Endoscope control method for neurosurgery robot based on hierarchical quadratic programming framework

Abstract

The application relates to a neurosurgery robot endoscope control method based on a hierarchical quadratic programming framework, and relates to the technical field of neurosurgery robot endoscope control technology, in particular to a neurosurgery robot endoscope control method based on a hierarchical quadratic programming framework, which comprises the following steps: determining a current operation mode of a neurosurgery robot endoscope, wherein the operation mode comprises a pivot motion mode, a path navigation mode and a dynamic tracking mode; determining a corresponding mechanical arm motion constraint model based on a hierarchical quadratic programming method according to the operation mode; solving the mechanical arm motion constraint model based on the output information of a position sensor and a force sensor to obtain a control speed of a robot joint; and controlling the robot joint to move based on the control speed, so as to finally complete neurosurgery robot endoscope control; and the application can improve the safety of endoscope control and reduce the complexity of control.

Description

Technical Field

[0001] This invention relates to the field of endoscopic control technology for neurosurgical robots, specifically to an endoscopic control method for neurosurgical robots based on a hierarchical quadratic programming framework. Background Technology

[0002] Neuroendoscopic surgery is an effective method for treating neurosurgical diseases such as cerebral hemorrhage. Endoscopes, as important surgical instruments, are widely used in minimally invasive surgeries. Traditional endoscopic procedures mainly rely on manual control by the surgeon, which not only increases the surgeon's workload but may also lead to problems such as unstable images and limited field of vision due to human factors.

[0003] With the continuous development of surgical robot technology, existing technologies use robotic arms to achieve endoscopic motion control, especially the multi-arm active surgical robots that have matured in the field of laparoscopic surgery. This approach has also been explored in clinical operations of the skull base in neurosurgery, but its end effector is relatively large and suitable for procedures with large surgical areas such as the abdominal cavity, making its application in the field of neurosurgery still difficult. Therefore, at present, neurosurgery mostly uses a single-arm approach to achieve endoscopic motion control.

[0004] Existing technologies for controlling the movement of robotic arms to drive endoscopes have significant shortcomings in neurosurgery. First, the method cannot recognize environmental factors, leading to low surgical safety. During the process of the endoscope moving from the skull point into the lesion area, the risk of collision with cerebral blood vessels and important brain tissues, as well as the state of the robotic arm, must be considered. However, controlling the robotic arm movement via buttons cannot take these factors into account in real time, increasing surgical risks. Second, the various movement modes of the endoscope (such as forward, backward, and rotation) need to be manually switched at different stages, increasing the complexity of the surgeon's operation. Therefore, traditional control methods are insufficient in both safety and ease of operation. Summary of the Invention

[0005] In view of the above problems, the present invention provides an endoscopic control method for 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] This invention provides an endoscopic control method for a neurosurgical robot 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 robotic joints. The method is characterized by the following steps:

[0007] Step S1: Determine the current operating mode of the neurosurgical robot endoscope, including: pivot motion mode, path navigation mode, and dynamic tracking mode;

[0008] Step S2: Based on the operating mode, determine the corresponding robotic arm motion constraint model using a hierarchical quadratic programming method, specifically including:

[0009] If the operating mode is a pivot motion mode, then a first constraint relationship is established regarding the projection position of the pivot point on the endoscope body and the distance between the pivot point position and the speed of the robot joint, as the motion constraint model of the robotic arm;

[0010] If the operating mode is path navigation mode, then a second constraint relationship is established regarding the pose difference between the robotic arm and the target area, the distance between the endoscope and the key environmental structure, and the speed of the robot joints, as the motion constraint model of the robotic arm.

[0011] If the operating mode is dynamic tracking mode, a third constraint relationship is established regarding the pose difference between the robotic arm and the tracked device, the contact force at the end of the robotic arm, and the speed of the robot joint, as the motion constraint model of the robotic arm.

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

[0013] Step S4: Control the robot joints to move based on the control speed, and finally complete the endoscopic control of the neurosurgical robot.

[0014] Preferably, in step S1:

[0015] In the pivot movement mode, the endoscope body moves around the pivot point;

[0016] In the path navigation mode, the endoscope body moves through path navigation to reach the target area;

[0017] In the dynamic tracking mode, the endoscope body moves along with the instrument being tracked.

