Implantable dynamic nerve tract micro-traction robot and control method thereof

Abstract

According to the implantable dynamic nerve bundle micro-traction robot and the control method thereof, the double-side symmetrical springs are arranged between the elastic part support and the guide sleeve, the double springs can buffer force impact in the traction process, secondary damage to nerves caused by rigid traction is prevented, and meanwhile through linear correlation between the spring deformation quantity and the traction force, the traction force can be accurately adjusted. A mechanical basis is provided for subsequent force control; when the spiral piece rotates, the traction robot is driven to stably move in the axial direction, and nerve stress deviation in the traction process is avoided; the suture ring is fixed by the elastic part support and is directly connected with a damaged nerve through an operation line, so that the nerve can be accurately driven to directionally move during traction, and the nerve is prevented from slipping or shifting during traction.

Description

Technical Field

[0001] This invention belongs to the technical field of implantable microrobots, and particularly relates to an implantable dynamic neural bundle micro-traction robot and its control method. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Implantable microrobot systems represent the cutting edge of modern medical device technology, with their core being the integration of advanced mechatronics technology and biomedical engineering. These devices employ a fully integrated design concept, deploying miniature robots directly at the lesion site to achieve a level of precision unattainable by traditional surgery. The system architecture primarily consists of an integrated operating module, a remote control device, and an information sensing module. Compared to traditional surgical interventions, this technology demonstrates superior performance in reducing tissue damage, maintaining operational continuity, and improving positioning accuracy, making it particularly suitable for treating complex diseases requiring long-term, gradual treatment.

[0004] In the current field of neurosurgery, large robot-assisted systems are relatively mature in intracranial surgery, capable of performing routine procedures such as brain tissue puncture and hematoma aspiration. However, there are still technological gaps in peripheral nerve reconstruction treatment. Faced with the challenge of treating large-scale nerve tissue loss, such as damage exceeding 5 cm, current clinical protocols generally employ external traction systems for gradual nerve reconstruction. This treatment method requires frequent manual adjustments by medical staff, which not only consumes a large amount of medical resources but also exposes several key technical limitations: the need to construct a penetrating fixation mechanism significantly increases the probability of infection; reliance on manual periodic traction adjustments leads to large manual operation errors, making it impossible to precisely control the traction distance and force; the external operating system suffers from cumulative deviations, reducing the directional accuracy of nerve stretching; and there is a lack of embedded monitoring methods to obtain real-time biomechanical data of the nerve remodeling process.

[0005] Taking the repair of sciatic nerve injuries in the lower limbs as an example, this nerve is the main neural pathway for motor and sensory function in the lower limbs. Existing external traction techniques cannot provide precise and continuous traction control at the millimeter level, and there is also a risk of causing secondary nerve trauma. Therefore, constructing a microrobot platform for neural traction with intelligent control capabilities has become an urgent technical challenge to be overcome in this research field.

[0006] Existing implantable neural traction robots typically employ single-spring structures or simple rigid transmission methods, lacking sufficient mechanical control capabilities to achieve precise traction control of neural tissue while ensuring safety. Furthermore, research on magnetically driven implantable devices, where external permanent magnets drive internal magnets, is generally limited by energy transfer efficiency and rotational synchronization. Therefore, there is an urgent need to develop implantable microrobots and their control methods to ensure the safety and precision of the neural traction process. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, the present invention provides an implantable dynamic nerve bundle micro-traction robot and its control method, which realizes hybrid control of traction force and displacement, and ensures the safety and accuracy of the nerve traction process.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an implantable dynamic neural bundle micro-traction robot, comprising: a magnetic drive device, a guide sleeve, a spiral component, an elastic component support, a first elastic component, a second elastic component, and a suture ring fixed on the elastic component support; The first elastic element and the second elastic element are assembled in the mating area between the inner side of the elastic element bracket and the outer side of the guide sleeve; The spiral component is disposed between the magnetic drive device and the external pipe, and the guide sleeve is disposed on the outside of the magnetic drive device; When the magnetic drive device rotates, the helical component converts the rotational motion of the magnetic drive device into its own axial linear motion, thereby driving the magnetic drive device to move axially. Under the action of the axial thrust or pull of the magnetic drive device, the guide sleeve moves axially along the external pipe in sync, causing the first elastic component and the second elastic component to deform.

