Mechanical arm positioning method and system suitable for small orifice surgery

Abstract

The invention discloses a mechanical arm positioning method and system suitable for small orifice surgery, and relates to the technical field of mechanical intelligent control, and the method comprises the steps: collecting data, carrying out three-dimensional structure modeling on orifice tissues, and constructing a tactile perception map of the inner wall of an orifice by using an annular sliding positioning probe in combination with the three-dimensional structure modeling; a path cost map is generated based on three-dimensional structure modeling and a touch sensing map, a mechanical arm control scheme is generated according to tissue physiological characteristics in combination with an intelligent compliance adjustment function, and deviation of a mechanical arm positioning track is monitored in real time and corrected. By constructing a multi-dimensional sensing system fusing a structure and touch information, combining a path control scheme driven by physiological characteristics and a dynamic learning mechanism based on experience feedback, a fine positioning and control method oriented to a small orifice surgery scene is formed, and the environmental adaptability, path precision and operation safety of a mechanical arm are improved.

Description

Technical Field

[0001] This invention relates to the field of mechanical intelligent control technology, and in particular to a robotic arm positioning method and system suitable for small cavity surgery. Background Technology

[0002] With the continuous advancement of minimally invasive surgical techniques, robotic arms are increasingly widely used in the medical field, especially in endoscopic surgeries, where they demonstrate significant advantages. Robotic arms possess high-precision positioning and repetitive operation capabilities, effectively replacing complex procedures that are difficult for humans to perform in traditional surgeries. In recent years, with the development of technologies such as image guidance, sensor fusion, and artificial intelligence, key aspects such as 3D modeling, path planning, and multimodal perception have been significantly optimized. For example, some systems can now reconstruct the endoscopic environment using endoscopic images and assist surgeons in path planning. However, traditional solutions are mostly based on static image data, lacking real-time 3D models and data support highly correlated with tissue contact characteristics, resulting in insufficient positioning response capabilities of robotic arms in flexible or deformable tissue environments. Furthermore, in terms of control strategies, there are still bottlenecks such as low levels of intelligence and a lack of dynamic adjustment, limiting the adaptability and accuracy of robotic arms in complex endoscopic environments.

[0003] Current technologies still have room for improvement in several key aspects. First, the structure of cavitary tissues is complex and exhibits a degree of dynamic variability, making it difficult for existing modeling methods to achieve 3D reconstructions that highly match the actual tissue state, thus failing to provide comprehensive data support for path planning. Second, the inner walls of cavitary tissues typically exhibit strong physiological differences and tactile response characteristics, while most current systems fail to effectively integrate tactile information with geometric modeling, resulting in insufficient responsiveness of path planning to tissue properties. Furthermore, existing path planning methods are mostly based on static models to generate fixed trajectories, lacking mechanisms for monitoring and adjusting real-time errors during execution. Even in systems with deviation correction functions, the control model often relies on manual parameter adjustment, lacking the ability to learn and update autonomously using operational experience. Therefore, how to achieve dynamic modeling of cavitary tissues, construct a path cost map that integrates tactile perception, and adjust control strategies in conjunction with physiological characteristics, thereby achieving real-time error correction and model learning updates during execution, has become a crucial problem that urgently needs to be solved in this technological field. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a robotic arm positioning method and system suitable for small cavity surgery, solving the problem that the robotic arm path planning and precise positioning in small cavity surgery is limited by insufficient coupling between structural modeling and tactile perception.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a robotic arm positioning method suitable for small cavity surgery, which includes collecting data to perform three-dimensional structural modeling of cavity tissue, and using a circumferential sliding positioning probe in combination with three-dimensional structural modeling to construct a tactile perception map of the cavity inner wall; Based on 3D structural modeling and tactile perception atlas generation of path cost atlas, combined with intelligent compliant adjustment function to generate robotic arm control scheme according to tissue physiological characteristics; The deviation of the robotic arm's positioning trajectory is monitored and corrected in real time, and the model is driven to learn and update by using the experience data generated during the operation.

[0007] As a preferred embodiment of the robotic arm positioning method for small cavity surgery described in this invention, the method for three-dimensional structural modeling of the cavity tissue using acquired data refers to using structured light sparse point cloud reconstruction. A structural scanning module is installed at the front end of the robotic arm, and the surface of the inner wall of the small cavity is scanned using sparse structured light projection. Each scan acquires a segment of local point cloud, resulting in the local point cloud of the i-th segment of the cavity. ; A continuous and complete three-dimensional point cloud model of the cavity inner wall is formed by point cloud coordinate registration. .