[0018] Preferably, in step S2:

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

[0020] The second constraint relationship is used to constrain the movement speed of the robotic arm between its current pose and the pose at the target area, to constrain the robotic arm from colliding with critical environmental structures during its movement, and to constrain the upper limit of the robot joint speed.

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

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

[0023]

[0024]

[0025] r rcm =-‖P rcm -P current ||

[0026] Where min represents the objective function to be minimized, ‖·‖ 2 Let represent the calculation of the square of the vector's magnitude, and 'st' represent the satisfaction of the following constraints. Indicates the speed of the robot's joints. J is the maximum speed limit threshold. rcm Represents the Jacobian matrix of the RCM task. δP represents an estimate of the position vector between the pivot point and the projection point. rcm δq represents the position change of the pivot point, and r represents the position change of the robot joint. rcm For the residual of the RCM task, P rcm Indicates the pivot point position, P current ‖·‖ represents the position of the pivot point projected onto the neuroendoscopy axis, and ‖·‖ represents the magnitude of the calculated vector.

[0027] Preferably, in step S2, the expression for 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] Among them, K t1 ,K t2 K represents the weight coefficients for path planning and collision avoidance tasks, respectively. r1 ,K r2 J represents the residual scaling factor for path planning and collision avoidance tasks, respectively.trajPlanning The Jacobian matrix δT represents the path planning task. act r represents the change in the current pose of the path planning task. trajPlanning T represents the residual of the path planning task, log(·) represents the natural logarithm, and T focus T represents the pose upon reaching the target area. act -1 Represents the current pose T of the path planning task. act The inverse matrix, J coll d represents the Jacobian matrix for the collision avoidance task. i T d i The transpose of δd i T d i d i T With d i The change in the product of p c p indicates the location of key environmental structures. a The value r represents the closest point of the endoscope to the critical environmental structures. coll Represents the residual of the collision avoidance task.

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

[0035]

[0036]

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

[0038]

[0039] Among them, K t3 ,K t4 K represents the weighting coefficients for the tracking task and the collision avoidance task, respectively. r3 J is the residual scaling factor for collision-free missions. track The Jacobian matrix δX represents the tracking task. act r represents the change in the current pose of the tracking task. track X represents the residual of the tracking task. act X represents the current pose of the tracking task. act -1 X represents act The inverse matrix, X des j represents the target pose of the tracking task. forceThis represents the Jacobian matrix for the collision avoidance task, where α is the adjustment coefficient, and F... ernd This indicates the force at the end effector of the robotic arm.

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

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

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

[0043] Preferably, step S3 specifically includes:

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

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

[0046] (1) This invention divides the endoscope's operation into three modes: pivot motion, path navigation, and dynamic tracking, and establishes a corresponding robotic arm motion constraint model for each mode, thereby achieving precise control of the robotic endoscope. By constructing a constraint relationship between the projected position of the pivot point and its actual position, the precise rotation of the endoscope around its distal end point is ensured, effectively avoiding the risk of the robotic arm joints reaching their limits.

[0047] (2) This invention achieves intelligent avoidance of critical environmental structures by establishing constraints on the pose difference between the robotic arm and the target area, and the distance between the endoscope and the critical environmental structures. The control method based on hierarchical quadratic programming can automatically adjust the robotic arm's motion trajectory, minimizing the risk of damage to critical environmental structures during endoscope operation and reducing operational complexity.

[0048] (3) This invention incorporates the positional difference between the robotic arm and the tracked instrument, as well as the end-effector contact force, into the constraint model, thereby enabling the endoscope to track the surgical instrument. By processing the output information of the position sensor and force sensor in real time, the system can keep the surgical instrument always in the center of the field of view, while avoiding accidental contact with other instruments, thus improving the accuracy and smoothness of endoscopic operation. Attached Figure Description

[0049] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0050] Figure 1 The flowchart shows the endoscopic control method for a neurosurgical robot based on a hierarchical quadratic programming framework provided by this invention.

[0051] Figure 2 This invention provides a schematic diagram of the movement of the endoscope around a pivot point.

