Intelligent control six-connecting-rod type fracture reduction robot

Abstract

The invention discloses an intelligent control six-connecting-rod type fracture reduction robot which comprises two fixing rings used for installing kirschner wires, six connecting arms which are integrally and evenly distributed in the circumferential direction are arranged between the two fixing rings in a hinged mode, one ends of any two adjacent connecting arms are close to each other, and the other ends of any two adjacent connecting arms are separated to form a splayed shape. Each connecting arm is provided with a telescopic device, the system further comprises a control center (PC terminal), the control center is in communication connection with the telescopic devices and realizes telescopic control, and the control center is internally provided with a control module used for controlling relative position paths of the two fixing rings and a path planning module used for designing a control path. The path planning module has an improved RRT * path planning algorithm. According to the scheme, a doctor can be assisted in operation, accidental injury can be better avoided through an obstacle avoidance strategy, and the reliability of the robot used in the fracture rehabilitation assisting process is better improved.

Description

Technical Field

[0001] The invention relates to the technical field of fracture reduction robots, and in particular to an intelligently controlled six-link fracture reduction robot. Background Art

[0002] During the rehabilitation process of fracture patients, especially limb fracture patients, it is usually necessary to use an orthopedic external fixator to fix the fracture site to avoid secondary injury to the fracture site during the rehabilitation process and accelerate the growth and recovery of the fracture site.

[0003] Existing bone external fixation devices can be divided into three different types according to their basic components and structures, namely, unilateral / bilateral external fixation devices, circular external fixation devices (CEFs), and hybrid external fixation devices. In the treatment of long bone deformities of the lower limbs, the optimal use condition of external fixation devices is to allow micro-motion of the bone segments in the long bone axial direction while maintaining high stiffness in torsion, shear, and bending. Although unilateral / bilateral external fixation devices can provide higher rigidity and early load-bearing capacity, they limit the axial micro-motion of the bone injury site, which may delay the healing process. In addition, asymmetric compression of the bone injury site can often be observed in unilateral / bilateral external fixation devices, which will have an adverse effect on healing. Circular external fixation devices just eliminate these two problems. Appropriate Kirschner wires can be used in circular external fixation devices to control axial micro-motion. For situations where the axial load of the bone increases, appropriate Kirschner wires can be used in a targeted manner to eliminate excessive movement of the bone injury site. In addition, the high stiffness of the circular external fixator in torsion, shear and bending is conducive to the stability of the bone correction process; the multi-plane fixation provided by the circular external fixator can treat more complex bone injuries than the unilateral / bilateral external fixator. For these reasons, the circular external fixator has gained a high popularity in orthopedics. Due to the biomechanically favorable stiffness characteristics of the circular external fixator (CEF), it has been widely used in the treatment of various orthopedic diseases, especially fracture reduction.

[0004] The structure of the conventional annular external fixator (CEF) type fracture reduction robot includes two fixing rings for installing Kirschner wires, and six connecting arms are hingedly arranged between the two fixing rings, which are uniformly distributed along the circumference. Any two adjacent connecting arms are close together at one end and separated at the other end to form an eight-shaped shape, and each connecting arm is provided with a telescopic device to enable it to have a telescopic function. When it is used for fracture rehabilitation, the two fixing rings are fixed to the two ends of the limb fracture through Kirschner wires to keep the limb fixed. When this fracture reduction robot is used, it can rely on the control of each telescopic arm to achieve the adjustment and control of the relative position between the two fixing rings. The adjustment of the relative position between the two fixing rings will show different changes in relative position paths following the entire rehabilitation process of the fracture.

[0005] Therefore, preoperative path planning technology is an important technology in fracture reduction surgery. Path information is obtained through programs and computer equipment to provide surgeons with more accurate path guidance, thereby improving surgical safety. The existing path planning design of fracture reduction robots in the process of fracture rehabilitation usually adopts A* algorithm and RRT* algorithm. Among them, the A* algorithm has a long calculation time and high memory consumption, which needs further improvement. In addition, the gap between the generated path and the obstacle is very small, which will cause the robot to be unable to effectively avoid obstacles. In contrast, the RRT* algorithm can be effectively applied to path planning in complex environments, showing stronger search capabilities, so it is relatively more used.

[0006] However, during surgery, due to the complexity of the tissues around the broken bones, it is difficult for doctors to accurately determine the position of the broken bones. Therefore, collisions between bones can easily lead to secondary fractures and soft tissue damage. It is necessary to ensure that there is no collision between bones in the reduction trajectory and to be able to quickly find the optimal path, which puts more stringent requirements on the algorithm. The existing RRT* algorithm still lacks comprehensive consideration of the fracture reduction path, has poor search efficiency, low collision detection efficiency, and is difficult to obtain the optimal reduction path.

