Method for controlling endoscope of surgical robot based on hierarchical quadratic programming framework

Abstract

The invention relates to the technical field of surgical robot endoscope control, in particular to a surgical robot endoscope control method based on a hierarchical quadratic programming framework, and the method comprises the steps: determining the operation mode of the current surgical robot endoscope, the operation mode including a pivot motion mode, a path navigation mode and a dynamic tracking mode; according to the operation mode, a corresponding mechanical arm motion constraint model is determined based on a hierarchical quadratic programming method; the mechanical arm motion constraint model is solved based on output information of the position sensor and the force sensor, and the control speed of the robot joint is obtained; and controlling the robot joints to move based on the control speed, and finally completing endoscope control of the surgical robot. According to the invention, the endoscope control safety can be improved and the control complexity can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of endoscopic control of neurosurgical robots, and particularly to a method for endoscopic control of neurosurgical robots based on a hierarchical quadratic programming framework. Background Art

[0002] Neurosurgical endoscopy is an effective method for treating neurosurgical diseases such as cerebral hemorrhage. As an important surgical instrument, the endoscope is widely used in minimally invasive surgery. Traditional endoscopic operations mainly rely on manual control by doctors, which not only increases the surgical burden on doctors, but also may cause problems such as unstable images and limited vision due to human factors.

[0003] With the continuous development of surgical robot technology, the prior art uses robotic arms to achieve endoscopic motion control. In particular, the multi-arm active surgical robots that are well-developed in the field of laparoscopic surgery have also been explored in the clinical operation of the neurosurgical skull base. However, the size of its end effector is relatively large and is suitable for surgical procedures with larger surgical areas such as the abdominal cavity, and it is still difficult to apply in the field of neurosurgery. Therefore, at present, single-arm forms are mostly used in neurosurgery to achieve the motion control of the endoscope.

[0004] There are obvious deficiencies in using the prior art to control the motion of the robotic arm to drive the endoscope in neurosurgical operations. First of all, this method cannot identify environmental factors, resulting in low surgical safety. During the process of the endoscope entering the lesion area from the skull point, the collision risks of cerebral blood vessels and important brain tissues as well as the state of the robotic arm need to be considered. However, controlling the motion of the robotic arm to drive the endoscope through button presses cannot comprehensively consider these factors in real time, increasing the surgical risk. Secondly, various motion modes of the endoscope (such as forward, backward, rotation, etc.) need to be manually switched at different stages, increasing the operation complexity for doctors. Therefore, the traditional control method is insufficient in terms of both safety and operation convenience. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method for endoscopic control of neurosurgical robots based on a hierarchical quadratic programming framework, which solves the technical problems of low safety and high complexity in endoscopic control in the prior art.

[0006] The present invention provides a method for endoscopic control of neurosurgical robots based on a hierarchical quadratic programming framework. The neurosurgical robot endoscope includes an endoscope body, a robotic arm, a position sensor, and a force sensor. The robotic arm has multiple robot joints. The method is characterized by including the following steps:

[0007] Step S1, determining the current operation mode of the neurosurgical robot endoscope, where the operation mode includes: pivot motion mode, path navigation mode, and dynamic tracking mode;

[0008] Step S2. According to the operation mode, determine the corresponding robotic arm motion constraint model based on the hierarchical quadratic programming method, specifically including:

[0009] If the operation mode is the pivot motion mode, establish a first constraint relationship regarding the distance between the projection position of the pivot point on the endoscope body and the pivot point position and the speed of the robotic arm joints as the robotic arm motion constraint model;

[0010] If the operation mode is the path navigation mode, establish a second constraint relationship regarding the pose difference between the robotic arm and the target area, the distance between the endoscope body and the key environmental structures, and the speed of the robotic arm joints as the robotic arm motion constraint model;

[0011] If the operation mode is the dynamic tracking mode, establish a third constraint relationship regarding the pose difference between the robotic arm and the instrument to be tracked, the contact force at the end of the robotic arm, and the speed of the robotic arm joints as the robotic arm motion constraint model;

[0012] Step S3. Solve the robotic arm motion constraint model based on the output information of the position sensor and the force sensor to obtain the control speed of the robotic arm joints;

[0013] Step S4. Control the robotic arm joints to move based on the control speed, and finally complete the endoscope control of the neurosurgery robot.

