Surgical robot motion control method applied to neuroendoscope

Abstract

The invention relates to a surgical robot motion control method applied to a neuroendoscope, and belongs to the technical field of surgical robos.The surgical robot motion control method comprises the steps that an entry point of a target area is determined based on the long axis of the target area, kinematics parameters are established according to the motion mode of the endoscope, and a motion area in the target area is constructed; flexible operation of an end effector of the mechanical arm in a motion area is achieved through an RCM constraint algorithm of the mechanical arm; according to the method, the motion control precision is improved, the response delay is reduced, the adaptability to personalized operation requirements is enhanced, and the safety and effectiveness of the neuroendoscopic operation are guaranteed. When the mechanical arm end executor is controlled to move, the defects that in the prior art, a surgical robot motion control method applied to a neuroendoscope is low in precision, delayed in response and poor in adaptability are overcome.

Description

Technical Field

[0001] This invention relates to the field of surgical robot technology, and to a method for controlling the motion of a surgical robot, particularly to a method for controlling the motion of a surgical robot applied to neuroendoscopy. Background Technology

[0002] With the development of robotics technology, its functions are becoming more and more powerful, and it is being widely used in different fields.

[0003] Neuroendoscopic surgery, with its advantages of minimal invasiveness and clear visualization, is increasingly widely used in the field of neurosurgery. It is now widely applied in the treatment of brain diseases such as hydrocephalus, septum pellucidum cysts, and arachnoid cysts. However, current neuroendoscopic surgeries primarily rely on manual manipulation by the surgeon, requiring a high level of skill. Problems such as hand tremors and insufficient precision during the procedure can easily arise, affecting surgical outcomes and patient safety.

[0004] Currently, some surgical robot systems are used in neurosurgery, but these systems are mostly based on general-purpose robotic arm designs and are not optimized for the specific needs of neuroendoscopic surgery. Neuroendoscopic surgery requires high-precision operations in confined spaces and involves complex operating paths, making it difficult for traditional robotic arm control systems to meet the high demands for flexibility, stability, and accuracy during surgery.

[0005] Existing motion control methods for surgical robots used in neuroendoscopy have many shortcomings: blind planning of motion area: they rely heavily on preoperative fixed path planning and do not dynamically adjust the range of motion in combination with the spatial distribution characteristics of the lesion area (such as shape, centroid, and long axis direction), which can easily lead to the instrument moving beyond the safety boundary or failing to fully cover the lesion.

[0006] Imperfect constraint mechanism: Although remote central motion (RCM) constraint is introduced to reduce tissue traction at the incision site, it is not associated with the geometric features of the lesion area, which may result in a mismatch between the instrument movement range and the lesion location, leading to missed views or limited operation.

[0007] The kinematic model is out of touch with actual needs: The kinematic model built based on the general structural parameters of the robotic arm (such as DH parameters) is not optimized for the specific motion modes of the neuroendoscopy (such as the rotation of the endoscope body and the rotation of limited angles), making it difficult to achieve precise operation.

[0008] Insufficient real-time performance of trajectory planning: Traditional trajectory generation relies on pre-calculation and cannot be dynamically updated according to changes in the lesion area during surgery (such as tissue deformation and bleeding), resulting in poor adaptability.

[0009] Therefore, there is an urgent need for a robotic motion control method specifically designed for the characteristics of neuroendoscopic surgery to improve surgical precision, safety, and efficiency. Summary of the Invention

