Motion track planning system for needle-knife robot

Abstract

The invention relates to the technical field of robot control, and discloses a movement track planning system for a needle-knife robot, which comprises a sensor module, a kinematics modeling module, a path generation and correction module, an optimization module and an intelligent control module, by arranging the path generation and correction module and the optimization module, a basic trajectory can be obtained by dynamically correcting a path in combination with a tissue deformation prediction model, and by combining real-time force feedback and tissue deformation data, the dynamic adaptability is improved, trajectory deviation is effectively prevented, and the accuracy of the system is improved. Meanwhile, a smooth speed curve is generated by adopting a quintic polynomial interpolation method on the basis of the basic trajectory, the smoothness of the trajectory and the continuity of the acceleration are ensured, acceleration abrupt change is restrained through a dynamic compensation algorithm, impact control is conducted on the needle-knife robot, mechanical vibration caused by acceleration abrupt change of the needle-knife robot in the operation process is prevented, and the operation reliability is improved. The surgical risk is reduced, and the surgical safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and more particularly to a motion trajectory planning system for an acupuncture robot. Background Art

[0002] As a minimally invasive surgical method, needle knife technology has the advantages of less trauma, faster recovery, and fewer complications. However, traditional manual needle knife surgery has certain limitations, such as low surgical precision and high operational difficulty. With the development of robotic technology, combining needle knife technology with robots to develop needle knife robot systems can effectively improve surgical precision and efficiency.

[0003] However, the trajectory planning of existing acupuncture robots mostly adopts the method of Cartesian space interpolation or joint space discrete point control, which still has some shortcomings:

[0004] 1. Failure to combine real-time force feedback with tissue deformation data results in trajectory deviation and insufficient dynamic adaptability;

[0005] 2. Lack of impact control for the acupuncture robot. If the acupuncture robot experiences a sudden acceleration during surgery, it is easy to cause mechanical vibration, affecting surgical safety and increasing surgical risks.

[0006] 3. There is path redundancy and lack of global optimization during multi-axis collaborative motion, which leads to low efficiency.

[0007] In view of this, the present invention proposes a motion trajectory planning system for an acupuncture robot. Summary of the Invention

[0008] In order to overcome the above-mentioned defects of the prior art, the present invention provides a motion trajectory planning system for an acupuncture robot, which provides real-time feedback on the force and tissue deformation data of the acupuncture robot, suppresses possible acceleration mutations through a dynamic compensation algorithm, and dynamically corrects and optimizes the surgical path to solve the problems existing in the above-mentioned background technology.

[0009] The present invention provides the following technical solutions: a motion trajectory planning system for an acupuncture robot, comprising a sensor module, a kinematic modeling module, a path generation and correction module, an optimization module, and an intelligent control module;

[0010] The sensor module integrates a torque sensor, an optical positioning module, and a strain gauge to collect the force, position, and tissue deformation data of the needle knife tip in real time;

[0011] The kinematic modeling module is based on computer-aided design to build a three-dimensional model of the acupuncture robot; obtains a set of preoperative planning path points, initializes the robot arm configuration through the DH parameter table, and establishes a mapping relationship from the joint space to the operation space;

[0012] The path generation and correction module determines the surgical path and surgical area based on the preoperative planned path point set, generates an initial trajectory, and dynamically corrects the path in combination with the tissue deformation prediction model to obtain a basic trajectory;

[0013] The optimization module generates a smooth velocity curve based on the basic trajectory using a quintic polynomial interpolation method, and suppresses acceleration mutations through a dynamic compensation algorithm to generate an optimized trajectory;

[0014] The intelligent control module realizes intelligent control of the acupuncture robot based on the optimized trajectory.

