CN119055371B - Multi-objective autonomous traction method of surgical robot based on fuzzy logic - Google Patents
Multi-objective autonomous traction method of surgical robot based on fuzzy logic Download PDFInfo
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Abstract
本申请提供一种基于模糊逻辑的手术机器人的多目标自主牵引方法，涉及医疗器械技术领域，该方法包括：在机器人末端装配执行器械，将执行器械的末端运动简化为一个弹簧质点阻尼线性控制系统，构建多个目标控制器；针对各个控制目标分别计算预测评价梯度下降值并构建状态反馈机制；基于预设的状态反馈融合机制对多目标控制量进行加权融合，并将加权融合后的目标值输入弹簧质点阻尼线性控制系统。本申请针对手术场景的复杂性，通过多目标约束控制策略，将手术中的多种安全约束以控制目标的形式整合到机器人的自主运动规划中；通过预先定义牵引点，机器人自动安全地进行组织牵引，不需要任何手术场景或组织模型的先验知识。
The present application provides a multi-objective autonomous traction method for a surgical robot based on fuzzy logic, which relates to the field of medical device technology. The method includes: assembling an actuator at the end of the robot, simplifying the end motion of the actuator into a spring-mass damped linear control system, and constructing multiple target controllers; respectively calculating the predicted evaluation gradient descent value for each control target and constructing a state feedback mechanism; weighted fusion of the multi-objective control quantities based on a preset state feedback fusion mechanism, and inputting the weighted fused target values into the spring-mass damped linear control system. Aiming at the complexity of the surgical scene, the present application integrates various safety constraints in the operation into the autonomous motion planning of the robot in the form of control targets through a multi-objective constraint control strategy; by pre-defining the traction point, the robot automatically and safely performs tissue traction without requiring any prior knowledge of the surgical scene or tissue model.
Description
Technical Field
The invention relates to the technical field of medical instruments, in particular to a multi-target autonomous traction method of a surgical robot based on fuzzy logic.
Background
Tissue retraction procedures in laparoscopic surgery help to improve visualization and create a more favorable surgical environment, and also help to maintain tension in the pathologically attached tissue, and currently, common tissue retraction methods include manual tissue retraction methods and fixed mechanical structure tissue retraction methods.
In manual tissue traction methods, specialized clamping instruments or surgical hooks are used to control tissue traction, the magnitude and direction of force is adjusted as required, and in fixed mechanical structures used to traction tissue, mechanical structures are used instead of manual tissue, which generally fix tissue in certain positions, adjusted as required by the procedure. However, manual traction cannot maintain hand stability for a long period of time, mechanical devices typically fix tissue in a predetermined position, and the instrument needs to be frequently reset, affecting smoothness and efficiency.
For this reason, in the related art, the robot may be used for auxiliary traction, and the current common auxiliary traction method includes a visual method based on tissue positioning and segmentation, a model-free shape control method and a tissue traction method based on reinforcement learning, however, the visual method based on tissue positioning and segmentation intermittently detects tissue positions and cannot deal with the situation that surgical instruments are blocked, and cannot continuously and real-time traction tissue to match sharp anatomy, the model-free shape control method is too slow in adjustment speed to meet the requirement of large deformation of tissue in laparoscopic surgery, and has insufficient safety constraint consideration, the tissue traction method based on reinforcement learning requires a large amount of training data and time, is difficult to generalize to various complex surgical scenes, and in addition, the success rate of the tissue traction method based on reinforcement learning after being transferred to a real robot is not ideal.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides a multi-target autonomous traction method of a surgical robot based on fuzzy logic, which solves a series of unreliable problems of poor continuity, insufficient safety, non-ideal generalization and the like in the tissue traction process of the current robot.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
The embodiment of the application provides a multi-target autonomous traction method of a surgical robot based on fuzzy logic, which comprises the steps of assembling an execution instrument at the tail end of the robot to carry out autonomous traction of surgical robot hardware construction, simplifying tail end motion of the execution instrument into a spring particle damping linear control system, constructing a plurality of target controllers corresponding to a preset target group according to actual requirements of traction tissues, wherein the preset target group comprises traction angle targets, traction force targets and safe area targets, setting an evaluation function for the corresponding control targets based on state errors in each target controller, respectively calculating a predictive evaluation gradient descent value for each control target, constructing a state feedback mechanism, normalizing the multi-target control quantity and the gradient descent value, inputting the multi-target control quantity and the multi-gradient descent value into a preset fuzzy logic weight distribution algorithm, carrying out weighted fusion on the multi-target control quantity based on the preset state feedback fusion mechanism, inputting the weighted fused target values into the spring particle damping linear control system, converting motion speed and gesture change speed into joint angles of the robot, and carrying out robot motion control.
According to a first aspect of embodiments of the present application, a spring mass damping linear control system is used to characterize the tip motion state of an implement and satisfies the state equation:
Wherein, For the position state of the end of the instrument,For the speed of movement of the distal end of the instrument,For the acceleration of the end of the instrument,For the total control input of the spring mass damping linear control system,Is an inertial parameter,As a parameter of the damping it is possible to provide,The plurality of target controllers includes a traction angle controller, a traction controller, and a safety zone controller.
