Obstacle avoidance model generation method, obstacle avoidance method, obstacle avoidance system, device and apparatus

Abstract

The application relates to an obstacle avoidance model generation method, an obstacle avoidance method, an obstacle avoidance system, an apparatus and a device. The obstacle avoidance model generation method comprises the following steps: obtaining a medical image of a region of interest of an object to be avoided when a bed body carrying the object to be avoided is moved to a preset position; controlling the bed body to move from the preset position to different target positions, and controlling a shooting device to shoot a plurality of initial images corresponding to the preset position and each target position of the object to be avoided; and generating an obstacle avoidance model according to the medical image and the plurality of initial images. The obstacle avoidance model generated by the obstacle avoidance model generation method provided by the application has high efficiency.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and in particular to an obstacle avoidance model generation method, obstacle avoidance method, obstacle avoidance system, device and equipment. Background Technology

[0002] With the development of robotics technology, surgical robot systems equipped with robotic arms have emerged. These systems allow the robotic arm to be moved to a designated location along a pre-defined path during surgical preparation to perform the surgery. This pre-defined path is planned based on a model of obstacles the robotic arm needs to avoid during its movement.

[0003] In traditional techniques, medical images of obstacles are typically obtained using computed tomography (CT) scans, and the model reconstructed from these medical images is used as the model of the obstacle.

[0004] However, methods that use traditional techniques to obtain obstacle models are inefficient. Summary of the Invention

[0005] Therefore, it is necessary to provide an obstacle avoidance model generation method, obstacle avoidance method, obstacle avoidance system, device, and equipment that can improve the efficiency of generating obstacle avoidance models in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for generating an obstacle avoidance model, the method comprising:

[0007] When the bed carrying the object to be avoided moves to a preset position, a medical image of the region of interest of the object to be avoided is acquired;

[0008] The system controls the bed to move from a preset position to different target positions, and controls the camera to capture multiple initial images of the object to be avoided in the preset position and the corresponding target positions.

[0009] An obstacle avoidance model is generated based on medical images and multiple initial images.

[0010] In one embodiment, an obstacle avoidance model is generated based on medical images and multiple initial images, including:

[0011] A first model of the region of interest is constructed based on medical images;

[0012] Multiple initial images are stitched together to obtain a second model of the object to be avoided;

[0013] Generate an obstacle avoidance model based on the first and second models.

[0014] In one embodiment, an obstacle avoidance model is generated based on a first model and a second model, including:

[0015] The region corresponding to the first model in the second model is cropped to obtain the cropped second model;

[0016] The cropped second model and the first model are spliced ​​together to obtain the obstacle avoidance model.

[0017] Secondly, this application also provides an obstacle avoidance method, which includes:

[0018] The receiving workstation sends the planned path of the needle channel corresponding to the surgical robot; the planned path is generated by the workstation based on the obstacle avoidance model.

[0019] Determine whether the planned path of the needle track meets the preset requirements. If the planned path of the needle track does not meet the preset requirements, a new planned path is received, and it is determined whether the new planned path meets the preset requirements, until the planned path of the needle track meets the preset requirements.

[0020] In one embodiment, determining whether the planned path of the needle track meets preset requirements includes:

[0021] If the needle moves to the target needle position according to the planned path and does not come into contact with the first obstacle during the movement, then the planned path of the needle is determined to meet the preset requirements.

[0022] If the needle path comes into contact with the first obstacle during movement, it is determined that the planned path does not meet the preset requirements.

[0023] In one embodiment, if the method does not come into contact with the first obstacle during movement, it further includes:

[0024] If a second obstacle is stored at the target needle path position, the needle path moves to the next target needle path position according to the planned path of the next target needle path. If the needle path does not come into contact with the second obstacle during the movement, it is determined that the planned path of the needle path meets the preset requirements.

[0025] If the needle path comes into contact with a second obstacle during movement, it is determined that the planned path does not meet the preset requirements.

[0026] In one embodiment, when it is determined that the planned path of the needle track does not meet preset requirements, the method further includes:

[0027] The system returns a prompt message to the workstation, instructing it to adjust the planned path of the needle channel according to the obstacle avoidance model, and then sends the adjusted planned path of the needle channel to the surgical robot.

[0028] Thirdly, this application provides an obstacle avoidance system, which includes a workstation and a surgical robot, with the workstation and the surgical robot being communicatively connected. The workstation is used to execute the steps of the obstacle avoidance model generation method provided in the first aspect, and the surgical robot is used to execute the steps of the obstacle avoidance method provided in the second aspect.

[0029] Fourthly, this application provides an obstacle avoidance model generation device, the device comprising:

[0030] The acquisition module is used to acquire medical images of the region of interest of the object to be avoided when the bed carrying the object to be avoided moves to a preset position;

[0031] The control module is used to control the bed to move from a preset position to different target positions, and to control the shooting device to capture multiple initial images of the obstacle avoidance object in the preset position and each target position.

[0032] The generation module is used to generate an obstacle avoidance model based on medical images and multiple initial images.

[0033] Fifthly, this application provides an obstacle avoidance device, which includes:

[0034] The receiving module is used to receive the planned path of the needle channel corresponding to the surgical robot sent by the workstation; the planned path is generated by the workstation based on the obstacle avoidance model.

[0035] The determination module is used to determine whether the planned path of the needle track meets the preset requirements. If the planned path of the needle track does not meet the preset requirements, a new planned path is received and it is determined whether the new planned path meets the preset requirements, until the planned path of the needle track meets the preset requirements.

[0036] Sixthly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the methods provided in the first and second aspects above.

[0037] In a seventh aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the methods provided in the first and second aspects above.

[0038] Eighthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods provided in the first and second aspects described above.

