Master-slave control method and system for coordinated operation of multiple instruments in a narrow anatomical passageway

Abstract

The application discloses a kind of robot master-slave control method and system for the cooperative operation of multiple instruments in narrow anatomical passageway, it is related to medical robot technical field, the robot master-slave control method and system for the cooperative operation of multiple instruments in narrow anatomical passageway disclosed, by obtaining multiple instrument coupling state data, and based on this, dynamic mapping processing and reverse coupling compensation are carried out, effectively offset the space coupling interference, can solve the coupling interference problem when the cooperative operation of multiple instruments in narrow anatomical passageway, improve the movement precision and operation intuition of surgical instrument, reduce the cognitive burden of doctor.

Description

Technical Field

[0001] This application relates to the field of medical robot technology, and in particular to a robot master-slave control method and system for collaborative operation of multiple instruments in narrow anatomical passages. Background Technology

[0002] In minimally invasive surgical practice, scenarios such as transforaminal endoscopic spinal surgery and transnasal or transurethral natural orifice endoscopic surgery commonly present challenges due to the highly limited anatomical structures. Surgeons must manipulate two or more rigid surgical instrument poles through narrow entrances only a few millimeters in diameter, such as the intervertebral foramen, nasal passage, or urethral orifice, to enter deep into the body for delicate operations. The physical size of such narrow entrances is much smaller than the cross-section of the instrument poles, resulting in tight spatial geometric constraints among multiple instrument poles in the entrance area. Current mainstream multi-instrument surgical robot systems generally adopt an independent master-slave mapping control architecture, treating each instrument arm as an isolated kinematic chain and directly translating the master arm's operating commands linearly into the corresponding slave instrument movements. However, this control strategy causes a severe "chopstick effect" in narrow-channel scenarios: when multiple instrument poles share the same tiny entrance, the geometric constraints at the entrance couple the degrees of freedom of the instrument poles, and the translational or rotational movement of one instrument pole will forcibly change the pose of adjacent instrument poles. For example, during spinal surgery, when moving the first instrument to dissect tissue, the second instrument may be accidentally pushed or twisted due to entry constraints, deviating from the intended trajectory.

[0003] Existing control methods lack real-time perception and dynamic compensation mechanisms for the coupling state of multiple instruments within the entry constraint domain, resulting in a non-intuitive mapping relationship between the master operator's operation and the slave operator's response. This mapping distortion reduces the precision of instrument movement, posing significant challenges for surgeons performing high-precision collaborative tasks. For example, during the simultaneous stabilization of the endoscope and suturing, additional cognitive resources are required to predict and counteract coupling interference. The resulting loss of operational intuition, excessive mental workload, and decreased operational efficiency not only prolong surgical time but also pose safety hazards such as tissue damage or loss of vision due to accidental instrument displacement.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a robot master-slave control method and system for collaborative operation of multiple instruments in narrow anatomical passages, aiming to improve the motion accuracy of surgical instruments.

[0006] To achieve the above objectives, this application proposes a robot master-slave control method for collaborative operation of multiple instruments within narrow anatomical passages, the method comprising: Acquire multi-instrument coupling state data, which characterizes the spatial geometric constraint relationship between multiple surgical instrument rods at the entrance of the narrow anatomical channel; Based on the multi-instrument coupling state data and the first master hand motion command data from the master hand operator, first slave instrument motion control data is generated through dynamic mapping processing. The dynamic mapping processing is used to map the first master hand motion command data into the first slave instrument motion control data that controls the movement of the first slave instrument, and at the same time, dynamically adjusts the mapping relationship according to the multi-instrument coupling state data. Based on the motion control data of the first slave device, second master hand guidance data is generated through reverse coupling compensation calculation. The second master hand guidance data is used to update the control mapping relationship of the master hand operator corresponding to the second slave device, so as to guide the operation of the master hand operator and thus cancel the spatial coupling interference caused by the motion of the first slave device to the second slave device. The motion control data of the first slave device is sent to the drive mechanism of the first slave device to execute the motion of the first slave device.

[0007] Acquiring multi-device coupling status data includes: Obtain the entrance constraint domain definition data; the entrance constraint domain definition data is used to define a virtual constraint region that surrounds or contains the actual entrance of the narrow anatomical passage; Acquire real-time pose data of each surgical instrument lever; Based on the defined entry constraint domain data and the real-time pose data of each surgical instrument lever, the multi-instrument coupling state data is calculated. The multi-instrument coupling state data includes: the incision point coordinates of each surgical instrument lever within the virtual constraint region, the direction vector of each surgical instrument lever, and the intersection angle between any two surgical instrument levers calculated based on the incision point coordinates and direction vectors.

[0008] Obtaining the entry constraint domain definition data includes: Acquire intraoperative medical image data or optical navigation marker data of the narrow anatomical passage; Based on the intraoperative medical image data or optical navigation marker data, the actual entrance location and boundary of the narrow anatomical passage are identified through image segmentation or spatial registration algorithms; Using the actual entrance location as the center, generate the initial geometric parameters of the virtual constraint region based on the boundary; Based on the real-time pose data of each surgical instrument lever, the smoothness of motion and the degree of constraint conflict of each surgical instrument lever within the virtual constraint area are evaluated to obtain constraint evaluation result data. Based on the constraint evaluation results, the initial geometric parameters of the virtual constraint region are fine-tuned, and the fine-tuned geometric parameters are used as the final entry constraint domain definition data.

[0009] Based on the multi-instrument coupling state data and the first master hand motion command data from the master hand operator, the step of generating the first slave end instrument motion control data through dynamic mapping processing includes: Receive the first main hand motion command data; the first main hand motion command data originates from the operation of the first main hand operator. Determine the current control mode data; Based on the current control mode data, the multi-instrument coupling state data, and the first master hand motion command data, the first slave instrument motion control data is generated through processing by a dynamic workspace mapping function. When the current control mode data is in a collaborative mode, the dynamic workspace mapping function performs nonlinear adjustment on the mapping relationship between the first master hand motion command data and the first slave instrument motion control data according to the multi-instrument coupling state data.

[0010] The dynamic workspace mapping function performs a nonlinear adjustment on the mapping relationship between the first master hand motion command data and the first slave device motion control data based on the multi-device coupling state data, including: Extract the intersection angle between the first slave device and the second slave device within the virtual constraint region from the multi-device coupling state data; The relationship between the mapping scaling factor and the intersection angle is established based on preset rules; the preset rules include: when the intersection angle decreases, the motion mapping scaling factor of the first slave device in the direction perpendicular to the shaft is increased, while the motion mapping scaling factor in the direction along the shaft is decreased; The first master hand motion command data is scaled using an adjusted mapping scaling factor to generate the first slave device motion control data.

[0011] The steps to determine the current control mode data include: Acquire mode switching request data, which is derived from user input signals or automatic decision signals; Based on the multi-device coupling state data, determine whether the current spatial geometric relationship of the multi-devices meets the preset cooperative mode conditions or independent mode conditions, and generate mode feasibility data. Arbitration is performed based on the mode switching request data and the mode feasibility data to determine the current control mode data. Specifically, if the target mode requested by the mode switching request data is consistent with the feasible mode indicated by the mode feasibility data, then the current control mode data is determined to be the target mode; otherwise, the current control mode data is determined to be either maintaining the original mode or switching to the feasible mode indicated by the mode feasibility data.