[0009] Secondly, the present invention provides a control method for an implantable dynamic neural bundle micro-traction robot, comprising: The force error is mapped to a desired axial velocity reference or a desired axial displacement reference through the admittance model; Based on the mapping relationship between the number of rotations of the external magnet and the axial displacement of the traction robot, and combined with the online estimated or calibrated coupling ratio, the desired axial velocity reference or desired axial displacement reference is mapped to the external magnet rotation number command or external magnet speed command. Under the condition that the external magnet rotation number command or external magnet speed command meets the safety constraints, the external magnet is driven to rotate, thereby driving the robot to move along the axis through the magnetic drive device.

[0010] The above one or more technical solutions have the following beneficial effects: In this invention, a double-sided symmetrical spring is provided between the elastic component support and the guide sleeve. The double spring can buffer the force impact during traction and prevent secondary damage to the nerve caused by rigid traction. At the same time, the linear correlation between the spring deformation and the traction force provides a mechanical basis for subsequent force control. When the spiral component rotates, it drives the traction robot to move smoothly along the axial direction, avoiding nerve displacement due to force during traction. The elastic component support fixes the suture ring, which is directly connected to the damaged nerve through the surgical suture. During traction, it can accurately drive the nerve to move in a directional manner, avoiding nerve slippage or displacement during traction.

[0011] In this invention, the force error between the actual traction force and the desired traction force is mapped to a desired axial velocity reference or a desired axial displacement reference through an admittance model. The system compliance can be dynamically adjusted according to the stiffness characteristics of the neural tissue. Based on the mapping relationship between the number of rotations of the external magnet and the axial displacement of the traction robot, combined with the online estimated or calibrated coupling ratio, the desired axial velocity reference or desired axial displacement reference is mapped to an external magnet rotation command or an external magnet speed command, thereby achieving millimeter-level axial displacement control.

[0012] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0014] Figure 1 This is a schematic diagram of the overall structure of the magnetically controlled micro-sized neural traction robot in this invention; Figure 2 This is an exploded view of the structure of the magnetically controlled micro-neural traction robot of the present invention; Figure 3 This is a schematic diagram of the elastic support structure in the magnetically controlled micro-neural traction robot of the present invention; Figure 4 This is a schematic diagram of the guide sleeve in the magnetically controlled micro-neural traction robot of the present invention; Figure 5 This is a schematic diagram of the external pipe structure in this invention; Figure 6 This is a schematic diagram of the magnetic drive device in the magnetically controlled micro-neural traction robot of the present invention; Figure 7 This is a control flow diagram of the present invention; In the diagram, 1 is the external pipe, 11 is the control chip, 12 is the button battery, and 13 is the thin-film strain sensor. 2. Traction robot, 21. Elastic component support, 211. Thin film strain sensor, 22. First spring, 23. Helical component, 24. Intermediate connector, 25. Guide sleeve, 251. Relative displacement sensor, 252. Absolute displacement sensor, 26. Magnetic drive device, 261. Gear support, 262. Gear, 263. Gear ring, 264. Housing, 265. Base, 266. Bearing, 267. Motor shaft, 268. Magnet, 27. Second spring. Detailed Implementation