[0008] As a preferred embodiment of the robotic arm positioning method for small cavity surgery described in this invention, the method of constructing a tactile perception map of the cavity wall using a circumferential sliding positioning probe combined with three-dimensional structural modeling refers to setting a tactile probe with a flexible suspension system and circumferential sliding capability at the front end of the robotic arm, and obtaining circumferential tactile data by performing circumferential sliding contact within the cavity. ; 3D point cloud model Voxelization is performed to generate a structural voxel network V; Each circumferential tactile data Mapped to the corresponding voxel node Calculate the average contact stiffness value for each voxel node. ; Finally, a tactile perception map was obtained. .

[0009] As a preferred embodiment of the robotic arm positioning method for small cavity surgery described in this invention, wherein: the path cost map generation based on three-dimensional structural modeling and tactile perception atlas refers to calculating the path cost of each voxel node. ; The path cost graph C is obtained based on the path cost of voxel nodes.

[0010] As a preferred embodiment of the robotic arm positioning method for small cavity surgery described in this invention, wherein: the step of generating a robotic arm control scheme based on tissue physiological characteristics by combining an intelligent compliance adjustment function refers to constructing a compliance factor for each voxel node through the intelligent compliance adjustment function. ; Based on the path cost spectrum and compliance factor, the new path cost of each voxel node is calculated. ; The new path cost graph is obtained based on the new path cost value of voxel nodes. ; According to the new path cost map Find the lowest cost path ; Output the lowest cost path This is a control scheme for a robotic arm.

[0011] As a preferred embodiment of the robotic arm positioning method for small cavity surgery described in this invention, the real-time monitoring and correction of the robotic arm positioning trajectory deviation refers to obtaining the current end effector position of the actuator in real time through optical tracking. Determine the relationship between the control scheme and the current position. nearest path point Based on path points With current location Calculate the error vector ; Set error threshold : like Greater than or equal to Then calculate the correction direction and... This is sent to the actuator as a correction control increment; like Less than If the plan is not adjusted, no adjustments will be made, and the process will continue according to the plan.

[0012] As a preferred embodiment of the robotic arm positioning method applicable to small cavity surgery described in this invention, the step of using experience data generated during operation to drive model learning and updating refers to collecting error data and execution status information of the robotic arm during execution in real time as experience data and uploading them to the database, using the experience data to enable the model to learn and update, and establishing a closed-loop feedback.

[0013] Secondly, the present invention provides a robotic arm positioning system suitable for small cavity surgery, comprising, The data acquisition module is used to collect cavity tissue data; The 3D modeling module is used to process the collected cavity tissue data and generate a spatial model that can be used for path planning. The positioning probe module is used to acquire tactile feedback data of the inner wall of the cavity through circumferential sliding operation; The tactile mapping module is used to combine the tactile information collected by the probe with the three-dimensional structural model to generate a tactile perception map of the cavity wall. The path cost map generation module is used to construct a path cost map using a 3D structural model and a tactile perception map. The control scheme generation module is used to combine the path cost map and tissue physiological characteristics to generate a specific robotic arm trajectory scheme through a compliant adjustment function; The model learning and update module uses the experience data accumulated during operation to learn and update the parameters of the control model.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the robotic arm positioning method for small cavity surgery as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the robotic arm positioning method for small cavity surgery as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: Data acquisition enables three-dimensional structural modeling of cavity tissues, achieving digital reconstruction of the internal spatial morphology of the surgical area and accurately presenting the geometric features and spatial distribution of the tissue. Using a circumferential sliding positioning probe combined with three-dimensional structural modeling to construct a tactile perception map of the cavity wall achieves a fusion expression of the physical properties of the cavity surface and its spatial structure, effectively supplementing the shortcomings of visual modeling in perceiving soft tissue stress states and providing more comprehensive surgical environment information. Generating a path cost map and combining it with an intelligent compliance function to generate a robotic arm control scheme realizes a coordinated response of the control strategy to the complexity of the spatial structure and the physical properties of the tissue at the path planning level. This supports continuous fine-tuning control of the robotic arm in irregular soft tissue environments, enhancing the compliance and flexibility of the operation process. By constructing a multi-dimensional perception system integrating structural and tactile information, combining a path control scheme driven by physiological characteristics, and a dynamic learning mechanism based on experience feedback, a refined positioning and control method for small-cavity surgical scenarios is constituted, significantly improving the environmental adaptability, path accuracy, and operational safety of the robotic arm. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a robotic arm positioning method suitable for small cavity surgery in Example 1.