[0012] The steps for generating second master hand guidance data based on the first slave device motion control data through reverse coupling compensation calculation include: Based on the motion control data of the first slave device, the coupling state data of the multiple devices, and the current actual pose data of the second slave device, the target compensation pose data of the second slave device is calculated through inverse kinematics and coupled geometric model; the target compensation pose data is the position and posture that minimizes the spatial coupling interference to the first slave device. Based on the target compensated pose data and the current actual pose data of the second slave device, pose deviation data is calculated. The pose deviation data is converted into the second master hand guidance data.

[0013] The method further includes: The reference guiding force vector is calculated based on the guiding vector or guiding force field defined by the second master guide data; Identify the current actual movement direction data of the second master hand controller; Calculate the angle between the current actual motion direction data and the direction of the reference guiding force vector; Based on the included angle, the magnitude and / or force feedback characteristics of the reference guiding force vector are dynamically modulated to generate force feedback data. Specifically, this includes: when the included angle is less than a first threshold, generating force feedback data with viscous damping force characteristics as the main component; and when the included angle is greater than a second threshold, generating force feedback data with elastic traction force characteristics as the main component.

[0014] The method further includes: Based on the multi-device coupling state data, the current theoretical coupling interference degree experienced by the first slave device is calculated; The current theoretical coupling interference degree is used as a real-time parameter and input into the dynamic workspace mapping function to dynamically adjust the intensity or strategy of the nonlinear adjustment. And / or, The current theoretical coupling interference degree is converted into a tactile encoded signal, superimposed on the force feedback data, and output to the second main hand operator.

[0015] Furthermore, to achieve the above objectives, this application also proposes a robot master-slave control system for collaborative operation of multiple instruments within a narrow anatomical passage. The robot master-slave control system includes: a memory, a processor, and a robot master-slave control program for collaborative operation of multiple instruments within a narrow anatomical passage, stored in the memory and executable on the processor. The robot master-slave control program for collaborative operation of multiple instruments within a narrow anatomical passage is configured to implement the steps of the robot master-slave control method for collaborative operation of multiple instruments within a narrow anatomical passage.

[0016] The robot master-slave control method and system proposed in this application for multi-instrument collaborative operation in narrow anatomical passages effectively counteracts spatial coupling interference by acquiring multi-instrument coupling state data and performing dynamic mapping processing and reverse coupling compensation based on this data. This solves the coupling interference problem during multi-instrument collaborative operation in narrow anatomical passages, improves the motion accuracy and operational intuition of surgical instruments, and reduces the cognitive burden on doctors. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the robot master-slave control method for collaborative operation of multiple instruments in a narrow anatomical passage, as provided in this application. Figure 2 This is a schematic diagram of a robot master-slave control system for collaborative operation of multiple instruments in a narrow anatomical passage, as provided in this application.

[0020] Explanation of icon numbers: 10. Memory; 20. Processor.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] In existing technologies, minimally invasive surgical robot systems typically treat each instrument arm as an independent kinematic chain when performing multi-instrument collaborative operations within narrow anatomical passages. This approach fails to effectively address the spatial geometric coupling and mechanical interference of multiple instrument arms at the entry point, known as the "chopstick effect." This results in poor intuitiveness of the master hand operation, decreased accuracy of the slave instruments, and increased workload and potential risks for the surgeon.

[0025] Based on this, embodiments of this application provide a robot master-slave control method for collaborative operation of multiple instruments within narrow anatomical passages, referring to... Figure 1 The robot master-slave control method for multi-instrument collaborative operation in narrow anatomical passages includes steps S100 to S600, wherein: Step S100: Obtain multi-instrument coupling state data, which represents the spatial geometric constraint relationship between multiple surgical instrument rods at the entrance of the narrow anatomical channel. Step S200: Based on the multi-instrument coupling state data and the first master hand motion command data from the master hand operator, first slave instrument motion control data is generated through dynamic mapping processing; the dynamic mapping processing is used to map the first master hand motion command data to the first slave instrument motion control data that controls the movement of the first slave instrument, and at the same time dynamically adjusts the mapping relationship according to the multi-instrument coupling state data. Step S300: Based on the motion control data of the first slave device, second master hand guidance data is generated through reverse coupling compensation calculation; the second master hand guidance data is used to update the control mapping relationship of the master hand operator corresponding to the second slave device, so as to guide the operation of the master hand operator and thereby cancel the spatial coupling interference caused by the motion of the first slave device to the second slave device. Step S400: Send the motion control data of the first slave device to the drive mechanism of the first slave device to execute the motion of the first slave device.

[0026] In this embodiment, multi-instrument coupling state data is used to characterize the spatial geometric constraints between multiple surgical instrument poles at the entrance of a narrow anatomical passage. This data may include the relative position and orientation of each instrument pole, as well as information on possible contact or proximity between them. A narrow anatomical passage refers to the restricted path for surgical instruments to enter the body during minimally invasive surgery, such as the intervertebral foramen in transforaminal spinal surgery, the natural orifice in natural cavity surgery, or a pre-designed working channel. The master hand manipulator is a device used by the surgeon to input operating commands; it typically has force feedback functionality, capable of sensing the surgeon's hand movements and converting them into digital commands. The first master hand motion command data consists of the raw motion commands generated by the master hand manipulator based on the surgeon's operations, such as position, orientation, or speed commands. Dynamic mapping processing is an algorithmic process that converts master hand operation commands into slave instrument motion control commands; this process can adjust the mapping relationship according to the real-time environment or system state.

[0027] In this embodiment, the motion control data of the first slave instrument is a command used to directly control the movement of the first slave instrument after dynamic mapping processing, such as joint angles, end effector pose, or speed commands. The first slave instrument refers to the instrument controlled by the robot system to perform surgical operations, such as needle forceps, electrosurgical units, or grasping forceps. The reverse coupling compensation calculation is a calculation process used to evaluate and counteract mutual interference between multiple instruments. It predicts the impact of one instrument's movement on another instrument and generates corresponding compensation commands. The second master hand guidance data is the result of the reverse coupling compensation calculation and is used to adjust the force feedback or motion mapping of the master hand manipulator corresponding to the second slave instrument to guide the surgeon's operation, thereby reducing coupling interference. The second slave instrument refers to another robot-controlled surgical instrument that operates in conjunction with the first slave instrument. The drive mechanism is a mechanical and electronic component responsible for receiving motion control data and actually driving the slave instruments to move, such as motors, reducers, and linkage mechanisms.

[0028] In this embodiment, the robot master-slave control method for collaborative operation of multiple instruments within a narrow anatomical passage first acquires multi-instrument coupling state data. This data characterizes the spatial geometric constraints between multiple surgical instrument poles at the entrance of the narrow anatomical passage. For example, sensors can be installed on the surgical instrument poles to measure the relative position and orientation of each instrument pole in real time, and geometric relationships such as distances and angles between the instrument poles can be calculated based on these measurements. As another implementation, the coupling state data can be obtained through theoretical calculations using a pre-established geometric model, combined with the known dimensions of the instrument poles and the geometric information of the entrance region.

[0029] In this embodiment, based on the multi-instrument coupling state data and the first master hand motion command data from the master hand operator, first slave instrument motion control data is generated through dynamic mapping processing. This dynamic mapping processing aims to convert the first master hand motion command data into control data for controlling the movement of the first slave instrument. For example, a basic linear mapping relationship can be used to directly map the movement of the master hand operator proportionally to the movement of the slave instrument. Based on this, the mapping relationship can be adjusted in real time according to the acquired multi-instrument coupling state data. For example, when it is detected that the distance between instrument levers is too close, the movement speed of the slave instrument can be simply reduced or its movement range in certain directions can be limited to avoid potential collisions.

