An igjo-based peanut-shaped magneto-rheological capsule robot live tissue sampling controller

Abstract

The application discloses a peanut-shaped magnetorheological capsule robot living tissue sampling controller based on IGJO, which comprises mechanical analysis of a living tissue sampling module for the peanut-shaped magnetorheological capsule robot, research on the driving effect of an external driving permanent magnet on the capsule robot, establishment of the living tissue sampling controller of the capsule robot, design of an improved golden jackal optimization algorithm, introduction of Fuch mapping theory and an elite population strategy in the population initialization stage, optimization initialization of the golden jackal population, introduction of an adaptive inertia weight omega and a searcher position updating strategy in the improved sparrow algorithm in the iterative output stage of the population, repeated optimization of the output solution of the golden jackal population, and effective improvement of the global search capability of the algorithm. The application improves the algorithm performance of the golden jackal algorithm, avoids falling into local optimization, realizes the optimization and adjustment of the living tissue sampling controller parameters of the capsule robot, optimizes the control effect, and improves the control precision.

Description

Technical Field

[0001] This invention relates to a live tissue sampling controller, specifically a peanut-shaped magnetorheological capsule robot live tissue sampling controller based on IGJO, belonging to the field of intelligent control technology for medical devices. Background Technology

[0002] In recent years, capsule robots, as an emerging intelligent medical device, have seen widespread development. They can safely and effectively perform non-invasive diagnosis and treatment of gastrointestinal diseases, avoiding the drawbacks of traditional gastroscopy and colonoscopy, such as significant pain and poor efficacy. Currently, research on capsule robots mainly focuses on movement, walking, and shape change, with relatively few functional magnetically controlled software systems suitable for practical environments.

[0003] Meanwhile, some research institutions utilize magnetic field control for functional modules such as targeted drug delivery and biopsy. However, due to the unique and limited nature of magnetically controlled materials, the control accuracy and reliability of most existing functional module control algorithms are relatively low. Therefore, designing adaptive and self-compensating control strategies based on the complex contact environment within the gastrointestinal tract and the motion characteristics of medical capsules remains a challenge. Summary of the Invention

[0004] The purpose of this invention is to provide a living tissue sampling controller for a peanut-shaped magnetorheological capsule robot based on IGJO in order to solve at least one of the above-mentioned technical problems.

[0005] The present invention achieves the above objectives through the following technical solution: a peanut-shaped magnetorheological capsule robot tissue sampling controller based on IGJO, comprising a peanut-shaped magnetorheological capsule robot and a permanent magnet. The peanut-shaped magnetorheological capsule robot includes a soft capsule shell and a sampling component disposed within the soft capsule shell. The soft capsule shell is provided with a ferromagnetic ring unit and a ferrofluid unit. The sampling component includes a camera module and a retractable sampling needle.

[0006] Motion control of a peanut-shaped magnetorheological capsule robot includes the following steps:

[0007] Step 1: Mechanical analysis was performed on the living tissue sampling module of the peanut-shaped magnetorheological capsule robot to study the driving effect of the external driving permanent magnet on the capsule robot and to establish a living tissue sampling controller for the capsule robot.

[0008] Step 2: Design an improved Golden Jackal Optimization Algorithm (IGJO), introducing three major improvements. In the population initialization stage, Fuchs mapping theory and elite population strategy are introduced to optimize and initialize the Golden Jackal population. In the iterative output stage of the population, adaptive inertia weight ω and the explorer position update strategy from the improved Sparrow Algorithm are introduced to update the solution of the improved Sparrow Algorithm again.

[0009] Step 3: Repeatedly optimize the output solution of the golden jackal population to effectively improve the global search capability of the algorithm. Select the optimal and second-best golden shots as the positions of the male and female shots, and calculate the prey's selection energy E and the random number of levy flight motion.

[0010] As a further embodiment of the present invention: the soft capsule shell has two cavities. The outer cavity contains a first ferromagnetic ring and a second ferromagnetic ring that constitute a ferromagnetic ring unit, and the inner cavity contains a first ferromagnetic fluid and a second ferromagnetic fluid that constitute a ferromagnetic fluid unit.

[0011] As a further aspect of the present invention: the camera module of the sampling component is located at the opening at one end of the soft capsule shell, and a wireless transmission module for transmitting the image data captured by the camera module and a battery for powering the camera module are also fixedly connected inside the soft capsule shell.

[0012] As a further embodiment of the present invention: the retractable sampling needle of the sampling component is located at the opening at the other end of the soft capsule shell, and the support connected to the tail end of the retractable sampling needle is placed on the extrusion plate fixedly connected to the inner wall of the soft capsule shell. The front end of the retractable sampling needle is provided with a vibration plate and a buffer plate, and a gasket is placed at the connection between the vibration plate and the buffer plate.