[0052] Figure 3 A schematic diagram of the hierarchical quadratic programming control framework provided by the present invention. Detailed Implementation

[0053] 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.

[0054] For robotic endoscopy in neurosurgery, the movement of the robotic endoscope is divided into three modes: pivot movement, path navigation, and dynamic tracking. During the movement of the robotic endoscope, the operations performed in these three modes differ, and therefore the corresponding control tasks are also inconsistent. In actual surgery, the endoscope movement cannot touch important critical environmental structures, which corresponds to control constraints for the robotic arm control. Simultaneously, the robotic arm system has joint limitations and singularity limitations. Therefore, this invention provides motion control and motion constraints for the robotic arm control in each mode. A hierarchical controller enables unified scheduling of multiple objectives within multiple modes, allowing different motion task objectives to be considered simultaneously. This achieves safe control of endoscopic operations and smooth transitions between tasks, eliminating the need for manual button switching to change the endoscope movement. Furthermore, it considers environmental factors in each movement, improving the safety of endoscopic movement.

[0055] To illustrate the effectiveness of the method proposed in this invention, the following detailed description of the above technical solution is provided through a specific embodiment, such as... Figure 1 As shown, a method for controlling the endoscope of a neurosurgical robot based on a hierarchical quadratic programming framework is disclosed. The neurosurgical robot endoscope includes an endoscope body, a robotic arm, position sensors, and force sensors, etc. The robotic arm has multiple robotic joints. The specific implementation steps are as follows:

[0056] Step S1: Determine the current operating mode of the neurosurgical robot endoscope, including: pivot motion mode, path navigation mode, and dynamic tracking mode;

[0057] For neurosurgical robotic endoscopes, the movement of the robotic endoscope is divided into three modes: pivot movement mode, path navigation mode, and dynamic tracking mode. Each mode has different control tasks, and constraints for the robotic arm movement need to be determined for these control tasks.

[0058] In this step, the control tasks for three modes are pre-planned and defined. For the pivot motion mode, the focus is on achieving rotational movement of the endoscope body around a pivot point. For the path navigation mode, path optimization is required to ensure progress along a preset trajectory and avoid collisions with critical environmental structures. For the dynamic tracking mode, the endoscope body needs to track the movement of other surgical instruments and minimize contact forces with them.

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

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

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

[0062] If the operating mode is dynamic tracking mode, then the constraint relationship between the pose difference between the robotic arm and the tracked device, the contact force at the end of the robotic arm and the speed of the robot joint is established as the motion constraint model of the robotic arm.

[0063] (1) Pivot motion mode

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

[0065] To ensure that the neuroendoscopy rotates around the pivot point, the optimization problem established in this invention is to minimize the pivot point P. rcm ∈R 3*1 Projection point P on the neuroendoscopy axis current ∈R3*1 To pivot point P rcm ∈R 3*1 The distance R is the distance between them. 3*1 It represents a three-dimensional column vector space.

[0066] Determine the Jacobian matrix J for the robot joint velocities and pivot point positions in the RCM task. rcm ∈R 1*6 , where R 1*6 The 6-dimensional row vector space is represented by the following expression:

[0067]

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

[0069] The residuals of the RCM task in pivot motion mode are used to describe the distance between the current projection point and the pivot point, and are expressed as:

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

[0071] Where, r rcm p is the residual of the RCM task. e Let represent the position vector between the pivot point and the projection point, and let ||·| represent the magnitude of the position vector.

[0072] During the neuroendoscopic motion in pivot mode, the self-constraints of the robotic arm need to be considered, and the speed of the robot joints needs to be limited, as expressed by:

[0073]

[0074] Based on the above formula, the objective function for the pivot motion mode is obtained as follows:

[0075]

[0076] Where min represents the objective function to be minimized, ‖·‖ 2 Let represent the calculation of the square of the vector's magnitude, and 'st' represent the satisfaction of the following constraints. Indicates the speed of the robot's joints. This is the maximum speed limit threshold.

[0077] In some embodiments, the pivot point P rcmThe position can be obtained in real time by the position sensor of the neurosurgical robot endoscope, and the pivot point is projected onto the neuroendoscopic axis as point P. current The position can be calculated from the geometric relationship based on the position of the pivot point.

