Intelligent auxiliary positioning navigation method and system for surgical manipulator

Abstract

The invention relates to the technical field of manipulator positioning and navigation processing, in particular to an intelligent auxiliary positioning and navigation method and system for a surgical manipulator. Obtaining the selection probability that the current discrete point moves to the next discrete point according to the change of the moving path parameters of the current discrete point and the previous discrete point; obtaining a selection result of the next discrete point according to the selection probability of moving the current discrete point to the next discrete point by adopting a theoretical turntable algorithm; updating the pheromone concentration of the next discrete point; acquiring the neighborhood concentration difference degree of the next discrete point; according to the neighborhood concentration difference degree of the next discrete point, the pheromone concentration of the next discrete point is updated to optimize the ant colony algorithm iteration process, and positioning navigation of the manipulator path is completed. The invention aims to improve the reliability of intelligent auxiliary positioning and navigation by using the surgical manipulator.

Description

Technical Field

[0001] The present application relates to the technical field of manipulator positioning, navigation and processing, and in particular to an intelligent auxiliary positioning and navigation method and system for a surgical manipulator. Background Art

[0002] Surgery is a complex and precise medical operation that places very high demands on doctors. In traditional surgery, doctors mainly rely on experience and intuition to navigate and position the surgery, but this often involves certain risks and errors. The robot's intelligent assisted positioning and navigation technology can help surgeons of different experience levels perform precise surgery, provide more accurate surgical navigation and positioning, reduce the impact of human factors during surgery, improve surgical accuracy and safety, reduce the risk of errors and complications during surgery, and reduce the rate of surgical accidents; at the same time, it can also provide doctors with real-time intraoperative guidance and feedback to help doctors make better decisions.

[0003] The manipulator intelligent assisted positioning and navigation system is an important technological innovation in the field of surgery. It provides surgeons with powerful tools and support, enabling them to better complete complex surgical operations, improve surgical results and patient treatment experience. When using the ant colony algorithm to deal with path planning problems, since the existing path planning algorithms all deal with two-dimensional path planning problems, due to the complexity of three-dimensional path planning, when dealing with three-dimensional path planning problems, there is no relatively unified, suitable and specific algorithm for manipulator intelligent assisted positioning and navigation. Therefore, this application uses an improved two-dimensional ant colony algorithm to complete path planning for the above problem. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an intelligent auxiliary positioning and navigation method and system for a surgical manipulator, and the technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides an intelligent assisted positioning and navigation method for a surgical manipulator, the method comprising the following steps: Obtain the search space for manipulator positioning and navigation; Obtain the safety factor of each discrete point in the search space; for the current discrete point in the iterative process of the ant colony algorithm, obtain the fitness value of the current discrete point moving to the next discrete point according to the change of the moving path parameters of the current discrete point and the previous discrete point; obtain the heuristic function of the current discrete point moving to the next discrete point according to the fitness value of the current discrete point moving to the next discrete point and the safety factor of the current discrete point moving to the next discrete point; obtain the selection probability of the current discrete point moving to the next discrete point according to the heuristic function of the current discrete point moving to the next discrete point; use the turntable algorithm to calculate the selection probability of the current discrete point moving to the next discrete point to obtain the selection result of the next discrete point; The pheromone concentration of the next discrete point is updated according to the heuristic function of moving the current discrete point to the next discrete point; the pheromone concentrations of all discrete points in the search space are clustered using a clustering algorithm to obtain clusters; the neighborhood concentration difference of the next discrete point is obtained; the concentration dilution coefficient of the next discrete point is obtained according to the neighborhood concentration difference of the next discrete point; the pheromone concentration of the next discrete point is updated according to the concentration dilution coefficient of the next discrete point to complete the optimization of the iterative process of the ant colony algorithm; the positioning and navigation of the robot path are completed using the ant colony algorithm.

[0005] Preferably, the step of obtaining the security factor of each discrete point in the search space includes: For each discrete point in the search space, any discrete point is taken as the current discrete point, and the neighborhood matrix of the current discrete point is constructed as the security factor of the current discrete point. The discrete points in the security factor of the current discrete point with the same grayscale value as the current discrete point are marked as 1, and the rest are marked as 0.