According to a first aspect of an embodiment of the present application, the traction angle controller is configured to define a traction angle that satisfies the expression:
In the formula, In order for the draft angle to be the same,The representation is the position of the cutting instrument,For the position state of the end of the instrument,The cutting direction vector is represented by a vector,Representation ofIs a norm of (2);
the traction angle controller satisfies the expression:
In the formula, The control amount of the traction angle is indicated,AndTo the traction angle controllerThe parameters of the control process are set to be,The error derived for the chord length formula,Is thatIs used as a first derivative of (a),In order for the angle to be a desired angle,Is the viewing angle direction.
According to a first aspect of embodiments of the present application, the traction controller is provided with a desired traction force to maintain tissue tension;
the dynamic characteristics of the 7-degree-of-freedom robot corresponding to the spring mass point damping linear control system in the Cartesian space satisfy the expression:
In the formula, Representing a vector consisting of force and moment,The joint vector is represented by a vector of the joint,Representing a matrix of cartesian inertia and,AndIs a velocity vector and a gravity vector in cartesian space,Representing the position and pose of the robotic end effector in cartesian space,Is thatIs used for the first derivative of (c),Is thatIs the first derivative of (a);
environmental interaction forces acting on the implement when the robot is in contact with the environment during movement The expression is satisfied:
Wherein, Indicating the net force acting on the implement,The corresponding force mapping relationship satisfies:, Represents the moment of the joint and the moment of the joint, A jacobian representing a distance from joint space to cartesian space;
the traction controller satisfies the expression:
Wherein, Indicating the control amount of the traction force,、To provide for traction controlThe parameters of the control process are set to be,In order for the traction force to be desired,As a result of the current force being applied,Is the error between the desired traction force and the current force;
Current force The expression is satisfied:
Wherein, Is a diagonal array of 3 rows and 3 columns,To perform force and torque vectors of the instrument and interact with the environmentHas a corresponding mapping relation.
According to a first aspect of the embodiment of the present application, the safety area set in the safety area controller is a radius ofHigh isIs a cylinder region of (2);
in the event that the implement exceeds the boundary of the cylinder region, the safety region controller generates a control quantity directed toward the interior of the cylinder region;
The safe area controller satisfies the expression:
Wherein, Indicating the control amount of the safety zone,AndTo control the safety areaThe parameters of the control process are set to be,Indicating a deviation of the current location from the safe area,Representation ofIs used as a first derivative of (a),AndRespectively representAt the position ofPlane surfaceThe component in the direction of the light is,As a vector towards the central axis,Is the distance of the implement tip to the coordinate axis,Is the distance of the implement tip from the cylinder base.
According to a first aspect of an embodiment of the present application, in each of the foregoing target controllers, setting an evaluation function for a corresponding control target based on a state error, calculating a predictive evaluation gradient descent value for each control target, respectively, and constructing a state feedback mechanism, including:
designing an evaluation function of a traction angle target based on the square of the chord length corresponding to the error between the current traction angle and the expected angle;
designing an evaluation function of the traction force target as the square of the error between the current traction force and the expected traction force;
designing an evaluation function of a safety area target according to the square of the current implementation instrument tip position and the established safety area boundary distance;
And constructing a state feedback mechanism in a state space to perform gradient optimization so as to enable the state of the robot to be converged rapidly.
According to a first aspect of an embodiment of the application, the evaluation function of the traction angle target:
Evaluation function of traction force target:
Evaluation function of safety area target:
Wherein, A predicted distance value representing the tip of the implement from the coordinate axis,Representing a predicted distance value of the implement tip from the cylinder base,Indicating that the desired force is to be applied,Representing the current force predicted by the system,Is a coefficient of a real-time variation,Indicating the current time of day and,As a function of the time variable,To perform the position state of the instrument tip,The representation is the position of the cutting instrument,In order for the angle to be a desired angle,Is the predicted angle.
The embodiment of the application provides a multi-target autonomous traction system of a surgical robot based on fuzzy logic, which comprises a hardware construction module, a system construction module, a calculation module, a weight distribution module and a fusion module, wherein the hardware construction module is used for assembling an execution instrument at the tail end of the robot so as to carry out autonomous traction of the surgical robot, the system construction module is used for simplifying the tail end motion of the execution instrument into a spring particle damping linear control system, a plurality of target controllers corresponding to a preset target group are constructed according to the actual requirement of traction tissues, the calculation module is used for setting an evaluation function on the corresponding control target based on a state error in each target controller, respectively calculating a predictive evaluation gradient descent value for each control target and constructing a state feedback mechanism, the weight distribution module is used for normalizing the multi-target control quantity and the gradient descent value and inputting the multi-target control quantity into a preset fuzzy logic weight distribution algorithm so as to distribute weights to each control target, and the fusion module is used for carrying out weighted fusion on the multi-target control quantity based on the preset state feedback fusion mechanism and inputting the weighted fused target value into the spring particle damping linear control system so as to convert the motion speed and the gesture of the movement of the robot into the motion speed of the robot.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, and a program stored on the memory and executable on the processor, where the program when executed by the processor implements the multi-objective autonomous traction method of the surgical robot based on fuzzy logic in the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a program or instructions that when executed by a processor implement the multi-target autonomous traction method of a fuzzy logic based surgical robot of the first aspect described above.
