruction corresponds to the operation button, the up button and the down button represent the coarse adjustment mode, and the fine adjustment button group represents the fine adjustment mode.

[0051] After determining the operation mode, the motor drives the sliding table 2 to move according to the pressing force, and the minimum movement distance and the maximum movement speed are set in the coarse adjustment mode. The parameter setting principle is safety first, balance of efficiency and accuracy, and dynamic adaptability, to ensure that the sliding table 2 does not approach the dangerous area (such as blood vessels and important organs) due to excessive speed or movement distance in the coarse adjustment stage, and to shorten the positioning time in the coarse adjustment stage by reasonably setting the parameters under the premise of ensuring safety, and to adjust the parameters according to the type of surgery (such as cholecystectomy and gastrointestinal anastomosis) and individual differences of patients (such as obesity and tissue elasticity).

[0052] Specifically, the minimum movement distance refers to the minimum displacement of the sliding table 2 in response to the control instruction in the coarse adjustment mode, and the calculation formula is as follows:

[0053]

[0054] wherein, is the minimum displacement amount; is the closest distance from the initial position to the target region, measured by the preoperative lesion three-dimensional model, i.e. the distance from the closest boundary point of the target region to the initial position; is the distance of the safety threshold; is the estimated maximum number of steps in the coarse adjustment mode, which is an estimated step number (e.g. 5-12 steps) obtained by comparative analysis according to the current closest distance and historical data. For example, in cholecystectomy, the target region is 30 mm away from the initial position, the safety buffer is 2 mm, and the coarse adjustment step number is set to 6 steps, then: In actual application, the upward rounding can be taken, i.e. 5 mm.

[0055] The maximum moving speed is the highest allowable speed of the slide table 2 in the coarse adjustment mode, which needs to be dynamically adjusted in combination with the pressing force and the safety threshold, and the calculation formula is:

[0056]

[0057] wherein, is the highest allowable speed; is the physical speed limit of the motor and guide rail; is the distance of the safety threshold; is the response time, which refers to the reaction moving time from the system receiving the user control instruction to the end of moving; a safety margin is set on the calculation result to fully ensure safety, for example, the maximum moving speed is calculated to be 50, and a safety margin of 20 is set, then the actual highest allowable speed is 30 mm / s.

[0058] In some embodiments, a dynamic adjustment strategy is implemented, i.e. distance-based deceleration, when the distance between the end of the secondary control arm 3 and the target region is ≤10 mm, the maximum speed limit is automatically reduced to 10 mm / s. When the distance is ≤5 mm, the fine adjustment mode is switched to. It also includes adjustment based on patient characteristics, for obese patients, the minimum moving distance standard should be increased.

[0059] Specifically, the auxiliary instrument is fixed on the secondary control arm 3, the up or down button is used to control the slide table 2 to move the secondary control arm 3 to the initial position, i.e. the position that needs to be fine adjusted, the fine adjustment button group is used to control the secondary control arm 3, and the auxiliary instrument is placed at the target position, which is determined according to the preset coordinate position and the bidirectional confirmation of the doctor.

[0060] Specifically, the user controls the moving direction of the sliding table 2 through the control buttons on the operator 5 (such as an operation panel or a handheld operator 5), the up button controls upward movement, and the down button controls downward movement. When it is detected that the up button of the control button is pressed, it is determined that the sliding table 2 moves towards the upper end of the master control arm 1, and when it is detected that the down button of the control button is pressed, it is determined that the sliding table 2 moves towards the lower end of the master control arm 1. The pressure of the control button at the previous time and the next time is detected, and the pressure at the previous time and the next time is converted into the force at the previous time and the next time, respectively; the acceleration of the sliding table 2 is obtained according to the force at the previous time and the next time, and the current speed of the sliding table 2 is obtained according to the acceleration.

[0061] The force applied to the button is obtained, and the moving speed of the sliding table 2 is determined according to the corresponding relationship. The moving speed of the sliding table 2 is sent to the motor, and the motor is used to drive the movement of the sliding table 2. The moving speed is controlled according to the pressing degree (0-100%), and the highest speed limit is set to prevent the sliding table 2 from moving too much. The position of the end of the sub-control arm 3 is obtained in real time by an optical navigation system, and the Euclidean distance to the nearest boundary of the target area is calculated.

[0062] Specifically, a reflective ball is installed at the end of the sub-control arm 3, which is tracked by an infrared camera with an accuracy of ±0.2 mm. Real-time output of end (position + attitude) data, using IMU data (angular velocity / acceleration) to assist compensation of positioning error when optical signal is blocked.

[0063] The position of the end of the sub-control arm 3 is obtained in real time by an optical navigation system, and the Euclidean distance to the nearest boundary of the target area is calculated as the real-time distance. The distance heat map (red→yellow→green represents far→near, for example, red area represents distance to target greater than 10 mm, yellow represents between 5-10 mm, green represents ≤5 mm) is displayed on the display screen 6 in real time. The doctor and the system judge whether to enter the fine adjustment mode based on the distance. The safety threshold is set according to historical data, and the average pressing force and the average moving distance are calculated according to the pressing force and the corresponding moving distance in the historical data. On the basis of the average moving distance, an error distance (0.5-1 mm) is added, that is, the safety threshold is set, for example, 5 mm, if the distance ≤5 mm, the system automatically stops moving and prompts to enter the fine adjustment mode, and the doctor confirms whether to enter the fine adjustment mode.