[0010] In view of the above problems, this invention provides a motion control method for surgical robots applied to neuroendoscopy, which improves motion control accuracy, reduces response latency, enhances adaptability to personalized surgical needs, and ensures the safety and effectiveness of neuroendoscopic surgery. This invention determines the entry point of the target region based on its long axis and establishes kinematic parameters according to the endoscope's movement mode, constructing a motion region within the target region. The RCM constraint algorithm of the robotic arm enables flexible operation of the robotic arm's end effector within the motion region. When controlling the movement of the robotic arm's end effector, this invention overcomes the shortcomings of existing motion control methods for surgical robots applied to neuroendoscopy, such as low accuracy, response latency, and poor adaptability. This invention provides a method for motion control of a surgical robot, comprising: Step S1: Determine the operating area of ​​the surgical robot in the environmental information acquisition module; Acquire the target region and its corresponding 3D point cloud data within the operating area, and determine the major axis direction of the target region and the entry point of the target region; Step S2: Input the long axis direction of the target region and the tangent point of the target region into the motion region construction module to determine the motion region model of the robot end effector; Step S3: Establish motion constraints for the robot end effector based on the motion control constraint module to ensure that the robot end effector's field of view covers the entire target area; Step S4: Construct a forward kinematics model based on the link structure parameters DH parameters of the robotic arm; Step S5, let t =1, when t =1 indicates the initial time. Step S6: Input the forward kinematics model into the real-time monitoring module. A t , obtain the moment t Robot end-effector pose matrix; Based on time t Timing of pose matrix acquisition for robot end effector t Joint angles; Step S7: Obtain the time based on the forward kinematics model t The geometric relationships of the links in the robotic arm; Step S8: Input the robot end effector motion constraints and the robot end effector motion region model into the motion control command generation module. B t Get robot moments t The trajectory points; Step S9, Set the time t The geometric relationship input safety adjustment module of the robotic arm link C tConstruct the corresponding Jacobian matrix D t Based on the corresponding Jacobian matrix A t Obtaining robotic arm moment t The relationship between joint velocity and end-effector Cartesian velocity; Step S10: Set the robotic arm time. t The relationship between joint velocity and end-effector Cartesian velocity and time t Joint angle input dynamic optimization module E t , obtain the moment t Joint velocity; Based on time t Joint speed and robotic arm timing t The trajectory points are used to obtain the moment of the robotic arm. t The updated trajectory points serve as the robotic arm's moment. t +1 trajectory points and record them; Step S11, Judgment t Is it greater than or equal to? T‘ , T‘ Represents the total number of moments; if so, we get... T The trajectory points of the robotic arm at each moment are used as the travel path of the robot's end effector in the target area; otherwise, let t = t +1, return to step S6.

[0011] Optionally, step S1 includes the following specific steps: The operating area of ​​the surgical robot is determined by the environmental information acquisition module; Acquire the target area and its corresponding 3D point cloud data within the operating area; Obtain the centroid of the target region and construct the covariance matrix C to reflect the spatial distribution characteristics; Eigenvalue decomposition is performed on the covariance matrix C to obtain the vector features of the point cloud along each axis and the corresponding vector variance. The axial vector feature corresponding to the maximum vector variance is selected as the major axis direction of the target region. The intersection of the extension of the long axis of the target area and the boundary of the area to be operated is taken as the entry point E of the target area.

[0012] Optionally, the expression for the covariance matrix C is:

[0013] in, For the first j Point cloud coordinates, For the centroid of the target region, J The total number of point clouds,T Indicates transpose. Let represent the covariance matrix.

[0014] Optionally, the specific steps for determining the motion region model of the robot end effector in step S2 include: In the motion region construction module, a cone is constructed with the major axis of the target region as the z-axis and the center of the target region's centroid and the center of the target region's entry point E as the origin. This cone is defined as the motion region of the robot's end effector. Based on the motion mode of the robot end effector in the operating area, determine multiple kinematic parameters of the robot end effector's motion area; Determine the center of motion of the robot's end effector's motion area; A model of the robot end effector's motion region is established based on the robot end effector's motion region, multiple kinematic parameters, and the center of motion.

[0015] Alternatively, the expression for the origin of the cone is:

[0016] Where (x,y,z) are the coordinates in three-dimensional space. r Let the radius of the conical ground be . h The height of the cone. α For the cone angle, Let be the coordinates of the origin of the cone. For cone x The coordinates of the origin of the axis For cone y The coordinates of the origin of the axis For cone z The coordinates of the origin of the axis The coordinates of the entry point in the target area. Entry point for the target area x Axis coordinates Entry point for the target area y Axis coordinates Entry point for the target area z Axis coordinates The coordinates of the centroid of the target region are given. For the target area x Axial centroid coordinates For the target area y Axial centroid coordinates For the target area z Axis centroid coordinates.

[0017] Optionally, the kinematic parameters include the center of motion, translation direction, and rotation angle.

[0018] Optionally, the rotation angle of the robot's end effector is smaller than the cone angle.

[0019] Optionally, the motion constraint of the robot end effector is that the robot end effector rotates or translates around the center of motion.

[0020] Optionally, the expression for the motion constraint of the robot end effector is: =0 in, The linear velocity of the center of motion, RCM. This is the vector of the angular velocity of the robotic arm joints.

[0021] The second objective of this invention is to provide an application of a surgical robot motion control method, which is applied to a neuroendoscopic surgical control system; the neuroendoscopic surgical control system includes an environmental information acquisition module, a motion region construction module, a motion control constraint module, a real-time monitoring module, a motion control command generation module, a safety adjustment module, and a dynamic optimization module; The target area of ​​the surgical robot is the lesion area.