[0007] Therefore, for those skilled in the art, how to provide an algorithm technology that can better help avoid important parts and avoid surgical injuries through obstacle avoidance strategies has become a problem to be considered and solved. Summary of the invention

[0008] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is: how to provide an intelligently controlled six-link fracture reduction robot that can better avoid important parts through obstacle avoidance strategies and avoid surgical injuries.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: An intelligent controlled six-link fracture reduction robot comprises two fixed rings for installing Kirschner wires, six connecting arms which are evenly distributed along the circumferential direction are hingedly arranged between the two fixed rings, one end of any two adjacent connecting arms are close together and the other end is separated to form an eight-shaped shape, each connecting arm is provided with a telescopic device, and also comprises a control center (PC end), the control center is connected with the telescopic device in communication and realizes telescopic control, a control module for controlling the relative position path of the two fixed rings and a path planning module for designing the control path are arranged in the control center, and the path planning module has an improved RRT* path planning algorithm; the improved RRT* path planning algorithm comprises the following steps: Step S1: Initialize the map and set the force field value and other related parameters; Step S2: Introduce the artificial potential field method to generate random sampling pointsq new ; Step S3: Using the cylindrical envelope method, q new Establish a cylinder and detect whether a collision occurs; if a collision occurs, return to step S2 to reselect and sample; if no collision occurs, continue to step S4; Step S4: Find the tree node closest to the random sampling point q n , and use this node as q new The parent node of Step S5: Find q new The minimum total path and reconnect; Step S6: Determination q new arrive Q goal Is the distance less than the step length? If not, return to step 2 for sampling; if so, continue to step S7; Step S7: using Gaussian smoothing algorithm for fitting processing, adding the processed points to a new smoothing path set; Step S8: Generate the final smooth planning path and end the path planning task.

[0010] Thus, in this scheme, based on the traditional intelligent control six-link fracture reduction robot, an improved path planning algorithm is adopted in the path planning module of the system. The proposed improved algorithm is used for path planning on the PC side, and then the generated path node posture information is imported into the control module, thereby controlling the rotation of the motor to achieve the purpose of control reduction.

[0011] Furthermore, the step S1 comprises the following steps: 1) initializing the map, setting the size of the map to 2×2×2 cm, setting the starting point to (-1,-1,-1), and setting the end point to (1,1,1); 2) setting the radius and height of the cylindrical obstacle to 0.05 and 0.2, respectively, and the position information to (0, 0, -0.1), (-0.6, -0.6, 0.4), (0.6, -0.6, 0.4), (-0.6,0.6, 0.4), (0.6, 0.6, 0.4), (-0.6, -0.6, -0.4), (0.6, -0.6, -0.4) (-0.6, 0.6, -0.4) (0.6, 0.6, -0.4) -0.4); 3) Set the parameters of the artificial force field, the relative strength of gravity is 1, the relative strength of repulsion is 2, the repulsive influence range is 0.3, and the step size is 0.04.

[0012] This is because the above-mentioned environment, start and end points, and obstacle setting data are based on a comprehensive consideration of the fracture environment characteristics and actual task requirements. At the same time, the advantages of the setting of potential field parameters are reflected: the relative strength of gravity is set to 1, indicating that the gravitational field has a moderate impact on path planning, which can guide the path to the end point, but will not be too strong to cause the path to be too direct and ignore obstacles. The relative strength of repulsion is set to 2, which is higher than the relative strength of gravity, indicating that obstacle avoidance is the primary consideration in path planning, which helps to ensure the safety of the path. The repulsion influence range is set to 0.3. This range should be large enough to form a safe buffer zone around obstacles, but not so large that the path is too circuitous. Furthermore, the step S2 comprises the following steps: 1) firstly creating a gravitational field for the end point and a repulsive field for the obstacle; 2) randomly selecting a position: randomly selecting a position in the sampling space as a potential sampling point q new ; 3) Calculate the potential field value: for randomly generated points q new , calculate its gravitational potential and repulsive potential. This usually involves the location information of the target point and the obstacle; 4) Calculate the total potential field value: If the potential field value meets the predetermined conditions, then accept q new As an effective random sampling point.

[0013] This is because by creating a gravitational field for the end point and a repulsive field for the obstacle, the random sampling points are more likely to choose a position facing the target and away from the obstacle, thereby improving the effectiveness and success rate of path planning and strengthening the algorithm's guidance; at the same time, the repulsive field ensures that the random sampling points will not be too close to the obstacle, reducing the potential collision risk in path planning. In addition, the gravitational field helps guide the search process toward the target point, which can accelerate the convergence speed of the algorithm and make path planning faster.