[0015] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0016] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0017] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0018] Example 1 This embodiment discloses an implantable dynamic neural bundle micro-traction robot, including: a magnetic drive device 26, a guide sleeve 25, a spiral component 23, an elastic component support 21, a first elastic component, a second elastic component, and a suture ring disposed on the elastic component support 21. The first elastic element and the second elastic element are assembled in the mating area between the inner side of the elastic element bracket 21 and the outer side of the guide sleeve 25; The spiral component 23 is disposed between the magnetic drive device 26 and the external pipe 1, and the guide sleeve 25 is disposed on the outside of the magnetic drive device 26. When the magnetic drive device 26 rotates, the screw 23 converts the rotational motion of the magnetic drive device 26 into its own axial linear motion, thereby driving the magnetic drive device 26 to move axially. Under the action of the axial thrust or pull of the magnetic drive device 26, the guide sleeve 25 moves axially along the external pipe 1 in sync, causing the first elastic element and the second elastic element to deform.

[0019] In this embodiment, a double-sided symmetrical spring is provided between the elastic element bracket and the guide sleeve. The double spring can buffer the force impact during traction and prevent secondary damage to the nerve caused by rigid traction. At the same time, the linear correlation between the spring deformation and the traction force provides a mechanical basis for subsequent force control. When the spiral component rotates, it drives the traction robot to move smoothly along the axis, avoiding nerve displacement due to force during traction. The elastic element bracket fixes the suture ring, which is directly connected to the damaged nerve through the surgical suture. During traction, it can accurately drive the nerve to move in a directional manner, avoiding nerve slippage or displacement during traction.

[0020] The following is combined Figure 1 and Figure 2 This embodiment provides a detailed description of an implantable dynamic neural bundle micro-traction robot: The traction robot 2 consists of an elastic support 21, a first elastic element (i.e., a first spring 22), a second elastic element (i.e., a second spring 27), a spiral element 23, an intermediate connector 24, a guide sleeve 25, and a magnetic drive device 26. One end of the elastic support 21 is fixed with a suture ring, which connects the nerve to the suture ring via surgical sutures for directional traction of the nerve. The other end engages with the magnetic drive device 26. The magnetic drive device 26 is connected to the spiral element 23 via the intermediate connector 24, allowing the spiral element 23 to rotate with the magnetic drive device 26. The magnetic drive device 26 is externally fitted with a guide sleeve 25.

[0021] like Figure 3 As shown, annular grooves are respectively opened on the inner sides of both ends of the elastic element bracket 21 for placing and fixing springs. Specifically, annular grooves for fixing the first spring 22 and the second spring 27 are respectively opened on the inner sides of both ends of the elastic element bracket 21. One end of the elastic element bracket 21 is fitted with the magnetic drive device 26, and a sewing ring is fixed to the other end of the elastic element bracket 21.

[0022] A slot is cut into the inner side of the top of the elastic element support 21 to place the first thin film strain sensor 211, which is used to cooperate with the relative displacement sensor 251 to measure the relative displacement of the magnetically controlled implantable dynamic nerve bundle micro-traction robot in the elastic element support.

[0023] Specifically, the first thin-film strain sensor 211 senses deformation in the same direction as the axial direction of the elastic element support 21; the top of the guide sleeve 25 corresponds to the slot position on the top of the elastic element support 21, and the relative displacement sensor 251 is fixed at the corresponding position on the top of the guide sleeve 25. The detection end of the relative displacement sensor 251 maintains a small gap or flexible contact with the first thin-film strain sensor 211 to ensure that when the guide sleeve 25 and the elastic element support 21 undergo relative displacement, the two can capture the position change-related signal in real time.

[0024] The guide sleeve 25 only moves linearly along the axial direction and does not rotate with the magnetic drive device. The elastic element support 21 moves axially synchronously with the magnetic drive device 26 and the spiral element 23. The difference in movement between the two is directly manifested as relative axial displacement. When this relative axial displacement occurs, the elastic element support 21 will cause the first thin film strain sensor 211 on its top to undergo a slight deformation, such as stretching or compression, due to the change in position relative to the guide sleeve 25. The relative displacement sensor 251 on the guide sleeve 25 uses itself as a fixed reference to detect the amount of position change corresponding to the deformation.