The application provides a multi-target autonomous traction method of a surgical robot based on fuzzy logic. Compared with the prior art, the method has the following beneficial effects:
the application designs a plurality of target controllers to pay attention to traction angle targets, traction force targets and safety area targets and realize autonomous tissue traction of a robot, calculates predictive evaluation gradient descent values for each control target respectively, constructs a state feedback mechanism, normalizes multi-target control quantity and the gradient descent values and distributes weights to each control target, carries out weighted fusion on the multi-target control quantity, and inputs the weighted fusion target value into a spring particle damping linear control system so as to convert the speed of motion speed and posture change into joint angles of the robot, and carries out motion control of the robot. Aiming at the complexity of a surgical scene, the application integrates various safety constraints in the operation into the autonomous motion planning of the robot in a control target mode through a multi-target constraint control strategy, and the robot automatically and safely performs tissue traction through predefining traction points without any prior knowledge of the surgical scene or tissue model.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart illustrating a multi-objective autonomous traction method of a surgical robot based on fuzzy logic according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a multi-objective autonomous traction method for a fuzzy logic based surgical robot according to an embodiment of the present application;
FIG. 3 is a schematic view of a trajectory of an implement tip provided by an embodiment of the present application;
FIG. 4 is a schematic illustration of the error between the current position of the tip of the implement instrument and the defined target position provided by an embodiment of the present application;
FIG. 5 is a schematic view of a snapshot of a porcine liver tissue edge portion during resection according to an embodiment of the present application;
FIG. 6 is a schematic view of another motion profile of an implement tip provided in an embodiment of the present application;
FIG. 7 is a schematic view of angles during towing provided by an embodiment of the present application;
FIG. 8 is a force schematic diagram of a traction process provided by an embodiment of the present application;
FIG. 9 is a schematic diagram of a multi-objective autonomous traction system of a surgical robot based on fuzzy logic provided by an embodiment of the present application;
Fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The embodiment of the application solves a series of unreliable problems of poor continuity, insufficient safety, non-ideal generalization and the like in the tissue traction process of the current robot by providing the multi-target autonomous traction method of the surgical robot based on the fuzzy logic.
The technical scheme in the embodiment of the application aims to solve the technical problems, and the overall thought is as follows:
tissue retraction procedures in laparoscopic surgery help to improve visualization and create a more favorable surgical environment, and also help to maintain tension in the pathologically attached tissue, and currently, common tissue retraction methods include manual tissue retraction methods and fixed mechanical structure tissue retraction methods.
In manual tissue traction methods, specialized clamping instruments or surgical hooks are used to control tissue traction, the magnitude and direction of force is adjusted as required, and in fixed mechanical structures used to traction tissue, mechanical structures are used instead of manual tissue, which generally fix tissue in certain positions, adjusted as required by the procedure. However, the hand cannot be kept stable for a long time during manual traction, the mechanical device usually fixes the tissue at a predetermined position, the instrument needs to be frequently reset, the fluency and the efficiency are affected, and each time the fixing instrument is adjusted, additional stress or damage can be caused to the tissue, and the risk of a patient is increased.
In the related art, a robot can be used for auxiliary traction, and the current common auxiliary traction method comprises a visual method based on tissue positioning and segmentation, a model-free shape control method and a tissue traction method based on reinforcement learning, wherein the visual method based on tissue positioning and segmentation is used for positioning tissues which shield the surgical field through a computer visual method, segmenting tissue edges based on an algorithm, and then retracting the shielding tissues through the robot. The model-free shape control method utilizes a multi-mapping relation to accurately control the snake-shaped continuum robot and the deformable object, aligns the marked points in the pixel space, and then pulls the target point to a desired position by controlling the continuum robot. The tissue traction method based on reinforcement learning builds a surgical scene in a virtual environment, and makes an intelligent agent learn how to correctly execute a tissue traction task by designing a reward function, and finally the tissue traction method is executed in a real robot system.
However, the visual method based on tissue positioning segmentation intermittently detects tissue positions, cannot deal with the condition that surgical instruments are blocked, cannot continuously pull tissues in real time to match sharp dissection, the model-free shape control method is too slow in adjusting speed, cannot meet the requirement of large deformation of tissues in laparoscopic surgery, is insufficient in consideration of safety constraint, the tissue pulling method based on reinforcement learning requires a large amount of training data and time, is difficult to generalize to various complex surgical scenes, and in addition, the success rate of the tissue pulling method based on reinforcement learning after being transferred to a real robot is not ideal.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
The following first describes a multi-target autonomous traction method of a surgical robot based on fuzzy logic provided by the embodiment of the application.
The flow chart of the multi-target autonomous traction method for realizing the surgical robot based on the fuzzy logic according to the embodiment of the application is shown in fig. 1, and the method can comprise the following steps S110-S150.
S110, assembling an execution instrument at the tail end of the robot so as to carry out the hardware construction of the autonomous traction surgical robot.