[0030] In this embodiment, based on the motion control data of the first slave device, second master hand guidance data is generated through reverse coupling compensation calculation. This second master hand guidance data is used to update the control mapping relationship with the master hand operator corresponding to the second slave device, guiding the operation of the master hand operator and thus counteracting the spatial coupling interference caused by the motion of the first slave device on the second slave device. For example, when the first slave device moves, its potential interference to the second slave device can be predicted based on its motion trajectory and coupling state data. Then, using a simple force feedback model, a guiding force with a fixed direction and magnitude is calculated and applied to the second master hand operator, prompting the operator to adjust the position of the second slave device to reduce interference.

[0031] In this embodiment, the motion control data of the first slave device is finally sent to the drive mechanism of the first slave device to execute the motion of the first slave device. After receiving the control data, the drive mechanism will drive the first slave device to perform corresponding movements according to the instructions. For example, the drive mechanism can be a servo motor system, which precisely controls the movement of the joints or end effector of the slave device according to the received position or speed instructions.

[0032] In this embodiment, by actively acquiring multi-instrument coupling status data and dynamically adjusting the master-slave mapping relationship based on this data, and simultaneously generating master-hand guidance data through reverse coupling compensation calculation, the "chopstick effect" problem existing in multi-instrument operation within narrow anatomical passages is effectively solved. This improves the intuitiveness of master-hand operation and the precision of slave instrument movement, reduces the mental burden on surgeons during complex collaborative operations, and enhances surgical efficiency and safety.

[0033] In one feasible implementation, acquiring multi-instrument coupling state data includes: acquiring entrance constraint domain definition data; the entrance constraint domain definition data is used to define a virtual constraint region that surrounds or contains the actual entrance of the narrow anatomical passage; acquiring real-time pose data of each surgical instrument lever; calculating the multi-instrument coupling state data based on the entrance constraint domain definition data and the real-time pose data of each surgical instrument lever; the multi-instrument coupling state data includes: the incision point coordinates of each surgical instrument lever within the virtual constraint region, the direction vector of each surgical instrument lever, and the intersection angle between any two surgical instrument levers calculated based on the incision point coordinates and the direction vector.

[0034] In this embodiment, obtaining the entry constraint domain definition data refers to determining a virtual, three-dimensional spatial region that precisely surrounds or contains the actual entry point of the narrow anatomical passage. This virtual constraint region is the basis for analyzing the spatial geometric constraint relationships between surgical instrument rods. It simulates the physical limitations when instruments enter the human body, ensuring that the coupling state data calculated subsequently is valid information for the actual operating environment. The entry constraint domain definition data can be a geometric model, such as a sphere, ellipsoid, cylinder, or convex hull of any shape, whose parameters (such as center coordinates, radius, major and minor axes, direction, etc.) define the size, shape, and position of the region. These parameters can be constructed and updated using preoperative planning data, intraoperative real-time image analysis (such as segmentation results from CT, MRI, and ultrasound images), or marker point data acquired through an optical / electromagnetic navigation system. For example, a standardized geometry can be pre-defined and then scaled and positioned according to the actual anatomical structure.

[0035] In this embodiment, acquiring real-time pose data for each surgical instrument lever refers to the precise position and orientation information of each lever in three-dimensional space during the surgical procedure. This data is a crucial input for dynamically calculating the spatial geometric relationships between instruments, reflecting their current motion state. Real-time pose data can be acquired using various sensor technologies. For example, optical markers can be integrated onto the surgical instrument levers, and the three-dimensional coordinates of these markers can be captured in real time using an optical tracking system to calculate the position and orientation of the levers. Alternatively, an electromagnetic tracking system can be used, integrating miniature electromagnetic sensors into the levers and determining their pose by measuring the sensors' response in an electromagnetic field. Furthermore, the pose of the levers can also be calculated using the robot's own encoder data and kinematic model, combined with force / torque sensor data from the end effector.

[0036] Based on this, the multi-instrument coupling state data is calculated using the defined entry constraint domain data and the real-time pose data of each surgical instrument lever. This step combines static entry constraint domain information with dynamic instrument pose information, and through geometric calculation and analysis, derives "multi-instrument coupling state data" that characterizes the spatial interaction relationship between instruments. This is a transformation process from raw data to meaningful coupling information. The calculation process typically involves spatial geometric algorithms. First, the boundary of the virtual constraint region is determined according to the defined entry constraint domain data. Then, for each surgical instrument lever, the intersection point between the lever and the boundary of the virtual constraint region, i.e., the "entry point coordinates," is calculated using its real-time pose data (position and direction vectors). Simultaneously, the lever's own "direction vector" is also part of its pose data. Finally, based on these entry point coordinates and direction vectors, the "intersection angle" between any two surgical instrument levers can be further calculated, for example, by calculating the angle between two direction vectors, or by analyzing the relative position and orientation of the levers within the virtual constraint region.

[0037] In this embodiment, the multi-instrument coupling state data includes: the coordinates of the entry point of each surgical instrument rod within the virtual constraint area, the direction vector of each surgical instrument rod, and the intersection angle between any two surgical instrument rods calculated based on the entry point coordinates and direction vectors. These data items are core elements comprehensively describing the spatial geometric constraint relationship of the instruments at the entrance of the narrow passage. The entry point coordinates can be obtained using a ray-to-geometry intersection algorithm. Each surgical instrument rod is considered as a ray, with its starting point at the instrument base and its direction being the direction of the instrument rod's axis. The first intersection point of this ray with the boundary of the virtual constraint area is the entry point coordinates. The direction vector is usually the unit vector of the instrument rod's axis direction in the global coordinate system, which can be directly extracted from real-time pose data or obtained through coordinate transformation. For any two surgical instrument rods, their respective direction vectors can be extracted, and then the angle between them can be obtained by calculating the dot product of these two direction vectors and combining their magnitudes. This angle can reflect the tendency of the instrument rods to move closer or further apart at the entrance, as well as their relative posture in space.

[0038] In this embodiment, a virtual constraint region is defined to simulate the actual entrance of a narrow anatomical passage. Combined with the real-time pose data of each surgical instrument lever, the coordinates of the entry point, direction vector, and intersection angle between the levers at the entrance can be accurately calculated. This detailed and crucial geometric information constitutes comprehensive multi-instrument coupling state data, providing a solid data foundation for subsequent dynamic mapping processing and reverse coupling compensation calculations. This enables the robotic system to more accurately perceive and predict spatial interactions between instruments, thereby achieving more precise and safer collaborative operations within the narrow anatomical passage, effectively avoiding instrument collisions and jamming, and improving surgical efficiency and patient safety.

[0039] In one feasible implementation, obtaining the entry constraint domain definition data includes: acquiring intraoperative medical image data or optical navigation marker data of the narrow anatomical passage; based on the intraoperative medical image data or optical navigation marker data, identifying the actual entry position and boundary of the narrow anatomical passage through image segmentation or spatial registration algorithms; generating initial geometric parameters of the virtual constraint region centered on the actual entry position and according to the boundary; evaluating the smoothness of motion and the degree of constraint conflict of each surgical instrument lever within the virtual constraint region based on the real-time pose data of each surgical instrument lever, obtaining constraint evaluation result data; and fine-tuning the initial geometric parameters of the virtual constraint region based on the constraint evaluation result data, and using the fine-tuned geometric parameters as the final entry constraint domain definition data.