[0006] Preferably, obtaining the adaptation value of the current discrete point moving to the next discrete point according to the change of the moving path parameters of the current discrete point and the previous discrete point includes: Get the angle value of the clockwise angle formed by the moving path from the previous discrete point to the current discrete point and the moving path from the current discrete point to the next discrete point and the origin of the coordinate system; The difference between the angles of the previous discrete point moving to the current discrete point and the angles of the current discrete point moving to the next discrete point is calculated, the absolute value of the ratio of the difference to the moving speed when the current discrete point moves to the next discrete point and changes direction is calculated, the inverse of the product of the absolute value of the ratio and the vibration amplitude when the current discrete point moves to the next discrete point is used as the exponent of an exponential function with a natural constant as the base, and the calculation result of the exponential function is used as the adaptation value of the current discrete point moving to the next discrete point.

[0007] Preferably, the heuristic function of moving the current discrete point to the next discrete point is obtained according to the fitness value of the current discrete point moving to the next discrete point and the safety factor of the current discrete point moving to the next discrete point, including: Get the Euclidean distance between the next discrete point and the target discrete point, and the fitness value of the current discrete point moving to the next discrete point; The product of the fitness value of the current discrete point moving to the next discrete point and the safety factor of the current discrete point moving to the next discrete point is calculated, and the ratio of the product to the Euclidean distance is used as the heuristic function of the current discrete point moving to the next discrete point.

[0008] Preferably, obtaining the selection probability of the current discrete point moving to the next discrete point according to the heuristic function of moving from the current discrete point to the next discrete point includes: Get the pheromone concentration and marking value of the next discrete point; The product of the heuristic function for the current discrete point to move to the next discrete point and the pheromone concentration is calculated, and the ratio of the product to the mark value is used as the selection probability of the current discrete point to move to the next discrete point.

[0009] Preferably, the updating of the pheromone concentration of the next discrete point according to the heuristic function of moving from the current discrete point to the next discrete point comprises: The heuristic function of moving the current discrete point to the next discrete point and the sum of the pheromone concentration of the next discrete point are updated to obtain the pheromone concentration of the next discrete point.

[0010] Preferably, obtaining the neighborhood concentration difference of the next discrete point includes: The average of the pheromone concentration differences between the next discrete point and all discrete points in the neighborhood of the next discrete point is taken as the neighborhood concentration difference of the next discrete point.

[0011] Preferably, obtaining the concentration dilution coefficient of the next discrete point according to the neighborhood concentration difference of the next discrete point includes: Get the mean value of pheromone concentration of all discrete points in the search space; The difference between the pheromone concentration of the next discrete point and the mean is calculated, and the ratio of the product of the difference and the neighborhood concentration difference of the next discrete point and the product of the number of iterations and the important coefficient of the number of iterations is used as the exponent of an exponential function with a natural constant as the base, and the normalized value of the calculation result of the exponential function is used as the concentration dilution coefficient of the next discrete point.

[0012] Preferably, the step of updating the pheromone concentration of the next discrete point according to the concentration dilution coefficient of the next discrete point comprises: The product of the result of subtracting the concentration dilution coefficient of the next discrete point from 1 and the pheromone concentration of the next discrete point is used as the update value of the pheromone concentration of the next discrete point, so as to realize the update of the pheromone concentration of the next discrete point.

[0013] In the second aspect, an embodiment of the present application also provides an intelligent assisted positioning and navigation system for a surgical manipulator, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0014] This application has at least the following beneficial effects: This application constructs a three-dimensional search space based on the surgical three-dimensional model collected by existing equipment, constructs the selection probability of discrete points according to the search logic of the ant colony algorithm in two-dimensional space, and jointly analyzes the probability of the next discrete point being selected from the local and overall perspectives, so that the selection of each discrete point in the search space is combined with many factors in the surgical manipulator operation process, making the selection result more accurate; according to the characteristics of the release of the next discrete point during the manipulator movement process, the influence on the movement path of the discrete points in the subsequent iteration process, while considering the dilution of the pheromone of each discrete point, and then updating the pheromone of the discrete point, optimizing each iteration process of the ant colony algorithm, so that the ant colony algorithm can obtain a more accurate movement path of the manipulator, greatly increasing the global search ability of the ant colony algorithm, and taking into account the movement characteristics of the manipulator, the final path obtained can make the manipulator move faster, and produce less vibration during the movement, reducing the risk of surgery. This application improves the reliability of intelligent assisted positioning and navigation using surgical manipulators. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flow chart of an intelligent assisted positioning and navigation method for a surgical manipulator provided in this application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the intelligent auxiliary positioning and navigation method and system of a surgical manipulator proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of the intelligent assisted positioning and navigation method and system for a surgical manipulator provided by the present application is described in detail below with reference to the accompanying drawings.