[0039] The aforementioned obstacle avoidance model generation method, obstacle avoidance method, obstacle avoidance system, device, and equipment acquire medical images of the region of interest (ROI) of the object to be avoided when the bed carrying the object to be avoided moves to a preset position; control the bed to move from the preset position to different target positions, and control the imaging device to capture multiple initial images of the object at the preset position and each target position; and generate an obstacle avoidance model based on the medical images and multiple initial images. In this embodiment, the obstacle avoidance model generated by combining medical images and multiple initial images is highly efficient. Furthermore, acquiring medical images of the ROI of the object to be avoided using medical imaging equipment can prevent the object from receiving large doses of radiation, improving safety, and ensuring the accuracy of the ROI, so that the generated obstacle avoidance model meets surgical requirements. Acquiring multiple initial images of the object to be avoided using imaging equipment can improve the generation efficiency of the obstacle avoidance model. In addition, in this embodiment, medical images are acquired when the bed moves to a preset position, and multiple initial images are acquired during the process of the bed moving out of the preset position (i.e., moving from the preset position to different target positions). In this way, during the process of completing a scanning process using medical imaging equipment, both medical images and multiple initial images can be acquired, which can reduce the complexity of the process of generating obstacle avoidance models and improve the efficiency and accuracy of generating obstacle avoidance models. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the obstacle avoidance model generation device in one embodiment;

[0041] Figure 2 This is a flowchart illustrating the steps of an obstacle avoidance model generation method in one embodiment.

[0042] Figure 3 This is a schematic diagram illustrating the direction of bed movement in one embodiment;

[0043] Figure 4 This is a flowchart illustrating the steps of an obstacle avoidance model generation method in another embodiment;

[0044] Figure 5 This is a flowchart illustrating the steps of an obstacle avoidance model generation method in another embodiment;

[0045] Figure 6 This is a flowchart illustrating the steps of an obstacle avoidance method in another embodiment;

[0046] Figure 7 This is a flowchart illustrating the steps of an obstacle avoidance method in another embodiment;

[0047] Figure 8 This is a flowchart illustrating the steps of an obstacle avoidance method in another embodiment;

[0048] Figure 9 This is a schematic diagram of the needle sorting rule in one embodiment;

[0049] Figure 10 This is a schematic diagram of the needle sorting rule in another embodiment;

[0050] Figure 11 This is a flowchart illustrating the steps of an obstacle avoidance model generation method in another embodiment;

[0051] Figure 12 This is a flowchart illustrating the steps of an obstacle avoidance method in another embodiment;

[0052] Figure 13 This is a schematic diagram of the obstacle avoidance system in one embodiment;

[0053] Figure 14 This is a schematic diagram of the obstacle avoidance model generation device in one embodiment;

[0054] Figure 15 This is a schematic diagram of the obstacle avoidance device in one embodiment;

[0055] Figure 16 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] The serial numbers assigned to components in this article, such as "first" and "second", are used only to distinguish the objects being described and have no sequential or technical meaning.

[0058] Before detailing the technical solutions of the embodiments of this application, the technical background and evolution of the embodiments of this application are first introduced. In the medical field, with the development of robotics technology, surgical human systems with robotic arms have emerged. Using a surgical robot system, the robotic arm can be moved to a designated position according to a preset path to perform surgery during surgical preparation. The preset path is planned based on a model of obstacles that the robotic arm needs to avoid during its movement to the designated position. In traditional technology, a structured light camera is typically used to scan the human body, and the scan results are modeled to generate a corresponding human body model. Before surgery, the generated human body model is used to plan needle trajectories and verify their feasibility. When using a surgical robot, this model can be applied to obstacle avoidance by the robotic arm. However, the accuracy of the human body model obtained using a structured light camera does not meet the requirements of surgery. To address this, traditional technology proposes obtaining medical images of obstacles using a computed tomography (CT) scanner and reconstructing a model from the medical images as the obstacle model. However, this model generation method is inefficient. Therefore, this application provides a method for generating an obstacle avoidance model.

[0059] The technical solutions involved in the embodiments of this application will be described below in conjunction with the application scenarios.

[0060] The obstacle avoidance model provided in this application can be applied to an obstacle avoidance model generation device, the structure of which is as follows: Figure 1 As shown, the system includes a medical imaging device 11, an imaging device 12, and a workstation 13. Both the medical imaging device 11 and the imaging device 12 are communicatively connected to the workstation 13. The connection method can be wired or wireless; this embodiment does not limit this. The medical imaging device 11 is used to acquire medical images of the region of interest of the object to be avoided. The medical imaging device 11 can be a computed tomography (CT) scanner or a magnetic resonance imaging (MRI) scanner. Specifically, as... Figure 1The medical imaging device 11 shown is a CT scanning device, which includes a CT scanner and a CT host computer. The CT host computer controls the CT scanner to scan the object to be avoided on the bed to obtain CT images of the region of interest. The imaging device 12 can be any one of a structured light camera, a time-of-flight depth camera, or a binocular camera. The imaging device 12 can acquire three-dimensional point cloud data of the object to be avoided. The workstation 13 is used to receive the medical images acquired by the medical imaging device 11 and the three-dimensional point cloud data of the object to be avoided acquired by the imaging device, and to generate an obstacle avoidance model based on the medical images and the three-dimensional point cloud data. A detailed description of the obstacle avoidance model generation method can be found in the description of the obstacle avoidance model generation method in the following embodiments. The workstation 13 can be a computer device, which can be, but is not limited to, an industrial computer, a laptop computer, and a tablet computer. The workstation 13 performs path planning based on the obstacle avoidance model and sends the planned path to the surgical robot 14 so that the surgical robot 14 can use the planned path to avoid obstacles. A detailed description of the surgical robot using the planned path to avoid obstacles can be found in the description of the obstacle avoidance method in the following embodiments.