[0040] In this embodiment, intraoperative medical image data or optical navigation marker data of the narrow anatomical passage are acquired. The intraoperative medical image data can be derived from real-time intraoperative X-ray fluoroscopy images, ultrasound images, endoscopic images, or optical coherence tomography (OCT) images, which provide real-time anatomical information of the narrow anatomical passage and its surrounding tissues. Optical navigation marker data can be obtained by pre-placing optical markers near the narrow anatomical passage or on the instruments, and then using an optical tracking system (e.g., a stereoscopic vision-based infrared tracking system) to capture the three-dimensional spatial coordinates of these markers in real time. This data provides the foundation for subsequent accurate identification of the passage entrance.

[0041] In this embodiment, based on the intraoperative medical image data or optical navigation marker data, the actual entrance location and boundary of the narrow anatomical passage are identified using image segmentation or spatial registration algorithms. Specifically, if intraoperative medical image data is used, image processing techniques, such as deep learning-based semantic segmentation algorithms (e.g., U-Net), thresholding, region growing, or active contour models, can be applied to accurately extract the contour of the narrow anatomical passage from the image, thereby determining its actual entrance location and boundary. If optical navigation marker data is used, the actual entrance location and boundary can be determined by spatially registering the marker data with a pre-established anatomical model, or by directly utilizing the distribution of markers at the passage entrance. This step ensures accurate identification of key anatomical regions.

[0042] In this embodiment, the initial geometric parameters of the virtual constraint region are generated based on the boundary, with the actual entrance location as the center. For example, the geometric center of the identified entrance boundary can be calculated as the center point of the virtual constraint region, and the initial radius, major and minor axes, or length and width of the virtual constraint region can be determined according to the shape and size of the boundary (such as maximum width, perimeter, etc.). The virtual constraint region can be defined as a circle, ellipse, rectangle, or arbitrary polygon, etc., and its initial geometric parameters are intended to initially encompass the actual entrance of the narrow anatomical passage.

[0043] In this embodiment, based on the real-time pose data of each surgical instrument lever, the smoothness of motion and the degree of constraint conflict of each surgical instrument lever within the virtual constraint area are evaluated to obtain constraint evaluation result data. Motion smoothness evaluation can analyze the continuity and rate of change of the instrument lever's trajectory within the virtual constraint area, for example, by calculating the stability index of the instrument lever's velocity or acceleration. The constraint conflict degree evaluation involves real-time detection of the minimum distance between each instrument lever and between the instrument lever and the boundary of the virtual constraint area, and prediction of potential collision or friction risks. For example, when the distance between instrument levers is less than a preset safety threshold, or when the instrument lever approaches the boundary of the virtual area, the degree of conflict increases. These evaluation results provide quantitative feedback on the applicability of the current virtual constraint area.

[0044] In this embodiment, the initial geometric parameters of the virtual constraint region are fine-tuned based on the constraint evaluation results, and the fine-tuned geometric parameters are used as the final entry constraint domain definition data. For example, if the evaluation results show a high degree of constraint conflict, the size of the virtual constraint region can be appropriately increased or its shape adjusted to provide more operating space; if the motion smoothness is low, the center or direction of the region can be fine-tuned to guide the instrument rod through more smoothly. This fine-tuning process can be implemented using an iterative optimization algorithm to maximize the flexibility and smoothness of operation while ensuring safety.

[0045] In this embodiment, the above-described technical solution overcomes the potential static or inaccurate nature of the entry constraint domain definition data in traditional methods. By acquiring intraoperative medical image data or optical navigation marker data in real time, and combining it with image segmentation or spatial registration algorithms, the actual entry position and boundary of the narrow anatomical passage can be accurately identified, thereby generating initial geometric parameters of the virtual constraint region that better reflect the actual situation. Furthermore, by evaluating the smoothness of movement and the degree of constraint conflict of each surgical instrument lever within the virtual constraint region in real time, and adaptively fine-tuning the geometric parameters of the virtual constraint region based on the evaluation results, it is ensured that the entry constraint domain definition data can dynamically adapt to minor changes in anatomical structure or instrument pose during surgery. This makes the acquisition of multi-instrument coupling state data more accurate and robust, thus providing a reliable foundation for subsequent dynamic mapping processing and reverse coupling compensation calculations. Ultimately, this solution improves the safety, accuracy, and smoothness of multi-instrument collaborative operation within narrow anatomical passages, effectively reducing the risk of instrument collisions or tissue damage, and improving surgical efficiency and operational experience.

[0046] In one feasible implementation, the step of generating first slave device motion control data through dynamic mapping processing based on the multi-device coupling state data and first master hand motion command data from the master hand operator includes: receiving the first master hand motion command data; the first master hand motion command data originates from the operation of the first master hand operator; determining the current control mode data; and generating the first slave device motion control data by processing the current control mode data, the multi-device coupling state data, and the first master hand motion command data through a dynamic workspace mapping function; wherein, when the current control mode data is a collaborative mode, the dynamic workspace mapping function nonlinearly adjusts the mapping relationship from the first master hand motion command data to the first slave device motion control data according to the multi-device coupling state data.

[0047] In this embodiment, receiving the first master hand motion command data refers to the operation signal received by the robot master-slave control system from the master hand manipulator, which represents the operator's intention to have the first slave device perform a movement. This data can be acquired in various ways. For example, an encoder or position sensor integrated inside the master hand manipulator can detect changes in the operator's hand position and posture in real time and convert them into digital signals; or, a force / torque sensor installed on the master hand manipulator can sense the force and torque applied by the operator and use it as input for motion commands. This command data typically includes information such as position, posture, velocity, or force, and is the basis for driving the movement of the slave device.

[0048] In this embodiment, determining the current control mode data refers to the current operating mode of the system, such as cooperative mode, independent mode, or auxiliary mode. This mode can be determined manually by the user through interface selection, voice commands, or physical buttons, or it can be automatically determined and switched by the system based on preset conditions. For example, when the distance between multiple slave devices is less than a certain threshold, or when the system detects a potential collision risk, it can automatically switch to cooperative mode to initiate a more refined obstacle avoidance and coordinated control strategy.

[0049] Based on this, the current control mode data, the multi-instrument coupling state data, and the first master hand motion command data are processed through a dynamic workspace mapping function to generate the first slave instrument motion control data. The dynamic workspace mapping function is the core component for realizing master-slave motion conversion. It converts the master hand's motion commands into specific motion control commands for the first slave instrument based on the current operating mode, the coupling state between instruments, and the operator's commands. This function can be a complex mathematical model, such as a kinematic mapping based on the Jacobian matrix, or an adaptive mapping model incorporating artificial intelligence algorithms (such as neural networks). Its processing aims to ensure that the movement of the slave instrument accurately reflects the operator's intentions while effectively avoiding spatial constraints and interference between instruments.

