Double-arm type tooth implantation robot positioning method applying oral cavity scanning

Abstract

According to the double-arm tooth implantation robot positioning method applying oral scanning, an implantation mechanical arm and an oral scanning mechanical arm which operate independently are included, a dental handpiece used for preparing an implantation cavity is installed at the tail end of the implantation mechanical arm, and an equipment probe used for obtaining the three-dimensional characteristics of the oral cavity is installed at the tail end of the oral scanning mechanical arm. The equipment probe is electrically connected with a computer which is used for acquiring image data acquired by the equipment probe and calculating a spatial pose; compared with the prior art, through the planting mechanical arm and the mouth sweeping mechanical arm which are independently arranged, the overall size of a single mechanical arm is reduced, so that the dental handpiece and the equipment probe are suitable for a narrow oral space environment.

Description

Technical Field

[0001] The present invention relates to the technical field of implant robots, and in particular to a positioning method for a double-arm tooth implant robot using oral scanning. Background Art

[0002] In recent years, modern engineering technologies such as robots and artificial intelligence have gradually been widely used in the medical field. Implant doctors can complete operations with the assistance of implant robots, which can greatly improve surgical accuracy, reduce surgical errors, maximize the goal of minimally invasive surgery, reduce surgical risks, shorten operation time, and achieve standardized operations of implant surgery.

[0003] When a robot assists an implant surgeon in performing oral implant surgery, one of the important steps is to determine the relative position of the drill under the robot flange. Currently, the drill positioning of the oral implant robot mainly uses an infrared binocular camera to emit infrared light. After being reflected by the reflective balls on the sign board, the infrared binocular camera can accurately identify a collection of multiple reflective balls and calculate the position of the oral implant robot drill through a relative coordinate transformation algorithm, thereby determining the position relationship between the oral implant robot drill and the patient's affected tooth. However, the above method for determining the position relationship between the oral implant robot drill and the patient's affected tooth has the following problems: 1. If the infrared light emitted by the infrared emitter is blocked, the infrared calibration system will not be able to detect the location of the marker. In the complex environment of oral implantology, it is difficult for doctors and patients to ensure that the infrared light path will not be blocked, so this positioning method is not stable.

[0004] 2. The reflective balls in the marking system are consumables and are expensive, which greatly increases the cost of treatment.

[0005] 3. The above-mentioned positioning system uses a fixed marker to assist in positioning during the positioning process. This marker is divided into two parts: a fixed module and a marker module. Before the operation, the patient wears the fixed module of the marker on his teeth to take a CT scan. During the operation, the patient wears the same fixed module to connect the marker module for positioning. However, such fixed modules are standard parts and do not match the shape of the patient's teeth. They must be filled with fillings such as gutta-percha / silicone rubber / EVA to fix them to the patient's teeth. After these fillings are made, they will produce uncontrollable deformation over time, resulting in inaccurate position of the connected marker, which will cause errors in the optical positioning and navigation system. The errors in the robot positioning and navigation system can easily lead to inaccurate positioning of the end effector (dental drill) due to inaccurate coordinates of the target position, thereby increasing the risk of implant surgery.

[0006] Chinese Publication No. CN113693723A discloses a cross-modal navigation and positioning system and method for oral and pharyngeal surgery, designed for real-time navigation and positioning of a surgical robot during surgery. The cross-modal navigation and positioning system includes a self-made mouth opener for supporting the oral cavity; self-identifying visual markers for assisting positioning; a visual positioning device for detecting and locating the self-identifying visual markers on the self-made mouth opener and the surgical robot; and a control host for preoperative registration and fusion of multi-source scan data, visual registration of a three-dimensional model of the self-identifying visual markers, and calibration between various coordinate systems. The visual positioning device then performs real-time detection and positioning of the self-identifying visual markers during surgery.

[0007] The positioning system disclosed above is achieved by installing a self-identifying visual marker on a homemade mouth opener. However, when the patient's oral space is small or the patient has difficulty opening his mouth wider due to his own reasons, it is difficult to achieve stable installation of the homemade mouth opener, thereby affecting the installation and identification of the overall visual marker and affecting the navigation and positioning effects of the surgical robot. Summary of the Invention

[0008] The present invention aims to overcome the above-mentioned defects in the prior art and provide a positioning method for a dual-arm dental implant robot using oral scanning, which has accurate positioning and is suitable for use in narrow oral spaces.