[0064] In the fine adjustment mode, based on the preoperative lesion three-dimensional model, the doctor circles the target area (such as the gallbladder triangle area) on the display screen 6, and the system automatically extracts the boundary coordinates and generates a virtual fence (the boundary is expanded by 2 mm as a safety buffer zone), and further reduces the moving speed and moving distance, and reduces the maximum moving speed and unit moving distance.

[0065] For example, in laparoscopic cholecystectomy, a patient's abdomen is scanned using a 64-slice or higher CT device to generate DICOM format data of the gallbladder and surrounding tissues. A three-dimensional model of the gallbladder region is generated by an image processing system, and the gallbladder triangle region is labeled as the target area for surgery. The doctor circles the target area on the display screen 6, and the system automatically extracts the boundary coordinates and generates a virtual fence (2mm outside the boundary as a safety buffer).

[0066] The slide table 2 moves on the master arm 1, and the doctor controls the slide table 2 to move the auxiliary instrument as a whole to the gallbladder region through the hand-held operator 5 button. When the distance is ≤5mm, the system triggers a warning and prompts to switch to fine adjustment mode, which is confirmed by the doctor. After confirmation, the fine adjustment button group on the hand-held operator 5 button is used to continue fine adjustment control of the slave arm 3 to adjust the surgical instrument to the target position.

[0067] The present application realizes high-precision motion control of the laparoscopic surgery robot slide table 2 through optical navigation, fusion positioning, coarse-fine adjustment cooperative control and target boundary technology, accurately provides auxiliary surgical instruments, avoids shaking caused by medical staff holding surgical instruments, can significantly shorten the operation time, reduce the risk of complications, and provides a safe and efficient solution for minimally invasive surgery.

[0068] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0069] The present application adopts coarse and fine adjustment modes to realize high-precision motion control of the laparoscopic surgery robot slide table 2; by reasonably controlling the movement of the slide table 2, the auxiliary instrument is quickly moved to the vicinity of the target area, and accurate fine adjustment is realized, which significantly shortens the operation time and accurately provides auxiliary surgical instruments, avoids shaking caused by medical staff holding surgical instruments, more accurately positions the auxiliary instrument, and improves the efficiency and quality of the operation.

[0070] Embodiment two: In embodiment one, two operation modes of coarse adjustment and fine adjustment are given, which further improves the control accuracy of the surgical robot and the accuracy of the doctor's regulation and control. In the fine adjustment mode, the user's pressing force cannot be accurately adjusted, and the user cannot accurately apply his own force. Therefore, the number of control times and control time are increased. The present embodiment further improves the above content.

[0071] In step S2, S21: record the user's operation data, classify the user, and obtain the corresponding force-speed curve, force-displacement sequence and habitual force, establish the comprehensive control model and error compensation model.

[0072] In some embodiments, the operation data includes historical data and operation data in the current surgery coarse adjustment mode, and the operation data includes pressing force, duration, slide table 2 movement distance and positioning error. Based on the operation data, first analysis data and second analysis data are obtained, the first analysis data refers to the average force, pressing duration and total movement distance in the coarse adjustment stage; and the second analysis data refers to the force distribution histogram and final positioning error in the fine adjustment stage. In actual application, if there is no historical data of the current user, only the operation data in the coarse adjustment stage is analyzed, i.e., the first analysis data is analyzed; based on the first analysis data and the second analysis data of the user as input features, the user is classified, the clustering algorithm is used, the clustering category is set to 3, and the optimal clustering center is found through multiple iterations. According to the clustering result, the doctors are divided into three types, i.e., light pressing type, standard type and heavy pressing type.

[0073] Specifically, the light pressing type means that the pressing force is relatively light, the positioning error is relatively large, and the operation control times are relatively many, for example, the pressing force is less than 40%, and the error is greater than 0.4 mm; the standard type means that the pressing force is moderate, and the situation analysis is in a moderate condition, for example, the pressing force is between 40% and 60%, and the error is between 0.2 mm and 0.4 mm; and the heavy pressing type means that the pressing force is relatively large, and the positioning error is relatively small, for example, the pressing force is greater than 60%, and the error is less than 0.2 mm.

[0074] In some embodiments, the force-speed curve is a curve corresponding to the pressing force and the slide table 2 movement speed, records the force input and the corresponding slide table 2 speed output data of different users in various operation scenes (different distances, different target operations, etc.), and is used to describe the mapping relationship between the user operation force and the slide table 2 speed, and reflects the speed that the user expects the slide table 2 to reach when applying different forces. The ideal force-speed curve will be different for different user types and operation scenes.

[0075] For the light pressing type user, it is usually desired that the slide table 2 has a relatively sensitive speed response to a small force input, so as to realize fine operation, and therefore the slope of the force-speed curve is relatively large when the force is small, i.e., a small force change can cause a large speed change.