[0013] As a further aspect of the present invention: In step one, during mechanical analysis, to ensure successful sampling, the retractable sampling needle can puncture and retract to a specified depth, establishing its needle pressure. Total spring force With permanent magnet magnetic force relation:

[0014] ;in, For the stress that damages gastrointestinal tissue, The area of ​​the needle tip. Let be the spring constant. This represents the spring elongation.

[0015] During sampling, the permanent magnet is radially magnetized, with the direction being along... Axial direction. The field point coordinate vector is... The field source coordinate vector is If expressed in cylindrical coordinates, the coordinates of the source point are: The field point coordinates are The distance between the two points is For ease of understanding and calculation, the origin of the coordinate system is... Set at the geometric center of the robot, that is, the coordinate system The origin is placed at the center of the cylindrical permanent magnet; based on Gauss's law of Maxwell's equations, the magnetic scalar potential at any point in the external space of the permanent magnet can be obtained;

[0016] ;

[0017] in, For volume magnetic charge density, , This refers to the magnetization, which is the volume density of the magnetic dipole moment. The outward normal unit vector of the permanent magnet boundary surface. for and The angle between the two points is L, and the distance between the two points is L.

[0018] When no bulk magnetic charge exists:

[0019] ;

[0020] The magnetic field strength can be obtained by calculating the divergence of the magnetic scalar potential. The vacuum permeability here ;

[0021] Based on the above relationships, the magnetic field strength and magnetic induction intensity can be obtained as follows:

[0022] ;

[0023] in is the vacuum permeability.

[0024] From this, we can obtain the magnetic flux density of a cylindrical permanent magnet at any point in space:

[0025] ;

[0026] In the formula, It is the outer radius of the cylindrical permanent magnet; For the bottom surface of the permanent magnet Axis coordinate values; For the top surface of the permanent magnet Axis coordinate values;

[0027] In cylindrical coordinates, the distance between the field source and the field point is... From the formula for the distance between two points, we get:

[0028] ;

[0029] make ,have to:

[0030] ;

[0031] in , , These are the unit vectors in the radial, circumferential, and axial directions, respectively. From the above three equations, the spatial magnetic field induction intensity of the permanent magnet in the radial, circumferential, and axial directions can be obtained.

[0032] 1) Radial magnetic field induction intensity

[0033] ;

[0034] 2) Circumferential magnetic field induction intensity

[0035] ;

[0036] 3) Axial magnetic field induction intensity

[0037]

[0038] After vector synthesis, the magnetic flux density at any point outside the permanent magnet can be obtained:

[0039] ;

[0040] The above derivation can be used to obtain the magnetic field strength of the external driving permanent magnet for the sampling module, and the corresponding magnetic field driving force can be derived.

[0041] Through the above process, a mathematical model of the externally driven permanent magnet and the capsule robot sampling is established, a capsule robot sampling control system is built, and the sampling PID method is used for control.

[0042] As a further aspect of the present invention: In step two, the improved Golden Jackal Optimization Algorithm (IGJO) optimizes the selection of PID control parameters. The basic Golden Jackal Optimization Algorithm simulates the hunting behavior of a golden jackal, whose hunting process mainly consists of three basic stages:

[0043] (1) Search for prey and approach it;

[0044] (2) Surround the prey and stimulate it until it stops moving;

[0045] (3) Attacking prey;

[0046] The algorithm flow is as follows:

[0047] First, perform population initialization:

[0048] ;

[0049] In the formula: This indicates the location of the initial golden jackal population. It is a random number in the range [0,1]. and These are the upper and lower boundaries of the problem to be solved;

[0050] In the GJO algorithm, the prey matrix is ​​represented as:

[0051] ;

[0052] In the formula: For the prey matrix; For the first The first prey Dimensional position; This refers to the population size of the golden jackal; The dimensions for solving the problem;

[0053] During the optimization process, the fitness value of each golden jackal during the hunting process is calculated using the target fitness function, and its fitness value matrix is ​​represented as follows:

[0054] ;

[0055] In the formula: This is the fitness value matrix of the prey; The fitness function or objective function is defined as follows: the jackal with the best fitness value is designated as the male, and the jackal with the second best fitness value is designated as the female.