[0025] like Figure 4As shown, a relative displacement sensor 251 is fixed to the top of the guide sleeve 25, and an absolute displacement sensor 252 is fixed to the bottom.

[0026] Specifically, the external pipe 1 is fixed to the bone by surgical sutures through a small semi-circular ring on its outside, which is the absolute fixed reference of the entire measurement system. The bottom of the external pipe 1 is slotted to embed and fix the second thin film strain sensor 13. The position of the second thin film strain sensor 13 is always relatively stationary with respect to the bone, and it only serves as a reference anchor point for displacement measurement. Its position does not change with the movement of the traction robot.

[0027] The bottom of the guide sleeve 25 corresponds to the position of the second thin-film strain sensor 13 at the bottom of the external pipe 1, and the absolute displacement sensor 252 is fixed at the bottom of the guide sleeve 25. The detection end of the absolute displacement sensor 252 maintains a small gap or flexible contact with the second thin-film strain sensor 13 to ensure that the absolute displacement sensor 252 can capture the positional difference with the external pipe in real time when the guide sleeve 25 moves.

[0028] The absolute displacement sensor 252 and the thin-film strain sensor 13 are both installed in a direction that is strictly parallel to the axis of the external pipe 1. They are only sensitive to axial displacement signals, thus eliminating the interference of lateral displacement on measurement accuracy.

[0029] like Figure 5 As shown, a button battery 12 is fitted at the end of the external pipe 1, and a control chip 11 is placed in a slot at the bottom. Several small semicircular rings are fixed to the external pipe 1 for passing through surgical sutures and connecting with the bone to fix the external pipe 1. The external pipe 1 is hollow inside and has threads machined on it, which can cooperate with the helical component 23 through a helical relationship, so that the rotation of the helical component 23 can drive the traction robot 2 to move inside the external pipe 1.

[0030] like Figure 6 As shown, the magnetic drive device 26 consists of a gear bracket 261, a gear 262, a gear ring 263, a housing 264, a base 265, a bearing 266, a motor shaft 267, and a magnet 268. The gear bracket 261, gear 262, and gear ring 263 constitute a transmission and speed-changing assembly, while the magnet 268 and motor shaft 267 constitute a power assembly. The gear bracket 261 acts as a main shaft, connecting several gears 262, gear ring 263, base 265, bearing 266, motor shaft 267, and magnet 268 in series. The magnet 268 is encased in the housing 264 to prevent it from loosening or falling off.

[0031] During movement, magnet 268 rotates in an oriented manner under the action of an external magnetic field. Several gears 262 and gear ring 263 cooperate with each other, adjust the rotation speed, and then transmit power to gear support 261. Gear support 261 also rotates in an oriented manner at an appropriate speed, thereby driving the helical component connected to gear support 261, thus driving the axial movement of traction robot 2.

[0032] The basic transmission principle of the traction robot is that the magnetic drive device 26 rotates under the control of an external magnetic field, driving the spiral component 23 to rotate. Since the external tube 1 is fixed inside the body, the spiral component 23 will drive the implanted dynamic nerve bundle micro-traction robot 2 to move axially during rotation, pulling the nerve connected to the suture ring to move. At this time, a relative displacement will occur between the guide sleeve 25 and the elastic support 21, and an absolute displacement will occur between the guide sleeve 25 and the external tube 1, which are detected by the relative displacement sensor 251 at the top of the guide sleeve 25 and the absolute displacement sensor 252 at the bottom. Due to the generation of relative displacement, the springs on both sides will be stretched or compressed respectively, thus producing deformation. The resulting tension is directly proportional to the deformation. Therefore, the magnitude of the tension can be calculated by the magnitude of the relative displacement, realizing force-position coupling measurement.