[0014] Preferably, in step S1:

[0015] In the pivot motion mode, the endoscope body rotates around the pivot point;

[0016] In the path navigation mode, the endoscope body reaches the target area through path navigation;

[0017] In the dynamic tracking mode, the endoscope body moves following the instrument to be tracked.

[0018] Preferably, in step S2:

[0019] The first constraint relationship is used to constrain the robotic arm to move around the pivot point and to constrain the upper limit of the speed of the robotic arm joints;

[0020] The second constraint relationship is used to constrain the motion speed of the robotic arm between the current pose and the pose of reaching the target area, to constrain that the robotic arm does not collide with the key environmental structures during motion, and to constrain the upper limit of the speed of the robotic arm joints;

[0021] The third constraint relationship is used to constrain the relative motion between the robotic arm and the instrument to be tracked, to constrain the magnitude of the force at the end of the robotic arm, and to constrain the upper limit of the speed of the robotic arm joints.

[0022] Preferably, in step S2, the expression of the first constraint relationship is:

[0023]

[0024]

[0025] r rcm = -‖P rcm - P current ‖

[0026] where min represents minimizing the objective function, ‖·‖ 2 represents calculating the square of the vector norm, s.t. represents satisfying the following constraint conditions, represents the velocity of the robot joint, is the maximum velocity limit threshold, J rcm represents the Jacobian matrix of the RCM task, represents the estimator of the position vector between the pivot point and the projection point, δP rcm represents the position change of the pivot point, δq represents the position change of the robot joint, r rcm is the residual of the RCM task, P rcm represents the pivot point position, P current represents the position of the projection point of the pivot point on the neuroendoscope axis, ‖·‖ represents calculating the norm of the vector.

[0027] Preferably, in step S2, the expression of the second constraint relationship is:

[0028]

[0029] r trajPlanning = log(T focus T act -1 )

[0030]

[0031] r coll = ‖d i ‖

[0032] d i = p a - p c

[0033] where K t1 , K t2 are the weight coefficients of the path planning and collision avoidance tasks respectively, K r1 , K r2 are the residual ratio coefficients of the path planning and collision avoidance tasks respectively, JtrajPlanning The Jacobian matrix representing the path planning task, δT act The change in the current pose of the path planning task, r trajPlanning The residual of the path planning task, log(·) represents the natural logarithm, T focus The pose reaching the target area, T act -1 The current pose T of the path planning task act The inverse matrix of, J coll The Jacobian matrix representing the collision avoidance task, d i T Represents d i The transpose of, δd i T d i Represents d i T And d i The change in the product of, p c The position of the key environmental structure, p a The position of the closest point of the endoscope from the key environmental structure, r coll Represents the residual of the collision avoidance task

[0034] Preferably, in step S2, the expression of the third constraint relationship is:

[0035]

[0036]

[0037] r track = log(T des T act -1 )

[0038]

[0039] Where, K t3 , K t4 Are respectively the weight coefficients of the tracking task and the collision avoidance task of the instrument, K r3 Is the residual ratio coefficient of the collision avoidance task of the instrument, J track Represents the Jacobian matrix of the tracking task, δX act The change in the current pose of the tracking task, r track Represents the residual of the tracking task, X act Represents the current pose of the tracking task, X act -1 Represents X act The inverse matrix of, X des Represents the target pose of the tracking task, j forceThe Jacobian matrix representing the task of avoiding instrument collisions, α is the adjustment coefficient, and F ernd represents the force at the end of the robotic arm.

[0040] Preferably, in the pivot motion mode, the position sensor of the neurosurgical robot endoscope acquires the pivot point position P rcm in real time; the projection point position P current is calculated from the position of the pivot point according to geometric relationships;

[0041] In the path navigation mode, the current pose T act and the position p of the closest point of the endoscope to the key environmental structure are acquired in real time by the position sensor of the neurosurgical robot endoscope; a ;

[0042] In the dynamic tracking mode, the force F at the end of the robotic arm is acquired in real time by the force sensor; end .