[0009] To achieve the above-mentioned purpose, the present invention adopts the following technical solution: a dual-arm dental implant robot positioning method using oral scanning, based on independently operating implant robotic arms, oral scanning robotic arms, and CT machines, wherein the implant robotic arms are equipped with a dental handpiece for preparing implant cavities, and the oral scanning robotic arms are equipped with a device probe for obtaining three-dimensional features of the oral cavity, and the device probe is electrically connected to a computer for collecting image data and calculating spatial posture; the method comprises the following steps: Step S1: Obtain the patient's oral information, obtain the global oral 3D feature A of the patient's desired implant area through the device probe, and obtain the oral 3D data B through the CT machine, and align the global oral 3D feature A and the oral 3D data B to establish a unified coordinate system N xyz , taking the center point of the implant area required by the patient as the coordinate system N xyz The base point, the lingual side is the positive direction of the X axis, and the maxillofacial normal is the positive direction of the Z axis of the coordinate system; Step S2: Pull the scanning robot arm to the desired implant area, align the device probe with the patient's desired implant area, and set the local coordinate system of the device probe to S xyz , the local coordinate system of the mouth scanning robot is Q xyz , use the homogeneous transformation matrix R ST to represent the local coordinate system Q of the device probe in the mouth scanning robot arm xyz The pose relationship in the figure is set as D xyz, the local coordinate system of the implant robot is R xyz , use the homogeneous transformation matrix R DT to represent the local coordinate system R of the dental mobile phone in the implant robot arm xyz The posture relationship in Step S3: Execute oral 3D feature acquisition, obtain the patient's real-time oral 3D features C through the device probe, and establish the coordinate system M xyz , taking the center point of the implant area required by the patient as the coordinate system M xyz The base point, the lingual side is the positive direction of the X axis, and the maxillofacial normal is the positive direction of the Z axis of the coordinate system; Step S4: In the set local coordinate system S xyz Under the action of xyz With the local coordinate system S xyz The relative pose of the coordinate system M xyz With the local coordinate system S xyz The relative pose of is the homogeneous coordinate transformation matrix S MT, and the coordinate system M is obtained by registering the global oral 3D feature A with the real-time oral 3D feature C. xyz With coordinate system N xyz The relative pose of the coordinate system M xyz With coordinate system N xyz The relative pose is the homogeneous coordinate transformation matrix N MT, in the set local coordinate system Q xyz With the local coordinate system R xyz Under the action of the homogeneous coordinate transformation matrix R QT, the local coordinate system Q is obtained. xyz With the local coordinate system R xyz The relative pose of the local coordinate system D xyz With the local coordinate system R xyz Under the action of the homogeneous coordinate transformation matrix R DT, the local coordinate system D is obtained xyz With the local coordinate system R xyz The relative pose of the coordinate system N is obtained by the following calculation formula xyz With the local coordinate system D xyz The relative pose of is as follows:

[0010] Step S5: Obtain confirmation of the posture relationship between the dental handpiece and the patient's oral cavity, thereby controlling the motion path of the dental handpiece according to the posture relationship between the dental handpiece and the patient's oral cavity to achieve the implantation operation in the patient's oral cavity.

[0011] As a preferred embodiment of the present invention, the global oral three-dimensional feature A of step S1 is the global three-dimensional feature of the patient, and the global oral three-dimensional feature A is composed of the patient's teeth and mucosa. The oral three-dimensional data B is the patient's global three-dimensional data, and the oral three-dimensional data B is composed of the patient's teeth, mucosa, and alveolar bone taken by a CT machine.

[0012] As a preferred solution of the present invention, the dental handpiece is fixedly installed at the end of the implant robotic arm, and the relative position of the dental handpiece and the implant robotic arm can be obtained through calibration, and the equipment probe is fixedly installed at the end of the oral scanning robotic arm, and the relative position of the equipment probe and the oral scanning robotic arm can be obtained through calibration.