[0033] In this embodiment, the application of a magnetic drive device significantly reduces energy consumption, thereby reducing the number of batteries and consequently the size of the traction device. The application of magnetic control technology can greatly reduce the size and energy consumption of implantable devices, thus reducing the invasiveness of micro-robots. Simultaneously, the displacement and force during the traction process are monitored in real time by relative displacement sensors, absolute displacement sensors, and springs, providing timely feedback and improving the accuracy of the repair process.

[0034] Example 2 This embodiment provides a control method for an implantable dynamic neural bundle micro-traction robot, including: The force error is mapped to a desired axial velocity reference or a desired axial displacement reference through the admittance model; Based on the mapping relationship between the number of rotations of the external magnet and the axial displacement of the traction robot, and combined with the online estimated or calibrated coupling ratio, the desired axial velocity reference or desired axial displacement reference is mapped to the external magnet rotation number command or external magnet speed command. Under the condition that the external magnet rotation number command or external magnet speed command meets the safety constraints, the external magnet is driven to rotate, thereby driving the robot to move along the axis through the magnetic drive device.

[0035] like Figure 7As shown, firstly, the system receives the traction target and safety threshold input by the doctor, providing reference and boundary conditions for subsequent control. Then, through sensor acquisition and external-to-internal magnetic coil conversion, the actual displacement, traction force, and theoretical displacement of the robot are obtained, enabling real-time perception of the traction robot's operating status. Based on this, force and displacement errors are processed separately, and corresponding velocity references are generated using admittance control and position control. A comprehensive velocity command is obtained through a fusion strategy, thus balancing compliant adjustment of traction force and precise displacement advancement. Before entering the execution phase, the comprehensive velocity command undergoes safety constraint processing, ensuring that the difference between traction force, displacement, and spring force does not exceed a set threshold through control obstacle functions or other optimization methods, avoiding tissue damage. Finally, the corrected velocity command drives the rotation of the external large magnet, causing the internal small magnet and helical components to achieve axial movement, traction on the nerve; simultaneously, the motion results are fed back to the sensing and conversion module in real time, forming a closed-loop control. This embodiment enables force-position hybrid control in an implantable environment, ensuring the safety and accuracy of the nerve traction process.

[0036] The control method for an implantable dynamic neural bundle micro-traction robot proposed in this embodiment will be described in detail below: Under the magnetic dipole approximation, the external magnetic dipole moment Location within the body A magnetic field is generated at the location Near-field dipole expression:

[0037] in, The permeability of free space, It is a unit vector, M represents the dipole moment, and the subscript ext represents the external magnet.

[0038] Internal magnetic dipole moment Moment:

[0039] Internal magnet torque The internal rotor is driven and converted into axial displacement through a gear system, gear ring, and helical components.

[0040] By approximating a linear mapping between the internal and external rotation numbers, we obtain:

[0041] in, This represents the robot's absolute axial displacement. The rotational speed of the external permanent magnet. The coupling ratio needs to be estimated or calibrated online in a practical system. This is the internal mechanical equivalent transmission ratio. The pitch of the screw component.

[0042] The controller must use this relationship to back-calculate the desired axial displacement / velocity into the number of outer magnetic coils / rotation speed command.

[0043] Let the left and right endpoints be known fixed positions. , (Relative to the external pipeline reference). If the central module is located at... Then the spring compression It can be represented as:

[0044] in, x Indicates displacement. Indicates the amount of compression.

[0045] Spring force:

[0046] Static equilibrium:

[0047] in, For friction / damping / additional load. If the suture loop is on the left end and directly transmits nerve force to the left end, then The above expression provides the center position The analytical relationship between the coupling of the left and right spring forces is the mathematical basis of the center position calculation module.

[0048] The controller consists of three layers: an outer loop, a middle loop, and an inner loop, with a safety constraint layer (CBF) and several observers / estimaters running in parallel. Outer loop (admittance / force reference generation): This generates force error. The force tracking is mapped to a desired axial velocity / displacement reference via an admittance model, ensuring compliance and stability. For the desired tensile force, This refers to the actual tensile force.