[0078] (2) Path navigation mode

[0079] In 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 their current pose T. act pose T of the target region for the path planning task focus .

[0080] The pose includes position and spatial orientation, which is used to fully describe the state of the robotic joints of the robotic arm in three-dimensional space. When the endoscope reaches the target area, it is necessary to consider not only whether the spatial position point is accurate, but also whether the orientation of the endoscope is appropriate, so as to ensure the accuracy and safety of the endoscope movement.

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

[0082]

[0083] Among them, J trajPlanning The Jacobian matrix δT represents the path planning task. act It indicates the change in the current pose.

[0084] The residual in a path planning task describes the deviation between the current pose and the target pose, and is expressed as:

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

[0086] Among them, e trajPlanning T represents the residual of the path planning task, log(·) represents the natural logarithm, and T focus T represents the pose upon reaching the target area. act -1 Represents the current pose T of the path planning task. act The inverse matrix.

[0087] During its movement to the target area, the endoscope needs to determine whether it has collided with any critical environmental structures to ensure path safety. The location of the critical environmental structure is p. c The closest point between the endoscope and the critical environmental structure is p. a Define the vector of the key environmental structure and the nearest point as d. i =pa -p c The expression for the Jacobian matrix, which represents the change in distance between the endoscope and the nearest point of the critical environmental structure, is as follows:

[0088]

[0089] Among them, J coll Let d represent the Jacobian matrix for the collision avoidance task, ||·|| represent the calculation of the magnitude of the vector, and d i T d i The transpose of δd i T d i d i T With d i The change in the product of .

[0090] The residual r of the collision avoidance task coll The distance used to describe the distance between the endoscope and key environmental structures is expressed as:

[0091] r coll =‖d i ||

[0092] In actual operation, the range of motion of each axis of the robotic arm and its own constraints should be limited to further ensure safety. The expression is also as follows:

[0093] Based on the above formula, the objective function of the path navigation mode is obtained as follows:

[0094]

[0095] Among them, K t1 ,K t2 K represents the weight coefficients for path planning and collision avoidance tasks, respectively. r1 ,K r2 These are the residual ratio coefficients for path planning and collision avoidance tasks, respectively.

[0096] In some embodiments, the current pose T act The position sensor of the endoscope of the neurosurgical robot can acquire the pose T of reaching the target area in real time. focus It can be manually set during endoscopic movement. The location of key environmental structures p c The point p, where the endoscope is closest to key environmental structures, can be manually set during endoscope movement. a The position sensor of the endoscope in the neurosurgical robot can be obtained in real time.

[0097] (3) Dynamic tracking mode

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

[0099] The goal of the tracking task is to track the robotic joints of the robotic arm from their current pose (X). act Reaching the target pose X of the tracking task des The Jacobian matrix expression for the relationship between the current pose and the joint angle in the tracking task is:

[0100]

[0101] Among them, J track The Jacobian matrix δX represents the tracking task. act This indicates the change in the current pose of the tracking task.

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

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

[0104] Where, r rrack T represents the residual of the tracking task. act -1 The inverse matrix is ​​represented.

[0105] During movement in dynamic tracking mode, collisions with other surgical instruments should be avoided as much as possible to reduce the risk of robotic arm vibration. Secondly, if accidental contact occurs, the robotic arm's movement speed needs to be adjusted based on the contact force to further reduce the collision risk. Let the magnitude of the force at the robotic arm's end effector acquired during the procedure be ||F||. end ‖, then the Jacobian matrix for the task of avoiding machine collisions is:

[0106]

[0107] Among them, J force X represents the Jacobian matrix for the task of avoiding machine collisions. act This represents the current pose of the tracking task, and α is the adjustment coefficient.

[0108] During operation, the range of motion of each axis of the robotic arm and its self-constraints should be limited to further ensure safety. The expression is also...

[0109] Based on the above formula, the objective function for the dynamic tracking mode is:

[0110]

[0111] Among them, Kt3 ,K t4 K represents the weighting coefficients for the tracking task and the collision avoidance task, respectively. r3 The residual ratio factor for tasks that avoid collisions with machinery.