[0019] Figure 2 This is a structural diagram of a robotic arm positioning system suitable for small cavity surgery in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a robotic arm positioning method suitable for small cavity surgery, including the following steps: S1: Collect data to perform three-dimensional structural modeling of the cavity tissue, and use circumferential sliding positioning probes in combination with three-dimensional structural modeling to construct a tactile perception map of the cavity inner wall.

[0024] Specifically, the data acquisition for 3D structural modeling of the cavity tissue involves using structured light sparse point cloud reconstruction. A structural scanning module is installed at the front end of the robotic arm, and sparse structured light projection is used to scan the surface of the inner wall of the small cavity. Each scan acquires a segment of local point cloud, resulting in the local point cloud of the i-th segment of the cavity. : , in, Let j be the coordinates of the j-th point on the x, y, and z axes. The number of points scanned in the local point cloud at the i-th position; A continuous and complete three-dimensional point cloud model of the cavity inner wall is formed by point cloud coordinate registration. : , Where N is the total number of scans. The rigid body transformation is used to register the i-th segment of the point cloud to a unified coordinate system.

[0025] By using structured light sparse point cloud reconstruction, sparse structured light projection scanning, and point cloud coordinate registration, a precise 3D model of the inner wall of a small cavity was successfully achieved. This solved the problems of high scanning difficulty, insufficient data acquisition accuracy, and poor continuity of the inner wall surface of the small cavity. It also provided a high-quality and operable 3D data model for the subsequent generation of robotic arm control schemes, improving the accuracy and stability of the overall system.

[0026] Furthermore, using a circumferential sliding positioning probe combined with 3D structural modeling to construct a tactile perception map of the cavity wall involves placing a tactile probe with a flexible suspension system and circumferential sliding capability at the front end of the robotic arm, and obtaining circumferential tactile data by performing circumferential sliding contact within the cavity. : , in, This represents the circumferential tactile data at the i-th segment within the cavity. The position of the k-th angle. For angle The feedback force value is given by K, where K is the total number of circumferential sampling points. 3D point cloud model Voxelization is performed to generate a structural voxel network V: , in, This is the voxelized node at the i-th segment of the cavity; Each circumferential tactile data Mapped to the corresponding voxel node Calculate the average contact stiffness value for each voxel node. : , in, This represents the average contact stiffness value at the i-th segment of the cavity. This represents the radial difference between the actual contact position of the probe and the ideal structural surface. Finally, a tactile perception map was obtained. : , in, The total number of voxels.

[0027] By combining a circumferential sliding positioning probe with three-dimensional structural modeling, a tactile perception map of the cavity wall was successfully constructed, solving the problems of inaccurate data acquisition and lack of real-time feedback in traditional tactile perception. Through the mapping of voxelized three-dimensional models and tactile data, the physical properties of the cavity wall were efficiently quantified, providing more accurate and comprehensive tactile feedback for the subsequent generation of robotic arm control schemes, and improving the operational precision and safety during surgery.

[0028] S2: Based on 3D structural modeling and tactile perception map generation, a path cost map is generated, and combined with intelligent compliant adjustment function, a robotic arm control scheme is generated according to the physiological characteristics of the tissue.

[0029] Specifically, the path cost map generation based on 3D structural modeling and tactile perception mapping refers to calculating the path cost of each voxel node. : , in, voxel nodes The value of the time, Let be the normalized stiffness value at the i-th segment of the cavity. , The weighting coefficients for the set stiffness value and distance. The location of the voxel center. For the target location; The path cost graph C is obtained based on the path cost of voxel nodes: , The final output C is the path cost graph.

[0030] By evaluating the path cost of each voxel node and constructing a path cost map, the 3D structural modeling and tactile perception data were successfully transformed into optimization data that can be used for path planning. This improved the adaptability of path planning in complex structural and small-space cavity environments and enhanced the sensitivity to changes in soft tissue characteristics. As a result, it provided safer, more reasonable, and personalized decision support for the robotic arm's operation path, reducing the risk of accidental touch and the probability of tissue damage.

[0031] Furthermore, by combining the intelligent compliance adjustment function with the generation of robotic arm control schemes based on tissue physiological characteristics, a compliance factor is constructed for each voxel node through the intelligent compliance adjustment function. : , in, Let be the compliance factor of the i-th voxel node in the cavity. The sensitivity value is set based on the physiological sensitivity of the tissue. This is a smart compliant adjustment function; Smart Compliance Function Specifically, this can be expressed as: , in, , These are the weights of the set contact stiffness value and sensitivity value, respectively; Based on the path cost spectrum and compliance factor, the new path cost of each voxel node is calculated. : , in, Let $i$ be the new path value obtained by the i-th voxel node after combining the compliance factor. The new path cost graph is obtained based on the new path cost value of voxel nodes. : , According to the new path cost map Find the lowest cost path : , , in, As the starting voxel node, For the target voxel node; Output the lowest cost path This is a control scheme for a robotic arm.