[0015] Preferably, the method of obtaining the preoperative planning path point set is:

[0016] Acquire preoperative CT data and MRI data and import them into preoperative planning software; extract key structures from the CT data and MRI data based on the preoperative planning software to construct a three-dimensional surface model of the surgical tissue;

[0017] Based on the preoperative planning software and the three-dimensional surface model of the surgical tissue, a preoperative planning path point set is obtained; the preoperative planning path point set is recorded as P = {P1, P2, P3, ..., P n}; where P represents the set of preoperative planning path points, P i represents the spatial coordinates of the i-th preoperative planning path point, P i =(x i ,y i ,z i ); where x i represents the coordinate point of the i-th preoperative planning path point on the x-axis, y i represents the coordinate point of the i-th preoperative planning path point on the y-axis, z i represents the coordinate point of the i-th preoperative planning path point on the z-axis; n represents the total number of preoperative planning path points, i = 1, 2, 3, ..., n.

[0018] Preferably, the specific method of initializing the robot arm configuration through the DH parameter table and establishing the mapping relationship from the joint space to the operation space is:

[0019] According to the mechanical structure of the acupuncture robot, a DH parameter table of each joint is defined to establish a homogeneous transformation relationship between the joint coordinate systems; the DH parameter table includes the rotation angle of the joint around the z-axis, the connecting rod offset along the z-axis, the connecting rod length along the x-axis, and the joint angle;

[0020] Through the forward kinematics model, the joint angle is mapped to the posture of the end effector, forming a mapping from the joint space to the operation space;

[0021] If the acupuncture robot has m joints, then for the jth joint, the rotation angle around the z axis is expressed as a j , the length of the connecting rod along the x-axis is denoted as b j , the link offset along the z-axis is denoted as c j , the joint rotation angle is represented by d j ; j = 1, 2, 3, …, m.

[0022] Preferably, the forward kinematics model is expressed as:

[0023] in, Mapping of the joint angles of the acupuncture robot to the position of the end effector; represents the transformation matrix from the j-1th joint to the jth joint; It can be expressed as a matrix:

[0024]

[0025] Preferably, the specific manner in which the path generation and correction module determines the surgical path and surgical area based on the preoperative planned path point set is:

[0026] Determine the surgical path based on the preoperative planned path point set;

[0027] Setting a safety boundary constraint function based on the CT data and the MRI data;

[0028] The dangerous areas within the surgical area that meet the safety boundary constraint function are marked.

[0029] Preferably, in the basic trajectory obtained by dynamically correcting the path in combination with the tissue deformation prediction model, the tissue deformation prediction model is expressed by the formula:

[0030] F(k,t)=λ1I+λ2B+λ3B 2 ; Wherein, F(k,t) represents the deformation state at position k at time t, which is used to characterize the deformation of soft tissue in different directions and degrees; the k represents the spatial vector coordinate of the soft tissue, which represents the specific position of the soft tissue in space; t represents the time when the soft tissue deformation occurs, which is used to characterize the change of deformation over time; λ1, λ2, and λ3 represent material parameters, which characterize the response characteristics of the soft tissue under different deformation states; I represents the unit tensor, which is used to characterize the rigid part of the soft tissue, that is, the initial state before deformation; B represents the deformation tensor, which is used to characterize the metric changes of the soft tissue before and after deformation.

[0031] Preferably, the specific way of obtaining the dynamic correction path in the basic trajectory based on the tissue deformation prediction model is as follows:

[0032] Based on the tissue deformation prediction model, a predicted deformation region and a corresponding predicted deformation displacement are obtained, wherein the predicted deformation region is a region in which deformation is predicted to occur.

[0033] A current path point P new is obtained. new The intersection probability U(P new ) of the current path point P new and the predicted deformation region is obtained.

[0034] If U(P new )>YU, dynamic correction is triggered, and a corrected path point set P * is obtained.

[0035] The above operation is performed on all path points, and a basic trajectory is generated based on the path point set after the last dynamic correction.

[0036] Preferably, the dynamic compensation algorithm is expressed by a formula as follows:

[0037] wherein A comp (t) represents an acceleration compensation value at time t; K p represents a proportional coefficient, K d represents a differential coefficient, K p =0.8, K d =0.2; and ΔL(t) represents a difference value between a real-time force feedback value at time t and a preset value.