[0013] As a preferred solution of the present invention, step S3 obtains a real-time display image of oral features through the device probe, obtains data on the location of the patient's teeth and mucosa based on the patient's teeth and mucosa features in the real-time display image, adjusts the position of the device probe to align the device probe with the patient's desired implant area, and obtains the patient's local real-time oral three-dimensional features C through the device probe.

[0014] As a preferred solution of the present invention, step S3 includes the following sub-steps: Step S3.1: The device probe transmits a real-time display of the oral features captured to a computer. The computer extracts feature points from each image and matches them with feature points in the global oral 3D feature A to find similar feature point pairs. Based on the matched feature point pairs, the computer calculates the geometric transformation relationship between the multiple images captured by the device probe and performs corresponding transformations on the images captured by the device probe to align them in the same coordinate system, achieving preliminary matching. Step S3.2: Extract feature points from the preliminarily matched image, perform feature matching, and verify whether the registration result meets the requirements. If not, perform preliminarily registration on the image again. Step S3.3: A set of point cloud data on the oral surface is generated based on the matched image under the action of triangulation method, the point cloud data is converted into mesh data, and a smooth surface model of the oral structure is generated based on the mesh data using a surface reconstruction algorithm.

[0015] As a preferred solution of the present invention, the computer in step S3.1 pre-processes the collected images before comparison, and the feature points extracted from the images are the patient's teeth, mucosa, and alveolar bone.

[0016] As a preferred solution of the present invention, the feature points extracted from the image in step S3.1 are the patient's teeth, mucosa, and alveolar bone.

[0017] As a preferred solution of the present invention, step S3 further includes the following sub-steps: Step S3.4: Multi-level feature fusion and segmentation grids are used to separate the features extracted from the global oral 3D data A into separate grids, and the real-time oral 3D features C are carefully registered with the divided feature grids. Step S3.5: Let the vertex set of the mesh reconstructed from the global oral 3D data A be , the vertex set of the real-time oral 3D feature C grid is , the transformation of the global oral 3D data A and the real-time oral 3D feature C grid registration is , making and The spatial distance between corresponding points is minimized, and the registration formula can be defined as follows: Step S3.6: Locally align the tooth feature points in the real-time oral 3D feature grid C with the tooth feature points reconstructed from the global oral 3D data A, so as to achieve one-to-one alignment between the tooth feature points in the real-time oral 3D feature grid C and the tooth feature points reconstructed from the global oral 3D data A, use the iterative closest point algorithm to estimate the local transformation matrix, and obtain the final refined alignment effect by fusing the alignment results of all tooth feature points.

[0018] As a preferred solution of the present invention, the local registration in step S3.6 is performed by mapping the tooth categories in the real-time oral 3D feature C to the reconstructed grid of the global oral 3D data A. Assume that the real-time oral 3D feature C contains M groups of tooth point clouds. Find the center point with the smallest average distance , ,in for The number of point clouds, Point Cloud The kth point of the tooth is t, and t is the tooth number of the center point with the smallest distance. At this time, the tooth t in the global oral 3D data A and the tooth i in the real-time oral 3D feature grid C are considered to be of the same category number. Finally, M groups of optimal transformation matrices can be obtained, which are expressed as , the transformation matrix of the entire IOS grid Calculated by the following formula: , where W is the weight coefficient, is the registration fitness of the i-th group of teeth, which indicates the proportion of point pairs successfully matched in the source point cloud to the total point cloud in the source point cloud.

[0019] Compared with existing technologies, the device probe acquires overall information about the teeth and mucosa, and the CT machine acquires information about the bones and implant plan. The three-dimensional oral features acquired by the device probe and the three-dimensional oral data acquired by the CT machine are matched to obtain a unified coordinate system. The device probe then acquires local tooth and mucosal information during the actual scan to obtain real-time three-dimensional oral features. By matching the real-time three-dimensional oral features with the three-dimensional oral features, the relative position of the patient in the unified coordinate system during the actual scan is obtained, completing the positioning. The independent implant robotic arm and oral scanning robotic arm reduce the overall size of a single robotic arm, making the dental handpiece and equipment probe suitable for use in a narrow oral space environment; Suitable for dental implants at the patient's molar position. Under the combined action of the mouth-scanning robot arm and the implant robot arm, synchronous support can be achieved on both sides of the patient's molars, making it easier for the patient to hold and support the implant when the mouth is wide open, thereby improving the positioning accuracy and implant stability of the dental implant robot; The use of biological oral three-dimensional features for positioning avoids the problems of high cost of external markers, susceptibility to external interference, and inaccurate positioning caused by uncontrollable deformation of fillings over time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural schematic diagram of the present invention; Figure 2 It is a structural diagram of the end effector; Figure 3 is a schematic diagram of the extracted oral three-dimensional data B; Figure 4 It is a schematic flow diagram of the present invention; Figure 5 is a sub-step flow chart of step S3; Figure 6 is a schematic diagram of grid separation in step S3; Figure numerals: patient 1, computer 2, oral scanning robot arm 3, implant robot arm 4, dental handpiece 5, equipment probe 6. DETAILED DESCRIPTION