[0061] In one embodiment, such as Figure 2 As shown, an obstacle avoidance model generation method is provided. This embodiment applies this method to, for example, obstacle avoidance model generation. Figure 1 The workstation shown is described in detail. In this embodiment, the method may include the following steps:

[0062] Step 200: When the bed carrying the object to be avoided moves to the preset position, acquire a medical image of the region of interest of the object to be avoided.

[0063] The medical imaging equipment's bed supports the object to be avoided, which can be a human model or other obstacle. The preset position is determined by the user based on the region of interest (ROI) of the object, specifically the surgical area. After the user moves the bed to the preset position, the medical imaging equipment scans the ROI, acquiring a medical image of it, and sends this image to the workstation. The type of medical image depends on the type of medical imaging equipment. If the equipment is a CT scanner, the acquired image is a CT image; if it is an MRI scanner, the acquired image is an MRI image.

[0064] The workstation receives medical images sent by the medical imaging equipment, which can be sent to the workstation in real time.

[0065] Step 210: Control the bed to move from the preset position to different target positions, and control the shooting device to capture multiple initial images of the object to be avoided in the preset position and the corresponding target positions.

[0066] After receiving a medical image, the workstation sends a control command to the medical imaging device to control the bed in the medical imaging device to move from a preset position to a different target position according to a preset moving distance (bed movement value). The preset moving distance can be determined by the user based on the shooting range of the imaging device and the distance that the bed can move (from the preset position to the outermost position of the medical imaging device). This embodiment does not limit this.

[0067] While sending control commands to the medical imaging equipment, the workstation also sends control commands to the imaging device. This controls the imaging device to capture images of the obstacle to be avoided when the bed carrying the obstacle is in a preset position and at various target positions, obtaining multiple initial images of the obstacle. In other words, when the bed is in the preset position, the imaging device captures an initial image of the obstacle; as the bed moves to each target position, the imaging device captures another initial image of the obstacle at different target positions, thus obtaining multiple initial images. After obtaining these multiple initial images, the imaging device sends them to the workstation.

[0068] In one embodiment, the workstation controls the direction of bed movement as follows: Figure 3 As shown.

[0069] Step 220: Generate an obstacle avoidance model based on medical images and multiple initial images.

[0070] After receiving the medical image of the region of interest of the object to be avoided from the medical imaging equipment, and multiple initial images of the object to be avoided from the imaging equipment, the workstation can obtain the obstacle avoidance model, i.e., the three-dimensional image of the object to be avoided, based on the medical image and the multiple initial images.

[0071] The obstacle avoidance model generation method provided in this application involves acquiring medical images of the region of interest (ROI) of the object to be avoided when the bed carrying the object moves to a preset position; controlling the bed to move from the preset position to different target positions, and controlling an imaging device to capture multiple initial images of the object at the preset position and each target position; and generating an obstacle avoidance model based on the medical images and the multiple initial images. In this embodiment, the obstacle avoidance model generated by combining medical images and multiple initial images is highly efficient. Furthermore, acquiring medical images of the ROI of the object using medical imaging equipment avoids the object receiving large doses of radiation, improving safety, and ensuring the accuracy of the ROI, thus enabling the generated obstacle avoidance model to meet surgical requirements. Acquiring multiple initial images of the object using an imaging device further improves the generation efficiency of the obstacle avoidance model. In addition, in this embodiment, medical images are acquired when the bed moves to a preset position, and multiple initial images are acquired during the process of the bed moving out of the preset position (i.e., moving from the preset position to different target positions). In this way, during the process of completing a scanning process using medical imaging equipment, both medical images and multiple initial images can be acquired, thereby reducing the complexity of the process of generating obstacle avoidance models and improving the efficiency and accuracy of generating obstacle avoidance models.

[0072] In one embodiment, such as Figure 4 As shown, this relates to an implementation method for generating an obstacle avoidance model based on medical images and multiple initial images. The steps of this implementation include:

[0073] Step 400: Construct a first model of the region of interest based on the medical image.

[0074] After receiving a medical image of the region of interest (ROI) of the obstacle to be avoided from the medical imaging equipment, the workstation constructs a first model of the ROI based on the medical image. The medical imaging equipment acquires multiple two-dimensional tomographic images of the ROI. The workstation reconstructs a three-dimensional model of the ROI, i.e., the first model, based on these multiple two-dimensional tomographic images. This embodiment does not limit the specific method for constructing the first model of the ROI from the medical image, as long as the function is achieved.

[0075] The workstation can send control commands to the bed and imaging equipment while simultaneously constructing a first model of the region of interest based on the medical images.

[0076] In an optional embodiment, after obtaining multiple two-dimensional tomographic images, the workstation generates coronal and sagittal plane images based on the tomographic images, extracts the contour of the region of interest, and uses a region growing tool to perform edge segmentation, removal of redundant data, selective encoding, and hole filling on each tomographic image, thereby generating a first model of the region of interest.

[0077] Step 410: Stitch together multiple initial images to obtain the second model of the object to be avoided.

[0078] After acquiring multiple initial images of the obstacle to be avoided captured by the imaging device, the workstation stitches these initial images together to obtain a second model of the obstacle to be avoided. The multiple initial images acquired from the imaging device are 3D point cloud data, and there may be overlapping regions among them. By stitching together the overlapping regions from the multiple initial images, the workstation can obtain complete 3D point cloud data of the obstacle to be avoided, i.e., the second model of the obstacle to be avoided. This embodiment does not limit the specific method of stitching multiple initial images, as long as the function is achieved.