[0022] Compared with the prior art, the present invention has at least the following beneficial effects: (1) This invention limits the range of motion of the robot end effector by restricting its movement area, thus avoiding accidental contact with other anatomical structures during operation, resulting in high safety. (2) This invention realizes multiple motion modes of the robot end effector through the RCM constraint algorithm of the robotic arm, which is simple to operate; (3) The present invention establishes an RCM constraint model and calculates the joint angle of the robotic arm through the Jacobian matrix to ensure that the robot end effector always moves around the remote center point; (4) The present invention improves work efficiency by determining the driving path based on the shape of the target area and establishing the motion area, and by using a robotic arm to operate the end effector. Attached Figure Description

[0023] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0024] Figure 1 This is a schematic diagram of the movement area of ​​the surgical robot in an embodiment of the present invention; Figure 2 This is a schematic diagram of the entry point for the motion region visualization target region based on VTK in an embodiment of the present invention; Figure 3 This is a schematic diagram of the model visualization rotation center based on VTK in an embodiment of the present invention. Detailed Implementation

[0025] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0026] A specific embodiment of the present invention, such as Figure 1-3 A method for motion control of a surgical robot applied to neuroendoscopy is disclosed, and the specific implementation steps are as follows: Step S1: Determine the operating area of ​​the surgical robot in the environmental information acquisition module; Acquire the target area and its corresponding 3D point cloud data within the operating area; Obtain the centroid of the target region and construct the covariance matrix C to reflect the spatial distribution characteristics; Optionally, the operating area may be the patient's skull; Optionally, the target area can be obtained through preoperative imaging data; The preoperative impact data refers to CT or MRI image data; For example, the target area could be a hemorrhage or tumor in the patient's skull.

[0027] Optionally, the expression for the covariance matrix C is:

[0028] in, For the first j Point cloud coordinates, For the center of mass of the point cloud, J The total number of point clouds, T Indicates transpose. Let represent the covariance matrix. For discrete points, the centroid is the mean of the coordinates of each point cloud. The expression is:

[0029] Eigenvalue decomposition is performed on the covariance matrix C to obtain the vector features of the point cloud along each axis and the corresponding vector variance. The axial vector feature corresponding to the maximum vector variance is selected as the major axis direction of the target region. The intersection of the extension of the long axis of the target area and the boundary of the area to be operated is taken as the entry point E of the target area; Step S2: Input the long axis direction of the target region and the tangent point of the target region into the motion region construction module to determine the motion region model of the robot end effector; Optionally, the robotic end effector is a neuroendoscope.

[0030] Optionally, the specific steps for determining the motion region model of the robot end effector in step S2 include: In the motion region construction module, a cone is constructed with the major axis of the target region as the z-axis and the center of the target region's centroid and the center of the target region's entry point E as the origin. This cone is defined as the motion region of the robot's end effector. Based on the motion mode of the robot end effector in the operating area, determine multiple kinematic parameters of the robot end effector's motion area; Determine the center of motion of the robot end effector's motion region ; A model of the robot end effector's motion region is established based on the robot end effector's motion region, multiple kinematic parameters, and the center of motion.

[0031] Alternatively, the expression for the origin of the cone is:

[0032] Where (x,y,z) are the coordinates in three-dimensional space. r Let the radius of the conical ground be . h The height of the cone. α For the cone angle, Let be the coordinates of the origin of the cone. For cone x The coordinates of the origin of the axis For cone y The coordinates of the origin of the axis For cone z The coordinates of the origin of the axis The coordinates of the entry point in the target area. Entry point for the target area x Axis coordinates Entry point for the target area y Axis coordinates Entry point for the target area z Axis coordinates The coordinates of the centroid of the target region are given. For the target area x Axial centroid coordinates For the target area y Axial centroid coordinates For the target area z Axis centroid coordinates.

[0033] Optionally, the kinematic parameters include the center of motion. Translation direction and rotation angle ); Optionally, the rotation angle All are smaller than the cone angle α This indicates that the neuroendoscopic movement will not extend beyond the conic region.

[0034] Optionally, the robotic end effector can move in the patient's brain in the following ways: forward, backward, translation, rotation, and mirror rotation. Optionally, the center of motion is a translational motion along the origin of the cone, expressed as:

[0035] in, Let the coordinates of the center of motion be , For sports center x Axis coordinates For sports center y Axis coordinates For sports center z Axis coordinates The coordinates are for the translation direction. For translation x Axial coordinates, For translation y Axial coordinates, For translation z Axial coordinates.