[0021] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] like Figures 1-6 As shown, the dental implant robot positioning method using oral scanning is based on independently operated implant robotic arm 4, oral scanning robotic arm 3 and CT machine, the end of the implant robotic arm 4 is equipped with a dental handpiece 5 for preparing implant cavities, the end of the oral scanning robotic arm 3 is equipped with a device probe 6 for obtaining three-dimensional features of the oral cavity, and the device probe 6 is electrically connected to a computer 2 for collecting image data and calculating spatial posture.

[0023] The dental handpiece 5 is installed at the end of the implant robotic arm 4. Under the action of the implant robotic arm 4, the dental handpiece 5 can be moved within a certain range of space. During the movement, the dental handpiece 5 synchronously drives the movement of the dental drill. Under the action of the implant robotic arm 4, the prepared cavity position of the dental handpiece 5 is controlled, and the dental handpiece 5 is connected to the implant robotic arm 4 with a flange.

[0024] The device probe 6 is installed at the end of the oral scanning robotic arm 3. Under the action of the oral scanning robotic arm 3, the device probe 6 can be moved within a certain range of space, thereby obtaining the three-dimensional features of the patient's 1 oral cavity through the device probe 6.

[0025] The actual use process includes the following steps: Step S1: Obtain the patient's oral information, obtain the global oral three-dimensional features A of the patient's required implant area through the device probe 6, and obtain the oral three-dimensional data B through the CT machine.

[0026] The device probe 6 captures the global three-dimensional features A of the oral cavity and reconstructs the three-dimensional model. The device probe 6 can be, but is not limited to, oral scanning or optical coherence tomography (OCT). Oral scanning technologies include three mainstream approaches: laser triangulation, such as the 3Shape R700, which uses the time difference of laser reflections to perform three-dimensional reconstruction; structured light technology, such as 3DSS STDII, which uses phase shifts of coded gratings to acquire high-precision data; and stereo photogrammetry, such as 3dMD Face, which uses the parallax of multiple cameras to determine spatial coordinates. OCT technology, based on the principle of optical interference, achieves micron-level resolution and can non-invasively examine subsurface structures of teeth.

[0027] The global oral 3D feature A in step S1 is the patient's global 3D feature, which is composed of the patient's teeth and mucosa. The oral 3D data B is the patient's global 3D data, which is composed of the patient's teeth, mucosa, and alveolar bone taken by a CT machine. The global oral 3D feature A and the oral 3D data B are registered to establish a unified coordinate system N. xyz , taking the center point of the implant area required by the patient as the coordinate system N xyz The base point is the lingual side as the positive direction of the X axis, and the maxillofacial normal is the positive direction of the Z axis of the coordinate system.

[0028] Step S2: After the device probe 6 is installed on the end of the oral scanning robot arm 3, the follow-up function of the oral scanning robot arm 3 is turned on, and the position of the device probe 6 is pulled by holding the end of the oral scanning robot arm 3. The device probe 6 is pulled to the desired implant area, and the device probe 6 is aligned with the patient's desired implant area. The local coordinate system of the device probe 6 is set to S xyz , the local coordinate system of the scanning robot arm 3 is Q xyz , use the homogeneous transformation matrix R ST to represent the local coordinate system Q of the device probe 6 in the mouth scanning robot arm 3 xyzThe position relationship in the figure is set as D xyz , the local coordinate system of the implant robot 4 is R xyz , use the homogeneous transformation matrix R ST to represent the local coordinate system R of the dental mobile phone 5 in the implant robot 4 xyz The posture relationship in .