[0014] Furthermore, the step S3 comprises the following steps: 1) q new Point Cylinder: At random sampling points q new A cylinder is built at the position of q new , radius is 0.05, height is 0.2; 2) Perform the following detection on each obstacle: calculate the minimum distance between the obstacle and the cylinder; if the minimum distance ≤ 0.1, record the collision; 3) Process the collision result: if a collision is detected, return to step S2 and regenerate a random sampling point q new , and re-establish the cylinder for collision detection; if no collision is detected, the sampling pointq new Valid, continue to execute subsequent path planning steps.

[0015] This is because by avoiding collisions, a cylinder is built around the random sampling points and collision detection is performed to ensure that the sampling points are not located inside or near obstacles, thereby avoiding collisions with obstacles during path planning. Effectively excluding sampling points that are too close to obstacles helps to generate a smoother and more direct path, reduce the complexity and detours of the path, and improve the overall quality of the path. The principle is to perform collision detection by calculating whether the horizontal and vertical distances of two cylinders overlap.

[0016] Furthermore, the step S4 includes the following steps: 1) traversing the path tree: traversing all nodes in the current path tree; 2) calculating the distance: for each node, calculating its distance to the random sampling point q new 3) Compare and select the minimum distance: Compare all calculated distances and find the node with the minimum distance, which is the nearest node. q n ; 4) Update the tree structure: In the path tree, q n Mark as q new The parent node of q n Add to the list of child nodes q new ; 5) Storage connection information: record q n and q new The connection information between them is used to facilitate subsequent path search and optimization.

[0017] This is because this step is the key step in the RRT algorithm for expanding the path tree and maintaining its structural integrity. In this way, the algorithm can gradually explore the search space until a feasible path from the starting point to the target point is found, ensuring the efficiency and robustness of the algorithm, making it suitable for various complex path planning problems.

[0018] Furthermore, the step S5 comprises the following steps: 1) calculating the distance from the root to q n The known optimal path cost and add it to the cost from q n arrive q new The new path cost is q new The initial total path cost; 2) traversal qnew Neighboring nodes: Check all nodes with q new directly connected nodes (neighboring nodes), which means checking q n All child nodes of q n 3) For each neighboring node, calculate the path from the root to the neighboring node, and then through the neighboring node to q new 4) Compare path costs: Compare each neighboring node to q new The path cost is compared with the current minimum total path cost; if a path cost through a neighboring node is found to be lower, the update q new 5) Reconnect: If a lower-cost path is found, then q new From the original parent node q n Disconnect it and connect it to its new parent node (i.e., a neighboring node with a lower path cost).

[0019] This is because this step continuously searches for and updates a better path, and the resulting path will be better in length and efficiency, improving the path quality; at the same time, it reduces the amount of calculation, and by timely updating the parent node of the node, it reduces unnecessary path expansion in subsequent searches and calculations, improving the operating efficiency of the algorithm; and further enhances the adaptability of the algorithm. Through dynamic adjustment and optimization strategies, the algorithm can adapt to complex planning environments and find effective paths even in situations where obstacles are dense or space is limited.

[0020] Furthermore, the step S6 comprises the following steps: 1) calculating q new and Q goal 2) Compare the calculated distance with the step length: If the distance is less than the step length, it is considered q new close enough Q goal , you can continue to step S7. If the distance is greater than or equal to the step length, it is considered that you need to get closer Q goal Therefore, the process returns to step S2 to perform new sampling.

[0021] This distance-based decision is a common practice and simplifies the implementation of the algorithm because it provides a clear criterion for deciding when to stop sampling and start connecting the final paths.

[0022] Furthermore, the step S7 includes the following steps: 1) collecting path points: collecting the path points from the starting point to the current point. q new All path points that make up the original non-smoothed path; 2) Apply Gaussian smoothing algorithm: Gaussian smoothing is a commonly used signal processing technique that uses Gaussian functions as weights to perform weighted averages on data points to reduce noise and fluctuations. In path smoothing, the Gaussian smoothing algorithm will act on the original path points and calculate the weighted average of each point. This process usually includes the following steps: a. Determine the parameters of the Gaussian function, such as the standard deviation (σ). The standard deviation determines the degree of smoothing. A larger σ will result in a smoother path, but may also lose details of the path.

[0023] b. For each point on the path, use Gaussian weights to calculate the weighted average of its surrounding points. The weight decreases as the distance increases, and the farther the distance, the lower the weight.

[0024] c. Replace each original path point with its weighted average, or add new points to form the smoothed path.