[0076] The heavy pressing type user pays more attention to the stability of operation, and needs a relatively large force to make the slide table 2 reach a relatively high speed, so the slope of the force-speed curve is relatively small when the force is small, and the speed is significantly increased after the force reaches a certain degree.

[0077] The force-speed curve of the standard type user is between the light pressing type and the heavy pressing type, and reflects a relatively balanced operation characteristic.

[0078] In some embodiments, corresponding force-speed curves are constructed for different types of users, and mathematical functions (such as linear functions, polynomial functions, etc.) are used to fit the relationship between force and speed. For example, a linear function is used for light-pressing users, and a quadratic function is used for fitting for heavy-pressing users. During actual operation, force input and distance information are obtained in real time according to actual conditions. According to the user type and real-time distance, the pre-set force-speed curve is dynamically adjusted according to the established strategy. For example, for light-pressing users, the slope of the curve in the low-force segment is appropriately increased; for heavy-pressing users, the slope of the curve in the low-force segment is reduced; and according to the adjusted force-speed curve, the speed of the slide 2 is controlled to match the doctor's operating intention.

[0079] For example, for a light-pressing user, the basic force-speed curve is , the force F range is [0, 1], and the velocity v range is [0, 1];

[0080] For standard users, the basic force-speed curve is ;

[0081] For heavy-pressing users, the basic force-speed curve is ;

[0082] In actual operation, for example, for heavy-pressing users, the real-time distance is monitored. When the real-time distance is less than the safety threshold (i.e., entering the fine-tuning mode), the force-speed curve is compressed to , which reduces the response of speed to force. Under the same force, the speed is reduced; then the force-speed curve is dynamically adjusted according to different user types and real-time distance to achieve more precise and safe operation control.

[0083] In some embodiments, a personalized force-speed curve is provided based on the operating habits and types of different users, allowing the slide 2 to better adapt to the doctor's operating style. In combination with real-time distance information, the force-speed curve is dynamically adjusted so that the slide 2 always maintains optimal speed response characteristics during operation. This allows for more natural and smooth control of the slide 2, reducing operational discomfort caused by speed mismatch and improving operational smoothness and comfort. When approaching the target position, the speed is reduced by compressing the force-speed curve, effectively reducing overshoot and improving positioning accuracy. At the same time, safety risks such as false touches caused by excessive speed are reduced, ensuring operational safety.

[0084] In some embodiments, the force displacement sequence is a sequence formed by arranging the number of presses, the pressing force, and the actual displacement distance over time. Based on the force displacement sequence, the user's habitual pressing force is obtained. The habitual force refers to the force generated by the user's habitual pressing and is the force characteristic that is least easily changed or adjusted by the user.

[0085] In some embodiments, statistical analysis is performed on the collected force data to find the frequently used pressing force interval of the user in a period of time. For example, the force distribution of the user's pressing in different operation stages (such as coarse adjustment, fine adjustment) is statistically analyzed, the frequency of occurrence of each force interval is calculated, and the typical value in the pressing force interval with the highest frequency of occurrence or the most frequently used by the user is taken as the habitual pressing force. For example, if the user's pressing force is concentrated between 30%-40% in most operations, and the number of pressing force in this interval is much higher than that in other intervals, the middle value 35% of this interval can be taken as the habitual pressing force. The habitual force is an important manifestation of the user's operation habit, and as an important feature of user classification, personalized control can reduce the time for the user to adjust the operation force and improve the operation efficiency. The user operates at his own habitual pressing force, which can more stably control the sliding table 2 and reduce operation errors.

[0086] According to the user's habitual pressing force, the parameters of the surgical robot are personalized set. In the fine adjustment mode, the response speed and accuracy of the sliding table 2 to the habitual pressing force are optimized, so that the sliding table 2 can more accurately move to the target position when the user applies the habitual pressing force.

[0087] In some embodiments, the comprehensive control model is a comprehensive control model between the user's pressing force and the actual displacement of the sliding table 2 obtained according to the user's historical operation data. Due to the difference in operation habit and skill, the actual displacement of the sliding table 2 under the same pressing force may be different for different doctors. Based on the comprehensive control model, the displacement of the sliding table 2 that different users should move under a certain force can be accurately predicted, so that the control of the sliding table 2 is more in line with the operation habit of the doctor, and the operation comfort and efficiency of the doctor are improved. Accurate force-displacement mapping can reduce the positioning error caused by the mismatch between force and displacement, so that the sliding table 2 can more accurately reach the target position, thereby improving the positioning accuracy of the operation.

[0088] For example, user A and user B operate the sliding table 2 respectively. In the historical data (or data in the previous coarse adjustment mode), the average displacement of the sliding table 2 when user A operates with habitual force is 5mm, and the average displacement of the sliding table 2 when user B operates with habitual force is 3mm. The comprehensive control model learns these historical data, and when user A presses with habitual force again, it will predict that the sliding table 2 should move 5mm; when user B presses with habitual force, it will predict that the sliding table 2 should move 3mm. According to the control mode and the actual displacement, the displacement is controlled to ensure a stable corresponding displacement, so that the control of the sliding table 2 can better adapt to the operation characteristics of different users.