[0029] The device probe 6 in step S2 is fixedly installed at the end of the oral scanning robot arm 3. The relative position of the device probe 6 and the oral scanning robot arm 3 can be obtained through calibration. Similarly, the dental handpiece 5 is fixedly installed at the end of the implant robot arm 4. The relative position of the dental handpiece 5 and the implant robot arm 4 can be obtained through calibration.

[0030] Step S3: Execute oral 3D feature acquisition, use the device probe 6 to acquire the real-time oral 3D feature C of the patient 1, and establish the coordinate system M xyz , taking the center point of the implant area required by the patient as the coordinate system M xyz The base point is the lingual side as the positive direction of the X axis, the maxillofacial normal is the positive direction of the Z axis of the coordinate system, and the real-time oral 3D feature C is aligned with the global oral 3D feature A to establish a coordinate system association.

[0031] In step S3, the device probe 6 is used to obtain a real-time display image of the oral features, and data on the positions of the patient's teeth and mucosa are obtained based on the features of the patient's teeth and mucosa in the real-time display image. The position of the device probe 6 is adjusted to align the device probe 6 with the patient's desired implant area, and the patient's local real-time oral three-dimensional features C are obtained through the device probe 6.

[0032] During the process of the device probe 6 acquiring the local real-time three-dimensional oral features C of the patient, the device probe 6 is aimed at the patient's desired implant area, and the oral scanning robotic arm 3 is manually pulled and slowly moved, so that the oral scanning robotic arm 3 moves slowly back and forth near the patient's desired implant area until the device probe 6 scans the complete patient's desired implant area, thereby acquiring a clear real-time three-dimensional oral feature C of the patient's mouth.

[0033] After the scanning program determines that complete data of the patient's desired implant area has been obtained, the operator stops the back-and-forth movement of the traction device probe 6 according to the prompt and waits for the current posture to be calculated.

[0034] Step S3 includes the following sub-steps: Step S3.1: The device probe 6 transmits the real-time display image of the captured oral features to the computer 2. The computer 2 extracts feature points from each image and matches them with the feature points in the global oral three-dimensional features A to find similar feature point pairs. Based on the matched feature point pairs, the computer 2 calculates the geometric transformation relationship between the multiple images captured by the device probe 6, and performs corresponding transformations on the images captured by the device probe 6 so that they are aligned in the same coordinate system to achieve preliminary matching.

[0035] Computer 2 pre-processes the collected images before comparison, including operations such as noise removal and contrast enhancement to ensure image quality. Feature points extracted from the images are the patient's teeth, mucosa, and alveolar bone.

[0036] Step S3.1 is to align multiple images using one of the feature points in the patient's teeth and mucosa to achieve preliminary registration.

[0037] Step S3.2: Perform preliminary registration on the aligned images, align the coordinate systems of the aligned images to the same standard, extract feature points from the preliminarily matched images, perform feature matching, and verify whether the registration results meet the requirements. If not, perform preliminary registration on the images again.

[0038] Step S3.2 is to re-register the image after the preliminary registration. Based on the preliminary registration, this re-registration identifies whether the other two feature points are in a matching state when one of the feature points is aligned, thereby performing a secondary detection of the image matching state.

[0039] Step S3.3: Based on the matched image, a set of point cloud data on the oral surface is generated under the action of triangulation. The point cloud data is converted into mesh data to represent the geometric shape of the oral surface. Based on the mesh data, a smooth surface model of the oral structure is generated using a surface reconstruction algorithm. Finally, the quality of the reconstructed three-dimensional model is evaluated, including checking indicators such as surface smoothness, topological structure, and model accuracy to verify whether the constructed three-dimensional model meets the requirements.

[0040] Step S3.4: Multi-level feature fusion segmentation grid, each feature extracted from the global oral 3D data A is divided into separate grids, and the real-time oral 3D feature C is carefully aligned with each divided feature grid.

[0041] Each tooth extracted from the global oral 3D data A is accurately meshed, and each tooth is rendered in a different color to clearly mesh each tooth. The crown mesh uses the tooth mesh instance segmentation result as the alignment benchmark.