[0038] Preferably, the dangerous region is represented as: R Danger ={(x,y,z)|blood vessel or nerve boundary}; wherein R Danger represents a dangerous region; and (x,y,z) represents a coordinate point, which corresponds to an x-axis coordinate point, a y-axis coordinate point and a z-axis coordinate point, respectively.

[0039] Preferably, the safety boundary constraint function is represented as: d(P i ,R Danger )≥δ; wherein d(P i ,R Danger ) represents a minimum safety distance of P i from the dangerous region, and δ represents a safety boundary layer.

[0040] Technical effects and advantages of the present application are as follows:

[0041] The present invention is provided with a path generation and correction module and an optimization module, which is conducive to obtaining a basic trajectory by dynamically correcting the path in combination with a tissue deformation prediction model. It combines real-time force feedback with tissue deformation data to improve dynamic adaptability and effectively prevent trajectory deviation. At the same time, a smooth velocity curve is generated based on the basic trajectory using a fifth-order polynomial interpolation method to ensure the smoothness of the trajectory and the continuity of acceleration. At the same time, a dynamic compensation algorithm is used to suppress acceleration mutations, and impact control is performed on the needle knife robot to prevent mechanical vibration caused by acceleration mutations during surgery, thereby reducing surgical risks and improving surgical safety. The construction of a three-dimensional model and the adoption of a global optimization mode improve the work efficiency of the needle knife robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a structural diagram of the motion trajectory planning system for the acupuncture robot of the present invention. DETAILED DESCRIPTION

[0043] The technical solutions of the present invention will be described clearly and completely below in conjunction with the drawings in the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples. The motion trajectory planning system for a needle knife robot involved in the present invention is not limited to the various structures described in the following embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0044] like Figure 1 As shown, the present invention provides a motion trajectory planning system for an acupuncture robot, comprising a sensor module, a kinematic modeling module, a path generation and correction module, an optimization module, and an intelligent control module;

[0045] The sensor module integrates a torque sensor, an optical positioning module, and a strain gauge to collect the force, position, and tissue deformation data of the acupuncture tip in real time; the tissue deformation data includes but is not limited to the position of the soft tissue in space, the time point when the soft tissue deforms, and the state of the soft tissue before and after deformation;

[0046] The kinematic modeling module is based on computer-aided design (CAD) to build a three-dimensional model of the acupuncture robot. The module obtains a set of preoperative planning path points, initializes the robotic arm configuration through the DH parameter table, and establishes a mapping relationship from the joint space to the operation space. The three-dimensional model of the acupuncture robot includes, but is not limited to, the robotic arm body, the acupuncture knife, the surgical path, the surgical area, and the human tissue model, etc., with the aim of providing a digital foundation for subsequent kinematic analysis and trajectory planning.

[0047] The path generation and correction module is configured to determine a surgical path and a surgical region based on a preoperative planning path point set, generate an initial trajectory, and dynamically correct the path in combination with a tissue deformation prediction model to obtain a basic trajectory; the purpose is to determine the surgical path and the surgical region, thereby marking the dangerous region in the surgical region, and to make the surgical path avoid the dangerous region as much as possible when generating the initial trajectory, thereby reducing the risk;

[0048] The optimization module generates a smooth speed curve based on the basic trajectory using a quintic polynomial interpolation method, ensures the smoothness of the trajectory and the continuity of the acceleration, and generates an optimized trajectory by suppressing acceleration mutations through a dynamic compensation algorithm.

[0049] The intelligent control module is configured to execute the optimized trajectory to decompose it into joint-level instructions, solve the driving motor through inverse kinematics, and realize intelligent control of the needle knife robot.