[0043] Preferably, step S3 specifically includes:

[0044] Based on the output information of the position sensor and the force sensor, the constraint conditions in the robotic arm motion constraint model are optimized and solved to obtain the real-time speed of the optimal robot joints as the control speed of the robot joints.

[0045] Compared with the prior art, the present invention has at least the following beneficial effects:

[0046] (1) By dividing the operation mode of the endoscope into three modes: pivot motion, path navigation, and dynamic tracking, and establishing a corresponding robotic arm motion constraint model for each mode, the present invention realizes precise control of the robot endoscope. By constructing the constraint relationship between the projected position and the actual position of the pivot point, the precise rotation of the endoscope around the distal point is ensured, effectively avoiding the risk of the robotic arm joints reaching the limit range.

[0047] (2) By establishing the constraint relationships between the pose difference between the robotic arm and the target area and the distance between the endoscope body and the key environmental structure, the present invention realizes intelligent avoidance of the key environmental structure. The control method based on hierarchical quadratic programming can automatically adjust the motion trajectory of the robotic arm, minimizing the risk of damaging the key environmental structure during the operation of the endoscope and reducing the operation complexity.

[0048] (3) By incorporating the pose difference between the robotic arm and the instrument to be tracked and the end contact force into the constraint model, the present invention realizes the tracking of the surgical instrument by the endoscope. By processing the output information of the position sensor and the force sensor in real time, the system can keep the surgical instrument always at the center of the field of view and avoid accidental contact with other instruments, improving the accuracy and smoothness of the endoscope operation. Brief Description of the Drawings

[0049] The drawings are only for the purpose of illustrating specific embodiments and are not considered as a limitation to the present invention.

[0050] Figure 1 It is a flowchart of the endoscopic control method for a neurosurgical robot based on a hierarchical quadratic programming framework provided by the present invention.

[0051] Figure 2 It is a schematic diagram of the movement of the endoscope around the pivot point provided by the present invention.

[0052] Figure 3 It is a schematic diagram of the hierarchical quadratic programming control framework provided by the present invention. Detailed Description of the Embodiments

[0053] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0054] For the neurosurgical robot endoscope, the movement of the robot endoscope is divided into three modes, namely pivot movement, path navigation, and dynamic tracking. During the movement of the robot endoscope, the operations achieved by the three modes are different, so the corresponding control tasks are also inconsistent. In actual surgery, the movement of the endoscope cannot touch important key environmental structures, which corresponds to control constraints for the manipulator control. At the same time, the manipulator system has joint limitations, singularity limitations, etc. In this regard, the present invention has motion control and motion constraints in the control of the manipulator in each mode. Through a hierarchical controller, unified scheduling of multiple targets within multiple modes is achieved, so that different motion task targets can be considered simultaneously, thereby realizing the safe control of endoscopic operations and the smooth transition between tasks, without the need to manually switch buttons to achieve the switching of endoscopic movement, and at the same time, environmental factors can be considered in each movement, improving the safety of endoscopic movement.

[0055] In order to illustrate the effectiveness of the method proposed by the present invention, the above technical solutions of the present invention will be described in detail below through a specific embodiment, as Figure 1 shown, a neurosurgical robot endoscopic control method based on a hierarchical quadratic programming framework is disclosed. The neurosurgical robot endoscope includes an endoscope body, a manipulator, a position sensor, a force sensor, etc. There are multiple robot joints on the manipulator. The specific implementation steps are as follows:

[0056] Step S1: Determine the current operating mode of the neurosurgical robotic endoscope. The operating modes include: pivot motion mode, path navigation mode, and dynamic tracking mode;

[0057] For the neurosurgical robotic endoscope, the movement of the robotic endoscope is divided into three modes, namely pivot motion mode, path navigation mode, and dynamic tracking mode. Each mode has different control tasks. For these control tasks, it is necessary to determine the constraints on the movement of the robotic arm.