[0025] 3) Generate smoothed paths: Generate a set of new smoothed path points by applying Gaussian smoothing to each original path point. These new smoothed path points should be smoother than the original path while still maintaining the overall shape and direction of the path. 4) Join the smoothed path set: Add the processed smoothed path points to the new smoothed path set.

[0026] The principle and purpose of the above steps: In order to ensure the continuity and smoothness of the planned path, the Gaussian smoothing algorithm is used to eliminate the inflection points in the distal motion trajectory of the fracture, so as to achieve a smoother and more natural motion trajectory. By integrating the Gaussian smoothing algorithm, the dynamic transition can be optimized and the potential mechanical stress can be reduced, so that the robot can ensure that no secondary injury will be caused as much as possible during the resetting movement.

[0027] Further, the step S8 includes the following steps: 1) Integrate smooth path points: Integrate the set of smooth path points generated in step S7 into a continuous path. This may involve connecting adjacent smooth points and ensuring the continuity and smoothness of the path. 2) Generate the final path: Define the optimized smooth path as the final path. This path should be the best feasible path from the starting point to the target point, taking into account all constraints and planning goals. 3) Output the path: Output the final path in an appropriate data structure or format so that it can be used for the reset task. 4) End the path planning task: After successfully generating the final path, the task of the path planning algorithm ends. At this point, the algorithm can release resources or prepare to initialize for the next path planning task.

[0028] This makes routine operations more convenient to implement.

[0029] Thus, in this method, in the RRT* path planning algorithm adopted, the expansion node step of the RRT* code is changed, the code of the artificial potential field algorithm is written, and it is reasonably adjusted to purposefully generate nodes toward the end point under the influence of the potential field; in this process, a cylinder is constructed at the node to determine whether it collides with the cylinder on the path; finally, the smoothing algorithm is used to write the end of the source code to obtain the final smooth path. Therefore, the algorithm integrates the artificial potential field algorithm into the RRT* algorithm to enhance the purpose of generating random points; at the same time, the cylindrical envelope obstacle avoidance strategy is added, and the physiological conditions of the fractured bone are simulated to enhance the obstacle avoidance performance of the algorithm; and then the Gaussian smoothing algorithm is combined to eliminate the inflection point in the distal motion trajectory of the fracture, and a smoother and safer motion trajectory is obtained, providing surgeons with more accurate path guidance.

[0030] Furthermore, a tension and pressure sensor is provided on the connecting arm, and the tension and pressure sensor is connected to the control center for communication, so that the tensile stress data of the connecting arm can be detected, its stress condition can be determined, and the telescopic control of the connecting arm can be assisted.

[0031] Furthermore, the telescopic device is an electric cylinder device. In this way, the electric cylinder device is a motor driving the lead screw nut to realize telescopic control, so that the control of the telescopic process and telescopic size is more accurate and stable, and the electric cylinder device itself is a mature existing technology, easy to implement production, and easy to accurately control.

[0032] Furthermore, the two ends of the connecting arm are hinged by a Hooke hinge and a fixing ring, so that the structure is more reliable and does not affect the motion control of the connecting arm in all directions.

[0033] In the robot of the present invention, a new algorithm strategy is used in the path planning module for path planning design, which can provide doctors with a better solution to avoid collisions when performing fracture reduction operations. Compared with other algorithms, the improved RRT* algorithm in this application integrates the APF algorithm with high purpose, reduces the generation of redundant points, and reduces the running time. The time required for clinical fracture reduction path planning is greatly reduced. The cylindrical envelope method is introduced, and then combined with an artificial repulsive field to generate a collision-free path. The Gaussian smoothing algorithm is used to eliminate the inflection points in the motion trajectory to obtain a smoother and safer motion trajectory.

[0034] In summary, the present application scheme can assist doctors in their operations, so as to better avoid accidental injuries through obstacle avoidance strategies, and better improve the reliability of the robot in the fracture rehabilitation assistance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a structural schematic diagram of the intelligently controlled six-link fracture reduction robot of the present invention.

[0036] Figure 2 It is a structural schematic diagram of the control part of the intelligently controlled six-link fracture reduction robot of the present invention.

[0037] Figure 3 In order to verify the feasibility of the path planning algorithm used in this application, a schematic diagram of the planned path obtained by using the existing RRT algorithm is shown.

[0038] Figure 4 In order to verify the feasibility of the path planning algorithm used in this application, a schematic diagram of the planned path obtained by using the existing RRT* algorithm is used.

[0039] Figure 5 In order to verify the feasibility of the path planning algorithm used in this application, a schematic diagram of the planned path is obtained using the existing A* algorithm.

[0040] Figure 6 In order to verify the feasibility of the path planning algorithm used in this application, a schematic diagram of the planned path is obtained by using the improved RRT* algorithm in this application.