[0089] In some embodiments, the comprehensive control model is established by extracting key features from the force-velocity curve, such as the slope of the curve, the position of the inflection point, the change in velocity response at different force intervals, etc.; extracting features from the force-displacement sequence, such as the average pressing force, the pressing frequency, the displacement rate of change, etc.; and taking the habitual force as an independent feature to represent the user's operation habits.

[0090] A neural network model is constructed, which includes an input layer, a hidden layer, and an output layer. The input layer of the comprehensive control model receives the force-velocity curve features, the force-displacement sequence features, and the habitual force features; the hidden layer is used to learn the complex relationships between the features; and the output layer outputs the ideal force value, the ideal displacement of the slide 2, and the velocity control instruction.

[0091] The preprocessed data is divided into a training set, a validation set, and a test set. The training set is used to train the model, and the weights and biases of the model are continuously adjusted through the backpropagation algorithm to minimize the prediction error. The validation set is used to monitor the training process of the model to prevent overfitting. According to the performance of the validation set, the structure and hyperparameters of the model, such as the learning rate and the number of hidden layer nodes, are adjusted to improve the performance of the model.

[0092] In some embodiments, the error compensation model is established based on the overshoot or undershoot data records generated by the user operating the slide 2 in historical surgeries (for users without historical data, the corresponding control data in the current surgery is analyzed in the coarse adjustment mode). Overshoot refers to the actual movement distance of the slide 2 exceeding the target distance, and undershoot refers to the actual movement distance of the slide 2 not reaching the target distance. By analyzing these historical data, the error patterns of different users in different operating situations are found out.

[0093] In real-time surgery control, when the system detects that the current positioning situation has similar characteristics to the historical data (such as a specific doctor at a specific target distance and operating force), the output displacement will be inversely corrected according to the historical error value. The meaning of inverse correction is that if the historical data shows that the doctor overshoots a certain distance in this situation, the corresponding displacement output will be reduced in actual control; if the historical data shows that it undershoots a certain distance, the corresponding displacement output will be increased.

[0094] The error compensation model is used after the adjustment using the comprehensive control model to inversely correct the movement error. In real-time surgery, when the doctor operates, the system real-time acquires the current target distance, pressing force, etc. information, and judges whether the current operating situation matches the similar situation in the historical data according to the error compensation model. If it matches, the output displacement of the slide 2 will be corrected according to the error value output by the model.

[0095] By compensating for historical errors, overshooting or undershooting of the sliding table 2 during actual movement can be reduced, the sliding table 2 can more accurately reach the target position, and the positioning accuracy of the surgery can be improved; repeated adjustment operations caused by inaccurate positioning are avoided, surgery time is saved, and surgery efficiency is improved; the sliding table 2 control system can better adapt to different doctors' operation habits and error changes in different surgery scenarios, and the stability and reliability of the system are enhanced.

[0096] For example, user A moves the sliding table 2 by an average of 5 mm at a habitual force of 35%. A comprehensive control model is established, and a formula for force and displacement is obtained by fitting the training data, that is, d = 0.14F + 0.1. In actual operation, when user A applies a habitual force of 35%, the predicted displacement according to the model is d = 5 mm. However, due to some nonlinear factors of the surgical robot, the actual displacement is 4.8 mm, and the error is 0.2 mm. Through error analysis, it is found that the error is linearly related to the pressing force, and an error compensation model is established to calculate the error as -0.005F. In subsequent operation, when user A applies a force of 35% again, the displacement is first predicted to be 5 mm according to the comprehensive control model, and then the error is calculated to be -0.175 mm according to the error compensation model, and finally the sliding table 2 moves a displacement of 5-0.175 = 4.825 mm, thereby improving the positioning accuracy.

[0097] S22: input the real-time distance, user type and force-displacement sequence into the comprehensive control model to determine the control scheme, and simultaneously correct the output displacement in the reverse direction according to the real-time positioning error. The comprehensive control model outputs an ideal force value, an ideal displacement of the sliding table 2 and a speed control instruction. The auxiliary surgical instrument is moved to the target position by controlling the sub-control arm 3 according to the corrected results, and subsequent surgical operations are performed.

[0098] In summary, the existing technology uses voice control or direct positioning, which will have the problem of secondary adjustment, and there will be greater data reaction delay; in practice, it is shown that the method of directly controlling the movement of the auxiliary instrument by the doctor (user) is the fastest way to adjust to reach the target position, but the force of a person is the most difficult to control, especially the habitual force of each person will not change in a short time, and the problem solved by the present application is how to achieve accurate positioning of the target position based on the habitual force of the user, improve the accuracy, smoothness and comfort, avoid the problem of repeated adjustment and inaccurate positioning caused by different habitual forces of the user, that is, the user's pressing force is not accurate in the fine adjustment mode, cannot accurately apply the force, causes the increase of the number of adjustments and the adjustment time, improves the control accuracy of the surgical robot and the accuracy of the doctor's adjustment.