[0058] In this step, the control tasks of the three modes are determined through pre-planning. For the pivot motion mode, the focus is on achieving the rotational movement of the endoscope body around the pivot point. For the path navigation mode, path optimization needs to be completed to ensure forward movement along the preset trajectory and avoid colliding with key environmental structures. For the dynamic tracking mode, it is necessary to complete the tracking movement of the endoscope body with respect to other surgical instruments and reduce the contact force with other surgical instruments.

[0059] Step S2: According to the operating mode, determine the corresponding robotic arm motion constraint model based on hierarchical quadratic programming, specifically including:

[0060] If the operating mode is the pivot motion mode, establish a constraint relationship between the distance between the projection position of the pivot point on the endoscope body and the pivot point position and the speed of the robotic joints as the robotic arm motion constraint model;

[0061] If the operating mode is the path navigation mode, establish a constraint relationship between the pose difference between the robotic arm and the target area, the distance between the endoscope body and the key environmental structures, and the speed of the robotic joints as the robotic arm motion constraint model;

[0062] If the operating mode is the dynamic tracking mode, establish a constraint relationship between the pose difference between the robotic arm and the instrument to be tracked, the contact force at the end of the robotic arm, and the speed of the robotic joints as the robotic arm motion constraint model;

[0063] (1) Pivot motion mode

[0064] As Figure 2 shown, for the pivot motion mode, the endoscope is used for exploration operations. At this time, it is necessary to use the kinematic model to achieve the movement of the robotic arm around the pivot point. Among them, the present invention determines the pivot point as the remote center of motion (RCM) and determines the above movement task as the RCM task.

[0065] To ensure the rotation of the neuroendoscope around the pivot point, the optimization problem established by the present invention is to minimize the pivot point P rcm ∈R 3*1 on the projection point P current ∈R3*1 to the pivot point P rcm ∈R 3*1 the distance. Where R 3*1 represents a three-dimensional column vector space.

[0066] Determine the Jacobian matrix J of the robot joint velocity and pivot point position for the RCM task rcm ∈R 1*6 where R 1*6 represents a 6-dimensional row vector space, and the expression is:

[0067]

[0068] where, J rcm represents the Jacobian matrix of the RCM task, represents an estimator of the position vector between the pivot point and the projection point, δP rcm represents the position change of the pivot point, and δq represents the position change of the robot joint.

[0069] The residual of the RCM task in the pivot motion mode is used to describe the distance between the current projection point and the pivot point, and the expression is:

[0070] r rcm =-‖p e ‖=-‖P rcm -P current ‖

[0071] where, r rcm is the residual of the RCM task, p e represents the position vector between the pivot point and the projection point, and ‖·‖ represents the modulus of the position vector.

[0072] During the neuroendoscopic motion in the pivot motion mode, the self-constraint of the robotic arm needs to be considered, and the speed of the robot joint is restricted. The expression is:

[0073]

[0074] According to the above formula, the objective function of the pivot motion mode is obtained as:

[0075]

[0076] where, min means that the objective function needs to be minimized, ‖·‖ 2 represents calculating the square of the vector modulus, s.t. means subject to the following constraints, represents the speed of the robot joint, is the maximum speed limit threshold.

[0077] In some embodiments, the pivot point P rcmThe position of can be obtained in real time by the position sensor of the neurosurgical robotic endoscope. The projection point P of the pivot point on the axis of the neuroendoscope current The position of can be calculated from the geometric relationship based on the position of the pivot point.

[0078] (2) Path navigation mode

[0079] For the path navigation mode, the endoscope needs to reach the target area through path planning. The goal of the path planning task is for the robotic joints of the robotic arm to move from the current pose T of the path planning task act to the pose T of the target area of the path planning task focus .

[0080] The pose includes position and spatial orientation, which are used to completely describe the state of the robotic joints of the robotic arm in three-dimensional space. When the endoscope reaches the target area, not only the accuracy of the reached spatial position point needs to be considered, but also the orientation of the endoscope needs to be appropriate, so as to ensure the accuracy and safety of the endoscope movement.