[0020] An embodiment of the present application provides an intelligent assisted positioning and navigation method and system for a surgical manipulator.

[0021] Specifically, the following intelligent auxiliary positioning and navigation method for a surgical manipulator is provided, see Figure 1 , the method comprises the following steps: Step S001, obtaining the search space for positioning and navigation of the manipulator during a surgical operation.

[0022] This embodiment uses robot processing technology to perform intelligent positioning and navigation of the surgical manipulator. Since the manipulator and the operating table are in a three-dimensional space, a three-dimensional reconstruction technology is used to obtain a three-dimensional model of the manipulator and the corresponding operating space.

[0023] Since the robot only moves in the surgical space, that is, it has the most basic moving space during the robot surgical operation, the search space will be obtained according to the position of the robot and the surgical space, and then the robot movement path of the surgical operation will be calculated based on the search space.

[0024] Surgical operations have a fixed target surgical position, which determines the starting position of the robot and the target position to which the robot ultimately needs to move.

[0025] At this point, this method can be used to obtain the search space for positioning and navigation of the manipulator during surgical operations, making it easier to analyze the distribution characteristics in the search space.

[0026] Step S002, optimizing the ant colony algorithm iteration process according to analyzing the distribution characteristics of each discrete point in the search space.

[0027] The entire search space is discretized into a series of three-dimensional discrete points. In this embodiment, the search space, i.e., the space where the manipulator and the corresponding three-dimensional model are located, is divided into w*d*h equal parts. These discrete points are the nodes that the ant colony algorithm needs to search. The discrete points are represented by (x, y, z, p), where x, y, and z represent the coordinates of the discrete points in the search space, respectively. P represents the pixel value of the discrete point. A pixel value of 0 indicates that the discrete point does not actually exist.

[0028] Mark the 3*3*3 discrete points around each discrete point in the search space. When the pixel value of the surrounding discrete points is the same as the pixel value of the current discrete point, the surrounding discrete points are marked as 1, otherwise they are marked as 0. Each discrete point has a 3*3*3 matrix to record the safety factor of the discrete point for the surrounding path. This matrix is ​​recorded as the safety matrix of the discrete point. At the same time, the safety factor of the safety matrix can be manually changed to ensure that the robot can enter the brain. A safety factor of 1 means that the surrounding discrete points of the discrete point are safer, which may be the normal path for the robot to operate.

[0029] The average moving speed of the manipulator in each posture and the corresponding moving speed when different postures are converted are obtained according to historical data. According to the scene in this embodiment, there are a total of 26 manipulator moving directions, so the corresponding average moving speed of the manipulator in 26 directions can be obtained.

[0030] At the same time, since the vibration magnitudes of the manipulator in different positions are different, the vibrations generated by the manipulator in different types of discrete points are obtained according to historical data, and the vibration amplitude is obtained using sensors. It is normalized and recorded as B, and its value range is [0,1].

[0031] When the ant colony algorithm is first used for optimization, the pheromone concentration of each discrete point is initialized, the initial data volume of the population is 1000, and the maximum number of iterations is recorded as K=50.

[0032] For any number of iterations, the current discrete point where the iteration number is located The next step is to select the discrete point m as an example for analysis.

[0033] Considering that during surgical operations, the smaller the number of changes in the direction of the corresponding moving path of the manipulator during movement and the smaller the angle of change in direction, the smoother the robot moves and the faster its moving speed. At the same time, the manipulator will generate vibrations during movement, and the degree of vibration may affect the operation. Therefore, the movement speed and direction of the current discrete point to the next discrete point are combined, and the magnitude of vibration during the movement of the manipulator is considered to obtain the fitness value of the manipulator moving from the current discrete point to the next discrete point in the moving path. Taking the mth discrete point in the neighborhood of the current discrete point as the next discrete point of the current discrete point as an example, the fitness value of the current discrete point moving to the next discrete point is obtained: In the formula, Indicates the fitness value of the current discrete point moving to the next discrete point. is an exponential function with the natural constant e as base, Indicates the clockwise angle between the moving path from the previous discrete point to the current discrete point and the origin of the coordinate system. Indicates the clockwise angle between the moving path of the current discrete point to the next discrete point and the origin of the coordinate system. Indicates the angle of motion conversion when the robot moves to the next discrete point The corresponding moving speed, Indicates the vibration amplitude generated when the robot moves from the current discrete point to the next discrete point.