S120, simplifying the tail end motion of the executing instrument into a spring mass point damping linear control system, and constructing a plurality of target controllers corresponding to a preset target group according to the actual demand of traction tissues, wherein the preset target group comprises a traction angle target, a traction force target and a safety area target.
S130, in each target controller, setting an evaluation function for the corresponding control target based on the state error, respectively calculating a predictive evaluation gradient descent value for each control target, and constructing a state feedback mechanism.
And S140, normalizing the multi-target control quantity and the gradient descent value, and inputting the multi-target control quantity and the gradient descent value into a preset fuzzy logic weight distribution algorithm to distribute weights to all the control targets.
S150, carrying out weighted fusion on the multi-target control quantity based on a preset state feedback fusion mechanism, and inputting the weighted fusion target value into a spring mass point damping linear control system so as to convert the movement speed and the speed of posture change into the joint angle of the robot, and carrying out robot movement control.
In the embodiment of the application, referring to fig. 2, a plurality of target controllers are designed to pay attention to traction angle targets, traction force targets and safety area targets, so as to realize autonomous tissue traction of a robot, a predictive evaluation gradient descent value is calculated for each control target, a state feedback mechanism is constructed, multiple target control amounts and the gradient descent value are normalized, weights are distributed to each control target, the multiple target control amounts are weighted and fused, and the weighted and fused target values are input into a spring particle damping linear control system so as to convert the speed of motion speed and posture change into joint angles of the robot, and the robot motion control is performed. According to the application, aiming at the complexity of the surgical scene, various safety constraints in the surgery are integrated into the autonomous motion planning of the robot in the form of control targets, and the robot automatically and safely performs tissue traction by predefining traction points without any prior knowledge of the surgical scene or tissue model.
In one example, the spring mass damping linear control system is a mass-damping-spring linear system that is used to characterize the motion state of the robotic end-effector.
In some embodiments, the plurality of target controllers includes a traction angle controller, a traction controller, and a safety zone controller. The spring mass point damping linear control system is used for representing the tail end motion state of the execution instrument and meets the state equation:
Wherein, For the position state of the end of the instrument,For the speed of movement of the distal end of the instrument,For the acceleration of the end of the instrument,For the total control input of the spring mass damping linear control system,Is an inertial parameter,As a parameter of the damping it is possible to provide,Is a stiffness parameter.
In some embodiments, the draft angle controller is used to define a draft angle that satisfies the expression:
In the formula, In order for the draft angle to be the same,The representation is the position of the cutting instrument,For the position state of the end of the instrument,The cutting direction vector is represented by a vector,Representation ofIs a norm of (2);
the traction angle controller satisfies the expression:
In the formula, The control amount of the traction angle is indicated,AndTo the traction angle controllerThe parameters of the control process are set to be,The error derived for the chord length formula,Is thatIs used as a first derivative of (a),In order for the angle to be a desired angle,Is the viewing angle direction.
In some embodiments, the traction controller is provided with a desired traction force to maintain tissue tension, and the dynamics of the corresponding 7-degree-of-freedom robot in Cartesian space of the spring mass point damping linear control system satisfies the expression:
In the above-mentioned method, the step of, Representing a vector consisting of force and moment,The joint vector is represented by a vector of the joint,Representing a matrix of cartesian inertia and,AndIs a velocity vector and a gravity vector in cartesian space,Representing the position and pose of the robotic end effector in cartesian space,Is thatIs used for the first derivative of (c),Is thatIs a first derivative of (a).
In one example, the environmental interaction forces acting on the implement are when the robot is in contact with the environment during movementThe expression is satisfied:
Wherein, Indicating the net force acting on the implement,The corresponding force mapping relationship satisfies:, Represents the moment of the joint and the moment of the joint, Representing a jacobian matrix from joint space to cartesian space.
In one example, the traction controller satisfies the expression:
Wherein, Indicating the control amount of the traction force,、To provide for traction controlThe parameters of the control process are set to be,In order for the traction force to be desired,As a result of the current force being applied,Is the error between the desired traction force and the current force.
In one example, the current forceThe expression is satisfied:
Wherein, Is a diagonal array of 3 rows and 3 columns,To perform force and torque vectors of the instrument and interact with the environmentHas a corresponding mapping relation.
In some embodiments, the safety zone provided in the safety zone controller is a radius ofHigh isIn the case of an implement beyond the boundaries of the cylinder region, the safety region controller generates a control quantity directed toward the interior of the cylinder region.
The safe area controller satisfies the expression:
Wherein, Indicating the control amount of the safety zone,AndTo control the safety areaThe parameters of the control process are set to be,Indicating a deviation of the current location from the safe area,Representation ofIs used as a first derivative of (a),AndRespectively representAt the position ofPlane surfaceThe component in the direction of the light is,As a vector towards the central axis,Is the distance of the implement tip to the coordinate axis,Is the distance of the implement tip from the cylinder base.
In some embodiments, in each of the target controllers, the foregoing setting an evaluation function for a corresponding control target based on the state error, calculating a predicted evaluation gradient descent value for each control target, and constructing a state feedback mechanism, that is, the foregoing S130 may specifically include the steps of:
s210, designing an evaluation function of the traction angle target based on the square of the chord length corresponding to the error between the current traction angle and the expected angle.