[0036] Optionally, the VTK visualization toolkit can be used to create and visualize the parametric model of the motion region of the robot end effector, in which multiple motion parameters can be adjusted in real time.

[0037] Step S3: Establish motion constraints for the robot end effector based on the motion control constraint module to ensure that the robot end effector's field of view covers the entire target area; Optionally, the robot end effector constraint is a remote center motion (RCM) constraint; Optionally, the motion constraint of the robot end effector is that the robot end effector rotates or translates around the motion center; Step S4: Construct a forward kinematics model based on the link structure parameters DH parameters of the robotic arm; Step S5, let t =1, when t =1 indicates the initial time. Step S6: Input the forward kinematics model into the real-time monitoring module. A t , obtain the moment t Robot end-effector pose matrix; Based on time t Timing of pose matrix acquisition for robot end effector tJoint angles; Step S7: Obtain the time based on the forward kinematics model t The geometric relationships of the links in the robotic arm; Step S8: Input the robot end effector motion constraints and the robot end effector motion region model into the motion control command generation module. B t Get robot moments t The trajectory points; Optionally, the surgical robot is a UR5e robotic arm, and the linkage structure parameters DH parameters of the UR5e robotic arm are shown in Table 1: Table 1 DH Parameters

[0038] Step S9, Set the time t The geometric relationship input safety adjustment module of the robotic arm link C t Construct the corresponding Jacobian matrix D t Based on the corresponding Jacobian matrix A t Obtaining robotic arm moment t The relationship between joint velocity and end-effector Cartesian velocity; Optionally, the geometric relationships of the robotic arm links include joint angles, link lengths, link offsets, and torsion angles; For example, the robotic arm is a Universal Robots or KUKA robot; The Universal Robots are 6-axis robotic arms, while the KUKA Robots are 7-axis robotic arms.

[0039] Alternatively, the expression for the Jacobian matrix is:

[0040] in, It is a Jacobian matrix. For linear velocity, ω is the angular velocity.

[0041] Alternatively, the expression for linear velocity is:

[0042] in, for x Direction and position For the first i The angle of each joint The first of the linear velocity Jacobian matrix i The angle of each joint affects the robot's end effector. x Partial derivatives of direction and position, For the first The length of the connecting rod at each joint For the first Offset of the connecting rod of each joint. i =1,2,3... I , I This indicates the total number of joints in the robotic arm.

[0043] Alternatively, the expression for angular velocity is:

[0044] in, For the first i The projection of the z-axis of the coordinate system of each joint onto the standard coordinate system. Angular velocity, Step S10: Set the robotic arm time. t The relationship between joint velocity and end-effector Cartesian velocity and time t Joint angle input dynamic optimization module E t , obtain the moment t Joint velocity; Based on time t Joint speed and robotic arm timing t The trajectory points are used to obtain the moment of the robotic arm. t The updated trajectory points serve as the robotic arm's moment. t +1 trajectory points and record them; Step S11, Judgment t Is it greater than or equal to? T‘ , T‘ Represents the total number of moments; if so, we get... T The trajectory points of the robotic arm at each moment are used as the travel path of the robot's end effector in the target area; otherwise, let t = t +1, return to step S6.

[0045] Optionally, the expression for the motion constraint of the robot end effector is: =0 in, The linear velocity of the center of motion, RCM. This is the vector of the angular velocity of the robotic arm joints.

[0046] This invention relates to an endoscopic control method that determines the surgical path based on the lesion morphology and constructs a motion constraint area in combination with the endoscopic movement mode, and uses a robotic arm RCM constraint to achieve the endoscopic control method.

[0047] Another objective of this invention is to provide a surgical robot motion control method for application in a neuroendoscopic surgical control system; the neuroendoscopic surgical control system includes an environmental information acquisition module, a motion region construction module, a motion control constraint module, a real-time monitoring module, a motion control command generation module, a safety adjustment module, and a dynamic optimization module.