[0050] In this embodiment, it needs to be specifically pointed out that the kinematic modeling module establishes a three-dimensional model of the needle knife robot, which is specifically:

[0051] Modeling the needle knife: obtaining the length, diameter and blade angle of the needle knife, using parameterized sketches to draw the outline of the needle knife, and generating a three-dimensional solid model through stretching, chamfering and other operations;

[0052] Modeling the mechanical arm configuration: modeling the key parts of the mechanical arm, including but not limited to harmonic reducer, servo motor, end effector and end effector interface, etc., the end effector being the needle knife, and the end effector interface being the needle knife holder;

[0053] The length of the needle knife is the effective working length of the needle knife, which is used to ensure that the cutting range covers the lesion area; the blade angle of the needle knife is the angle between the surgical cutting surface and the axis of the needle knife, which is used to optimize the tissue separation efficiency and reduce the resistance.

[0054] In this embodiment, it needs to be specifically pointed out that the way to obtain the preoperative planning path point set is:

[0055] Obtain CT data and MRI data before surgery and import them into preoperative planning software; based on the preoperative planning software, extract key structures such as nerves and blood vessels from the CT data and MRI data, and construct a three-dimensional surface model of the surgical tissue;

[0056] Based on the preoperative planning software and the three-dimensional surface model of the surgical tissue, obtain the preoperative planning path point set; the preoperative planning path point set is denoted as P={P1,P2,P3,…,P n};wherein P represents the preoperative planning path point set, P i represents the spatial coordinates of the i-th preoperative planning path point, and P i =(xi ,y i ,z i ); where x i represents the coordinate point of the i-th preoperative planning path point on the x-axis, y i represents the coordinate point of the i-th preoperative planning path point on the y-axis, z i represents the coordinate point of the i-th preoperative planning path point on the z-axis; n represents the total number of preoperative planning path points, i = 1, 2, 3, ..., n;

[0057] The acquisition interval of the preoperative planned path point set is set to Δs, and the posture angle is set to θ. The value of Δs can be reasonably set by professionals in this field based on the specific surgical situation and the actual surgical tissue conditions to ensure the smoothness of the surgical trajectory. In this embodiment, Δs=1mm is selected. The posture angle, i.e., the angle between the needle knife edge direction and the tissue section, satisfies θ≤15°. At the same time, a safety boundary layer can be set to avoid accidental injury during surgery. The safety boundary layer is the minimum distance between the path and the tissue.

[0058] The preoperative planning software, such as Mimics, is widely used in surgical planning and three-dimensional model construction. It can convert medical imaging data such as CT and MRI into high-precision three-dimensional models, providing accurate simulation and planning for clinical surgery, thereby greatly improving the success rate and safety of surgery.

[0059] In this embodiment, it should be specifically explained that the specific method of initializing the robot arm configuration through the DH parameter table and establishing the mapping relationship from the joint space to the operation space is:

[0060] According to the mechanical structure of the acupuncture robot, a DH parameter table of each joint is defined to establish a homogeneous transformation relationship between the joint coordinate systems; the DH parameter table includes the rotation angle of the joint around the z-axis, the connecting rod offset along the z-axis, the connecting rod length along the x-axis, and the joint angle;

[0061] If the acupuncture robot has m joints, then for the jth joint, the rotation angle around the z axis is expressed as a j , the length of the connecting rod along the x-axis is denoted as b j , the link offset along the z-axis is denoted as c j , the joint rotation angle is represented by d j ; j = 1, 2, 3, ..., m; the joint angle determines the position of the end effector;

[0062] The joint angle is mapped to the position of the end effector through the forward kinematics model, forming a mapping from the joint space to the operation space; the forward kinematics model is expressed by the formula:

[0063] in, Mapping of the joint angle of the needle knife robot to the pose of the end effector; represents the transformation matrix from the j-1th joint to the jth joint; the is expressed by a matrix as:

[0064]

[0065] In this embodiment, it needs to be specifically pointed out that the specific manner in which the path generation and correction module determines the surgical path and the surgical region based on the preoperative planning path point set is as follows:

[0066] Determine the surgical path based on the preoperative planning path point set;

[0067] Based on the CT data and the MRI data, a safety boundary constraint function is set; the purpose is to clearly define the feasible region for surgery, and to provide a basis for subsequent division of the surgical path into several segments;