[0050] Furthermore, when the current control mode data is in a collaborative mode, the dynamic workspace mapping function nonlinearly adjusts the mapping relationship between the first master hand motion command data and the first slave device motion control data based on the multi-device coupling state data. In collaborative mode, due to the operation of multiple devices in a narrow space, the mutual influence between devices is greater. At this time, the dynamic workspace mapping function will nonlinearly adjust the master-slave mapping relationship based on the multi-device coupling state data. This nonlinear adjustment means that the mapping ratio or direction is no longer a fixed linear relationship, but dynamically changes according to the coupling state (such as the distance between devices, relative angle, potential collision risk, etc.). For example, when two devices approach each other, the system can nonlinearly reduce the motion sensitivity along the collision direction while increasing the motion sensitivity perpendicular to the collision direction to guide the operator to perform safer obstacle avoidance operations. In addition, nonlinear adjustment can also be achieved by introducing a virtual force field or damping effect, so that when the slave device approaches the constraint boundary, its motion response will change smoothly and nonlinearly, thereby effectively avoiding collisions without completely preventing the operator's intentions.

[0051] In this embodiment, by receiving the first master hand motion command data and determining the current control mode data, the system can flexibly adjust the control strategy according to different operating scenarios. Especially in collaborative mode, the dynamic workspace mapping function nonlinearly adjusts the mapping relationship between the first master hand motion command data and the first slave instrument motion control data based on real-time multi-instrument coupling status data. This nonlinear adjustment ensures that the movement of the slave instruments is no longer a simple linear follow, but rather intelligently adapts to the complex spatial constraints within narrow anatomical passages. For example, when the slave instruments are too close together or there is a potential risk of collision, the nonlinear adjustment can automatically reduce the motion gain along the conflict direction or increase the motion gain in the avoidance direction, thereby effectively guiding the slave instruments to avoid obstacles and preventing instrument collisions without affecting the operator's overall intent. This improves the safety, accuracy, and efficiency of multi-instrument collaborative operation in narrow spaces, enabling the operator to complete complex surgical tasks more intuitively and safely.

[0052] In one feasible implementation, the dynamic workspace mapping function, based on the multi-device coupling state data, performs a nonlinear adjustment to the mapping relationship between the first master hand motion command data and the first slave device motion control data. This includes: extracting the intersection angle between the first slave device and the second slave device within the virtual constraint region from the multi-device coupling state data; establishing a correlation between the mapping scaling factor and the intersection angle based on a preset rule; the preset rule includes: increasing the motion mapping scaling factor of the first slave device in the direction perpendicular to the shaft while decreasing the motion mapping scaling factor along the shaft when the intersection angle decreases; and scaling the first master hand motion command data using the adjusted mapping scaling factor to generate the first slave device motion control data.

[0053] In this embodiment, extracting the intersection angle between the first and second slave instruments within the virtual constraint region from the multi-instrument coupling state data refers to the system determining the relative angle between the axes of the first and second slave instruments within the virtual constraint region using geometric calculation methods, based on the acquired multi-instrument coupling state data, which includes the coordinates of the entry points and direction vectors of each surgical instrument rod within the virtual constraint region. This intersection angle is a key parameter for measuring the spatial proximity and relative posture between the two instrument rods; a smaller value generally indicates that the instrument rods are more parallel or about to make contact.

[0054] Based on this, a correlation is established between the mapping scaling factor and the intersection angle according to preset rules. This correlation can be a predefined functional relationship, a lookup table, or a rule set based on fuzzy logic. The purpose is to use the intersection angle calculated in real time as input and dynamically output a scaling factor to adjust the master-slave mapping relationship. For example, a nonlinear function can be designed so that the scaling factor changes when the intersection angle is within a certain critical range.

[0055] In this embodiment, the preset rule further clarifies that when the intersection angle decreases, the motion mapping ratio factor of the first slave device in the direction perpendicular to the pole is increased, while the motion mapping ratio factor along the pole is decreased. This means that when the two device poles become closer or parallel in a narrow space, the system enhances the operator's control sensitivity to the lateral (perpendicular to the pole) movement of the device, so that a small lateral operation at the master end can cause a more significant lateral displacement of the slave device, thereby facilitating the operator to perform precise obstacle avoidance or separation operations. At the same time, the system reduces the operator's control sensitivity to the longitudinal (along the pole) movement of the device, so that a large longitudinal operation at the master end only causes a small longitudinal displacement of the slave device, thereby effectively preventing the device from getting stuck, colliding, or being excessively squeezed due to excessive axial movement in a narrow channel.

[0056] In this embodiment, the first master hand motion command data is finally scaled using an adjusted mapping scaling factor to generate the first slave device motion control data. This step involves applying the scaling factor adjusted according to the above rules to the original motion command data from the master hand operator. For example, the displacement or velocity component of the master hand command is multiplied by the corresponding scaling factor to obtain the final motion control command sent to the first slave device drive mechanism.

[0057] In this embodiment, through the above technical solution, this application can extract the intersection angle between the first and second slave instruments within a virtual constraint area from multi-instrument coupling state data, and establish a correlation between the mapping scaling factor and the intersection angle based on preset rules. This allows for dynamic adjustment of the master-slave mapping sensitivity according to the actual spatial geometry between the instruments. Specifically, when the intersection angle decreases, i.e., the distance between instruments approaches or their postures become parallel, increasing the motion mapping scaling factor perpendicular to the lever direction allows the slave instrument to generate more lateral displacement when the operator makes minor lateral adjustments at the master end, effectively avoiding lateral collisions between instrument levers. Simultaneously, decreasing the motion mapping scaling factor along the lever direction reduces the sensitivity of the instrument's axial movement, preventing jamming or excessive compression of instruments in narrow spaces due to excessively rapid axial movement. This nonlinear adjustment mechanism provides operators with a more refined and safer control experience when performing multi-instrument collaborative operations in narrow anatomical passages, reducing the risk of collisions and entanglement between instruments and improving surgical precision and safety.

[0058] In one feasible implementation, the step of determining the current control mode data includes: acquiring mode switching request data, which originates from user input signals or automatic decision signals; based on the multi-device coupling state data, determining whether the spatial geometric relationship of the current multi-devices meets preset cooperative mode conditions or independent mode conditions, and generating mode feasibility data; and arbitrating based on the mode switching request data and the mode feasibility data to determine the current control mode data, specifically including: if the target mode requested by the mode switching request data is consistent with the feasible mode indicated by the mode feasibility data, then the current control mode data is determined to be the target mode; otherwise, the current control mode data is determined to be either maintaining the original mode or switching to the feasible mode indicated by the mode feasibility data.

[0059] In this embodiment, the mode switching request data is a signal that triggers a change in control mode. This data can come from various sources. For example, the operator may issue a switching request via buttons on the main hand controller, a foot switch, voice commands, or a graphical user interface; alternatively, the system may automatically generate a mode switching request based on a preset surgical procedure, instrument status (such as whether the instrument has entered the target area or completed a specific operation), or by analyzing the current surgical scenario using artificial intelligence algorithms. This request data clearly indicates the desired target control mode to switch to, such as switching from independent mode to collaborative mode, or vice versa.

[0060] In this embodiment, after acquiring the mode switching request data, the system uses real-time updated multi-instrument coupling state data to evaluate the spatial geometric relationship between all surgical instrument rods. The multi-instrument coupling state data, such as the coordinates of the entry point of each surgical instrument rod within the virtual constraint area, the direction vector, and the intersection angle between any two surgical instrument rods, provides detailed information on the relative positions and attitudes of the instruments. Preset cooperative mode conditions may include: when the intersection angle between any two or more surgical instrument rods is less than a certain threshold, the distance between the instrument rods is less than a certain safe distance, or there is a potential risk of intersection in the movement trajectories of the instrument rods, the system determines that it should enter cooperative mode for fine-tuned coordinated control. Conversely, preset independent mode conditions may include: when the intersection angle between all surgical instrument rods is greater than a certain threshold, the distance between the instrument rods is greater than a certain safe distance, or the movement trajectories of the instrument rods do not interfere with each other, the system determines that it can enter independent mode, allowing the instruments to move relatively independently. Through this determination, the system generates mode feasibility data, which indicates which control modes are safe and feasible under the current spatial geometric relationship.