[0042] Step S3.5: Let the vertex set of the mesh reconstructed from the global oral 3D data A be , the vertex set of the real-time oral 3D feature C grid is , the transformation of the global oral 3D data A and the real-time oral 3D feature C grid registration is where R Î SO (3) represents the rotation transformation, Translation transformation, so that and The spatial distance between corresponding points is minimized, and the registration formula can be defined as follows:

[0043] Step S3.6: Locally align the tooth feature points in the real-time oral 3D feature grid C with the tooth feature points reconstructed from the global oral 3D data A, so as to achieve one-to-one alignment between the tooth feature points in the real-time oral 3D feature grid C and the tooth feature points reconstructed from the global oral 3D data A, use the iterative closest point algorithm to estimate the local transformation matrix, and obtain the final refined alignment effect by fusing the alignment results of all tooth feature points.

[0044] The tooth instances in the real-time oral 3D feature C grid are locally aligned with the tooth instances reconstructed from the global oral 3D data A. For each pair of teeth, the Iterative Closest Point (ICP) algorithm is used to estimate the local transformation matrix. Finally, all the alignment results are fused to obtain the final refined alignment effect.

[0045] The local registration in step S3.6 maps the tooth categories in the real-time oral 3D feature C to the reconstructed mesh of the global oral 3D data A. Assume that the real-time oral 3D feature C contains M groups of tooth point clouds. , the N tooth center points in the global oral 3D data A are expressed as , for each set of point clouds Find the center point with the smallest average distance , ,in for The number of point clouds, Point Cloud The kth point of the tooth is t, and t is the tooth number of the center point with the smallest distance. At this time, the tooth t in the global oral 3D data A and the tooth i in the real-time oral 3D feature grid C are considered to be of the same category number. Finally, M groups of optimal transformation matrices can be obtained, which are expressed as , the transformation matrix of the entire IOS grid Calculated by the following formula: , where W is the weight coefficient, is the registration fitness of the i-th group of teeth, which indicates the proportion of point pairs successfully matched in the source point cloud to the total point cloud in the source point cloud.

[0046] Step S4: Establish a coordinate system M for the real-time oral 3D feature C obtained in step S3 xyz , taking the center point of the implant area required by the patient as the coordinate system M xyz The base point, the lingual side is the positive direction of the X axis, the maxillofacial normal is the positive direction of the coordinate system Z axis, in the set local coordinate system S xyz Under the action of xyz With the local coordinate system S xyz The relative pose of the coordinate system M xyz With the local coordinate system S xyz The relative pose of is the homogeneous coordinate transformation matrix S MT, and the coordinate system M is obtained by registering the global oral 3D feature A with the real-time oral 3D feature C. xyz With coordinate system N xyz The relative pose of the coordinate system M xyz With coordinate system N xyz The relative pose is the homogeneous coordinate transformation matrix N MT, in the set local coordinate system Q xyz With the local coordinate system R xyz Under the action of the homogeneous coordinate transformation matrix R QT, the local coordinate system Q is obtained. xyz With the local coordinate system R xyz The relative pose of the local coordinate system D xyz With the local coordinate system R xyz Under the action of the homogeneous coordinate transformation matrix R DT, the local coordinate system D is obtained xyz With the local coordinate system R xyz The relative pose of the coordinate system N is obtained by the following calculation formula xyz With the local coordinate system D xyz The relative pose of is as follows:

[0047] Step S5: Complete the confirmation of the posture relationship between the dental handpiece 5 and the patient's mouth, and then control the movement path of the dental handpiece 5 according to the posture relationship between the dental handpiece 5 and the patient's mouth, and realize the implantation operation in the patient's mouth. After determining the posture relationship between the dental handpiece 5 and the patient's mouth, the dental handpiece 5 is positioned by controlling the implantation robot arm 4, so that the relative posture of the dental handpiece 5 in the patient's mouth can be controlled according to actual needs, thereby realizing that the drill on the dental handpiece 5 operates in the required implantation area in the patient's mouth.

[0048] The above description of the disclosed embodiments will enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein, but is to be embodied in the widest possible manner consistent with the principles and novel features disclosed herein.