[0112] In some embodiments, the current pose T act The position sensor of the endoscope of the neurosurgical robot can be used to acquire the force F at the end of the robotic arm in real time. end This 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 force sensor to obtain the control speed of the robot joint;

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

[0115] During 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 residual terms.

[0116] Throughout the movement, the system continuously optimizes the joint speed by solving a quadratic programming problem. The goal of optimization is to minimize the objective function. The quadratic programming method can find the optimal speed of the robot joint as the control speed of the robot joint, while satisfying various constraints, thereby achieving efficient, smooth, and safe movement of the robotic arm.

[0117] Through the above methods, the neurosurgical robotic arm of the present invention maintains flexibility and reliability in complex task environments, can dynamically respond to real-time changes, optimize motion paths, and ensure the successful completion of tasks.

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

[0119] While the specific embodiments of the present invention depict actions or steps in a particular order, this should be understood as requiring such actions or steps to be performed in the shown specific order or sequential order, or requiring all illustrated actions or steps to be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations. The above descriptions are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention.

[0120] 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 within the scope of protection of the present invention.

Claims

1. An endoscopic control system for a neurosurgical robot based on a hierarchical quadratic programming framework, wherein the neurosurgical robot endoscope includes an endoscope body, a robotic arm, a position sensor, and a force sensor, and the robotic arm has multiple robotic joints, characterized in that, The system performs the following steps: Step S1: Determine the current operating mode of the neurosurgical robot endoscope, including: pivot motion mode, path navigation mode, and dynamic tracking mode; Step S2: Based on the operating mode, determine the corresponding robotic arm motion constraint model using a hierarchical quadratic programming method, specifically including: If the operating mode is a pivot motion mode, then a first constraint relationship is established regarding the projection position of the pivot point on the endoscope body and the distance between the pivot point position and the speed of the robot joint, as the motion constraint model of the robotic arm; If the operating mode is path navigation mode, then a second constraint relationship is established regarding the pose difference between the robotic arm and the target area, the distance between the endoscope and the key environmental structure, and the speed of the robot joints, as the motion constraint model of the robotic arm. If the operating mode is dynamic tracking mode, a third constraint relationship is established regarding the pose difference between the robotic arm and the tracked device, the contact force at the end of the robotic arm, and the speed of the robot joint, as the motion constraint model of the robotic arm. Step S3: Solve the motion constraint model of the robotic arm based on the output information of the position sensor and 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 neurosurgical robot; In step S1: In the pivot motion mode, the endoscope body moves around the 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 body moves following the instrument being tracked; In step S2: The first constraint relationship is used to constrain the movement of the robotic arm around the pivot point and to constrain the upper limit of the speed of the robot joints; The second constraint relationship is used to constrain the movement speed of the robotic arm between its current pose and the pose at the target area, to constrain the robotic arm from colliding with critical environmental structures during its movement, and to constrain the upper limit of the robot joint speed. The third constraint relationship is used to constrain the relative motion between the robotic arm and the tracked device, constrain the magnitude of the force at the end of the robotic arm, and constrain the upper limit of the speed of the robot joints.

2. The neurosurgical robot endoscopic control system based on a hierarchical quadratic programming framework according to claim 1, characterized in that: In the pivot motion mode, the position sensor of the endoscope of the neurosurgical robot acquires the pivot point position in real time. The position of the pivot point projected onto the neuroendoscopic axis is calculated using geometric relationships based on the pivot point's position. In the path navigation mode, the position sensor of the endoscope of the neurosurgical robot acquires the current pose of the path planning task and the closest point of the endoscope to the key environmental structure in real time. ; In the dynamic tracking mode, the force sensor acquires the contact force at the end of the robotic arm in real time.

3. The neurosurgical robot endoscopic control system based on a hierarchical quadratic programming framework according to claim 2, characterized in that, Step S3 specifically includes: Based on the output information of the position sensor and force sensor, the constraint conditions in the motion constraint model of the robotic arm are optimized and solved to obtain the optimal real-time speed of the robot joint, which is used as the control speed of the robot joint.

Images