[0049] The outer loop uses a second-order admittance model to map the force error to a velocity reference, in the form of:

[0050] Or it can be written as a state-space equation:

[0051] in, For virtual displacement variables, This is a virtual velocity variable. The virtual damping parameter of the admittance ring is larger; the larger it is, the "softer" the system becomes. The virtual mass parameter of the admittance loop, increasing which leads to a slower response, This is a virtual stiffness parameter for the admittance ring, which, when increased, tends towards a harder anchor position. The admittance output is used for subsequent mixing or direct application. This admittance is adapted... , , This ensures that overshoot does not occur when encountering a sudden increase in tensile force, thereby protecting the nerves.

[0052] Inner loop (displacement / velocity tracking and actuator mapping): Positioning the central target... or axial velocity reference Mapped to external magnet revolutions Or external magnet rotation speed command It also performs rate / acceleration limiting and instruction filtering.

[0053] If the suture loop is on the left side, the solution is:

[0054] in, This represents the initial pre-compression of the spring blade.

[0055] This formula assumes that the position / force on the right side will not violate the upper limit, and needs to be verified subsequently.

[0056] If the verification fails, constraint optimization will be used:

[0057] Will , Replace with and It can be obtained quickly using one-dimensional quadratic solutions or Newton's iteration. .

[0058] Depend on Obtain the target axial displacement It can also calculate the positional velocity reference. For mixing.

[0059] A weighted parallel hybrid strategy is adopted:

[0060] Weight The weights can be adjusted based on clinical stage, adaptive metrics, or safety policies. The weights can also be adjusted when the controller detects changes in coupling or a risk of loss of synchronization. This hybrid output, after being corrected by the safety layer, becomes the final velocity command.

[0061] Safety constraint layer (CBF+QP): The velocity command is modified by least squares / QP before the inner loop to meet constraints such as tension, symmetry, and displacement boundaries.

[0062] Define a set of security functions (Constraint set C):

[0063] in, , These represent the maximum permissible force (safety limit) of the left and right springs, respectively, which is determined by system design or experimentation. , This represents the actual force applied to the left and right spring blades at the current center position xc.

[0064] For each Constructing the CBF condition (after linearization):

[0065] in Speed ​​as a control variable Substituting the above inequalities, we obtain a set of linear inequality constraints. Finally, we use the following QP to solve for the safety correction velocity. :

[0066] Among them, matrix , The weight matrix is ​​generated by linearizing CBF. Adjustable. The QP is solved before actuator mapping to ensure that the output speed meets all safety constraints. If the QP has no feasible solution, the system triggers an emergency stop or returns to a safe attitude.

[0067] Axial velocity obtained through the safety layer Mapped to external magnetic rotation increment or rate:

[0068] Observer / Estimator: Coupling Coefficient Friction / uncertainty, sensor redundancy fusion ( (Integrated with absolute displacement).

[0069] Based on a symmetrical structure of magnetic dipole drive and dual-sided series elastic actuators, hybrid control of tension force and displacement is achieved. By assuming synchronization between the magnetic dipoles of the external rotating large magnet and the internal small magnet, accurate conversion between the number of external rotations and the robot's axial displacement is realized; a dual-spring topology enables compliant adjustment and accurate measurement of tension force; and force-position hybrid control is achieved through admittance control, position control, and safety constraint functions.

[0070] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 2. For brevity, further details are omitted here.

[0071] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0072] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0073] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 2.

[0074] The method in Example 2 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0075] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 2.