[0049] Although this document frequently uses the following terms: patient 1, computer 2, oral scanning robot 3, implant robot 4, dental handpiece 5, device probe 6, etc., other terms may be used. These terms are used solely to more conveniently describe and explain the essence of the present invention; any interpretation of them as additional limitations would be contrary to the spirit of the present invention.

Claims

1. A dual-arm dental implant robot positioning method using oral scanning, based on independently operated implant robotic arms (4), oral scanning robotic arms (3) and a CT machine, wherein the implant robotic arms (4) are provided with a dental handpiece (5) for preparing implant cavities, and the oral scanning robotic arms (3) are provided with a device probe (6) for acquiring three-dimensional features of the oral cavity, and the device probe (6) is electrically connected to a computer (2) for collecting image data and calculating spatial posture, characterized in that: The following steps are involved: Step S1: Obtain the patient's oral information, obtain the global oral three-dimensional features A of the patient's desired implant area through the device probe (6), and obtain the oral three-dimensional data B through the CT machine, and align the global oral three-dimensional features A and the oral three-dimensional data B to establish a unified coordinate system N xyz , taking the center point of the implant area required by the patient as the coordinate system N xyz The base point, the lingual side is the positive direction of the X axis, and the maxillofacial normal is the positive direction of the Z axis of the coordinate system; Step S2: Pull the scanning robot arm (3) to the desired implantation area, align the device probe (6) with the patient's desired implantation area, and set the local coordinate system of the device probe (6) to S xyz , the local coordinate system of the scanning robot (3) is Q xyz , the homogeneous transformation matrix RST is used to represent the local coordinate system Q of the device probe (6) in the mouth scanning robot arm (3) xyz The pose relationship in the figure is set, and the local coordinate system of the dental handpiece (5) is set as D xyz , the local coordinate system of the implantation robot (4) is R xyz , use the homogeneous transformation matrix R ST to represent the local coordinate system R of the dental handpiece (5) in the implant robot (4) xyz The posture relationship in Step S3: Execute the acquisition of oral three-dimensional features, obtain the patient's real-time oral three-dimensional features C through the device probe (6), and establish the coordinate system M xyz , taking the center point of the implant area required by the patient as the coordinate system M xyz The base point, the side of the tongue is the positive direction of the X axis, the normal line of the maxillofacial face is the positive direction of the Z axis of the coordinate system, and the real-time oral 3D feature C is aligned with the global oral 3D feature A, and unified in the coordinate system Q of the oral scanning robot arm (3) xyz middle; Step S4: In the set local coordinate system S xyz Under the action of xyz With the local coordinate system S xyz The relative pose of the coordinate system M xyz With the local coordinate system S xyz The relative pose of is the homogeneous coordinate transformation matrix S MT, and the coordinate system M is obtained by registering the global oral 3D feature A with the real-time oral 3D feature C. xyz With coordinate system N xyz The relative pose of the coordinate system M xyz With coordinate system N xyz The relative pose is the homogeneous coordinate transformation matrix N MT, in the set local coordinate system Q xyz With the local coordinate system R xyz Under the action of the homogeneous coordinate transformation matrix R QT, the local coordinate system Q is obtained. xyz With the local coordinate system R xyz The relative pose of the local coordinate system D xyz With the local coordinate system R xyz Under the action of the homogeneous coordinate transformation matrix RDT, the local coordinate system D is obtained. xyz With the local coordinate system R xyz The relative pose of the coordinate system N is obtained by the following calculation formula xyz With the local coordinate system D xyz The relative pose of is as follows: ； Step S5: Obtain confirmation of the positional relationship between the dental handpiece (5) and the patient's oral cavity, thereby controlling the motion path of the dental handpiece (5) according to the positional relationship between the dental handpiece (5) and the patient's oral cavity, and realizing the implantation operation in the patient's oral cavity.

2. The dual-arm dental implant robot positioning method using oral scanning according to claim 1, characterized in that: The global oral three-dimensional feature A of step S1 is the global three-dimensional feature of the patient, and the global oral three-dimensional feature A is composed of the patient's teeth and mucosa. The oral three-dimensional data B is the global three-dimensional data of the patient, and the oral three-dimensional data B is composed of the patient's teeth, mucosa, and alveolar bone taken by a CT machine.