[0068] Label the dangerous region in the surgical region that meets the safety boundary constraint function; it can be manually labeled by a person skilled in the art, or the dangerous region can be extracted by threshold segmentation or a deep learning model such as a U-Net model;

[0069] The dangerous region is represented as: R Danger ={(x, y, z) | blood vessel or nerve boundary}; wherein, R Danger represents a dangerous region; that is, when the path point is located at the blood vessel or nerve boundary, the region near the path point is a dangerous region;

[0070] The safety boundary constraint function is represented as: d(P i , R Danger ) ≥ δ; wherein, d(P i , R Danger ) represents the minimum safety distance of P i from the dangerous region, and δ represents the safety boundary layer, which is selected as δ = 5 mm in this embodiment; the specific value of the safety boundary layer can be set by a person skilled in the art according to the actual situation of different tissues; for example, for tissues with relatively dense blood vessel distribution and more nerves, the value of the safety boundary layer can be appropriately increased, and for tissues with relatively sparse blood vessel distribution and fewer nerves, the value of the safety boundary layer can be appropriately reduced;

[0071] When calculating the minimum safety distance of P i from the dangerous region, the distance between P i and the nearest dangerous region boundary is regarded as the minimum safety distance; the division value of the nearby region in the dangerous region can be set by a person skilled in the art according to the actual situation of the tissue; if the division value of the nearby region is 2 mm, the region within 2 mm of the path point is a dangerous region.

[0072] In this embodiment, it should be specifically noted that when determining the surgical path based on the preoperative planned path point set, the surgical path can be set as straight segments and curved segments according to tissue structural characteristics, and the marked dangerous area range is avoided as much as possible; the tissue structural characteristics include but are not limited to tissue hardness, curvature, and vascular density;

[0073] For example, for tissue hardness, if the tissue hardness E>50kPa, the path segment can be set as a straight path, otherwise it can be set as a curved path; the tissue hardness Among them, PA represents tissue pressure, τ represents tissue deformation; for vascular density, if vascular density ρ bold When the blood vessel density is >0.3, the path segment can be set as a curve, otherwise it can be set as a straight line. Among them, XS represents the number of blood vessel pixels, and QS represents the area.

[0074] In this embodiment, it should be specifically explained that in the dynamic correction path obtained by combining the tissue deformation prediction model to obtain the basic trajectory, the tissue deformation prediction model is expressed by the formula:

[0075] F(k,t)=λ1I+λ2B+λ3B 2 ; Wherein, F(k,t) represents the deformation state at position k at time t, which is a second-order tensor used to characterize the deformation of soft tissue in different directions and degrees; the k represents the spatial vector coordinate of the soft tissue, which represents the specific position of the soft tissue in space and is used to accurately locate the position where the deformation occurs; t represents the time when the soft tissue deformation occurs, which is used to characterize the change of deformation over time; λ1, λ2, and λ3 represent material parameters, which characterize the response characteristics of the soft tissue under different deformation states; I represents the unit tensor, which is a second-order unit matrix, used to characterize the rigid part of the soft tissue, that is, the initial state before deformation; B represents the deformation tensor, which is used to characterize the metric change of the soft tissue before and after deformation. It is a second-order tensor that reflects the degree of deformation of the soft tissue in different directions;

[0076] The tissue deformation prediction model can also be the Ogden model in the hyperelastic model, which characterizes the stress-strain relationship through the strain energy function. Since human tissues such as muscles and skin will undergo large deformation when subjected to external forces, and these tissues usually exhibit hyperelastic properties, through the Ogden model, professional and technical personnel in this field can predict tissue deformation under different load conditions.

[0077] In this embodiment, it should be specifically explained that the specific method of combining the dynamic correction path of the tissue deformation prediction model to obtain the dynamic correction path in the basic trajectory is:

[0078] Acquiring a predicted deformation region and a corresponding predicted deformation displacement based on a tissue deformation prediction model, wherein the predicted deformation region is a region predicted to be deformed;

[0079] Get the current path point P new The intersection probability U(P new );

[0080] If U(P new )>YU, then dynamic correction is triggered and the corrected path point set P is obtained. * ;

[0081] The above operations are performed on all path points, and the basic trajectory is generated based on the path point set after the last dynamic correction.