[0079] In an optional embodiment, the user can set markers on the bed, and the workstation can stitch together multiple initial images by extracting feature points from multiple initial images.

[0080] In another optional embodiment, multiple initial images are obtained by moving the bed from a preset position to different target positions. Then, the workstation can stitch the multiple initial images together according to the moving distance corresponding to each initial object.

[0081] Step 420: Generate an obstacle avoidance model based on the first model and the second model.

[0082] After obtaining the first model of the region of interest (ROI) of the object to be avoided and the first model of the object itself, the workstation generates an obstacle avoidance model based on the first and second models. The first model is a model of the ROI within the object to be avoided, and the second model is a complete model of the object. By splicing the two models, the obstacle avoidance model can be obtained.

[0083] In this embodiment, a first model of the region of interest is constructed using the obtained medical images, and a second model is constructed using multiple initial images. An obstacle avoidance model is then generated based on the first and second models. By constructing the first and second models separately, and then generating the obstacle avoidance model based on them, the efficiency of obstacle avoidance model generation can be improved. Furthermore, using medical images acquired during a single scan using medical imaging equipment and multiple initial images to construct the model ensures consistency between the first and second models, thereby improving the accuracy of the generated obstacle avoidance model.

[0084] In one embodiment, such as Figure 5 As shown, this relates to an implementation method for generating an obstacle avoidance model based on a first model and a second model. The steps of this implementation method include:

[0085] Step 500: Crop the region corresponding to the first model in the second model to obtain the cropped second model.

[0086] The second model is a complete model of the object to be avoided, while the first model is a model of the region of interest of the object. In other words, the second model includes the region of the first model. After the workstation determines the region corresponding to the first model in the second model, it clips that region out of the second model to obtain the clipped second model.

[0087] In an optional embodiment, the workstation can determine the region in the second model where the first model is located based on the position of the region of interest on the bed and the position of the obstacle to be avoided on the bed. Alternatively, the workstation can determine the region in the second model where the first model is located based on the position of the region of interest within the obstacle to be avoided.

[0088] Step 510: Combine the trimmed second model and the first model to obtain the obstacle avoidance model.

[0089] After obtaining the cropped second model, the workstation stitches the first model with the cropped second model. That is, it stitches the first model into the cropped area of ​​the first model to obtain a complete model of the object to be avoided, i.e., the obstacle avoidance model. The model of the region of interest in this obstacle avoidance model is a model constructed from medical images, which can meet the accuracy required for surgery; the models of other regions are models constructed from images obtained by the imaging device.

[0090] In this embodiment, the workstation obtains an obstacle avoidance model by cropping the region corresponding to the first model in the second model and then splicing the cropped second model with the first model. This method of obtaining the obstacle avoidance model is quick and easy to implement, and can improve the efficiency of generating the obstacle avoidance model.

[0091] Please see Figure 6 This application provides an obstacle avoidance method in one embodiment, which is applied to situations such as... Figure 1 The surgical robot shown is used for illustration. In this embodiment, the method includes the following steps:

[0092] Step 600: Receive the planned path of the needle channel corresponding to the surgical robot sent by the workstation; the planned path is generated by the workstation based on the obstacle avoidance model.

[0093] After generating an obstacle avoidance model, the workstation plans the path for the surgical robot's arm to move to the target position based on the model. The target position can be the location of a needle track pre-set by the user. In other words, the workstation can obtain the planned path for the corresponding needle track on the surgical robot based on the obstacle avoidance model. The surgical robot can have multiple needle tracks, each with its own planned path. After generating the planned path, the workstation sends it to the surgical robot, which receives the corresponding planned path. The workstation can either send the planned path directly to the surgical robot after generation, or it can store the generated planned path in the corresponding memory and send it to the surgical robot when needed, such as when it is idle.

[0094] Step 610: Determine whether the planned path of the needle track meets the preset requirements. If the planned path of the needle track does not meet the preset requirements, receive a new planned path and determine whether the new planned path meets the preset requirements until the planned path of the needle track meets the preset requirements.

[0095] After receiving the planned path for the corresponding needle channel, the surgical robot determines whether the planned path meets preset requirements. These preset requirements indicate that the surgical robot will not collide with obstacles when moving along the planned path. This embodiment does not limit the specific method for determining whether the planned path meets the preset requirements, as long as the function is achieved.

[0096] If the surgical robot determines that the planned path of the needle path does not meet the preset requirements, the workstation will replan the path according to the obstacle avoidance model and send the new planned path to the surgical robot. After receiving the new planned path, the surgical robot will determine whether the new planned path meets the preset requirements until the surgical robot determines that the received planned path of the needle path meets the preset requirements. In other words, when the surgical robot determines that the planned path of the needle path does not meet the preset requirements, it will return to the step of determining whether the planned path of the needle path meets the preset requirements until the planned path of the needle path meets the preset requirements.

[0097] The obstacle avoidance method provided in this embodiment receives the planned path of the needle path corresponding to the surgical robot sent by the workstation. The planned path is generated by the workstation based on the obstacle avoidance model. The method determines whether the planned path of the needle path meets the requirements. If the planned path does not meet the preset requirements, a new planned path is received, and its satisfaction is determined until the planned path of the needle path meets the preset requirements. In this embodiment, the surgical robot determines whether the planned path of the needle path generated by the obstacle avoidance model received from the workstation meets the preset requirements. If the planned path does not meet the preset requirements, the workstation re-plans the path based on the obstacle avoidance model and sends it to the surgical robot until the planned path meets the preset requirements. This ensures that the planned path of the needle path corresponding to the surgical robot obtained by the workstation through the obstacle avoidance model is more accurate.