3. The dual-arm dental implant robot positioning method using oral scanning according to claim 1, characterized in that: The dental handpiece (5) is fixedly mounted on the end of the implant robotic arm (4) to obtain the relative position of the dental handpiece (5) and the implant robotic arm (4), and the device probe (6) is fixedly mounted on the end of the oral scanning robotic arm (3) to obtain the relative position of the device probe (6) and the oral scanning robotic arm (3).

4. The dual-arm dental implant robot positioning method using oral scanning according to claim 1, characterized in that: In step S3, a real-time display screen of oral features is obtained through the device probe (6), data on the positions of the patient's teeth and mucosa are obtained based on the features of the patient's teeth and mucosa in the real-time display screen, the position of the device probe (6) is adjusted to align the device probe (6) with the patient's desired implant area, and the local real-time three-dimensional features C of the patient's oral cavity are obtained through the device probe (6).

5. The dual-arm dental implant robot positioning method using oral scanning according to claim 4, characterized in that: The step S3 includes the following sub-steps: Step S3.1: The device probe (6) transmits the real-time display image of the oral features captured to the computer (2). The computer (2) extracts feature points from each image and matches them with the feature points in the global oral three-dimensional feature A to find similar feature point pairs. Based on the matched feature point pairs, the computer (2) calculates the geometric transformation relationship between the multiple images captured by the device probe (6), and performs corresponding transformation on the images captured by the device probe (6) so that they are aligned in the same coordinate system to achieve preliminary matching. Step S3.2: Extract feature points from the preliminarily matched image, perform feature matching, and verify whether the registration result meets the requirements. If not, perform preliminarily registration on the image again. Step S3.3: A set of point cloud data on the oral surface is generated based on the matched image under the action of triangulation method, the point cloud data is converted into mesh data, and a smooth surface model of the oral structure is generated based on the mesh data using a surface reconstruction algorithm.

6. The dual-arm dental implant robot positioning method using oral scanning according to claim 5, characterized in that: The computer (2) in step S3.1 pre-processes the collected images before comparison, and extracts feature points from the images, namely, the patient's teeth, mucosa, and alveolar bone.

7. The dual-arm dental implant robot positioning method using oral scanning according to claim 1, characterized in that: The step S3 further includes the following sub-steps: Step S3.4: Multi-level feature fusion segmentation grid, each feature extracted from the global oral 3D data A is divided into individual grids, and the real-time oral 3D feature C is carefully aligned with each divided feature grid; Step S3.5: Let the vertex set of the mesh reconstructed from the global oral 3D data A be , the vertex set of the real-time oral 3D feature C grid is , the transformation of the global oral 3D data A and the real-time oral 3D feature C grid registration is , making and The spatial distance between corresponding points is minimized, and the registration formula can be defined as follows: ； Step S3.6: Locally align the tooth feature points in the real-time oral 3D feature grid C with the tooth feature points reconstructed from the global oral 3D data A, so as to achieve one-to-one alignment between the tooth feature points in the real-time oral 3D feature grid C and the tooth feature points reconstructed from the global oral 3D data A, use the iterative closest point algorithm to estimate the local transformation matrix, and obtain the final refined alignment effect by fusing the alignment results of all tooth feature points.

8. The dual-arm dental implant robot positioning method using oral scanning according to claim 7, characterized in that: The local registration in step S3.6 maps the tooth categories in the real-time oral 3D feature C to the reconstructed mesh of the global oral 3D data A. Assume that the real-time oral 3D feature C contains M groups of tooth point clouds. , the N tooth center points in the global oral 3D data A are expressed as , for each set of point clouds Find the center point with the smallest average distance , ,in for The number of point clouds, Point Cloud The kth point of the tooth is t, and t is the tooth number of the center point with the smallest distance. At this time, the tooth t in the global oral 3D data A and the tooth i in the real-time oral 3D feature grid C are considered to be of the same category number. Finally, M groups of optimal transformation matrices can be obtained, which are expressed as , the transformation matrix of the entire IOS grid Calculated by the following formula: , where W is the weight coefficient, is the registration fitness of the i-th group of teeth, which indicates the proportion of point pairs successfully matched in the source point cloud to the total point cloud in the source point cloud.

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