[0034] It should be noted that when the difference between the moving direction of the robot from the previous discrete point to the current discrete point is larger than the moving direction from the current discrete point to the next discrete point, that is, The smaller it is, the angle of the robot movement conversion direction in the historical mobile data is The larger the corresponding moving speed is, the smaller the vibration amplitude from the current discrete point to the next discrete point is, which means that the adaptation value of the current discrete point relative to the previous step is larger when it moves to the next discrete point, that is, The larger the value, the faster the robot can move in a smaller direction with less vibration.

[0035] The adaptation value of the current discrete point moving to the next discrete point m is the local consideration level of the current discrete point moving to the next discrete point combined with the historical moving path. However, for the overall level, it is necessary to consider not only the distance between the current discrete point and the final target discrete point, but also whether the next discrete point is a movable point, that is, to ensure that the position selection of the next discrete point is in line with the basic operation of the surgical operation, and there will be no large deviation error in the operation of the robot.

[0036] Based on this, combined with the local and global considerations of the current discrete point moving to the next discrete point, the heuristic function of the current discrete point moving to the next discrete point can be obtained: In the formula, Represents the heuristic function of moving the current discrete point to the next discrete point, Indicates the Euclidean distance between the current discrete point and the next discrete point and the target discrete point. Indicates the safety factor of moving from the current discrete point to the next discrete point. Indicates the fitness value of moving from the current discrete point to the next discrete point.

[0037] It should be noted that the closer the Euclidean distance of the next discrete point to the target discrete point, the safer the next discrete point, that is, the safety factor of the next discrete point m of the current discrete point is 1, and the smaller the difference in moving direction and speed between the previous discrete point and the current discrete point, the greater the fitness of the current discrete point in moving to the next discrete point, which means that the heuristic function of the current discrete point is larger, that is, the next movement of the current discrete point is more likely to be the preferred ant path.

[0038] According to the ant colony algorithm, the selection probability of the current discrete point for other points in the 3x3x3 neighborhood around it is calculated. The selection probability calculation method for the current discrete point to move to the next discrete point m is as follows: In the formula, Indicates the probability of moving from the current discrete point to the next discrete point. Indicates the mark value of the current discrete point moving to the next discrete point. The value is 0 or 1. 0 means that no ant passes through the path, and 1 means that an ant passes through the path. Indicates the pheromone concentration of the current discrete point moving to the next discrete point. The larger the value, the greater the possibility of selecting this point. Represents the heuristic function for moving from the current discrete point to the next discrete point.

[0039] It should be noted that when the mark value of the current discrete point moving to the next discrete point is 0, the selection probability of the next discrete point is indirectly increased, that is, the global search ability of the ants is increased in this way, so that each discrete point may be passed by ants; at the same time, the larger the pheromone concentration and heuristic function of the next discrete point, the more likely the next discrete point is to be the optimal search path for the ants, that is, the greater the selection probability of the current discrete point moving to the next discrete point, the more likely the next discrete point is to be selected.

[0040] According to the above obtained selection probability of moving the current discrete point to the next discrete point , the ant selection result is obtained using the wheel algorithm. The wheel algorithm is a well-known technology and will not be described in detail in this embodiment.

[0041] After passing the next discrete point, the next discrete point also serves as a reference point for the discrete points in the subsequent movement process, and its reference degree will also change. That is, when the ant passes the next discrete point, it will release pheromones, so that more ants will be attracted to choose this path. The calculation method of the pheromone concentration of the next discrete point after the ant passes the next discrete point is as follows: In the formula, Indicates the pheromone concentration of the current discrete point moving to the next discrete point. The larger the value, the greater the possibility of selecting this point. Represents the heuristic function of moving the current discrete point to the next discrete point, Represents the pheromone concentration at the next discrete point after the ant passes through the next discrete point.