[0098] In one embodiment, such as Figure 7 As shown, one implementation method involves determining whether the planned path of the needle track meets preset requirements. The steps of this implementation method include:

[0099] Step 700: Move to the target needle path position according to the planned needle path. If the needle path does not come into contact with the first obstacle during the movement, it is determined that the planned needle path meets the preset requirements.

[0100] When determining whether the planned path of the received needle track meets the preset requirements, the surgical robot uses simulation. The first obstacle is the phantom and scanning equipment used in the simulation. The surgical robot moves to the target needle track position according to the planned path; that is, the surgical robot controls the robotic arm to move to the target needle track position according to the planned path. During the movement, the surgical robot monitors in real time whether the robotic arm comes into contact with the first obstacle. If the surgical robot determines that it has not come into contact with the first obstacle during the movement, it determines that the planned path of the needle track meets the preset requirements.

[0101] Step 710: If the needle path comes into contact with the first obstacle during the movement, it is determined that the planned path does not meet the preset requirements.

[0102] If the surgical robot determines that the robotic arm comes into contact with the first obstacle during movement, it determines that the planned path of the needle channel does not meet the preset requirements.

[0103] In this embodiment, during the process of the surgical robot controlling the robotic arm to move according to the planned path of the received needle path, it is determined whether the planned path of the needle path meets the preset requirements by judging whether the robotic arm comes into contact with the first obstacle. This method of determining whether the planned path of the needle path meets the preset requirements is simple, fast and easy to implement.

[0104] In one embodiment, when the surgical robot receives multiple planned paths for needle channels, it is necessary to ensure that the surgical robot does not collide with other needle channels while moving to the target needle channel position according to the planned path. Therefore, as... Figure 8 As shown, if the surgical robot does not come into contact with the first obstacle during its movement—that is, if it does not come into contact with the first obstacle while moving along the planned path to the target needle path position for each needle path—the obstacle avoidance method further includes the following steps:

[0105] Step 800: If there is a second obstacle at the target needle path position, move to the next target needle path position according to the planned path of the next target needle path. If there is no contact with the second obstacle during the movement, it is determined that the planned path of the needle path meets the preset requirements.

[0106] After the surgical robot moves its robotic arm to the target needle path according to the planned path, it performs a puncture operation, that is, inserts a needle into the target needle path. The puncture operation is a simulation of a puncture in a real-world scenario.

[0107] In one optional embodiment, after the surgical robot moves the robotic arm to the target needle path according to the planned path, the doctor can manually insert the needle in the scanning room based on the position of the robotic arm; or the doctor can use a controller in the operating room outside the scanning room to adjust the posture of the robotic arm and remotely control the robotic arm to complete the puncture.

[0108] In one optional embodiment, while the surgeon adjusts the robotic arm's posture using a controller in the operating room outside the scanning room, the surgical robot monitors the proximity between the robotic arm and the phantom in real time. If the surgical robot detects that the distance between the robotic arm and the phantom is less than a preset distance threshold, it will issue a warning message to alert the surgeon that the distance is too close. If the surgical robot detects a collision between the robotic arm and the phantom, it will issue an alarm message to alert the surgeon that a collision has occurred.

[0109] If a second obstacle exists at the target needle path location, the surgical robot will control the robotic arm to move to the next target needle path location according to the planned path for the next needle path. During the movement, the surgical robot will monitor in real time whether the robotic arm comes into contact with the second obstacle; if the surgical robot determines that it has not come into contact with the second obstacle during the movement, it determines that the planned path for the needle path meets the preset requirements. Here, the second obstacle refers to the puncture needles of other needle paths.

[0110] In an optional embodiment, during the puncture, the medical imaging device continuously exposes the phantom to acquire images of the region of interest and displays them on a monitor in the scanning room to provide the doctor with a reference for the position of the puncture needle.

[0111] Step 810: If the needle path comes into contact with a second obstacle during the movement, it is determined that the planned path does not meet the preset requirements.

[0112] If the surgical robot determines that it comes into contact with a second obstacle during its movement, it is determined that the planned path of the needle channel does not meet the preset requirements.

[0113] In this embodiment, if the surgical robot determines that it has not come into contact with the first obstacle during its movement, after completing the puncture operation at the target needle path location, it will also determine whether the planned path of the needle path meets the preset requirements by determining whether it comes into contact with the second obstacle during its movement to the next target needle path location according to the planned path of the next target needle path. This can improve the accuracy and reliability of the needle path planning path finally determined by the obstacle avoidance model.

[0114] In an optional embodiment, when the phantom includes multiple needle channels, the sequence of the needle channels is obtained; based on the sequence of the needle channels and the planned path of the next target needle channel, the phantom moves to the position of the next target needle channel; the sequence of the multiple needle channels is based on a preset sequence rule, which is related to the shape of the needle positioner in the robotic arm. When the needle positioner is L-shaped, the sequence rule is to sort the needle channels from the furthest point from the robotic arm to the closest point, and from the outside of the aperture to the inside of the aperture. When the needle positioner is I-shaped, the sequence rule is to sort the needle channels from the inside of the aperture to the outside of the aperture, and from the furthest point from the robotic arm to the closest point. Here, "inside and outside the aperture" refers to the inside and outside of the aperture in a medical imaging device (CT), and the needle positioner is installed at the end of the robotic arm.

[0115] When the needle positioner is L-shaped, the planned needle path is as follows: Figure 9 As shown. Figure 9 Multiple small dots represent multiple needle paths, and the order of the needle paths is as follows: Figure 9 The order indicated by the middle arrow. When the needle positioner is I-shaped, the planned needle path is as follows: Figure 10 As shown, Figure 10 The document includes Figures a, b, and c. Figure a shows the order of the needles when multiple needles are arranged in a regular pattern; Figures b and c show the order of the needles when multiple needles are arranged in an irregular pattern. Figure 10 Multiple small dots represent multiple needle paths, and the order of the needle paths is as follows: Figure 10 The order indicated by the middle arrow.