[0099] The technical solutions in the embodiments of the present application have at least the following technical effects or advantages:

[0100] The application provides personalized force-speed curves and parameter settings according to the operation habits and types of different users, so that the sliding table 2 can better adapt to the operation style of the doctor; the force-speed curve is dynamically adjusted in combination with real-time distance information, so that the sliding table 2 always maintains the best speed response characteristic during operation, reduces the discomfort caused by speed mismatch, improves the smoothness and comfort of operation, and improves the control accuracy of the surgical robot and the accuracy of the doctor's control;

[0101] When approaching the target position, the speed is reduced by compressing the force-speed curve, effectively reducing the overshoot phenomenon; at the same time, the error compensation model is used to correct the movement error in the opposite direction, so that the sliding table 2 can more accurately reach the target position;

[0102] Precise force-displacement mapping reduces positioning errors caused by force and displacement mismatch, avoids repeated adjustment operations caused by inaccurate positioning, saves surgery time, reduces safety risks such as accidental touch caused by excessive speed, and ensures the safety of operation.

[0103] In the surgical process, the target area often changes in position due to tissue peristalsis, respiratory movement or surgical operation (such as cutting and suturing). The above content relies on preoperative lesion three-dimensional model and virtual fence positioning, and force-speed curve and error compensation model based on static target area, which leads to a decrease in positioning error accuracy in real-time response to dynamic changes in the target area.

[0104] In laparoscopic surgery, target tissues (such as tumors and blood vessels) often change dynamically (typical fluctuation range ±3mm) due to respiratory movement, organ peristalsis or surgical operation (such as pulling and cutting), which reduces the positioning accuracy of the above content.

[0105] The method further comprises: S3: acquiring an ultrasound image and a fluorescence image, calculating a first centroid based on the ultrasound image; extracting a blood vessel region based on the fluorescence image by threshold segmentation, and calculating a second centroid; obtaining a target point set based on real-time target position monitoring, calculating a third centroid and a standard deviation of the target point set; determining a target centroid according to the first centroid, the second centroid and the third centroid, and obtaining a new boundary of the region according to the target centroid and the standard deviation; determining a dynamic change difference value according to the new boundary of the region, and adjusting the force-speed curve according to the dynamic change value.

[0106] In some embodiments, corresponding ultrasound images and fluorescence images are obtained using a high-frequency ultrasound probe and a near-infrared fluorescence imaging system, and a hardware trigger signal is used to ensure that the ultrasound probe and the NIR camera can simultaneously capture the current frame image. The accuracy of the trigger signal is controlled at the microsecond level to ensure the synchronization of the two images in time; for example: the ultrasound imaging is set to a frame rate of 30 fps, and the gain, depth and other parameters of the ultrasound probe are adjusted according to the surgical site and the size of the tumor to obtain the best image quality; the near-infrared fluorescence imaging is set to a frame rate of 25 fps, and the exposure time and gain of the NIR camera are adjusted to ensure that the fluorescence signal of the blood vessels can be clearly captured.

[0107] The first centroid is calculated based on the ultrasound image, including: inputting the ultrasound image into a U-Net++ model for real-time inference, outputting a binary mask, calculating the centroid coordinates of the binary mask, and marking the first centroid.

[0108] The second centroid is calculated based on the fluorescence image by threshold segmentation to extract the blood vessel region, including: threshold segmentation is performed on the collected fluorescence image to extract the blood vessel region, the centroid coordinates of the blood vessel region are calculated, and the second centroid is obtained by mapping the centroid coordinates to a unified coordinate system.

[0109] The target point set is obtained based on real-time target position monitoring, and the third centroid and the standard deviation of the target point set are calculated, including: the target point set at time t is obtained The centroid coordinates are obtained as: The standard deviations of the target point set on the three coordinate axes are calculated according to the standard deviation formula. The target point set includes all feature points of the target region.

[0110] The target centroid is determined according to the first centroid, the second centroid and the third centroid, and the new boundary of the region is obtained according to the target centroid and the standard deviation, including: the first centroid, the second centroid and the third centroid are fused to obtain the target centroid; the semi-axis length of the target region is calculated based on the standard deviation; and the new boundary of the region is determined according to the target centroid and the semi-axis length.

[0111] Specifically, the target region is mapped to a unified coordinate system to form an ellipsoid shape as the boundary data of the target region. The ellipsoid has three mutually perpendicular semi-axes, represented by a, b and c, which determine the size and shape of the ellipsoid in three different directions. The semi-axis length of the target region is calculated based on the standard deviation, and the standard deviations of the target point set on the three coordinate axes are multiplied by 1.5 respectively to obtain three values as the semi-axis length of the ellipsoid.