[0042] It should be noted that the larger the heuristic function of the next discrete point is, the more likely it is that the discrete point is the optimal path, that is, the larger the heuristic function value of the point is, the more pheromones the corresponding ant releases when passing through the point.

[0043] In the process of real ants searching for a path, the ants passing through the path release pheromones. In order to prevent the ants from walking the same path repeatedly, that is, to prevent the ants from walking on the same path all the time or other situations, the pheromones released by the ants need to be diluted to simulate the process of real ants searching for a path and ensure that the ants do not fall into the local optimal solution when searching for a path.

[0044] According to the concentration of pheromone at the discrete points around the discrete point, in order to ensure the global search capability of the ant colony, in the initial iteration, the pheromone concentration of the discrete point is higher than that of the surrounding discrete points, and the pheromone concentration dilution coefficient is larger. The specific method is as follows: First, the pheromone concentrations of all discrete points are clustered and analyzed. The pheromone concentrations of all discrete points are clustered to obtain discrete points with similar pheromone concentrations. The pheromone concentrations of similar discrete points are kept similar. All clusters are re-grouped into clusters composed of adjacent discrete points according to the positions in the search space as new clusters. That is, the discrete points that were originally in the same cluster but far apart are updated according to the position relationship, and the discrete points with lower pheromone concentrations around them are suppressed, so that the pheromone concentration of the current discrete point is maintained at a higher pheromone concentration, increasing the possibility of the preferred path. Among them, the clustering algorithm of this embodiment uses the DBSCAN clustering algorithm. The DBSCAN clustering algorithm is a well-known technology and will not be described in detail in this embodiment. The corresponding discrete point pheromone concentration dilution method is calculated as follows: In the formula, Indicates the difference in neighborhood concentration of the next discrete point, Indicates the next discrete point and the corresponding discrete point in the 5*5*5 range around the next discrete point The difference in pheromone concentration between.

[0045] It should be noted that the greater the difference in pheromone concentration between the next discrete point and the surrounding discrete points, the more unique the pheromone concentration of the next discrete point is from other discrete points, that is, the point may be the intersection of many paths, or it may be a point that many paths do not pass through.

[0046] In order to further and more accurately determine whether the next discrete point is a corner point of many paths or a point that many paths do not pass through, the concentration dilution coefficient of the next discrete point is obtained by combining the pheromone concentration of the next discrete point and the number of iterations: In the formula, represents the concentration dilution factor of the next discrete point, is the normalization function, is an exponential function with the natural constant e as base, Indicates the difference in neighborhood concentration of the next discrete point, represents the pheromone concentration at the next discrete point after the ant passes through the next discrete point, represents the mean pheromone concentration of all discrete points in the search space, represents the important coefficient of the number of iterations, Indicates the number of iterations.

[0047] It should be noted that when the pheromone concentration of the next discrete point is greater than the mean pheromone concentration, it means that the next discrete point may be a discrete point under the intersection path, so the pheromone dilution coefficient is increased to reduce the possibility of ants choosing this path and enhance the possibility of ants choosing other paths, where k represents the number of iterations and n represents the important coefficient of the number of iterations. In this embodiment, n takes an empirical value of 0.05, which can be set by the implementer. That is, in the early stage of the iteration of the ant colony algorithm, the pheromone concentration of the discrete point with higher pheromone is suppressed, the possibility of ants going to other paths is increased, and its global search ability is increased. When the required k is smaller, that is, the number of iterations is smaller, and the difference between the pheromone concentration of the corresponding discrete point and the pheromone around it is greater, that is, the required The larger the difference, the positive difference, that is, If it is a positive number, it means that the pheromone concentration dilution coefficient corresponding to the discrete point is larger.

[0048] By analyzing the dilution degree of the pheromone concentration of the next discrete point, the concentration dilution coefficient of the discrete point is obtained, and then brought into each discrete point in the above iterative process. After each iteration is completed, in addition to updating the pheromone concentration of the shortest discrete point among all paths, the pheromone concentration of all discrete points must also be diluted and volatilized: In the formula, Indicates the pheromone concentration of the next discrete point after the current iteration is completed, that is, the initial pheromone concentration of the discrete point in the next iteration , represents the concentration dilution factor of the next discrete point, Represents the pheromone concentration at the next discrete point after the ant passes through the next discrete point.