[0082] In this embodiment, it should be specifically explained that the intersection probability U(P new ) is expressed as:

[0083] U(P new )=1-e -γ‖ΔX‖ ; Among them, ‖ΔX‖ represents the predicted deformation displacement, the unit is mm; γ represents the risk sensitivity coefficient, γ=1.5, the unit is mm -1 ;

[0084] YU represents a trigger threshold, and in this embodiment, YU=0.3 is selected.

[0085] In this embodiment, it should be specifically explained that the dynamic compensation algorithm is expressed as follows:

[0086] Among them, A comp (t) represents the acceleration compensation value at time t; K p Represents the proportionality coefficient, K d represents the differential coefficient, K p =0.8, K d =0.2; ΔL(t) represents the difference between the real-time force feedback value and the preset value at time t; its purpose is to dynamically adjust the acceleration through force feedback to suppress mechanical vibration and acceleration mutation; the preset value is a pre-set force feedback value.

[0087] In this embodiment, it should be specifically explained that the optimization module can also construct a dynamic repulsive field to achieve millimeter-level obstacle avoidance of the needle knife robot. The dynamic repulsive field is expressed by the formula:

[0088] Among them, U qrepresents the repulsive force, η represents the repulsive force gain coefficient, η is set to η = 1.2, θ_time represents the real-time obstacle distance, that is, the distance to obstacles such as key structures such as blood vessels or nerves, θ_safe represents the safety threshold of the obstacle distance, θ_safe ≥ 3mm; ∈(t) represents the time-varying attenuation coefficient, which is positively correlated with the movement speed; ω represents the control period, satisfying ω∈[1ms,10ms].

[0089] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0090] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A motion trajectory planning system for an acupuncture robot, characterized by: It includes sensor module, kinematic modeling module, path generation and correction module, optimization module and intelligent control module; The sensor module integrates a torque sensor, an optical positioning module, and a strain gauge to collect the force, position, and tissue deformation data of the needle knife tip in real time; The kinematic modeling module performs modeling based on computer-aided design to establish a three-dimensional model of the acupuncture robot; Obtain the preoperative planning path point set, initialize the robotic arm configuration through the DH parameter table, and establish the mapping relationship from the joint space to the operation space; The path generation and correction module determines the surgical path and surgical area based on the preoperative planned path point set, generates an initial trajectory, and dynamically corrects the path in combination with the tissue deformation prediction model to obtain a basic trajectory; The optimization module generates a smooth velocity curve based on the basic trajectory using a quintic polynomial interpolation method, and suppresses acceleration mutations through a dynamic compensation algorithm to generate an optimized trajectory; The intelligent control module realizes intelligent control of the acupuncture robot based on the optimized trajectory.

2. According to the motion trajectory planning system for an acupuncture robot according to claim 1, the method for obtaining the preoperative planning path point set is: Acquire preoperative CT data and MRI data and import them into preoperative planning software; extract key structures from the CT data and MRI data based on the preoperative planning software to construct a three-dimensional surface model of the surgical tissue; Based on the preoperative planning software and the three-dimensional surface model of the surgical tissue, a preoperative planning path point set is obtained; the preoperative planning path point set is recorded as P = {P1, P2, P3, ..., P n };in, P represents the set of preoperative planning path points, P i represents the spatial coordinates of the i-th preoperative planning path point, P i =(x i ,y i ,z i ); where x i represents the coordinate point of the i-th preoperative planning path point on the x-axis, y i represents the coordinate point of the i-th preoperative planning path point on the y-axis, z i represents the coordinate point of the i-th preoperative planning path point on the z-axis; n represents the total number of preoperative planning path points, i = 1, 2, 3, ..., n.