[0048] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A surgical robot motion control method characterized by, Comprise: Step S1, determining the operation area of the surgical robot in the environment information acquisition module; Obtain the target area in the operation area and the corresponding three-dimensional point cloud data, and determine the long axis direction of the target area and the target area incision point; Step S2, input the long axis direction of the target area and the target area incision point into the motion area construction module to determine the robot end effector motion area model; Step S3, establish robot end effector motion constraints based on the motion control constraint module to ensure that the field of view of the robot end effector covers the entire target area; Step S4, construct the forward kinematics model according to the link structure parameters DH parameters of the robot arm; Step S5, let t = 1, when t = 1 represents the initial moment; Step S6, input the forward kinematics model into the real-time monitoring module A t , obtain the moment t robot end position matrix based on time t pose matrix of the robot end effector at the time t joint angles; Step S7, obtaining the time point based on the forward kinematics model t geometric relationship of the robot arm link Step S8, input the robot end effector motion constraint and robot end effector motion region model into the motion control instruction generation module B t , obtain the trajectory point of the robot at time t ; Step S9, inputting the time t The geometric relationship of the mechanical arm connecting rod is input into the safety adjustment module C t , the corresponding Jacobian matrix is constructed D t , the corresponding Jacobian matrix is constructed A t The relationship between the joint speed and the end Cartesian speed of the mechanical arm at the time t is obtained. Step S10, input the relationship between the joint speed of the robot arm at time t and the end Cartesian speed and the joint angle at time t to the dynamic optimization module E t , obtain the joint speed at time t ; based on the joint velocity and the robot time point t based on the joint velocity and the robot time point t based on the joint velocity and the robot time point t based on the joint velocity and the robot time point t based on the joint velocity and the robot time point Step S11, judging t whether it is greater than or equal to T‘ , T‘ indicates the total number of time points, if yes, the trajectory points of the robot arm at T time points are obtained as the travel path of the robot end effector in the target area; if no, let t = t +1, return to step S6.

2. The surgical robotic motion control method of claim 1, wherein, The specific steps of step S1 include: Determine the operation area of the surgical robot in the environment information acquisition module; Obtain the target area in the operation area and the corresponding three-dimensional point cloud data; Obtain the centroid of the target area and construct the covariance matrix C to reflect the spatial distribution characteristics; Perform eigenvalue decomposition on the covariance matrix C to obtain vector features and corresponding vector variances of the point cloud in each axis; Select the axis vector feature corresponding to the maximum vector variance as the long axis direction of the target area; The intersection of the extension line of the long axis direction of the target area and the boundary of the to-be-operated area is taken as the target area incision point E.

3. The surgical robot motion control method according to claim 2, wherein The expression of the covariance matrix C is: wherein, is the j point cloud coordinate, is the target region centroid, J is the total number of point clouds, T denotes the transpose, denotes the covariance matrix.

4. The surgical robotic motion control method of claim 2, wherein, The specific steps of determining the robot end effector motion area model in step S2 include: In the motion area construction module, take the long axis direction of the target area as the z axis, and take the center of the target area centroid and the target area incision point E as the origin to establish a circular cone, which is defined as the robot end effector motion area: Determine a plurality of kinematic parameters of the robot end effector motion area according to the motion mode of the robot end effector in the operation area; Determine the motion center of the robot end effector motion area; Establish the robot end effector motion area model based on the robot end effector motion area, the plurality of kinematic parameters, and the motion center.

5. The surgical robotic motion control method of claim 4, wherein, The expression of the origin of the circular cone is: wherein (x, y, z) is the coordinate in three-dimensional space, r is the radius of the conic ground, h is the height of the conic, α is the angle of the conic, is the origin coordinate of the conic, is the x axis origin coordinate of the conic, is the y axis origin coordinate of the conic, is the z axis origin coordinate of the conic, is the target area cut-in point coordinate, is the target area cut-in point x axis coordinate, is the target area cut-in point y axis coordinate, is the target area cut-in point z axis coordinate, is the target area region centroid coordinate, is the target area region x axis centroid coordinate, is the target area region y axis centroid coordinate, is the target area region z axis centroid coordinate.

6. The surgical robotic motion control method of claim 4, wherein, The kinematic parameters include the motion center, the translation direction, and the rotation angle.

7. The surgical robotic motion control method of claim 4, wherein, The rotation angle of the robot end effector is less than the cone angle.

8. The surgical robotic motion control method of claim 1, wherein, The robot end effector motion constraint is the rotation or translation motion of the robot end effector around the motion center.

9. The surgical robotic motion control method of claim 1, wherein, The expression of the robot end effector motion constraint is: =0 wherein, is the linear velocity of the center of motion RCM, is the vector of joint angular velocities of the robot arm.

10. Use of the surgical robot motion control method according to any one of claims 1-9, characterized in that, Applied to a neuroendoscopic surgery control system; the neuroendoscopic surgery control system includes an environment information acquisition module, a motion area construction module, a motion control constraint module, a real-time monitoring module, a motion control instruction generation module, a safety adjustment module, and a dynamic optimization module; The target area of the surgical robot is the lesion area.

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