[0061] In this embodiment, the system then compares the mode switching request data with the mode feasibility data to arbitrate and determine the current control mode data. If the target mode requested by the operator or the automated decision-making system matches the feasible mode determined by the system based on real-time multi-instrument coupling status data—that is, the requested mode is safe and appropriate under the current spatial geometry—the system will approve the request and determine the current control mode data as the target mode. For example, when the instruments are far apart, requesting to switch to independent mode is feasible. However, if the requested target mode is inconsistent with the mode indicated by the mode feasibility data—for example, when the instruments are very close together and there is a risk of collision, but a request is made to switch to independent mode—the system will not directly execute the request. In this case, the system will adopt a safety strategy, such as maintaining the current control mode or forcibly switching to a safer mode indicated by the mode feasibility data (e.g., switching back from the requested independent mode to the cooperative mode) to avoid potential instrument collisions or operational errors.

[0062] In this embodiment, through the above-described technical solution, when determining the current control mode of the robot master-slave control system, this application not only considers the operator's intention or the preset automation process, but more importantly, introduces a dynamic evaluation of the real-time spatial geometric relationship of multiple instruments. This mechanism, which arbitrates based on mode switching request data and mode feasibility data, can effectively avoid inappropriate mode switching under unsafe spatial conditions. For example, when multiple surgical instrument rods are too close together in a narrow anatomical passage, even if the operator requests to switch to an independent mode, the system can determine that the mode is not feasible based on the real-time coupling state, thereby maintaining the collaborative mode or forcibly switching to the collaborative mode, ensuring a safe distance between instruments and reducing the risk of instrument collisions. This enables the robot system to perform multi-instrument collaborative operations more intelligently and safely in complex and ever-changing narrow anatomical passage environments, improving the accuracy and safety of surgery while also taking into account operational flexibility and efficiency.

[0063] In one feasible implementation, the step of generating second master hand guidance data based on the motion control data of the first slave device through reverse coupling compensation calculation includes: calculating the target compensation pose data of the second slave device based on the motion control data of the first slave device, the multi-device coupling state data, and the current actual pose data of the second slave device through inverse kinematics and coupled geometric model; the target compensation pose data is the position and posture of the second slave device that minimizes spatial coupling interference to the first slave device; calculating pose deviation data based on the target compensation pose data and the current actual pose data of the second slave device; and converting the pose deviation data into the second master hand guidance data.

[0064] In this embodiment, the aforementioned motion control data of the first slave instrument refers to the instructions controlling the motion of the first slave instrument, which include its target position, posture, or velocity information, reflecting the motion intention that the first slave instrument is about to execute or is currently executing. The aforementioned multi-instrument coupling state data characterizes the spatial geometric constraint relationship between multiple surgical instrument rods at the entrance of a narrow anatomical passage. Specifically, it may include the coordinates of the entry point of each surgical instrument rod within the virtual constraint area, the direction vector of each surgical instrument rod, and the intersection angle between any two surgical instrument rods calculated based on this information. This data is crucial for assessing and predicting potential coupling interference between instruments. The aforementioned current actual pose data of the second slave instrument refers to the actual spatial position and posture information of the second slave instrument at the current moment, typically acquired in real time through sensors within the robot system (e.g., encoders, force sensors, or external visual tracking systems) to accurately reflect its current spatial state.

[0065] In this embodiment, inverse kinematics refers to calculating the corresponding angles or displacements of each joint of the robot based on the target pose required by the robot's end effector (in this case, the second slave device). When calculating the target compensation pose data of the second slave device, inverse kinematics is used to transform abstract spatial pose requirements into executable joint motion commands for the robot. The coupled geometry model is a mathematical model used to describe the interaction and geometric constraints of multiple devices in a confined space. This model can establish collision detection, minimum distance calculation, or contact mechanics models based on the geometric parameters of the device's links (such as diameter and length), entry point position, and orientation, thereby predicting the impact of one device's motion on the spatial availability or potential collision risk of another device. Through this model, spatial coupling interference between devices can be quantified and predicted.

[0066] In this embodiment, the target compensated pose data of the second slave device is the ideal spatial position and attitude of the second slave device obtained according to the above calculation process. Its core purpose is to minimize spatial coupling interference with the first slave device without affecting the second slave device's own operational intent. This may involve fine-tuning its position to increase the safety distance or adjusting its attitude to avoid potential collisions between the poles. The pose deviation data is the difference between the target compensated pose data of the second slave device and its current actual pose data. It quantifies the gap between the current state and the ideal compensated state of the second slave device, typically expressed as positional deviations (such as X, Y, Z direction deviations in Cartesian coordinates) and attitude deviations (such as rotational deviations represented by Euler angles or quaternions). The second master hand guidance data is used to update the control mapping relationship with the master hand operator corresponding to the second slave device. It transforms the calculated pose deviation information into a form that the master hand operator can perceive, such as force feedback commands, visual cues, or tactile signals. Through this guidance data, the system can proactively guide the operator to adjust the operation of the second master manipulator, thereby indirectly controlling the second slave device to move toward the target compensation pose to counteract or avoid coupling interference.

[0067] In this embodiment, through the above technical solution, this application can accurately calculate the target compensation pose data of the second slave instrument based on the motion control data of the first slave instrument, the multi-instrument coupling state data, and the current actual pose data of the second slave instrument, using inverse kinematics and a coupled geometric model. This target compensation pose data aims to position the second slave instrument in a position and posture that minimizes spatial coupling interference to the first slave instrument. Subsequently, by calculating the deviation between the current actual pose and the target compensation pose and converting it into second master hand guidance data, the system can actively guide the operator of the second master hand manipulator, enabling them to predict and effectively counteract the spatial coupling interference caused by the movement of the first slave instrument to the second slave instrument. This not only improves the accuracy and safety of multi-instrument collaborative operation and avoids accidental collisions between instruments, but also reduces the cognitive burden on the operator, making complex surgical operations in narrow anatomical passages smoother and more efficient.

[0068] In one feasible implementation, the method further includes: calculating a reference guiding force vector based on the guiding vector or guiding force field defined by the second master hand guiding data; identifying the current actual movement direction data of the second master hand operator; calculating the angle between the current actual movement direction data and the direction of the reference guiding force vector; and dynamically modulating the magnitude and / or force feedback characteristics of the reference guiding force vector based on the angle to generate force feedback data, specifically including: generating force feedback data with viscous damping force characteristics as the main feature when the angle is less than a first threshold; and generating force feedback data with elastic traction force characteristics as the main feature when the angle is greater than a second threshold.

[0069] In this embodiment, the reference guiding force vector represents the direction and intensity of the system's desired movement of the master hand manipulator. It transforms the compensation direction and pose deviation indicated by the second master hand guiding data into a mechanical command perceptible to the operator. For example, if the second master hand guiding data indicates that the second slave device needs to move in a certain direction to avoid colliding with the first slave device, then the reference guiding force vector will point in that direction, and its magnitude is proportional to the required degree of compensation. Simultaneously, the method identifies the current actual movement direction data of the second master hand manipulator. This is typically achieved by real-time monitoring of sensor data (e.g., an encoder or inertial measurement unit) within the master hand manipulator to obtain its instantaneous velocity vector, thereby determining the operator's current movement intention. Subsequently, the angle between the current actual movement direction data and the direction of the reference guiding force vector is calculated. This angle quantifies the degree of consistency or deviation between the operator's current movement intention and the system's desired guiding direction.