S220, designing an evaluation function of the traction force target according to the square of the error between the current traction force and the expected traction force.
S230, designing an evaluation function of the safety area target according to the square of the distance between the tip position of the current execution instrument and the boundary of the established safety area.
S240, constructing a state feedback mechanism in a state space to perform gradient optimization so as to enable the state of the robot to be converged rapidly.
In one example, the merit function of the draft angle target:
Evaluation function of traction force target:
Evaluation function of safety area target:
Wherein, A predicted distance value representing the tip of the implement from the coordinate axis,Representing a predicted distance value of the implement tip from the cylinder base,Indicating that the desired force is to be applied,Representing the current force predicted by the system,Is a coefficient of a real-time variation,Indicating the current time of day and,As a function of the time variable,To perform the position state of the instrument tip,The representation is the position of the cutting instrument,In order for the angle to be a desired angle,Is the predicted angle.
In some embodiments, it is understood that in the state space, the gradient direction is the direction that converges fastest, and the gradient direction can be expressed as:
in order to limit the magnitude of the gradient to a reasonable range, the application designs a saturation suppression function The saturation suppression functionThe expression is satisfied:
Wherein, As a saturation-suppression parameter,In order to control the cumulative gradient descent of the target,
The single target controller after adding the state feedback mechanism is expressed as:
Obtaining input after single-objective optimization:
Wherein, As a result of the state feedback coefficient,For the initial control quantity, the control quantity,For the control amount after adding the state feedback mechanism,The control target is indicated to be a target of the control,In a corresponding state of the device,The optimal state is indicated and the method is performed,Is a parameter corresponding to the saturation suppression function.
In some embodiments, the application performs weight distribution based on fuzzy logic, and performs weighted fusion on the optimized multi-target control quantity of the three controllers;
It can be understood that the application only carries out centralized processing on traction angle targets, traction targets and safe area targets, and cannot solve the inherent conflict among different targets, thus the dynamic weight distribution of each target is indispensable, the dynamic weight distribution is carried out by utilizing fuzzy logic according to the actual requirement of tissue traction in operation, the application carries out normalized proportionality calculation on the control input and gradient value of each control target to obtain the proportionality factor Respectively representAndRespectively representExpressed as:
Three fuzzy sets with different sizes are designed for each input, each fuzzy set corresponds to a specific membership function, and a specific function is designed to ensure smoothness:
Wherein, 、AndCorresponding to three fuzzy sets with different sizes, parameters、、AndThe application designs 9 fuzzy rules to cover all different scenes, the traction angle target, traction target and safety area target are all correspondent to 6 output sets, the defuzzification process is based on the membership of output sets, and utilizes centroid method, every output set is correspondent to generating an important index parameterExpressed as:
A judgment matrix is constructed for evaluating the relative importance between each pair of control targets, and is expressed as follows:
Wherein, In response to the draft angle target,In response to the traction force target,In correspondence with the target of the safe area,Representing control targetsControl targetThe relative importance of the control targets in the whole problem can be determined by the geometric mean valueIt is given that,The expression is satisfied:
the obtained weight of the single control target in the global environment is expressed as:
The motion fusion of multiple targets in the traction process is realized by fusing the optimal input of state feedback of each controller, and is expressed as follows:
In the formula, For the actual system input to be made,For the weight corresponding to the traction angle target,For the weight corresponding to the traction force target,For the weight corresponding to the target of the safe area,,AndRespectively the optimal input including traction angle, traction force and safe space constraint target state feedback,The accumulated gradient of the three control targets, namely the traction angle target, the traction target and the safety area target, is reduced.
In some embodiments, the robotic motion control considers constraints of a fixed distal center of motion, expressed as:
Wherein, Representing the center point of motion of the distal end,Representation ofTime of day gesture matrixThe axial vector quantity is set to be equal to the axial vector quantity,Representation ofTime of day gesture matrixThe axial vector quantity is set to be equal to the axial vector quantity,Representing a gesture matrixThe axial vector quantity is set to be equal to the axial vector quantity,To correspond toThe position of the shaft is such that,Representing a gesture matrixThe axial vector quantity is set to be equal to the axial vector quantity,For the gesture of robot movement, functionRepresenting the transformation of the attitude rotation matrix into euler angles in a cartesian coordinate system,As euler angles in a cartesian coordinate system,For the rate of change of the euler angle,For the speed of the change in position of the robotic end effector in cartesian space,Is the rotation speed of the joint angle of the robot,A jacobian representing the space from the joint to the cartesian space,Is the inverse of the jacobian matrix.
In some embodiments, in order to verify the effectiveness of the multi-target autonomous traction method proposed by the present application to traction flexible tissue within a confined space, a target point traction experiment was performed. Four points are defined in the space, which are respectively located at different positions inside and outside the safety area. The multi-target autonomous traction method provided by the application is used for controlling the robot to hold the prosthesis tissue and traction the prosthesis tissue to each target point in sequence, the target points are marked as 1 to 4, the result is shown in figure 3, figure 3 shows the track of the tip of the execution instrument, figure 4 describes the error between the current position of the tip of the execution instrument and the defined target position, the robot successfully drags the tissue to the target point for the point 1 positioned in the defined safety area, the points 2, 3 and 4 are respectively positioned at different positions outside the safety area, the tip of the execution instrument is finally restrained on the boundary outside the safety area, and experiments prove that the method is effective in improving the autonomous traction safety of the robot.