[0081] The Jacobian matrix expression of the relationship between the current pose and the change of joint angle is:

[0082]

[0083] Among them, J trajPlanning represents the Jacobian matrix of the path planning task, and δT act represents the change of the current pose.

[0084] The residual of the path planning task is used to describe the deviation between the current pose and the target pose, and the expression is:

[0085] r trajPlanning = log(T focus T act -1 )

[0086] Among them, e trajPlanning represents the residual of the path planning task, log(·) represents the natural logarithm, T focus represents the pose of reaching the target area, and T act -1 represents the current pose T of the path planning task act 's inverse matrix.

[0087] During the movement of the endoscope to reach the target area, it is necessary to judge whether it touches the key environmental structures, so as to ensure the path safety. The position of the key environmental structure is p c , and the closest point of the endoscope to the key environmental structure is p a . Define the vector of the key environmental structure and the closest point as d i = pa -p c The expression of the Jacobian matrix representing the distance variation relationship between the endoscope and the nearest point of the critical environmental structure is as follows:

[0088]

[0089] where J coll represents the Jacobian matrix of the collision avoidance task, ‖·‖ represents calculating the modulus of a vector, and d i T represents d i transpose of, δd i T d i represents d i T and d i change amount of the product of.

[0090] The residual r coll of the collision avoidance task is used to describe the distance between the endoscope and the critical environmental structure, and the expression is:

[0091] r coll =‖d i ‖

[0092] In actual movement, the movement ranges and self-constraints of the various axes of the robotic arm should be restricted to further ensure safety, and the expression is also

[0093] According to the above formula, the objective function of the path navigation mode is:

[0094]

[0095] where K t1 , K t2 are the weight coefficients of the path planning and collision avoidance tasks respectively, and K r1 , K r2 are the residual ratio coefficients of the path planning and collision avoidance tasks respectively.

[0096] In some embodiments, the current pose T act can be obtained in real time by the position sensor of the endoscope of the neurosurgical robot, and the pose T focus reaching the target area can be manually set during the movement of the endoscope. The position p c of the critical environmental structure can be manually set during the movement of the endoscope, and the nearest point p a of the endoscope from the critical environmental structure can be obtained in real time by the position sensor of the endoscope of the neurosurgical robot.

[0097] (3) Dynamic tracking mode

[0098] For the dynamic tracking mode, the endoscope needs to track the positions of other surgical instruments in the field of view in real time.

[0099] The goal of the tracking task is for the robotic joints of the robotic arm to move from the current pose X of the tracking task act to the target pose X of the tracking task des , and the Jacobian matrix expression for the relationship between the current pose and the joint angles in the tracking task is:

[0100]

[0101] where J track represents the Jacobian matrix of the tracking task, and δX act represents the change in the current pose of the tracking task.

[0102] The residual expression of the tracking task is:

[0103] r track = log(X des X act -1 )

[0104] where r rrack represents the residual of the tracking task, and T act -1 represents the inverse matrix.

[0105] During the movement to the dynamic tracking mode, collisions with other surgical instruments should be avoided as much as possible to reduce the risk of robotic arm oscillation. Secondly, if an accidental touch occurs, the movement speed of the robotic arm needs to be adjusted according to the contact force to reduce the collision risk. Let the magnitude of the force at the end of the robotic arm obtained during the operation be ‖F end ‖, then the Jacobian matrix of the task of avoiding instrument collisions is:

[0106]

[0107] where J force represents the Jacobian matrix of the task of avoiding instrument collisions, X act represents the current pose of the tracking task, and α is an adjustment coefficient.

[0108] During the movement process, the movement ranges and self-constraints of each axis of the robotic arm should be restricted to further ensure safety, and the expression is also

[0109] According to the above formula, the objective function of the dynamic tracking mode is:

[0110]

[0111] where Kt3 , K t4 are the weight coefficients for the tracking task and the task of avoiding instrument collisions, respectively, and K r3 is the residual ratio coefficient for the task of avoiding instrument collisions.