[0041] Figure 7 In order to verify the feasibility of the path planning algorithm used in this application, a planning time comparison chart of four algorithms is used.

[0042] Figure 8 In order to verify the feasibility of the path planning algorithm used in this application, a comparison diagram of path nodes of four algorithms was used.

[0043] Fig. 9In order to verify the feasibility of the path planning algorithm used in this application, four algorithms were used to generate node comparison diagrams.

[0044] Fig.10 In order to verify the feasibility of the path planning algorithm used in this application, a comparison chart of the path lengths of four algorithms was used.

[0045] Fig.11 for Figure 7-10 Table of average values ​​of the obtained data. DETAILED DESCRIPTION

[0046] The following is combined with the drawings and specific implementation methods.

[0047] A preferred embodiment, an intelligently controlled six-link fracture reduction robot, comprising two fixing rings 1 for installing Kirschner wires, six connecting arms 2 that are evenly distributed along the circumferential direction are hingedly arranged between the two fixing rings 1, one end of any two adjacent connecting arms 2 is close together and the other end is separated to form an eight-shaped shape, each connecting arm 2 is provided with a telescopic device 3, and also includes a control center (PC end) 4, the control center 4 is connected to the telescopic device 3 for communication and realizes telescopic control, and the control center 4 is provided with a control module 5 for controlling the relative position path of the two fixing rings and a path planning module 6 for designing a control path, and the path planning module 6 has an improved RRT* path planning algorithm; the improved RRT* path planning algorithm includes the following steps: Step S1: Initialize the map and set the force field value and other related parameters; Step S2: Introduce the artificial potential field method to generate random sampling points q new ; Step S3: Using the cylindrical envelope method, q new Establish a cylinder and detect whether a collision occurs; if a collision occurs, return to step S2 to reselect and sample; if no collision occurs, continue to step S4; Step S4: Find the tree node closest to the random sampling point q n , and use this node as q new The parent node of Step S5: Find q new The minimum total path and reconnect; Step S6: Determination q new arrive Q goal Is the distance less than the step length? If not, return to step 2 for sampling; if so, continue to step S7; Step S7: using Gaussian smoothing algorithm for fitting processing, adding the processed points to a new smoothing path set; Step S8: Generate the final smooth planning path and end the path planning task.

[0048] Thus, in this scheme, based on the traditional intelligent control six-link fracture reduction robot, an improved path planning algorithm is adopted in the path planning module of the system. The proposed improved algorithm is used for path planning on the PC side, and then the generated path node posture information is imported into the control module, thereby controlling the rotation of the motor to achieve the purpose of control reduction.

[0049] During implementation, the step S1 includes the following steps: 1) initializing the map, setting the size of the map to 2×2×2 cm, setting the starting point to (-1,-1,-1), and setting the end point to (1,1,1); 2) setting the radius and height of the cylindrical obstacle to 0.05 and 0.2 respectively, and the position information to (0, 0, -0.1), (-0.6, -0.6, 0.4), (0.6, -0.6, 0.4), (-0.6,0.6, 0.4), (0.6, 0.6, 0.4), (-0.6, -0.6, -0.4), (0.6, -0.6, -0.4) (-0.6, 0.6, -0.4) (0.6, 0.6, -0.4). 3) Set the parameters of the artificial force field: the relative strength of gravity is 1, the relative strength of repulsion is 2, the repulsive influence range is 0.3, and the step size is 0.04.

[0050] When implemented, the step S2 includes the following steps: 1) firstly creating a gravitational field for the end point and a repulsive field for the obstacle; 2) randomly selecting a position: randomly selecting a position in the sampling space as a potential sampling point q new ; 3) Calculate the potential field value: for randomly generated points q new , calculate its gravitational potential and repulsive potential. This usually involves the location information of the target point and the obstacle; 4) Calculate the total potential field value: If the potential field value meets the predetermined conditions, then accept q new As an effective random sampling point.

[0051] The step S3 comprises the following steps: 1) q new Point Cylinder: At random sampling points q new A cylinder is built at the position of q new, radius is 0.05, height is 0.2; 2) Perform the following detection on each obstacle: Calculate the minimum distance between the obstacle and the cylinder. If the minimum distance ≤ 0.1, record a collision; 3) Process the collision result: If a collision is detected, return to step S2 and regenerate a random sampling point q new , and re-establish the cylinder for collision detection. If no collision is detected, the sampling point q new Valid, continue to execute subsequent path planning steps.