In addition, in an in-vitro experiment, in order to simulate tissue traction in a more real environment and evaluate the performance of the multi-target autonomous traction method in assisting in the excision of the actual attached tissue, pig liver tissue separated from an animal body is selected as an experimental object and subjected to traction and cutting operation, the partial excision of the edge part of the pig liver tissue is completed within 50 seconds, fig. 5 shows a snapshot of the experiment in the excision process, fig. 6 shows a motion track of an implement tip, and fig. 7 and 8 are respectively change curves of angles and forces in the traction process, and experimental results show that the multi-target autonomous traction method of the operation robot based on the fuzzy logic provided by the application can assist an operator to complete the partial excision task of the pig liver tissue.
In some embodiments, referring to fig. 9, the present application further provides a multi-target autonomous traction system 300 for implementing a fuzzy logic based surgical robot, the system 300 comprising:
A hardware set-up module 310 for assembling an implement at the end of the robot for autonomous traction surgical robot hardware set-up.
The system construction module 320 is configured to simplify the end motion of the implement into a spring mass damping linear control system, and construct a plurality of target controllers corresponding to the preset target group according to the actual requirement of the traction tissue.
The calculating module 330 is configured to set an evaluation function for a corresponding control target based on the state error in each target controller, calculate a predicted evaluation gradient descent value for each control target, and construct a state feedback mechanism.
The weight distribution module 340 is configured to normalize the multi-objective control amount and the gradient descent value, and input the normalized multi-objective control amount and the gradient descent value into a preset fuzzy logic weight distribution algorithm to distribute weights to the control objectives.
The fusion module 350 is configured to perform weighted fusion on the multi-target control amount based on a preset state feedback fusion mechanism, and input the weighted fusion target value into a spring mass point damping linear control system to convert the motion speed and the speed of the posture change into a joint angle of the robot, so as to perform motion control of the robot.
In some embodiments, the foregoing computing module 330 may be specifically configured to:
designing an evaluation function of a traction angle target based on the square of the chord length corresponding to the error between the current traction angle and the expected angle;
designing an evaluation function of the traction force target as the square of the error between the current traction force and the expected traction force;
designing an evaluation function of a safety area target according to the square of the current implementation instrument tip position and the established safety area boundary distance;
And constructing a state feedback mechanism in a state space to perform gradient optimization so as to enable the state of the robot to be converged rapidly.
In some embodiments, the present application provides an electronic device, and a schematic structural diagram of the electronic device is shown in fig. 10.
The electronic device may include a processor 410 and a memory 420 storing computer program instructions.
In particular, the processor 410 may include a Central Processing Unit (CPU), or an Application SPECIFIC INTEGRATED Circuit (ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
Memory 420 may include mass storage for data or instructions. By way of example, and not limitation, memory 420 may include a hard disk drive (HARD DISK DRIVE, HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a universal serial bus (Universal Serial Bus, USB) drive, or a combination of two or more of the foregoing. Memory 420 may include removable or non-removable (or fixed) media, where appropriate. Memory 420 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 420 is a non-volatile solid state memory.
Memory 420 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, memory 420 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and, when the software is executed (e.g., by one or more processors), it may perform the operations described by the multi-target autonomous traction method of a fuzzy logic based surgical robot of any of the above embodiments.
The processor 410 reads and executes the computer program instructions stored in the memory 420 to implement the multi-objective autonomous traction method of the surgical robot based on the fuzzy logic of any of the above embodiments.
In one example, the electronic device may also include a communication interface 430 and a bus 400. As shown in fig. 10, the processor 410, the memory 420, and the communication interface 430 are connected to each other through the bus 400 and perform communication with each other.
The communication interface 430 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiment of the present application.
Bus 400 includes hardware, software, or both, coupling components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 400 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
In addition, in combination with the multi-objective autonomous traction method of the surgical robot based on fuzzy logic in the above embodiment, the embodiment of the application can be realized by providing a computer storage medium. The computer storage medium is stored with computer program instructions which when executed by the processor implement any of the fuzzy logic based multi-objective autonomous traction methods of the surgical robot of the above embodiments.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. The method processes of the present application are not limited to the specific steps described and shown, but various changes, modifications and additions, or the order between steps may be made by those skilled in the art after appreciating the spirit of the present application.
The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. The present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In summary, compared with the prior art, the application has the following beneficial effects:
1. According to the application, various motion constraints in operation are converted into control targets, a plurality of target controllers are designed to pay attention to traction angle targets, traction force targets and safety area targets, so that autonomous tissue traction of the robot is realized, the motion speed and the speed of posture change are converted into joint angles of the robot, and the motion control of the robot is performed.
2. Aiming at the complexity of a surgical scene, the application integrates various safety constraints in the operation into the autonomous motion planning of the robot in a control target mode through a multi-target constraint control strategy, and the robot automatically and safely performs tissue traction through predefining traction points without any prior knowledge of the surgical scene or tissue model.