[0049] At this point, the above method can be used to complete the update of pheromone concentration in one iteration.

[0050] Step S003, completing the positioning and navigation of the robot path during the operation according to the ant colony algorithm.

[0051] By optimizing the above iterative process, the iterative process of moving the current discrete point to the next discrete point is completed. Based on this, the same method is used to complete each iteration of the surgical manipulator using the ant colony algorithm in the search space. Among them, the ant colony algorithm is not a well-known technology and will not be described in detail in this embodiment.

[0052] When the number of iterations is less than the maximum number of iterations K=50, the iteration process of the ant colony algorithm will continue until all the data in the population have completed the iteration process; when the number of iterations is greater than the maximum number of iterations K=50, the iteration process of the ant colony algorithm will be stopped, and the final path at this time will be taken as the final result of the ant colony algorithm; Repeat the above iterative process to complete the ant colony algorithm and obtain the final moving path of the manipulator.

[0053] The movement path of the manipulator is input into the manipulator control system, so that the manipulator can complete the surgical auxiliary positioning navigation function according to the obtained movement path.

[0054] At this point, the intelligent assisted positioning and navigation of the surgical manipulator is completed.

[0055] Based on the same inventive concept as the above-mentioned method, an embodiment of the present application also provides an intelligent assisted positioning and navigation system for a surgical manipulator, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned intelligent assisted positioning and navigation methods for a surgical manipulator when executing the computer program.

[0056] The embodiment of the present application constructs a three-dimensional search space based on the three-dimensional surgical model acquired by the existing equipment, constructs the selection probability of discrete points based on the search logic of the ant colony algorithm in the two-dimensional space, and jointly analyzes the probability of the next discrete point being selected from the local and overall perspectives, so that the selection of each discrete point in the search space is combined with many factors in the surgical manipulator operation process, making the selection result more accurate; Combined with the influence of the release characteristics of the next discrete point during the manipulator movement process on the movement path of the discrete points in the subsequent iteration process, the dilution of the pheromone of each discrete point is considered, and the pheromone of the discrete point is then updated, and the ant colony algorithm is optimized for each iteration process, so that the ant colony algorithm can obtain a more accurate movement path of the manipulator, which greatly increases the global search ability of the ant colony algorithm. At the same time, considering the movement characteristics of the manipulator, the final path obtained can make the manipulator move faster, and the vibration generated during the movement is smaller, reducing the risk of surgery. The embodiment of the present application increases the reliability of the use of surgical manipulator intelligent assisted positioning navigation and improves the success rate of surgery.

[0057] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0059] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An intelligent auxiliary positioning and navigation method for a surgical manipulator, characterized in that: The method comprises the following steps: Obtain the search space for manipulator positioning and navigation; Obtain the safety factor of each discrete point in the search space; for the current discrete point in the iterative process of the ant colony algorithm, obtain the fitness value of the current discrete point moving to the next discrete point according to the change of the moving path parameters of the current discrete point and the previous discrete point; obtain the heuristic function of the current discrete point moving to the next discrete point according to the fitness value of the current discrete point moving to the next discrete point and the safety factor of the current discrete point moving to the next discrete point; obtain the selection probability of the current discrete point moving to the next discrete point according to the heuristic function of the current discrete point moving to the next discrete point; use the turntable algorithm to calculate the selection probability of the current discrete point moving to the next discrete point to obtain the selection result of the next discrete point; The pheromone concentration of the next discrete point is updated according to the heuristic function of moving the current discrete point to the next discrete point; the pheromone concentrations of all discrete points in the search space are clustered using a clustering algorithm to obtain clusters; the neighborhood concentration difference of the next discrete point is obtained; the concentration dilution coefficient of the next discrete point is obtained according to the neighborhood concentration difference of the next discrete point; the pheromone concentration of the next discrete point is updated according to the concentration dilution coefficient of the next discrete point to complete the optimization of the iterative process of the ant colony algorithm; the positioning and navigation of the robot path are completed using the ant colony algorithm.