[0116] In one embodiment, if the planned path of the needle track does not meet the preset requirements, the obstacle avoidance method further includes the following steps:

[0117] The system returns a prompt message to the workstation, instructing it to adjust the planned path of the needle channel according to the obstacle avoidance model, and then sends the adjusted planned path of the needle channel to the surgical robot.

[0118] If the surgical robot determines that the planned needle path does not meet the preset requirements, it will send a prompt message to the workstation. This prompt message instructs the workstation to adjust the planned needle path according to the obstacle avoidance model. In other words, when the workstation receives the prompt message from the surgical robot, indicating that the previously sent planned needle path was inaccurate, the workstation will adjust the planned needle path according to the obstacle avoidance model and resend the adjusted path to the surgical robot. This allows the surgical robot to determine whether the received planned needle path can avoid obstacles, i.e., to execute steps 600 and 610.

[0119] In this embodiment, if the surgical robot determines that the planned path of the needle channel does not meet the preset requirements, it sends a prompt message to the workstation to instruct the workstation to adjust the planned path of the needle channel according to the obstacle avoidance model. This facilitates the workstation to adjust the planned path of the needle channel, so as to ensure the accuracy and reliability of the final planned path of the needle channel.

[0120] Please see Figure 11 This application provides a method for generating an obstacle avoidance model. In this embodiment, the medical imaging device is a CT scanning device, and the imaging device is a structured light camera. The method includes the following steps:

[0121] Step 111: The user moves the bed carrying the object to be avoided to the preset position;

[0122] Step 112: The CT scanning device scans the region of interest of the object to be avoided, and obtains a CT image;

[0123] Step 113: Send the CT images to the workstation;

[0124] Step 114: The workstation constructs a CT model of the region of interest based on the medical image;

[0125] Step 115: The workstation sends an image capture command to the structured light camera and a bed movement command to the CT scanning device at the same time.

[0126] Step 116: The CT scanning device controls the adult body to move from a preset position to different target positions according to the movement command;

[0127] Step 117: The structured light camera captures multiple initial images of the bed in a preset position and at each target position according to the shooting instructions;

[0128] Step 118: The structured light camera sends multiple initial images to the workstation;

[0129] Step 119: The workstation will stitch together multiple initial images to obtain a structured light model; the region corresponding to the first model in the second model will be cropped, and the cropped second model and the first model will be stitched together to obtain an obstacle avoidance model;

[0130] Step 120: Send the obstacle avoidance model to the surgical robot so that the surgical robot can determine the phantom based on the obstacle avoidance model.

[0131] Please see Figure 12 This application provides an obstacle avoidance method, the steps of which include:

[0132] Step 121: The user sorts the needle tracks according to the needle positioner of the surgical robot;

[0133] Step 122: Send the sorting results to the workstation;

[0134] Step 123: The workstation plans the planned path for each needle path based on the obstacle avoidance model and the sorting results;

[0135] Step 124: Send the planned paths of each needle channel to the surgical robot;

[0136] Step 125: The surgical robot controls the robotic arm to move to the target needle path position according to the planned path of the needle path, and ensures that it does not come into contact with the first obstacle during the movement. A simulated puncture operation is then performed at the target needle path position; the first obstacle is the phantom.

[0137] Step 126: After the simulated puncture operation is completed, the robotic arm is controlled to move to the next target needle path position according to the sorting rules and the planned path of the next target needle path; if it is determined that there is no contact with the second obstacle during the movement, it is determined that the planned path of the needle path meets the preset requirements.

[0138] Step 127: If it is determined that the needle path comes into contact with the second obstacle during the movement, then it is determined that the planned path of the needle path does not meet the preset requirements.

[0139] Step 128: Return a prompt message to the workstation, indicating that the workstation adjusts the planned path of the needle channel according to the obstacle avoidance model and sends the adjusted planned path of the needle channel to the surgical robot.

[0140] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0141] Please see Figure 13 One embodiment of this application provides an obstacle avoidance system 20, which includes a workstation 13 and a surgical robot 14. The workstation 13 is communicatively connected to the surgical robot 14. The workstation 13 is used to perform the steps of the obstacle avoidance model generation method described above, and the surgical robot 14 is used to perform the steps of the obstacle avoidance method described above.

[0142] The workstation 13 in the obstacle avoidance system 20 provided in this embodiment is used to execute the steps of the obstacle avoidance model generation method described above, and thus has all the beneficial effects of the obstacle avoidance model generation method; the surgical robot is used to execute the steps of the obstacle avoidance method described above, and thus has all the beneficial effects of the obstacle avoidance method, which will not be elaborated here.

[0143] Based on the same inventive concept, this application also provides an obstacle avoidance model device for implementing the obstacle avoidance model method described above, and an obstacle avoidance device for the obstacle avoidance method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more obstacle avoidance model devices and obstacle avoidance device embodiments provided below can be found in the limitations of the obstacle avoidance model method and obstacle avoidance method described above, and will not be repeated here.

[0144] In one embodiment, such as Figure 14 As shown, an obstacle avoidance model generation device 30 is provided, including: an acquisition module 31, a control module 32, and a generation module 33, wherein:

[0145] The acquisition module 31 is used to acquire medical images of the region of interest of the object to be avoided when the bed carrying the object to be avoided moves to a preset position.

[0146] The control module 32 is used to control the bed to move from a preset position to different target positions, and to control the shooting device to capture multiple initial images of the obstacle avoidance object in the preset position and each target position.

[0147] The generation module 33 is used to generate an obstacle avoidance model based on medical images and multiple initial images.