[0052] The step S4 includes the following steps: 1) traversing the path tree: traversing all nodes in the current path tree. 2) calculating the distance: for each node, calculating its distance to the random sampling point. q new 3) Compare and select the minimum distance: Compare all calculated distances and find the node with the minimum distance, which is the nearest node. q n ; 4) Update the tree structure: In the path tree, q n Mark as q new The parent node of q n Add to the list of child nodes q new . 5) Storage connection information: record q n and q new The connection information between them is used to facilitate subsequent path search and optimization.

[0053] The step S5 comprises the following steps: 1) calculating the distance from the root to q n The known optimal path cost and add it to the cost from q n arrive q new The new path cost is q new The initial total path cost. 2) Traverse q new Neighboring nodes: Check all nodes with q new directly connected nodes (neighboring nodes), which means checking q n All child nodes of q n 3) For each neighboring node, calculate the path from the root to the neighboring node, and then through the neighboring node toq new 4) Compare path costs: Compare each neighboring node to q new The path cost of is compared with the current minimum total path cost. If a path cost through a neighboring node is found to be lower, the update q new 5) Reconnect: If a lower-cost path is found, then q new From the original parent node q n Disconnect it and connect it to its new parent node (i.e., a neighboring node with a lower path cost).

[0054] The step S6 comprises the following steps: 1) calculating q new and Q goal 2) Compare the calculated distance with the step length: If the distance is less than the step length, it is considered q new close enough Q goal , you can continue to step S7. If the distance is greater than or equal to the step length, it is considered that you need to get closer Q goal Therefore, the process returns to step S2 to perform new sampling.

[0055] The step S7 includes the following steps: 1) collecting path points: collecting the path points from the starting point to the current point q new All path points of the original non-smoothed path. 2) Apply Gaussian smoothing algorithm: Gaussian smoothing is a commonly used signal processing technique that uses Gaussian functions as weights to perform weighted averages on data points to reduce noise and fluctuations. In path smoothing, the Gaussian smoothing algorithm will act on the original path points and calculate the weighted average of each point. This process usually includes the following steps: a. Determine the parameters of the Gaussian function, such as the standard deviation (σ). The standard deviation determines the degree of smoothing. A larger σ will result in a smoother path, but may also lose details of the path.

[0056] b. For each point on the path, use Gaussian weights to calculate the weighted average of its surrounding points. The weight decreases as the distance increases, and the farther the distance, the lower the weight.

[0057] c. Replace each original path point with its weighted average, or add new points to form the smoothed path.

[0058] 3) Generate smoothed paths: Generate a set of new smoothed path points by applying Gaussian smoothing to each original path point. These new smoothed path points should be smoother than the original path while still maintaining the overall shape and direction of the path. 4) Join the smoothed path set: Add the processed smoothed path points to the new smoothed path set.

[0059] The step S8 comprises the following steps: 1) Integrate smooth path points: Integrate the set of smooth path points generated in step S7 into a continuous path. This may involve connecting adjacent smooth points and ensuring the continuity and smoothness of the path. 2) Generate the final path: Define the optimized smooth path as the final path. This path should be the best feasible path from the starting point to the target point, taking into account all constraints and planning goals. 3) Output the path: Output the final path in an appropriate data structure or format so that it can be used for the reset task. 4) End the path planning task: After successfully generating the final path, the task of the path planning algorithm ends. At this point, the algorithm can release resources or prepare to initialize for the next path planning task.

[0060] Thus, in this method, in the RRT* path planning algorithm adopted, the expansion node step of the RRT* code is changed, the code of the artificial potential field algorithm is written, and it is reasonably adjusted to purposefully generate nodes toward the end point under the influence of the potential field; in this process, a cylinder is constructed at the node to determine whether it collides with the cylinder on the path; finally, the smoothing algorithm is used to write the end of the source code to obtain the final smooth path. Therefore, the algorithm integrates the artificial potential field algorithm into the RRT* algorithm to enhance the purpose of generating random points; at the same time, the cylindrical envelope obstacle avoidance strategy is added, and the physiological conditions of the fractured bone are simulated to enhance the obstacle avoidance performance of the algorithm; and then the Gaussian smoothing algorithm is combined to eliminate the inflection point in the distal motion trajectory of the fracture, and a smoother and safer motion trajectory is obtained, providing surgeons with more accurate path guidance.

[0061] The connecting arm is also provided with a tension pressure sensor 7, which is connected to the control center for communication. In this way, the tensile stress data of the connecting arm can be detected, its stress condition can be determined, and the telescopic control of the connecting arm can be assisted.

[0062] The telescopic device 3 is an electric cylinder device, which is a motor driving a lead screw nut to realize telescopic control, making the control of the telescopic process and telescopic size more precise and stable, and the electric cylinder device itself is a mature existing technology, easy to implement production, and easy to accurately control.