[0112] In some embodiments, the dynamic change difference value is determined according to the new region boundary, including: calculating the target blood vessel displacement velocity according to the optical flow method, taking the norm value of the target blood vessel displacement velocity as the target velocity, obtaining the current slide 2 average velocity, taking the ratio of the target velocity and the slide 2 average velocity as the dynamic change difference value, adjusting the force-speed curve according to the dynamic change value, and performing scaling or expansion. The optical flow method is a technique for estimating the motion information of objects in a sequence of images. It is based on the basic assumption that the gray value of a pixel in an image remains relatively stable between consecutive frames, that is, the gray value of the same object in different frame images changes little. By analyzing the change of pixel gray in adjacent frame images, the optical flow method can calculate the motion vector of each pixel point, and these motion vectors constitute the optical flow field, reflecting the motion of objects in the image.

[0113] The method further includes: S31: setting a state vector and an observation vector according to the new region boundary and the adjusted force-speed curve to obtain a prediction equation, predicting the displacement amount at the next time based on the actual displacement and the slide 2 velocity, and calculating a compensation amount based on the user's habitual force; setting a prediction time domain, a control time domain and a constraint condition, obtaining an optimal control sequence according to the prediction equation and the compensation amount, and determining a regulation and control scheme based on the first step control amount of the optimal control sequence.

[0114] In some embodiments, the state vector and the observation vector are set according to the new region boundary and the adjusted force-speed curve, including: the state vector is used to describe the motion state of the target object, and the state vector at time t is defined as

[0115]

[0116] wherein, respectively represent the displacement, velocity and acceleration of the target at time t;

[0117] The observation vector represents the position information and velocity information of the target object obtained from the sensor, and provides the new region boundary of the target object, and the boundary information is used to constrain the observation, and the observation vector at time t is defined as

[0118]

[0119] wherein, is the actual displacement observation value, is the slide 2 velocity observation value;

[0120] The prediction equation is:

[0121] wherein, A is a state transition matrix, B is a control input matrix, is the input value at time t-1;

[0122] The update equation is:

[0123]

[0124]

[0125] wherein, is the Kalman gain, is the predicted covariance matrix, is the observation matrix, is the observation noise covariance matrix. The Kalman filter receives the observation value, predicts the displacement at the next time, and calculates the compensation amount of the displacement based on the habitual force of the user, which is used for subsequent feedback control. In the prediction and update process, the motion range of the target object is limited by boundary constraints. For example, after the state prediction, it can be checked whether the predicted position exceeds the boundary. If it exceeds, the predicted position is adjusted to be within the boundary area. According to the new force-velocity curve, the process noise covariance matrix and the observation noise covariance matrix can be adjusted to reflect the changes in the motion characteristics of the target object. For example, if the force-velocity curve indicates that the motion speed of the target object is accelerated, the component related to the speed in the process noise covariance matrix can be increased to reflect greater uncertainty.

[0126] The update equation is used to correct the predicted state estimate according to the observation data, and the core is to fuse the predicted value and the observation value through the Kalman gain, so as to obtain a more accurate state estimate. The prediction equation is based on the system model and the state at the previous time to predict the state at the current time. The prediction equation provides a priori estimate, and the update equation corrects this estimate in combination with the observation data to form a posteriori estimate.

[0127] In some embodiments, the next time displacement is predicted based on the actual displacement and the speed of the slide 2, and the compensation amount is calculated according to the actual displacement obtained from the displacement at the next time and the habitual force of the user, that is, the compensation amount is obtained by advance prediction according to the predicted displacement and the displacement corresponding to the habitual force of the user, and then the optimal control sequence is further confirmed according to the compensation amount.

[0128] In some embodiments, the prediction horizon, the control horizon and the constraint condition are set. The prediction horizon and the control horizon are used to describe the planning range of the future behavior of the control algorithm. The control horizon is usually less than or equal to the prediction horizon, indicating how many steps of control input the control algorithm will optimize within the prediction horizon. The control horizon determines the number of control inputs that the control algorithm can directly optimize. For a rapidly changing system, a shorter prediction horizon and control horizon are needed to respond to system changes in a timely manner. For example, the prediction horizon N is set to 5 and the control horizon M is set to 3 to reduce the computational complexity while ensuring control accuracy. The constraint condition is set to ensure that the movement speed and acceleration of the surgical instrument are within a safe range, and the maximum value is dynamically set according to the actual situation. The optimization target is set in advance and is set according to the real-time distance (i.e., the distance between the end of the secondary control arm 3 and the boundary of the target region in Embodiment 1), the prediction equation and the compensation amount.

[0129] In some embodiments, the optimal control sequence is obtained according to the prediction equation and the compensation amount, including: determining the optimization target according to the prediction equation and the compensation amount, inputting the optimization target and the constraint condition into the OSQP solver, and solving to obtain the optimal control sequence. The first-step control amount based on the optimal control sequence determines the regulation scheme.

[0130] In some embodiments, based on the prediction of the dynamic changes of the target region, the optimal control sequence is obtained in advance, and then the first-step control amount is obtained. Based on the user's habitual strength, the target region position can be accurately positioned, the number of subsequent repeated adjustments is reduced, and personalized optimal sequences can be dynamically set according to the habitual strength of different users, ensuring the accuracy of fine adjustment and the comfort and smoothness of use and operation.