[0070] In this embodiment, based on the included angle, the system dynamically modulates the magnitude and / or force characteristics of the reference guiding force vector to generate force feedback data. Specifically, when the included angle is less than a first threshold, the system generates force feedback data primarily characterized by viscous damping force. Viscous damping force is proportional to the operator's movement speed and opposite in direction to the movement direction. It provides a smooth and stable "adhesive" feeling, helping the operator move more stably and smoothly along the desired path while maintaining a basic alignment with the guiding direction. It also allows for fine-tuning, avoiding a stiff, resistant feeling. This characteristic helps suppress hand tremors and improves operational stability. When the included angle is greater than a second threshold, the system generates force feedback data primarily characterized by elastic traction force. Elastic traction force is proportional to the operator's displacement deviation from the desired position or direction and points towards the desired position. When the operator's movement direction deviates from the guiding direction, this force characteristic more strongly "pulls" the operator's hand back onto the desired guiding path, providing a clear correction signal, effectively preventing the operator from moving in the wrong direction, and guiding them back to the correct operating area. Between the first and second thresholds, a smooth transition function can be used to blend the two force characteristics to ensure the continuity and naturalness of the force feedback.

[0071] In this embodiment, through the above-described technical solution, this application can transform abstract secondary master hand guidance data into intuitive and adaptive force feedback, improving the experience and safety of master-slave operation. When the operator's movement direction is consistent with the system's guidance direction, the feedback, primarily characterized by viscous damping force, provides smooth and stable assistance, making the operator feel natural and easy to control. This allows for more precise and fluid instrument movement within narrow anatomical channels, effectively avoiding unnecessary coupling interference. Conversely, when the operator's movement direction deviates from the guidance direction, the feedback, primarily characterized by elastic traction force, provides clear correction and guidance, promptly pulling the operator back to the correct operating path. This effectively prevents instrument collisions or tissue damage caused by operational errors, further ensuring the safety of multi-instrument collaborative operation. This dynamically modulated force feedback mechanism not only reduces the operator's cognitive load, allowing them to focus more on the surgery itself, but also greatly enhances the system's understanding and response to the operator's intentions, making multi-instrument collaborative operation more efficient, precise, and safe in complex and constrained environments.

[0072] In one feasible implementation, the method further includes: calculating the current theoretical coupling interference degree experienced by the first slave device based on the multi-device coupling state data; inputting the current theoretical coupling interference degree as a real-time parameter into the dynamic workspace mapping function for dynamically adjusting the intensity or strategy of the nonlinear adjustment; and / or converting the current theoretical coupling interference degree into a tactile encoded signal, superimposing it onto the force feedback data, and outputting it to the second master hand operator.

[0073] In this embodiment, the current theoretical coupling interference degree refers to a quantitative assessment of the spatial interaction or potential collision risk between the first slave instrument and other surgical instrument rods (e.g., the second slave instrument) within a narrow anatomical passage. This interference degree can be calculated based on multi-instrument coupling state data, such as by analyzing the tangent point coordinates, direction vectors, and intersection angles between any two surgical instrument rods within the virtual constraint region. The theoretical coupling interference degree increases accordingly when the distance between the instrument rods decreases, the intersection angle decreases, or the relative velocity increases. The calculation method can employ distance threshold judgment, an interaction force model based on potential field theory, or a collision risk assessment algorithm combining instrument motion prediction.

[0074] In this embodiment, the current theoretical coupling interference degree is input as a real-time parameter into the dynamic workspace mapping function to dynamically adjust the intensity or strategy of nonlinear adjustment. This aims to enable the master-slave mapping relationship to adaptively adjust based on actual coupling risks. For example, when the calculated current theoretical coupling interference degree is high, the dynamic workspace mapping function can enhance the nonlinear adjustment intensity from the first master hand motion command data to the first slave end device motion control data. This could involve further reducing the motion mapping scaling factor along the pole direction or more strictly limiting motion perpendicular to the pole direction to avoid potential collisions. Conversely, when the interference degree is low, the intensity of the nonlinear adjustment can be appropriately weakened to provide a more direct and sensitive control response. This adjustment strategy can be pre-set as a series of rules or implemented through an adaptive algorithm.

[0075] Furthermore, the current theoretical coupling interference level can be converted into a tactile encoded signal, superimposed on the force feedback data, and output to the second master hand actuator. The tactile encoded signal can be generated in various ways; for example, the magnitude of the interference level can be mapped to the damping sensation, vibration frequency, or texture of the force feedback. When the interference level is high, the master hand actuator can generate a larger damping force, a higher frequency vibration, or a rougher tactile sensation to warn the operator of potential coupling risks. This tactile encoded signal is superimposed on the force feedback data generated based on pose deviation data, for example, through vector summation or modulation, to jointly act on the master hand actuator, providing the operator with a more comprehensive and intuitive tactile perception.

[0076] In this embodiment, on the one hand, the current theoretical coupling interference degree is input as a real-time parameter to the dynamic workspace mapping function, enabling the master-slave mapping relationship to adaptively adjust according to the actual coupling risk. This allows for more precise restriction or guidance of the movement of the first slave instrument when coupling interference is high, effectively avoiding collisions between instruments and improving the accuracy and safety of the operation. On the other hand, by converting the coupling interference degree into a tactile encoded signal and superimposing it on the force feedback data, the master operator can intuitively perceive the coupling risk faced by the slave instrument through touch. This provides richer and more accurate perceptual information during operation, enhancing the immersion and intuitiveness of control. It allows the operator to adjust the operation strategy more promptly and accurately, further reducing the risk of misoperation and improving the efficiency and safety of multi-instrument collaborative operation within narrow anatomical passages.

[0077] In the embodiments of this application, the robot master-slave control method for multi-instrument collaborative operation in narrow anatomical passages effectively counteracts spatial coupling interference by acquiring multi-instrument coupling state data and performing dynamic mapping processing and reverse coupling compensation based on this data. This solves the coupling interference problem when multi-instrument collaborative operation is performed in narrow anatomical passages, improves the motion accuracy and operational intuition of surgical instruments, and reduces the cognitive burden on doctors.

[0078] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the robot master-slave control method for multi-instrument collaborative operation in narrow anatomical passages. Any simple modifications based on this technical concept are within the protection scope of this application.

[0079] This application also provides a robot master-slave control system for collaborative operation of multiple instruments within narrow anatomical passages, referenced in [reference]. Figure 2 The robot master-slave control system for multi-instrument collaborative operation in a narrow anatomical passage includes: a memory 10, a processor 20, and a robot master-slave control program for multi-instrument collaborative operation in a narrow anatomical passage stored in the memory 10 and executable on the processor 20. The robot master-slave control program for multi-instrument collaborative operation in a narrow anatomical passage is configured to implement the steps of the robot master-slave control method for multi-instrument collaborative operation in a narrow anatomical passage.