[0032] By constructing compliance factors, updating path cost values, generating new path cost maps, and selecting the lowest-cost path as the control scheme, the physiological characteristics of tissues are effectively introduced into the path control process of the robotic arm, achieving a unity of compliance, adaptability, and safety. This comprehensively improves the robotic arm's operational capabilities in confined and complex environments and enhances the system's intelligent perception and compliant control level.

[0033] S3: Monitor and correct deviations in the robotic arm's positioning trajectory in real time, and use experience data generated during operation to drive model learning and updates.

[0034] Specifically, real-time monitoring and correction of deviations in the robotic arm's positioning trajectory refers to obtaining the current end effector position in real time through optical tracking. Determine the relationship between the control scheme and the current position. nearest path point Calculate the error vector : , Set error threshold : like Greater than or equal to Then calculate the correction direction and... This is sent to the actuator as a correction control increment; like Less than If so, no adjustments will be made, and the plan will continue to be implemented. The calculation correction direction : , in, To compensate in the opposite direction.

[0035] By introducing optical tracking, error vector calculation, error judgment mechanism and adaptive correction strategy, the robot can detect and accurately correct deviations in real time, effectively solving the problems of large delay, high rigidity and lack of self-correction capability in traditional path control, and improving the path tracking accuracy, operation stability and system robustness of the robot in complex cavity environments.

[0036] Furthermore, the model learning and updating is driven by the experience data generated during the operation. This means that error data and execution status information of the robotic arm during the execution process are collected in real time and uploaded to the database as experience data. The experience data is used to enable the model to learn and update, thus establishing a closed-loop feedback.

[0037] By collecting error and status data, uploading it to the database, learning and updating the model, and constructing a closed-loop feedback mechanism, a control model optimization process driven by real operational experience was realized, effectively solving the problems of fixed models, lagging learning, and the inability to translate operational experience into performance improvement in traditional systems.

[0038] This embodiment also provides a robotic arm positioning system suitable for small cavity surgery, including: The data acquisition module is used to collect cavity tissue data; The 3D modeling module is used to process the collected cavity tissue data and generate a spatial model that can be used for path planning. The positioning probe module is used to acquire tactile feedback data of the inner wall of the cavity through circumferential sliding operation; The tactile mapping module is used to combine the tactile information collected by the probe with the three-dimensional structural model to generate a tactile perception map of the cavity wall. The path cost map generation module is used to construct a path cost map using a 3D structural model and a tactile perception map. The control scheme generation module is used to combine the path cost map and tissue physiological characteristics to generate a specific robotic arm trajectory scheme through a compliant adjustment function; The model learning and update module uses the experience data accumulated during operation to learn and update the parameters of the control model.

[0039] This embodiment also provides a computer device applicable to a robotic arm positioning method for small cavity surgery, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a robotic arm positioning system for small cavity surgery as proposed in the above embodiment.

[0040] The computer device can be a terminal, comprising 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, carrier networks, NFC (Near Field Communication), or other technologies. 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 on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0041] This embodiment also provides a storage medium on which a computer program is stored. When executed by a processor, the program implements a robotic arm positioning system suitable for small-cavity surgery as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0042] In summary, this invention achieves digital reconstruction of the internal spatial morphology of the surgical area by collecting data to perform three-dimensional structural modeling of cavity tissues, accurately presenting the geometric features and spatial distribution of the tissues. Using a circumferential sliding positioning probe combined with three-dimensional structural modeling to construct a tactile perception map of the cavity wall, it achieves a fusion expression of the physical properties of the cavity surface and the spatial structure, effectively supplementing the shortcomings of visual modeling in perceiving soft tissue stress states and providing more comprehensive surgical environment information. Generating a path cost map and combining it with an intelligent compliance function to generate a robotic arm control scheme enables the control strategy to respond synergistically to the complexity of the spatial structure and the physical properties of the tissue at the path planning level. This supports continuous fine-tuning control of the robotic arm in irregular soft tissue environments, enhancing the compliance and flexibility of the operation process. By constructing a multi-dimensional perception system that integrates structural and tactile information, combining a path control scheme driven by physiological characteristics, and a dynamic learning mechanism based on experience feedback, a refined positioning and control method for small-cavity surgical scenarios is constituted, significantly improving the environmental adaptability, path accuracy, and operational safety of the robotic arm.