[0148] In one embodiment, the generation module 33 includes a construction unit and a generation unit. The construction unit is used to construct a first model of the region of interest based on the medical image; and to stitch together multiple initial images to obtain a second model of the object to be avoided; the generation unit is used to generate an obstacle avoidance model based on the first model and the second model.

[0149] In one embodiment, the generation unit is specifically used to crop the region corresponding to the first model in the second model to obtain the cropped second model; and to stitch the cropped second model and the first model together to obtain the obstacle avoidance model.

[0150] In one embodiment, such as Figure 15 As shown, an obstacle avoidance device 40 is provided, including a receiving module 41 and a determining module 42. Wherein,

[0151] The receiving module 41 is used to receive the planned path of the needle channel corresponding to the surgical robot sent by the workstation; the planned path is generated by the workstation based on the obstacle avoidance model.

[0152] The determination module 42 is used to determine whether the planned path of the needle track meets the preset requirements. If the planned path of the needle track does not meet the preset requirements, a new planned path is received and it is determined whether the new planned path meets the preset requirements until the planned path of the needle track meets the preset requirements.

[0153] In one embodiment, the determining module 42 is specifically used to move to the target needle track position according to the planned path of the needle track. If it does not come into contact with the first obstacle during the movement, it is determined that the planned path of the needle track meets the preset requirements; if it comes into contact with the first obstacle during the movement, it is determined that the planned path of the needle track does not meet the preset requirements.

[0154] In one embodiment, the determining module 42 is further configured to, when there is a second obstacle at the target needle path position, move to the next target needle path position according to the planned path of the next target needle path. If the needle path does not come into contact with the second obstacle during the movement, it is determined that the planned path of the needle path meets the preset requirements; if the needle path comes into contact with the second obstacle during the movement, it is determined that the planned path of the needle path does not meet the preset requirements.

[0155] In one embodiment, the obstacle avoidance device 40 further includes a sending module, which is used to return prompt information to the workstation to instruct the workstation to adjust the planned path of the needle channel according to the obstacle avoidance model, and send the adjusted planned path of the needle channel to the surgical robot.

[0156] The various modules in the aforementioned obstacle avoidance model generation device and obstacle avoidance device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0157] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 16 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements an obstacle avoidance model generation method and an obstacle avoidance method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0158] Those skilled in the art will understand that Figure 16 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0159] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0160] When the bed carrying the object to be avoided moves to a preset position, a medical image of the region of interest of the object to be avoided is acquired;

[0161] The system controls the bed to move from a preset position to different target positions, and controls the camera to capture multiple initial images of the object to be avoided in the preset position and the corresponding target positions.

[0162] An obstacle avoidance model is generated based on medical images and multiple initial images.

[0163] In one embodiment, when the processor executes the computer program, it further performs the following steps: constructing a first model of the region of interest based on the medical image; stitching together multiple initial images to obtain a second model of the object to be avoided; and generating an obstacle avoidance model based on the first model and the second model.

[0164] In one embodiment, when the processor executes the computer program, it further performs the following steps: cropping the region corresponding to the first model in the second model to obtain the cropped second model; and splicing the cropped second model and the first model to obtain the obstacle avoidance model.

[0165] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0166] The receiving workstation sends the planned path of the needle channel corresponding to the surgical robot; the planned path is generated by the workstation based on the obstacle avoidance model.

[0167] Determine whether the planned path of the needle track meets the preset requirements. If the planned path of the needle track does not meet the preset requirements, a new planned path is received, and it is determined whether the new planned path meets the preset requirements, until the planned path of the needle track meets the preset requirements.

[0168] In one embodiment, when the processor executes the computer program, it further performs the following steps: moving to the target needle track position according to the planned path of the needle track; if the needle track does not come into contact with the first obstacle during the movement, it is determined that the planned path of the needle track meets the preset requirements; if the needle track comes into contact with the first obstacle during the movement, it is determined that the planned path of the needle track does not meet the preset requirements.

[0169] In one embodiment, when the processor executes the computer program, it further performs the following steps: if there is a second obstacle at the target needle path position, it moves to the next target needle path position according to the planned path of the next target needle path; if it does not come into contact with the second obstacle during the movement, it is determined that the planned path of the needle path meets the preset requirements; if it comes into contact with the second obstacle during the movement, it is determined that the planned path of the needle path does not meet the preset requirements.

[0170] In one embodiment, the processor, when executing the computer program, also performs the following steps: returning a prompt message to the workstation to instruct the workstation to adjust the planned path of the needle track according to the obstacle avoidance model, and sending the adjusted planned path of the needle track to the surgical robot.

[0171] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0172] When the bed carrying the object to be avoided moves to a preset position, a medical image of the region of interest of the object to be avoided is acquired;

[0173] The system controls the bed to move from a preset position to different target positions, and controls the camera to capture multiple initial images of the object to be avoided in the preset position and the corresponding target positions.

[0174] An obstacle avoidance model is generated based on medical images and multiple initial images.

[0175] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: constructing a first model of a region of interest based on a medical image; stitching together multiple initial images to obtain a second model of the object to be avoided; and generating an obstacle avoidance model based on the first and second models.

[0176] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: cropping the region corresponding to the first model in the second model to obtain the cropped second model; and splicing the cropped second model and the first model to obtain the obstacle avoidance model.

[0177] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0178] The receiving workstation sends the planned path of the needle channel corresponding to the surgical robot; the planned path is generated by the workstation based on the obstacle avoidance model.

[0179] Determine whether the planned path of the needle track meets the preset requirements. If the planned path of the needle track does not meet the preset requirements, a new planned path is received, and it is determined whether the new planned path meets the preset requirements, until the planned path of the needle track meets the preset requirements.