[0112] In some embodiments, the current pose T act can be obtained in real time by the position sensor of the neurosurgical robot endoscope, and the force F at the end of the robotic arm end can be obtained in real time by the force sensor.

[0113] Step S3, solve the motion constraint model of the robotic arm based on the output information of the position sensor and the force sensor to obtain the control speed of the robot joints;

[0114] In this step, real-time data is obtained from the position sensor and the force sensor, and this data includes information such as the current pose of the endoscope and the contact force at the end.

[0115] During the movement, optimal control is achieved by dynamically adjusting the speed of the robot joints. As the endoscope moves, the system continuously monitors the state of the robotic arm and updates the Jacobian matrix and the residual term.

[0116] Throughout the movement process, the system solves a quadratic programming problem to continuously optimize the joint speed. The goal of the optimization is to minimize the objective function. The quadratic programming method can find the optimal speed of the robot joints as the control speed of the robot joints on the premise of satisfying various constraint conditions, so as to achieve the efficient, stable and safe movement of the robotic arm.

[0117] In the above way, the neurosurgical robotic arm of the present invention maintains flexibility and reliability in a complex task environment, can dynamically respond to real-time changes, optimize the motion path, and ensure the smooth completion of the task.

[0118] Step S4, control the robot joints to move based on the control speed, and finally complete the control of the neurosurgical robot endoscope.

[0119] While the specific embodiments of the present invention depict various actions or steps in a particular order, it should be understood that such actions or steps are required to be performed in the specific order shown or in a sequential order, or that all of the illustrated actions or steps should be performed to achieve the desired result. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although a number of specific implementation details are included in the foregoing description, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented in combination in a single implementation. Conversely, the various features described in the context of a single implementation may also be implemented separately or in any suitable sub-combination in multiple implementations. As described above, only the preferred specific embodiments of the present invention are provided, but the protection scope 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 technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

[0120] As described above, only the preferred specific embodiments of the present invention are provided, but the protection scope 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 technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A neurosurgery robot endoscope control method based on a hierarchical quadratic programming framework, wherein the neurosurgery robot endoscope comprises an endoscope body, a robotic arm, a position sensor and a force sensor, wherein the robotic arm has a plurality of robotic joints, and wherein: The following steps are involved: Step S1, determining the current operation mode of the neurosurgery robot endoscope, wherein the operation modes include: pivot motion mode, path navigation mode and dynamic tracking mode; Step S2: According to the operation mode, a corresponding robot arm motion constraint model is determined based on a hierarchical quadratic programming method, specifically including: If the operation mode is the pivot motion mode, a first constraint relationship is established regarding the distance between the projection position of the pivot point on the endoscope body and the position of the pivot point and the speed of the robot joint as the robot arm motion constraint model; If the operation mode is the path navigation mode, a second constraint relationship is established regarding the posture difference between the robot arm and the target area, the distance between the endoscope body and the key environmental structure, and the speed of the robot joint as the motion constraint model of the robot arm; If the operation mode is a dynamic tracking mode, a third constraint relationship is established regarding the posture difference between the robot arm and the device to be tracked, the contact force at the end of the robot arm, and the speed of the robot joint as the motion constraint model of the robot arm; Step S3, solving the robot arm motion constraint model based on the output information of the position sensor and the force sensor to obtain the control speed of the robot joint; Step S4: Control the robot joints to move based on the control speed, and finally complete the endoscopic control of the neurosurgery robot.

2. The neurosurgery robot endoscopic control method based on hierarchical quadratic programming framework according to claim 1 is characterized in that: In step S1: In the pivot motion mode, the endoscope body moves about a pivot point; In the path navigation mode, the endoscope body reaches the target area through path navigation movement; In the dynamic tracking mode, the endoscope moves following the instrument to be tracked.