[0076] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0077] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0078] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0079] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0080] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An implantable dynamic neural bundle micro-traction robot, characterized in that, include: A magnetic drive device, a guide sleeve, a spiral component, an elastic component bracket, a first elastic component, a second elastic component, and a stitching ring fixed on the elastic component bracket; The first elastic element and the second elastic element are assembled in the mating area between the inner side of the elastic element bracket and the outer side of the guide sleeve; The spiral component is disposed between the magnetic drive device and the external pipe, and the guide sleeve is disposed on the outside of the magnetic drive device; When the magnetic drive device rotates, the helical component converts the rotational motion of the magnetic drive device into its own axial linear motion, thereby driving the magnetic drive device to move axially. Under the action of the axial thrust or pull of the magnetic drive device, the guide sleeve moves axially along the external pipe in sync, causing the first elastic component and the second elastic component to deform.

2. The implantable dynamic neural bundle micro-traction robot as described in claim 1, characterized in that, Annular grooves for fixing the first elastic element and the second elastic element are respectively opened on the inner side of both ends of the elastic element bracket. One end of the elastic element bracket is fitted with the magnetic drive device, and the other end of the elastic element bracket is fixed with the suture ring.

3. The implantable dynamic neural bundle micro-traction robot as described in claim 1, characterized in that, A relative displacement sensor and an absolute displacement sensor are respectively provided at the top and bottom of the guide sleeve; the relative displacement sensor is used to measure the relative displacement between the guide sleeve and the elastic support; the absolute displacement sensor detects the absolute displacement between the guide sleeve and the external pipe.

4. The implantable dynamic neural bundle micro-traction robot as described in claim 3, characterized in that, A first thin-film strain sensor is provided on the top of the elastic element support, and the deformation direction of the first thin-film strain sensor is consistent with the axial direction of the elastic element support; the detection end of the relative displacement sensor maintains a small gap or flexible contact with the first thin-film strain sensor. A second thin-film strain sensor is installed on the external pipe. The position of the second thin-film strain sensor is always stationary relative to the bone. The detection end of the absolute displacement sensor maintains a small gap or flexible contact with the second thin-film strain sensor.

5. The implantable dynamic neural bundle micro-traction robot as described in claim 1, characterized in that, The magnetic drive device includes a power component and a transmission speed change component. The transmission speed change component includes a gear bracket, a gear, and a gear ring. The power component includes a magnet and a motor shaft. When the magnet drives the motor shaft to rotate, the gear and the gear ring cooperate with each other to adjust the speed and then transmit the power to the helical component connected to the gear bracket, thereby realizing speed adaptation and power transmission.

6. A control method for an implantable dynamic neural bundle micro-traction robot, characterized in that, include: The force error is mapped to a desired axial velocity reference or a desired axial displacement reference through the admittance model; Based on the mapping relationship between the number of rotations of the external magnet and the axial displacement of the traction robot, and combined with the online estimated or calibrated coupling ratio, the desired axial velocity reference or desired axial displacement reference is mapped to the external magnet rotation number command or external magnet speed command. Under the condition that the external magnet rotation number command or external magnet speed command meets the safety constraints, the external magnet is driven to rotate, thereby driving the robot to move along the axis through the magnetic drive device.

7. The control method for an implantable dynamic neural bundle micro-traction robot as described in claim 6, characterized in that, Based on the approximation of magnetic dipole theory and mechanical transmission structure parameters, the mapping relationship between the number of rotations of the external magnet and the axial displacement of the traction robot is determined.

8. The control method for an implantable dynamic neural bundle micro-traction robot as described in claim 6, characterized in that, The force error is mapped to the desired axial velocity reference using a second-order admittance model, and the desired axial displacement reference is obtained by integrating the desired axial velocity reference.

9. The control method for an implantable dynamic neural bundle micro-traction robot as described in claim 6, characterized in that, Also includes: By establishing a double-spring mechanical model to solve for the position of the central module, and by calculating the spring deformation in reverse using the position parameters, the traction force on the nerve can be finally obtained.

10. The control method for an implantable dynamic neural bundle micro-traction robot as described in claim 6, characterized in that, The specific safety constraints are as follows: before the inner loop, the speed command is modified using the least squares or QP algorithm to meet the constraints of tension, symmetry, and displacement boundaries.

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