3. The motion trajectory planning system for an acupuncture robot according to claim 2, characterized in that: The specific method of initializing the robot arm configuration through the DH parameter table and establishing the mapping relationship from the joint space to the operation space is as follows: According to the mechanical structure of the acupuncture robot, a DH parameter table of each joint is defined to establish a homogeneous transformation relationship between the joint coordinate systems; the DH parameter table includes the rotation angle of the joint around the z-axis, the connecting rod offset along the z-axis, the connecting rod length along the x-axis, and the joint angle; Through the forward kinematics model, the joint angle is mapped to the posture of the end effector, forming a mapping from the joint space to the operation space; If the acupuncture robot has m joints, then for the jth joint, the rotation angle around the z axis is expressed as a j , the length of the connecting rod along the x-axis is denoted as b j , the link offset along the z-axis is denoted as c j , the joint rotation angle is represented by d j ; j = 1, 2, 3, …, m.

4. The motion trajectory planning system for an acupuncture robot according to claim 3, characterized in that: The forward kinematics model is expressed as follows: in, is the mapping from the joint angle of the acupuncture robot to the position of the end effector; T j j-1 represents the transformation matrix from the j-1th joint to the jth joint; the T j j-1 It can be expressed as a matrix:

5. The motion trajectory planning system for an acupuncture robot according to claim 4, characterized in that: The specific method in which the path generation and correction module determines the surgical path and surgical area based on the preoperative planned path point set is: Determine the surgical path based on the preoperative planned path point set; Setting a safety boundary constraint function based on the CT data and the MRI data; The dangerous areas within the surgical area that meet the safety boundary constraint function are marked.

6. The motion trajectory planning system for an acupuncture robot according to claim 5, characterized in that: In the dynamic correction path obtained by combining the tissue deformation prediction model to obtain the basic trajectory, the tissue deformation prediction model is expressed by the formula: F(k,t)=λ1I+λ2B+λ3B 2 ; Wherein, F(k,t) represents the deformation state at position k at time t, which is used to characterize the deformation of soft tissue in different directions and degrees; the k represents the spatial vector coordinate of the soft tissue, which represents the specific position of the soft tissue in space; t represents the time when the soft tissue deformation occurs, which is used to characterize the change of deformation over time; λ1, λ2, and λ3 represent material parameters, which characterize the response characteristics of the soft tissue under different deformation states; I represents the unit tensor, which is used to characterize the rigid part of the soft tissue, that is, the initial state before deformation; B represents the deformation tensor, which is used to characterize the metric changes of the soft tissue before and after deformation.

7. The motion trajectory planning system for an acupuncture robot according to claim 6, characterized in that: The specific method of combining the dynamic correction path of the tissue deformation prediction model to obtain the dynamic correction path in the basic trajectory is: Acquiring a predicted deformation region and a corresponding predicted deformation displacement based on a tissue deformation prediction model, wherein the predicted deformation region is a region predicted to be deformed; Get the current path point P new The intersection probability U(P new ); If U(P new )>YU, then dynamic correction is triggered and the corrected path point set P is obtained. * ; The above operations are performed on all path points, and the basic trajectory is generated based on the path point set after the last dynamic correction.

8. The motion trajectory planning system for an acupuncture robot according to claim 7, characterized in that: The dynamic compensation algorithm is expressed as follows: Among them, A comp (t) represents the acceleration compensation value at time t; K p Represents the proportionality coefficient, K d represents the differential coefficient, K p =0.8, K d =0.2; ΔL(t) represents the difference between the real-time force feedback value and the preset value at time t.

9. The motion trajectory planning system for an acupuncture robot according to claim 8, characterized in that: The danger zone is represented by: R Danger = {(x, y, z) | blood vessel or nerve boundary}; where R Danger Indicates the danger zone; (x, y, z) represents the coordinate point, corresponding to the x-axis coordinate point, y-axis coordinate point, and z-axis coordinate point respectively.

10. The motion trajectory planning system for an acupuncture robot according to claim 9, characterized in that: The safety boundary constraint function is expressed as: d(P i ,R Danger )≥δ; where d(P i ,R Danger ) indicates P i The minimum safe distance from the dangerous area, δ represents the safety boundary layer.

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