3. The application provides a multi-target autonomous traction method of a surgical robot based on fuzzy logic, which can execute flexible tissue traction, save labor cost, integrate a plurality of control targets into autonomous motion planning of the robot based on the fuzzy logic, and solve a series of problems of poor continuity, insufficient safety, non-ideal generalization and the like in the tissue traction process of the current robot.
The foregoing embodiments are merely for illustrating the technical solution of the present invention, but not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that modifications may be made to the technical solution described in the foregoing embodiments or equivalents may be substituted for parts of the technical features thereof, and that such modifications or substitutions do not depart from the spirit and scope of the technical solution of the embodiments of the present invention in essence.
Claims (7)
1. A multi-target autonomous traction method of a surgical robot based on fuzzy logic, comprising:
Assembling an execution instrument at the tail end of the robot so as to carry out the hardware construction of the autonomous traction surgical robot;
Simplifying the tail end motion of the execution instrument into a spring mass point damping linear control system, and constructing a plurality of target controllers corresponding to a preset target group according to the actual requirement of traction tissues, wherein the preset target group comprises a traction angle target, a traction force target and a safety area target;
setting an evaluation function for a corresponding control target based on a state error in each target controller, respectively calculating a predictive evaluation gradient descent value for each control target, and constructing a state feedback mechanism;
normalizing the multi-target control quantity and the gradient descent value, and inputting the multi-target control quantity and the gradient descent value into a preset fuzzy logic weight distribution algorithm to distribute weights to the control targets;
The multi-target control quantity is subjected to weighted fusion based on a preset state feedback fusion mechanism, and the weighted fusion target value is input into the spring mass point damping linear control system so as to convert the speed of motion speed and posture change into the joint angle of the robot, and the robot motion control is performed;
Setting an evaluation function on the corresponding control target based on the state error in each target controller, respectively calculating a predictive evaluation gradient descent value for each control target, and constructing a state feedback mechanism, wherein the method comprises the following steps:
designing an evaluation function of a traction angle target based on the square of the chord length corresponding to the error between the current traction angle and the expected angle;
designing an evaluation function of the traction force target as the square of the error between the current traction force and the expected traction force;
designing an evaluation function of a safety area target according to the square of the current implementation instrument tip position and the established safety area boundary distance;
Constructing a state feedback mechanism in a state space to perform gradient optimization so as to enable the state of the robot to be converged rapidly;
The spring mass point damping linear control system is a mass-damping-spring linear system for representing the motion state of the robot tail end assembly execution instrument, and the plurality of target controllers comprise a traction angle controller, a traction force controller and a safety area controller;
The spring mass point damping linear control system satisfies the state equation:
Wherein, For the position state of the end of the instrument,For the speed of movement of the distal end of the instrument,For the acceleration of the end of the instrument,For the total control input of the spring mass damping linear control system,Is an inertial parameter,As a parameter of the damping it is possible to provide,Is a stiffness parameter;
the traction angle controller is used for defining a traction angle, and the traction angle meets the expression:
In the formula, In order for the draft angle to be the same,The representation is the position of the cutting instrument,For the position state of the end of the instrument,The cutting direction vector is represented by a vector,Representation ofIs a norm of (2);
the traction angle controller satisfies the expression:
In the formula, The control amount of the traction angle is indicated,AndTo the traction angle controllerThe parameters of the control process are set to be,The error derived for the chord length formula,Is thatIs used as a first derivative of (a),In order for the angle to be a desired angle,Is the viewing angle direction;
the traction controller is provided with a desired traction force to maintain tissue tension, the traction controller satisfying the expression:
Wherein, Indicating the control amount of the traction force,、To provide for traction controlThe parameters of the control process are set to be,In order for the traction force to be desired,As a result of the current force being applied,An error between the desired tractive effort and a current force;
the safety area arranged in the safety area controller is a radius of High isThe safe region controller satisfies the expression:
Wherein, Indicating the control amount of the safety zone,AndTo control the safety areaThe parameters of the control process are set to be,Indicating a deviation of the current location from the safe area,Representation ofIs used as a first derivative of (a),AndRespectively representAt the position ofPlane surfaceThe component in the direction of the light is,As a vector towards the central axis,Is the distance of the implement tip to the coordinate axis,Is the distance of the implement tip from the cylinder base.
2. The multi-target autonomous traction method of a surgical robot based on fuzzy logic according to claim 1,
The dynamic characteristics of the 7-degree-of-freedom robot corresponding to the spring mass damping linear control system in Cartesian space meet the expression:
In the formula, Representing a vector consisting of force and moment,The joint vector is represented by a vector of the joint,Representing a matrix of cartesian inertia and,AndIs a velocity vector and a gravity vector in cartesian space,Representing the position and pose of the robotic end effector in cartesian space,Is thatIs used for the first derivative of (c),Is thatIs the first derivative of (a);
Ambient interaction forces acting on the implement when the robot is in contact with the environment during movement The expression is satisfied:
Wherein, Indicating the net force acting on the implement,The corresponding force mapping relationship satisfies:, Represents the moment of the joint and the moment of the joint, A jacobian representing a distance from joint space to cartesian space;
The current force The expression is satisfied:
Wherein, Is a diagonal array of 3 rows and 3 columns,Force and torque vectors for the implement and interact with ambient forcesHas a corresponding mapping relation.