[0131] The technical solutions in the embodiments of the present application have at least the following technical effects or advantages:

[0132] The present application more accurately determines the position of the target region by multi-modal image fusion and centroid calculation, monitors the target position change in real time, dynamically adjusts the force-speed curve, and reduces the positioning error. The Kalman filter is used for state estimation, the predicted value is corrected in combination with the observation data, the resistance of the system to noise and uncertainty is improved, the movement speed and acceleration of the surgical instrument are ensured to be within a safe range by setting the constraint condition, the optimal control sequence is obtained according to the prediction equation and the compensation amount, more accurate control is achieved, prevention is performed in advance, the number of subsequent repeated adjustments is reduced, and the surgical efficiency is improved.

[0133] According to the habitual force of different users, the optimal sequence of individualization is dynamically set, and the regulation and control scheme is determined based on the first step control amount of the optimal control sequence, so that the displacement error problem in the subsequent regulation and control is eliminated from the beginning, and the accuracy of fine adjustment and the comfort and fluency of operation are further ensured.

[0134] Embodiment four also provides a sliding table motion control device of a laparoscopic surgery robot, which comprises: the surgery robot comprises a master control arm 1, a sliding table 2 and a slave control arm 3 for performing surgery; the master control arm 1 is located above or on one side of a patient, and the sliding table 2 is controlled to slide on the master control arm 1 by a motor. The master control arm 1 can drive the whole to move forward, backward, leftward and rightward, and the slave control arm 3 is movably connected to the sliding table 2 at one end and is provided with a fixed gripper 4 at the other end for fixing and holding auxiliary instruments, and a reflective ball is installed for being tracked by an infrared camera.

[0135] The surgery robot further comprises an operator 5 and a display screen 6, the operator 5 is provided with operation buttons; the operation buttons comprise up button, down button and fine adjustment button group; the fine adjustment button group comprises up button and down button; the user control instruction corresponds to the operation buttons, the up button and the down button represent coarse adjustment mode, and the fine adjustment button group represents fine adjustment mode; the user sends a control instruction by operating the operation buttons, obtains the force applied to the operation buttons and determines the moving speed of the sliding table 2 according to the corresponding relationship, a pressure sensor is arranged below each operation button to obtain the force, the force is converted into the moving speed and transmitted to the motor, and then the motor drives the sliding table 2 to slide; the display screen 6 is used for receiving a lesion CT image, a target region and boundary coordinates thereof and displaying a distance heat map in real time.

[0136] The user controls the surgery robot by using the operation buttons on the operator 5, moves the surgery auxiliary instruments to the target position, and realizes the effect of surgery assistance.

[0137] The above only describes the preferred embodiments of the present application and is not used to limit the present application, and the present application can be variously changed and modified for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A slide motion control device for a laparoscopic surgical robot, characterized in that: The surgical robot comprises a main control arm (1), a slide (2), and an auxiliary control arm (3) for performing surgery; the main control arm (1) is located above or on the side of the patient; the slide (2) is controlled by a motor to slide on the main control arm (1); one end of the auxiliary control arm (3) is movably connected to the slide (2); and the other end is provided with a fixed clamp (4) for fixing and clamping auxiliary equipment; The surgical robot further comprises an operator (5) and a display screen (6), wherein the operator (5) is provided with an operating button; The operation buttons include an up button, a down button, and a fine adjustment button group; the fine adjustment button group includes an up button and a down button; user control instructions correspond to the operation buttons, the up button and the down button represent a coarse adjustment mode, and the fine adjustment button group represents a fine adjustment mode; the user issues a control instruction through the operation buttons, obtains the force applied to the operation buttons, and determines the moving speed of the slide (2) according to the corresponding relationship; The display screen (6) is used to receive the lesion CT image, the target area and its boundary coordinates, and to display the distance thermal map in real time; The slide motion control device of the surgical robot is implemented by the following motion control method, which includes: S1: Build a 3D model of the lesion based on the CT image of the lesion, obtain the coordinates of the target area and its boundary, calculate the coordinate transformation matrix, perform spatial registration, and obtain a unified coordinate system; S2: Selecting a movement mode according to a user control instruction, the movement mode includes a coarse adjustment mode and a fine adjustment mode; in the coarse adjustment mode, the slide (2) is adjusted according to the user control instruction, and the real-time distance between the auxiliary control arm (3) and the target area is monitored in real time based on a unified coordinate system. If the real-time distance is less than a safety threshold, the movement is stopped and a prompt is given to enter the fine adjustment mode; S21: Record the user's operation data, classify the user to obtain the corresponding force-speed curve, force-displacement sequence and habitual force, and establish a comprehensive control model and error compensation model; S22: When entering the fine adjustment mode, the real-time distance, user type and force displacement sequence are input into the integrated control model to determine the control scheme, and the output displacement is reversely corrected according to the real-time positioning error so that the auxiliary surgical instrument reaches the target position.