[0180] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: moving to the target needle track position according to the planned path of the needle track; if the needle track does not come into contact with the first obstacle during the movement, it is determined that the planned path of the needle track meets the preset requirements; if the needle track comes into contact with the first obstacle during the movement, it is determined that the planned path of the needle track does not meet the preset requirements.

[0181] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if there is a second obstacle at the target needle path position, it moves to the next target needle path position according to the planned path of the next target needle path; if it does not come into contact with the second obstacle during the movement, it is determined that the planned path of the needle path meets the preset requirements; if it comes into contact with the second obstacle during the movement, it is determined that the planned path of the needle path does not meet the preset requirements.

[0182] In one embodiment, when the computer program is executed by the processor, it also performs the following steps: returning a prompt message to the workstation to instruct the workstation to adjust the planned path of the needle track according to the obstacle avoidance model, and sending the adjusted planned path of the needle track to the surgical robot.

[0183] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0184] When the bed carrying the object to be avoided moves to a preset position, a medical image of the region of interest of the object to be avoided is acquired;

[0185] The system controls the bed to move from a preset position to different target positions, and controls the camera to capture multiple initial images of the object to be avoided in the preset position and the corresponding target positions.

[0186] An obstacle avoidance model is generated based on medical images and multiple initial images.

[0187] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: constructing a first model of a region of interest based on a medical image; stitching together multiple initial images to obtain a second model of the object to be avoided; and generating an obstacle avoidance model based on the first and second models.

[0188] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: cropping the region corresponding to the first model in the second model to obtain the cropped second model; and splicing the cropped second model and the first model to obtain the obstacle avoidance model.

[0189] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0190] The receiving workstation sends the planned path of the needle channel corresponding to the surgical robot; the planned path is generated by the workstation based on the obstacle avoidance model.

[0191] Determine whether the planned path of the needle track meets the preset requirements. If the planned path of the needle track does not meet the preset requirements, a new planned path is received, and it is determined whether the new planned path meets the preset requirements, until the planned path of the needle track meets the preset requirements.

[0192] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: moving to the target needle track position according to the planned path of the needle track; if the needle track does not come into contact with the first obstacle during the movement, it is determined that the planned path of the needle track meets the preset requirements; if the needle track comes into contact with the first obstacle during the movement, it is determined that the planned path of the needle track does not meet the preset requirements.

[0193] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if there is a second obstacle at the target needle path position, it moves to the next target needle path position according to the planned path of the next target needle path; if it does not come into contact with the second obstacle during the movement, it is determined that the planned path of the needle path meets the preset requirements; if it comes into contact with the second obstacle during the movement, it is determined that the planned path of the needle path does not meet the preset requirements.

[0194] In one embodiment, when the computer program is executed by the processor, it also performs the following steps: returning a prompt message to the workstation to instruct the workstation to adjust the planned path of the needle track according to the obstacle avoidance model, and sending the adjusted planned path of the needle track to the surgical robot.

[0195] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0196] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0197] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for generating an obstacle avoidance model, characterized in that, The method includes: When the bed carrying the object to be avoided moves to a preset position, a medical image of the region of interest of the object to be avoided is acquired; The system controls the bed to move from the preset position to different target positions, and controls the imaging device to capture multiple initial images of the object to be avoided at the preset position and each of the target positions. An obstacle avoidance model is generated based on the medical images and the plurality of initial images; the obstacle avoidance model is used to plan the movement path of the surgical robot's robotic arm.

2. The method according to claim 1, characterized in that, The step of generating an obstacle avoidance model based on the medical image and the plurality of initial images includes: A first model of the region of interest is constructed based on the medical image; The multiple initial images are stitched together to obtain a second model of the object to be avoided; The obstacle avoidance model is generated based on the first model and the second model.

3. The method according to claim 2, characterized in that, The step of generating the obstacle avoidance model based on the first model and the second model includes: The region corresponding to the first model in the second model is cropped to obtain the cropped second model; The obstacle avoidance model is obtained by splicing the cut second model and the first model together.

4. The method according to claim 2, characterized in that, The medical image includes multiple two-dimensional tomographic images, and the construction of a first model of the region of interest based on the medical image includes: Based on the multiple two-dimensional tomographic images, generate coronal and sagittal plane images; Based on the coronal and sagittal images, the contour of the region of interest is extracted; Based on the contour of the region of interest, a region growing tool is used to grow each of the two-dimensional tomographic images to generate a first model of the region of interest; the growing process includes edge segmentation, removal of redundant data, selective encoding, and hole filling.

5. The method according to claim 2, characterized in that, The step of stitching together the multiple initial images to obtain the second model of the obstacle-avoiding object includes: The initial images are stitched together based on the movement distance corresponding to each initial image to obtain a second model of the obstacle to be avoided; or... The multiple initial images are stitched together by extracting feature points from them.

6. An obstacle avoidance system, characterized in that, The obstacle avoidance system includes a workstation and a surgical robot, the workstation being communicatively connected to the surgical robot; the workstation is used to perform the steps of the method according to any one of claims 1-5 and to plan a path according to the obstacle avoidance model, and the surgical robot is used to avoid obstacles using the planned path.

7. An obstacle avoidance model generation device, characterized in that, The device includes: The acquisition module is used to acquire medical images of the region of interest of the object to be avoided when the bed carrying the object to be avoided moves to a preset position; The control module is used to control the bed to move from the preset position to different target positions, and to control the shooting device to capture multiple initial images of the obstacle avoidance object in the preset position and each of the target positions; The generation module is used to generate an obstacle avoidance model based on the medical image and the plurality of initial images; the obstacle avoidance model is used to plan the movement path of the robotic arm of the surgical robot.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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