[0080] The robot master-slave control system for multi-instrument collaborative operation in narrow anatomical passages provided in this application, employing the robot master-slave control method for multi-instrument collaborative operation in narrow anatomical passages described in the above embodiments, can improve the motion accuracy of surgical instruments. Compared with the prior art, the beneficial effects of the robot master-slave control system for multi-instrument collaborative operation in narrow anatomical passages provided in this application are the same as the beneficial effects of the robot master-slave control method for multi-instrument collaborative operation in narrow anatomical passages provided in the above embodiments, and other technical features in the robot master-slave control system for multi-instrument collaborative operation in narrow anatomical passages are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0081] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0082] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A robot master-slave control method for collaborative operation of multiple instruments within a narrow anatomical passage, characterized in that, The method includes: Acquire multi-instrument coupling state data, which characterizes the spatial geometric constraint relationship between multiple surgical instrument rods at the entrance of the narrow anatomical channel; Based on the multi-instrument coupling state data and the first master hand motion command data from the master hand operator, first slave instrument motion control data is generated through dynamic mapping processing. The dynamic mapping processing is used to map the first master hand motion command data into the first slave instrument motion control data that controls the movement of the first slave instrument, and at the same time, dynamically adjusts the mapping relationship according to the multi-instrument coupling state data. Based on the motion control data of the first slave device, second master hand guidance data is generated through reverse coupling compensation calculation. The second master hand guidance data is used to update the control mapping relationship of the master hand operator corresponding to the second slave device, so as to guide the operation of the master hand operator and thus cancel the spatial coupling interference caused by the motion of the first slave device to the second slave device. The motion control data of the first slave device is sent to the drive mechanism of the first slave device to execute the motion of the first slave device; The steps for generating second master hand guidance data based on the first slave device motion control data through reverse coupling compensation calculation include: Based on the motion control data of the first slave device, the coupling state data of the multiple devices, and the current actual pose data of the second slave device, the target compensation pose data of the second slave device is calculated through inverse kinematics and coupled geometric model; the target compensation pose data is the position and posture that minimizes the spatial coupling interference to the first slave device. Based on the target compensated pose data and the current actual pose data of the second slave device, pose deviation data is calculated. The posture deviation data is converted into the second master hand guidance data; Acquiring multi-device coupling status data includes: Obtain the entrance constraint domain definition data; the entrance constraint domain definition data is used to define a virtual constraint region that surrounds or contains the actual entrance of the narrow anatomical passage; Acquire real-time pose data of each surgical instrument lever; Based on the defined entry constraint domain data and the real-time pose data of each surgical instrument lever, the multi-instrument coupling state data is calculated. The multi-instrument coupling state data includes: the incision point coordinates of each surgical instrument lever within the virtual constraint region, the direction vector of each surgical instrument lever, and the intersection angle between any two surgical instrument levers calculated based on the incision point coordinates and direction vectors. Based on the multi-instrument coupling state data and the first master hand motion command data from the master hand operator, the step of generating the first slave end instrument motion control data through dynamic mapping processing includes: Receive the first main hand motion command data; the first main hand motion command data originates from the operation of the first main hand operator. Determine the current control mode data; Based on the current control mode data, the multi-instrument coupling state data, and the first master hand motion command data, the first slave instrument motion control data is generated through processing by a dynamic workspace mapping function. When the current control mode data is in a collaborative mode, the dynamic workspace mapping function nonlinearly adjusts the mapping relationship from the first master hand motion command data to the first slave instrument motion control data based on the multi-instrument coupling state data. The dynamic workspace mapping function performs a nonlinear adjustment on the mapping relationship between the first master hand motion command data and the first slave device motion control data based on the multi-device coupling state data, including: Extract the intersection angle between the first slave device and the second slave device within the virtual constraint region from the multi-device coupling state data; The relationship between the mapping scaling factor and the intersection angle is established based on preset rules; the preset rules include: when the intersection angle decreases, the motion mapping scaling factor of the first slave device in the direction perpendicular to the shaft is increased, while the motion mapping scaling factor in the direction along the shaft is decreased; The first master hand motion command data is scaled using an adjusted mapping scaling factor to generate the first slave device motion control data.

2. The robot master-slave control method for multi-instrument cooperative operation in narrow anatomical passages as described in claim 1, characterized in that, Obtaining the entry constraint domain definition data includes: Acquire intraoperative medical image data or optical navigation marker data of the narrow anatomical passage; Based on the intraoperative medical image data or optical navigation marker data, the actual entrance location and boundary of the narrow anatomical passage are identified through image segmentation or spatial registration algorithms; Using the actual entrance location as the center, generate the initial geometric parameters of the virtual constraint region based on the boundary; Based on the real-time pose data of each surgical instrument lever, the smoothness of motion and the degree of constraint conflict of each surgical instrument lever within the virtual constraint area are evaluated to obtain constraint evaluation result data. Based on the constraint evaluation results, the initial geometric parameters of the virtual constraint region are fine-tuned, and the fine-tuned geometric parameters are used as the final entry constraint domain definition data.

3. The robot master-slave control method for collaborative operation of multiple instruments in narrow anatomical passages as described in claim 1, characterized in that, The steps to determine the current control mode data include: Acquire mode switching request data, which is derived from user input signals or automatic decision signals; Based on the multi-device coupling state data, determine whether the current spatial geometric relationship of the multi-devices meets the preset cooperative mode conditions or independent mode conditions, and generate mode feasibility data. Arbitration is performed based on the mode switching request data and the mode feasibility data to determine the current control mode data. Specifically, if the target mode requested by the mode switching request data is consistent with the feasible mode indicated by the mode feasibility data, then the current control mode data is determined to be the target mode; otherwise, the current control mode data is determined to be either maintaining the original mode or switching to the feasible mode indicated by the mode feasibility data.

4. The robot master-slave control method for multi-instrument cooperative operation in narrow anatomical passages as described in claim 1, characterized in that, The method further includes: The reference guiding force vector is calculated based on the guiding vector or guiding force field defined by the second master guide data; Identify the current actual movement direction data of the second master hand controller; Calculate the angle between the current actual motion direction data and the direction of the reference guiding force vector; Based on the included angle, the magnitude and / or force feedback characteristics of the reference guiding force vector are dynamically modulated to generate force feedback data. Specifically, this includes: when the included angle is less than a first threshold, generating force feedback data with viscous damping force characteristics as the main component; and when the included angle is greater than a second threshold, generating force feedback data with elastic traction force characteristics as the main component.

5. The robot master-slave control method for multi-instrument cooperative operation in narrow anatomical passages as described in claim 4, characterized in that, The method further includes: Based on the multi-device coupling state data, the current theoretical coupling interference degree experienced by the first slave device is calculated; The current theoretical coupling interference degree is used as a real-time parameter and input into the dynamic workspace mapping function to dynamically adjust the intensity or strategy of the nonlinear adjustment. And / or, The current theoretical coupling interference degree is converted into a tactile encoded signal, superimposed on the force feedback data, and output to the second main hand operator.

6. A robot master-slave control system for collaborative operation of multiple instruments within narrow anatomical passages, characterized in that, The robot master-slave control system for multi-instrument collaborative operation in a narrow anatomical passage includes: a memory, a processor, and a robot master-slave control program for multi-instrument collaborative operation in a narrow anatomical passage stored in the memory and executable on the processor. The robot master-slave control program for multi-instrument collaborative operation in a narrow anatomical passage is configured to implement the steps of the robot master-slave control method for multi-instrument collaborative operation in a narrow anatomical passage as described in any one of claims 1 to 5.

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