[0043] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A robotic arm positioning method suitable for small-cavity surgery, characterized in that: include, Data was collected to create a three-dimensional structural model of the cavity tissue, and a tactile perception map of the cavity inner wall was constructed using a circumferential sliding positioning probe combined with the three-dimensional structural model. Based on 3D structural modeling and tactile perception atlas generation of path cost atlas, combined with intelligent compliant adjustment function to generate robotic arm control scheme according to tissue physiological characteristics; The deviation of the robotic arm's positioning trajectory is monitored and corrected in real time, and the model is driven to learn and update by using the experience data generated during the operation.

2. The robotic arm positioning method for small-cavity surgery as described in claim 1, characterized in that: The data acquisition is used to perform three-dimensional structural modeling of the cavity tissue. This involves using structured light sparse point cloud reconstruction, installing a structural scanning module at the front end of a robotic arm, and scanning the inner wall surface of the small cavity using sparse structured light projection. Each scan acquires a segment of local point cloud, resulting in the local point cloud of the i-th segment of the cavity. ; A continuous and complete three-dimensional point cloud model of the cavity inner wall is formed by point cloud coordinate registration. .

3. The robotic arm positioning method for small-cavity surgery as described in claim 2, characterized in that: The method of constructing a tactile perception map of the cavity wall using a circumferential sliding positioning probe combined with three-dimensional structural modeling refers to setting a tactile probe with a flexible suspension system and circumferential sliding capability at the front end of the robotic arm, and obtaining circumferential tactile data by performing circumferential sliding contact within the cavity. ; 3D point cloud model Voxelization is performed to generate a structural voxel network V; Each circumferential tactile data Mapped to the corresponding voxel node Calculate the average contact stiffness value for each voxel node. ; Finally, a tactile perception map was obtained. .

4. The robotic arm positioning method for small-cavity surgery as described in claim 3, characterized in that: The path cost map generation based on 3D structural modeling and tactile perception mapping refers to calculating the path cost of each voxel node. ; The path cost graph C is obtained based on the path cost of voxel nodes.

5. The robotic arm positioning method for small-cavity surgery as described in claim 4, characterized in that: The aforementioned combination of intelligent compliance adjustment function to generate robotic arm control scheme based on tissue physiological characteristics refers to constructing compliance factor for each voxel node through intelligent compliance adjustment function. ; Based on the path cost spectrum and compliance factor, the new path cost of each voxel node is calculated. ; The new path cost graph is obtained based on the new path cost value of voxel nodes. ; According to the new path cost map Find the lowest cost path ; Output the lowest cost path This is a control scheme for a robotic arm.

6. The robotic arm positioning method for small-cavity surgery as described in claim 5, characterized in that: The real-time monitoring and correction of the robotic arm's positioning trajectory deviation refers to obtaining the current end position of the actuator in real time through optical tracking. Determine the relationship between the control scheme and the current position. nearest path point Based on path points With current location Calculate the error vector ; Set error threshold : like Greater than or equal to Then calculate the correction direction and... This is sent to the actuator as a correction control increment; like Less than If the plan is not adjusted, no adjustments will be made, and the process will continue according to the plan.

7. The robotic arm positioning method for small-cavity surgery as described in claim 6, characterized in that: The process of using experience data generated during operation to drive model learning and updating refers to collecting error data and execution status information of the robotic arm in real time as experience data and uploading them to the database. The experience data is then used to enable the model to learn and update, establishing a closed-loop feedback.

8. A robotic arm positioning system suitable for small-cavity surgery, based on the robotic arm positioning method for small-cavity surgery according to any one of claims 1 to 7, characterized in that: include, The data acquisition module is used to collect cavity tissue data; The 3D modeling module is used to process the collected cavity tissue data and generate a spatial model that can be used for path planning. The positioning probe module is used to acquire tactile feedback data of the inner wall of the cavity through circumferential sliding operation; The tactile mapping module is used to combine the tactile information collected by the probe with the three-dimensional structural model to generate a tactile perception map of the cavity wall. The path cost map generation module is used to construct a path cost map using a 3D structural model and a tactile perception map. The control scheme generation module is used to combine the path cost map with tissue physiological characteristics and generate a specific robotic arm trajectory scheme through a compliant adjustment function. The model learning and update module uses the experience data accumulated during operation to learn and update the parameters of the control model.

9. 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 robotic arm positioning method for small cavity surgery as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the robotic arm positioning method for small cavity surgery as described in any one of claims 1 to 7.

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