2. The slide motion control device of a laparoscopic surgical robot according to claim 1, characterized in that: The operation data includes the pressing force, duration, movement distance of the slide (2) and error record of the button; first analysis data and second analysis data are obtained based on the operation data, the first analysis data refers to the average force, pressing duration and total movement distance in the coarse adjustment stage; the second analysis data refers to the force distribution histogram and final positioning error in the fine adjustment stage; The control instruction refers to the operation button manipulated by the user and the corresponding pressing force applied, and the moving direction of the slide (2) is regulated based on the operation button, and the moving speed of the slide (2) is controlled based on the pressing force.

3. The slide motion control device of a laparoscopic surgical robot according to claim 1, characterized in that: Calculate the coordinate transformation matrix for spatial registration, including: Several optical markers are attached to the patient's body surface. The positions of the markers in the image coordinate system are obtained through CT scanning to generate a set of marker coordinates. The markers are tracked in real time based on optical navigation, and the transformation matrix between the image coordinates and the operating table coordinate system is calculated. The spatial registration is to map the transformation matrix to the master arm (1), dynamically align the image coordinate system and the operating table coordinate system, and perform zero point calibration on the guide rail of the master arm (1) and the slide (2).

4. The slide motion control device of a laparoscopic surgical robot according to claim 1, characterized in that: The method further comprises: in the coarse adjustment mode, setting a minimum moving distance and a maximum moving speed, wherein the minimum moving distance refers to the minimum displacement of the slide (2) in response to a control instruction each time in the coarse adjustment mode, and the formula is as follows: ； in, is the minimum displacement; It is the shortest distance from the initial position to the target area, which is the distance from the nearest boundary point of the target area to the initial position obtained by measuring the preoperative lesion three-dimensional model; is the distance of the safety threshold; is the estimated maximum number of steps in coarse adjustment mode; The maximum moving speed is the highest permissible speed of the slide (2) in the coarse adjustment mode, and the formula is: ； in, is the maximum permissible speed; It is the physical speed limit of the motor and guide rail; It is the response time, which refers to the reaction time from the system receiving the user control command to the end of the movement.

5. The slide motion control device of a laparoscopic surgical robot according to claim 1, characterized in that: The force-speed curve is a curve corresponding to the pressing force and the moving speed of the slide (2), which records the force input of different users in various operation scenarios and the corresponding slide (2) speed output data, and is used to describe the mapping relationship between the user's operation force and the slide (2) speed; Obtaining the user's habitual pressing force based on the force displacement sequence, where the habitual force refers to the force generated by the user's habitual pressing; The integrated control model is used to receive the force-speed curve characteristics, the force-displacement sequence characteristics and the habitual force characteristics, and output the ideal force value, the ideal displacement of the slide (2) and the speed control instruction; The error compensation model is established based on the overshoot or undershoot data records generated when the user operates the slide (2) during historical surgeries, and is used for reverse correction of output displacement during surgery.

6. The slide motion control device of a laparoscopic surgical robot according to claim 1, characterized in that: The method further includes: S3: acquiring an ultrasound image and a fluorescence image, calculating a first centroid based on the ultrasound image; extracting a blood vessel region based on the fluorescence image by threshold segmentation, and calculating a second centroid; acquiring a target point set based on real-time target position monitoring, and calculating a third centroid and a standard deviation of the target point set; determining a target centroid based on the first centroid, the second centroid, and the third centroid, and obtaining a new region boundary based on the target centroid and the standard deviation; Determine the dynamic change difference based on the new boundary of the area, and adjust the force-speed curve based on the dynamic change value; The collected fluorescence image is threshold segmented to extract the vascular area, the centroid coordinates of the vascular area are calculated, and the centroid coordinates are mapped to a unified coordinate system to obtain a second centroid; Get the target point set at time t as The coordinates of the center of mass are obtained as: , calculate the standard deviation of the target point set on the three coordinate axes according to the standard deviation formula; the target point set includes all feature points of the target area; The first, second, and third centroids are fused to obtain the target centroid; the semi-axis length of the target region is calculated based on the standard deviation; and the new boundary of the region is determined based on the target centroid and the semi-axis length. The displacement velocity of the target blood vessel is calculated according to the optical flow method, the norm value of the displacement velocity of the target blood vessel is used as the target velocity, the average velocity of the current slide (2) is obtained, and the ratio of the target velocity and the average velocity of the slide (2) is used as the dynamic change difference.

7. The slide motion control device of a laparoscopic surgical robot according to claim 6, characterized in that: The method further comprises: S31: Set the state vector and observation vector according to the new boundary of the region and the adjusted force-speed curve, obtain the prediction equation, predict the displacement at the next moment based on the actual displacement and the speed of the slide (2), and calculate the compensation amount based on the user's habitual force; Set the prediction time domain, control time domain and constraint conditions, obtain the optimal control sequence according to the prediction equation and compensation amount, and determine the control plan based on the first step control amount of the optimal control sequence.

8. The slide motion control device of a laparoscopic surgical robot according to claim 7, characterized in that: The state vector is used to describe the motion state of the target object. The state vector at time t is defined as ； in, They represent the displacement, velocity and acceleration of the target at time t respectively; The observation vector represents the acquired position and velocity information of the target object. The observation vector at time t is defined as ； in, is the actual displacement observation value, is the observed value of the velocity of the slide (2).

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