2. The intelligent auxiliary positioning and navigation method of a surgical manipulator according to claim 1, characterized in that: The step of obtaining the security factor of each discrete point in the search space includes: For each discrete point in the search space, any discrete point is taken as the current discrete point, and the neighborhood matrix of the current discrete point is constructed as the security factor of the current discrete point. The discrete points in the security factor of the current discrete point with the same grayscale value as the current discrete point are marked as 1, and the rest are marked as 0.

3. The intelligent auxiliary positioning and navigation method of a surgical manipulator according to claim 1, characterized in that: The step of obtaining the adaptation value of the current discrete point moving to the next discrete point according to the change of the moving path parameters between the current discrete point and the previous discrete point includes: Get the angle value of the clockwise angle formed by the moving path from the previous discrete point to the current discrete point and the moving path from the current discrete point to the next discrete point and the origin of the coordinate system; The difference between the angles of the previous discrete point moving to the current discrete point and the angles of the current discrete point moving to the next discrete point is calculated, the absolute value of the ratio of the difference to the moving speed when the current discrete point moves to the next discrete point and changes direction is calculated, the inverse of the product of the absolute value of the ratio and the vibration amplitude when the current discrete point moves to the next discrete point is used as the exponent of an exponential function with a natural constant as the base, and the calculation result of the exponential function is used as the adaptation value of the current discrete point moving to the next discrete point.

4. The intelligent auxiliary positioning and navigation method of a surgical manipulator according to claim 3, characterized in that: The heuristic function of moving the current discrete point to the next discrete point is obtained according to the fitness value of the current discrete point moving to the next discrete point and the safety factor of the current discrete point moving to the next discrete point, including: Get the Euclidean distance between the next discrete point and the target discrete point, and the fitness value of the current discrete point moving to the next discrete point; The product of the fitness value of the current discrete point moving to the next discrete point and the safety factor of the current discrete point moving to the next discrete point is calculated, and the ratio of the product to the Euclidean distance is used as the heuristic function of the current discrete point moving to the next discrete point.

5. The intelligent auxiliary positioning and navigation method of a surgical manipulator according to claim 4, characterized in that: The selection probability of moving the current discrete point to the next discrete point is obtained according to the heuristic function of moving the current discrete point to the next discrete point, including: Get the pheromone concentration and marking value of the next discrete point; The product of the heuristic function for the current discrete point to move to the next discrete point and the pheromone concentration is calculated, and the ratio of the product to the mark value is used as the selection probability of the current discrete point to move to the next discrete point.

6. The intelligent auxiliary positioning and navigation method of a surgical manipulator according to claim 1, characterized in that: The updating of the pheromone concentration of the next discrete point according to the heuristic function of moving from the current discrete point to the next discrete point comprises: The heuristic function of moving the current discrete point to the next discrete point and the sum of the pheromone concentration of the next discrete point are updated to obtain the pheromone concentration of the next discrete point.

7. The intelligent auxiliary positioning and navigation method of a surgical manipulator according to claim 1, characterized in that: The step of obtaining the neighborhood concentration difference of the next discrete point includes: The average of the pheromone concentration differences between the next discrete point and all discrete points in the neighborhood of the next discrete point is taken as the neighborhood concentration difference of the next discrete point.

8. The intelligent auxiliary positioning and navigation method of a surgical manipulator according to claim 7, characterized in that: The step of obtaining the concentration dilution coefficient of the next discrete point according to the neighborhood concentration difference of the next discrete point includes: Get the mean value of pheromone concentration of all discrete points in the search space; The difference between the pheromone concentration of the next discrete point and the mean is calculated, and the ratio of the product of the difference and the neighborhood concentration difference of the next discrete point and the product of the number of iterations and the important coefficient of the number of iterations is used as the exponent of an exponential function with a natural constant as the base, and the normalized value of the calculation result of the exponential function is used as the concentration dilution coefficient of the next discrete point.

9. The intelligent auxiliary positioning and navigation method of a surgical manipulator according to claim 8, characterized in that: The step of updating the pheromone concentration of the next discrete point according to the concentration dilution coefficient of the next discrete point includes: The product of the result of subtracting the concentration dilution coefficient of the next discrete point from 1 and the pheromone concentration of the next discrete point is used as the update value of the pheromone concentration of the next discrete point, so as to realize the update of the pheromone concentration of the next discrete point.

10. An intelligent auxiliary positioning and navigation system for a surgical manipulator, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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