3. The neurosurgery robot endoscopic control method based on hierarchical quadratic programming framework according to claim 2 is characterized in that: In step S2: The first constraint relationship is used to constrain the robot arm to move around the pivot point and constrain the upper speed limit of the robot joint; The second constraint relationship is used to constrain the movement speed of the robot arm between the current posture and the posture of reaching the target area, constrain the robot arm from colliding with key environmental structures during movement, and constrain the upper limit of the speed of the robot joint; The third constraint relationship is used to constrain the relative motion between the robot arm and the device to be tracked, constrain the force applied to the end of the robot arm, and constrain the upper speed limit of the robot joint.

4. The neurosurgery robot endoscopic control method based on hierarchical quadratic programming framework according to claim 3 is characterized in that: In step S2, the expression of the first constraint relationship is: r rcm =-‖P rcm -P current ‖ Among them, min means that the objective function needs to be minimized, ‖·‖ 2 Indicates the calculation of the square of the vector modulus, and st means that the following constraints are met: represents the velocity of the robot joint, is the maximum speed limit threshold, J rcm represents the Jacobian matrix of the RCM task, represents the estimate of the position vector between the pivot point and the projection point, δP rcm represents the position change of the pivot point, δq represents the position change of the robot joint, and r rcm is the residual of the RCM task, P rcm represents the pivot point position, P current represents the position of the projection point of the pivot point on the neuroendoscopic axis, and ‖·‖ represents the modulus of the calculated vector.

5. The neurosurgery robot endoscopic control method based on hierarchical quadratic programming framework according to claim 4 is characterized in that: In step S2, the expression of the second constraint relationship is: r trajPlanning =log(T focus T act -1 ) r coll ＝‖d i ‖ d i =p a -p c Among them, K t1 ,K t2 are the weight coefficients of path planning and collision avoidance tasks, K r1 ,K r2 are the residual ratio coefficients of path planning and collision avoidance tasks, J trajPlanning Represents the Jacobian matrix of the path planning task, δT act represents the change of the current pose of the path planning task, r trajPlanning represents the residual of the path planning task, log(·) represents the natural logarithm, T focus represents the position to reach the target area, T act -1 Represents the current pose T of the path planning task act The inverse matrix, J coll The Jacobian matrix of the collision avoidance task, d i T Indicates d i The transpose of δd i T d i Indicates d i T With d i The change in the product of c Indicates the location of key environmental structures, p a Indicates the closest point of the endoscope to the key environmental structure, r coll represents the residual of the collision avoidance task.

6. The neurosurgery robot endoscopic control method based on hierarchical quadratic programming framework according to claim 5 is characterized in that: In step S2, the expression of the third constraint relationship is: r track =log(X des X act -1 ) Among them, K t3 ,K t4 are the weight coefficients of the tracking task and the device collision avoidance task, K r3 The residual proportional coefficient for the task of avoiding instrument collision, J track Denotes the Jacobian matrix of the tracking task, δX act represents the change of the current pose of the tracking task, r track represents the residual of the tracking task, X act represents the current position of the tracking task, X act -1 Represents X act The inverse matrix, X des represents the target pose of the tracking task, J force represents the Jacobian matrix of the task of avoiding equipment collision, α is the adjustment coefficient, F end Represents the end force of the robot arm.

7. The neurosurgery robot endoscopic control method based on hierarchical quadratic programming framework according to claim 6 is characterized in that: In the pivot motion mode, the position sensor of the neurosurgery robot endoscope acquires the pivot point position P in real time. rcm ; The projection point position P is calculated based on the geometric relationship according to the position of the pivot point current ; In the path navigation mode, the position sensor of the neurosurgery robot endoscope acquires the current position T in real time. act and the closest point position p of the endoscope to the key environmental structure a ; In the dynamic tracking mode, the force sensor obtains the end force F of the robot arm in real time. end .

8. The neurosurgery robot endoscopic control method based on hierarchical quadratic programming framework according to claim 7 is characterized in that: Step S3 specifically includes: Based on the output information of the position sensor and the force sensor, the constraint conditions in the robot arm motion constraint model are optimized and solved to obtain the optimal real-time speed of the robot joint as the control speed of the robot joint.

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