[0063] Wherein, the two ends of the connecting arm 2 are hinged by a Hooke hinge 8 and a fixing ring, so that the structure is more reliable and does not affect the motion control of the connecting arm in all directions.

[0064] In the robot of the present invention, a new algorithm strategy is used in the path planning module for path planning design, which can provide doctors with a better solution to avoid collisions when performing fracture reduction operations. Compared with other algorithms, the improved RRT* algorithm in this application integrates the APF algorithm with high purpose, reduces the generation of redundant points, and reduces the running time. The time required for clinical fracture reduction path planning is greatly reduced. The cylindrical envelope method is introduced, and then combined with an artificial repulsive field to generate a collision-free path. The Gaussian smoothing algorithm is used to eliminate the inflection points in the motion trajectory to obtain a smoother and safer motion trajectory.

[0065] In order to verify the feasibility of the path planning algorithm proposed in this application, a verification experiment was designed to compare it with the other three commonly used path planning algorithms (RRT, RRT*, A*). Under ideal conditions, in order to simplify the algorithm, the distal end of the fracture is regarded as a mass point during the simulation process, but the cylindrical bounding box method is used in the algorithm to simulate its spatial position relationship. The map environment size is set to 2cm×2cm×2cm, the initial position of the center of the distal section of the fracture is (-1,-1,-1)cm, and the target position is (0.9,0.9,0.9)cm, that is, the center position of the proximal section of the fracture. The complex environment is set to 9 cylinders with a radius of 0.05cm and a height of 0.2cm to ensure the single variable principle of the experiment.

[0066] In this complex environment, multiple tests were conducted on RRT, RRT*, A* and the improved algorithm of this application to verify the stability and security of the improved algorithm of this application. Figure 3-6 shown.

[0067] In the comparative test, the path planning time, the number of path nodes, the number of generated nodes and the path length are used as evaluation indicators. The results are as follows: Figure 7-10 As shown in the figure, since the RRT* algorithm and the RRT algorithm have strong randomness during sampling, the path planning time, generated nodes and path length have a large range of variation, poor stability, and many inflection points; while the present application proposes an improved RRT* algorithm, which reduces the redundant nodes in the path, and the path planning time and path length have a smaller range of variation, good stability, and a smoother path. The experimental results show that the improved RRT* algorithm can obtain a feasible smooth path at a lower time cost and a shorter path length, and can safely reach the target position, which can effectively improve the efficiency of path planning and meet the needs of fracture reduction.

[0068] Fig.11 The table shows Figure 7-10The average of the items shown. Data analysis shows that the reset path planning time used by the improved RRT is significantly shortened by 99.3% compared with A* and 94.3% compared with RRT. In addition, the number of randomly generated points by the improved RRT is significantly reduced by 98.7% compared with A* and 96.8% compared with RRT. In terms of path length, the path length of the enhanced RRT is comparable to that of A*, only slightly longer than RRT, but significantly better than RRT. This shows that its path efficiency is at a relatively high level. Regarding the number of path points generated, the number of path points of the improved RRT is lower than that of RRT, but higher than that of RRT and A*. This shows that the improved RRT* achieves better optimization while ensuring the smoothness and accuracy of the path.

Claims

1. An intelligent controlled six-link fracture reduction robot, comprising two fixed rings for installing Kirschner wires, six connecting arms that are evenly distributed along the circumference are hingedly arranged between the two fixed rings, one end of any two adjacent connecting arms is close together and the other end is separated to form an eight-shaped shape, each connecting arm is provided with a telescopic device, and also includes a control center (PC end), the control center is connected to the telescopic device for communication and realizes telescopic control, and a control module for controlling the relative position path of the two fixed rings and a path planning module for designing a control path are arranged in the control center, characterized in that: The path planning module has an improved RRT* path planning algorithm; the improved RRT* path planning algorithm comprises the following steps: Step S1: Initialize the map and set the force field value and other related parameters; Step S2: Introduce the artificial potential field method to generate random sampling points q new ; Step S3: Using the cylindrical envelope method, q new Establish a cylinder and detect whether a collision occurs; if a collision occurs, return to step S2 to reselect and sample; if no collision occurs, continue to step S4; Step S4: Find the tree node closest to the random sampling point q n , and use this node as q new The parent node of Step S5: Find q new The minimum total path and reconnect; Step S6: Determination q new arrive Q goal Is the distance less than the step length? If not, return to step 2 for sampling; if so, continue to step S7; Step S7: using Gaussian smoothing algorithm for fitting processing, adding the processed points to a new smoothing path set; Step S8: Generate the final smooth planning path and end the path planning task.

2. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The connecting arm is also provided with a tension pressure sensor, which is communicatively connected with the control center.

3. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The telescopic device is an electric cylinder device.

4. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The two ends of the connecting arm are hinged by Hook hinge and fixed ring.

5. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The step S1 includes the following steps: 1) initializing the map, setting the map size to 2×2×2 cm, setting the starting point to (-1,-1,-1), and setting the end point to (1,1,1); 2) setting the radius and height of the cylindrical obstacle to 0.05 and 0.2 respectively, and the position information to (0, 0,-0.1), (-0.6, -0.6, 0.4), (0.6, -0.6, 0.4), (-0.6, 0.6, 0.4), (0.6, 0.6, 0.4), (-0.6, -0.6, -0.4), (0.6, -0.6, -0.4) (-0.6, 0.6, -0.4) (0.6, 0.6, -0.4) -0.4); 3) Set the parameters of the artificial force field, the relative strength of gravity is 1, the relative strength of repulsion is 2, the repulsive influence range is 0.3, and the step size is 0.

04.

6. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The step S2 comprises the following steps: 1) firstly creating a gravitational field for the end point and a repulsive field for the obstacle; 2) randomly selecting a position in the sampling space as a potential sampling point q new ; 3) Calculate the potential field value for the randomly generated points q new , calculate its gravitational potential and repulsive potential; 4) calculate the total potential field value; if the potential field value meets the predetermined conditions, then accept q new As an effective random sampling point.

7. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The step S3 comprises the following steps: 1) q new Point to build a cylinder, that is, at random sampling points q new A cylinder is built at the position of q new , radius is 0.05, height is 0.2; 2) Perform the following detection on each obstacle and calculate the minimum distance between the obstacle and the cylinder; if the minimum distance ≤ 0.1, record the collision; 3) Process the collision result. If a collision is detected, return to step S2 and regenerate a random sampling point q new , and re-establish the cylinder for collision detection; if no collision is detected, the sampling point q new Valid, continue to execute subsequent path planning steps.

8. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The step S4 comprises the following steps: 1) traversing the path tree, traversing all nodes in the current path tree; 2) calculating the distance, for each node, calculating its distance to the random sampling point q new 3) Compare and select the minimum distance, that is, compare all calculated distances and find the node corresponding to the minimum distance, which is the nearest node. q n ; 4) Update the tree structure. In the path tree, q n Mark as q new The parent node of q n Add to the list of child nodes q new ; 5) Store connection information and record q n and q new The connection information between them is used to facilitate subsequent path search and optimization.

9. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The step S5 comprises the following steps: 1) calculating the distance from the root to q n The known optimal path cost and add it to the cost from q n arrive q new The new path cost is q new The initial total path cost; 2) traversal q new Check all neighboring nodes q new Directly connected nodes, i.e. check q n All child nodes of q n 3) For each neighboring node, calculate the path from the root to the neighboring node, and then through the neighboring node to q new 4) Compare the path costs and compare each neighboring node to q new The path cost is compared with the current minimum total path cost; if a path cost through a neighboring node is found to be lower, the update q new The minimum total path cost is calculated and the new path is recorded; 5) Reconnect. If a lower cost path is found, the q new From the original parent node q n Disconnect it and connect it to the new parent node.

10. The intelligent controlled six-link fracture reduction robot according to claim 1, characterized in that: The step S6 comprises the following steps: 1) calculating q new and Q goal 2) Compare the calculated distance with the step length: If the distance is less than the step length, it is considered q new close enough Q goal , you can continue to step S7; if the distance is greater than or equal to the step length, it is considered that you need to get closer Q goal The sampling point is thus returned to step S2 for new sampling; The step S7 includes the following steps: 1) collecting path points: collecting the path points from the starting point to the current point q new All path points of , which constitute the original non-smooth path; 2) Apply the Gaussian smoothing algorithm to calculate the weighted average of each point, which includes the following steps: a. Determine the parameters of the Gaussian function, including the standard deviation (σ); b. For each point on the path, use the Gaussian weight to calculate the weighted average of its surrounding points; c. Replace each original path point with its weighted average, or add new points to form a smoothed path; 3) Generate a smooth path by applying Gaussian smoothing to each original path point to generate a set of new smooth path points; 4) Adding the smooth path set, that is, adding the processed smooth path points to the new smooth path set; The step S8 comprises the following steps: 1) integrating smooth path points: integrating the set of smooth path points generated in step S7 into a continuous path; 2) generating a final path: defining the optimized smooth path as the final path, which is the best feasible path from the starting point to the target point; 3) outputting the path, that is, outputting the final path in an appropriate data structure or format so that it can be used for the reset task; 4) ending the path planning task, that is, after successfully generating the final path, the task of the path planning algorithm ends.

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