3. The multi-target autonomous traction method of a surgical robot based on fuzzy logic according to claim 1,
In the event that the implement exceeds the boundary of the cylinder region, the safety region controller generates a control variable directed toward the interior of the cylinder region.
4. A multi-target autonomous traction method of a surgical robot based on fuzzy logic as claimed in claim 3, wherein the evaluation function of the traction angle target:
Evaluation function of the traction force target:
Evaluation function of the safety area target:
Wherein, A predicted distance value representing the tip of the implement from the coordinate axis,Representing a predicted distance value of the implement tip from the cylinder base,Indicating that the desired force is to be applied,Representing the current force predicted by the system,Is a coefficient of a real-time variation,Indicating the current time of day and,As a function of the time variable,To perform the position state of the instrument tip,The representation is the position of the cutting instrument,In order for the angle to be a desired angle,Is the predicted angle.
5. A multi-target autonomous traction system of a surgical robot based on fuzzy logic, comprising:
the hardware construction module is used for assembling an execution instrument at the tail end of the robot so as to carry out the hardware construction of the autonomous traction operation robot;
The system construction module is used for simplifying the tail end motion of the execution instrument into a spring mass point damping linear control system, and constructing a plurality of target controllers corresponding to a preset target group according to the actual requirement of traction tissues;
The calculation module is used for setting an evaluation function for the corresponding control target based on the state error in each target controller, calculating a predictive evaluation gradient descent value for each control target respectively, and constructing a state feedback mechanism;
the weight distribution module is used for normalizing the multi-target control quantity and the gradient descent value, and inputting the multi-target control quantity and the gradient descent value into a preset fuzzy logic weight distribution algorithm so as to distribute weights to the control targets;
the fusion module is used for carrying out weighted fusion on the multi-target control quantity based on a preset state feedback fusion mechanism, inputting the weighted fusion target value into the spring mass point damping linear control system, converting the speed of motion speed and posture change into a joint angle of the robot, and carrying out motion control on the robot;
Setting an evaluation function on the corresponding control target based on the state error in each target controller, respectively calculating a predictive evaluation gradient descent value for each control target, and constructing a state feedback mechanism, wherein the method comprises the following steps:
designing an evaluation function of a traction angle target based on the square of the chord length corresponding to the error between the current traction angle and the expected angle;
designing an evaluation function of the traction force target as the square of the error between the current traction force and the expected traction force;
designing an evaluation function of a safety area target according to the square of the current implementation instrument tip position and the established safety area boundary distance;
Constructing a state feedback mechanism in a state space to perform gradient optimization so as to enable the state of the robot to be converged rapidly;
The spring mass point damping linear control system is a mass-damping-spring linear system for representing the motion state of the robot tail end assembly execution instrument, and the plurality of target controllers comprise a traction angle controller, a traction force controller and a safety area controller;
The spring mass point damping linear control system satisfies the state equation:
Wherein, For the position state of the end of the instrument,For the speed of movement of the distal end of the instrument,For the acceleration of the end of the instrument,For the total control input of the spring mass damping linear control system,Is an inertial parameter,As a parameter of the damping it is possible to provide,Is a stiffness parameter;
the traction angle controller is used for defining a traction angle, and the traction angle meets the expression:
In the formula, In order for the draft angle to be the same,The representation is the position of the cutting instrument,For the position state of the end of the instrument,The cutting direction vector is represented by a vector,Representation ofIs a norm of (2);
the traction angle controller satisfies the expression:
In the formula, The control amount of the traction angle is indicated,AndTo the traction angle controllerThe parameters of the control process are set to be,The error derived for the chord length formula,Is thatIs used as a first derivative of (a),In order for the angle to be a desired angle,Is the viewing angle direction;
the traction controller is provided with a desired traction force to maintain tissue tension, the traction controller satisfying the expression:
Wherein, Indicating the control amount of the traction force,、To provide for traction controlThe parameters of the control process are set to be,In order for the traction force to be desired,As a result of the current force being applied,An error between the desired tractive effort and a current force;
the safety area arranged in the safety area controller is a radius of High isThe safe region controller satisfies the expression:
Wherein, Indicating the control amount of the safety zone,AndTo control the safety areaThe parameters of the control process are set to be,Indicating a deviation of the current location from the safe area,Representation ofIs used as a first derivative of (a),AndRespectively representAt the position ofPlane surfaceThe component in the direction of the light is,As a vector towards the central axis,Is the distance of the implement tip to the coordinate axis,Is the distance of the implement tip from the cylinder base.
6. An electronic device comprising a processor, a memory, and a program stored on the memory and executable on the processor, the program when executed by the processor implementing the multi-objective autonomous traction method of a fuzzy logic based surgical robot of any one of claims 1 to 4.
7. A computer readable storage medium, characterized in that it stores thereon a program or instructions, which when executed by a processor, implements a multi-objective autonomous traction method of a surgical robot based on fuzzy logic as claimed in any of claims 1 to 4.
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