[0056] Golden jackal populations rely on their own senses to search for prey, and the amount of energy the prey uses to escape directly affects the population behavior of golden jackals. The escape energy of prey can be calculated using the following formula:

[0057] ;

[0058] in, This indicates the initial energy state of the prey. This indicates the process of the prey's energy decreasing;

[0059] ;

[0060] In the formula: This represents the maximum number of iterations. It is a constant with a value of 1.5; This represents the current iteration number; throughout the entire iteration process, It decreases linearly from 1.5 to 0;

[0061] The formula mentioned above It is based on A vector of random numbers distributed as follows:

[0062] ;

[0063] in The Lévy flight function is represented and its calculation method is as follows:

[0064] ;

[0065] ;

[0066] In the formula: and A random number within the range (0,1); To use the default constant, the value is 1.5;

[0067] Once the desired location is found, the golden jackal population enters the search and exploration phase, with male jackals leading the hunt and female jackals following behind. Their group behavior is as follows:

[0068] ;

[0069] In the formula: This represents the current iteration number; For the first The location of the prey in the next iteration; , The first The positions of the male and female golden jackals in the next iteration; , The first The positions of the male and female golden jackals after the first iteration are updated; thus, the updated positions of the golden jackals after the first iteration are as follows:

[0070] ;

[0071] When the desired result is obtained At this stage, the golden jackal population enters the encirclement and attack phase, where the jackals surround and prey on the prey they found in the previous phase. The behavioral patterns of male and female golden jackals in this phase are as follows:

[0072] ;

[0073] The above method is used to update and obtain the first... The positions of male and female golden jackals after each iteration are determined, and the positions of the golden jackals are updated according to formula (26).

[0074] As a further aspect of the present invention: in step two, the population initialization of the golden jackal is optimized by introducing Fuch mapping, and an elite population strategy is used to optimize the selection of the population.

[0075] The Fuch mapping is a novel one-dimensional discrete mapping, and its expression is:

[0076] ;

[0077] in, ,

[0078] The Fuch mapping population is obtained using the above method. This invention employs an elite population strategy, merging the Fuch mapping population and the regular initialization population, calculating the fitness of each golden jackal, ranking them, and selecting the top-performing jackals. There are 1 elite individual, and the sequence of elite individuals is as follows:

[0079] ;

[0080] in, , , The number of individual golden jackals. , The first The positions of the male and female golden jackals in the next iteration.

[0081] As a further aspect of the present invention: in step two, an adaptive inertia weight ω is introduced to update the optimal solution obtained by the golden jackal population in each iteration;

[0082] To further balance the global and local search capabilities of the Golden Jackal algorithm at different stages, an adaptive inertia weight ω is proposed. In the early stage of iteration, the Golden Jackal moves at a faster speed to quickly reach the vicinity of the target value. In the later stage of iteration, the Golden Jackal moves at a slower speed to avoid getting trapped in local optima.

[0083] The formula for calculating the adaptive weighting factor is:

[0084] ;

[0085] in, This represents the maximum number of iterations. This represents the current iteration number; The optimized solution output after adaptive adjustment. The original output solution for this iteration is, i.e. , .

[0086] As a further aspect of this invention: In step two, the explorer position update strategy introduced from the sparrow algorithm is improved by pre-setting the sparrow search probability ST=0.8, and the improved formula is as follows:

[0087] ;

[0088] This allows for further optimization of the output solution for the golden jackal population, effectively improving the algorithm's global search capability.

[0089] The beneficial effects of this invention are as follows: First, a mechanical analysis was conducted on the living tissue sampling module of a peanut-shaped magnetorheological capsule robot, studying the driving effect of the external driving permanent magnet on the capsule robot and establishing a living tissue sampling controller for the capsule robot. Simultaneously, an improved Golden Jackal Optimization Algorithm (IGJO) was designed, introducing three major improvements. In the population initialization stage, Fuchs mapping theory and an elite population strategy were introduced to optimize and initialize the Golden Jackal population. In the iterative output stage of the population, an adaptive inertial weight ω and an explorer position update strategy from the improved sparrow algorithm were introduced to repeatedly optimize the output solution of the Golden Jackal population, effectively improving the algorithm's global search capability. Through the above methods, the performance of the Golden Jackal algorithm was effectively improved, avoiding getting trapped in local optima. This allows for the optimized adjustment of the parameters of the living tissue sampling controller for the capsule robot, improving the control effect and increasing control accuracy. The implementation of this method provides a theoretical reference for the precise control of the multifunctional module of the capsule robot and has certain reference value. Attached Figure Description

[0090] Figure 1 This is a schematic cross-sectional view of the peanut-shaped magnetorheological capsule robot of the present invention;

[0091] Figure 2 This is a schematic diagram of the sampling process of the peanut-shaped magnetorheological capsule robot in this invention;

[0092] Figure 3 This is a schematic diagram of the living tissue sampling controller for the peanut-shaped magnetorheological capsule robot based on IGJO in this invention;

[0093] Figure 4 This is a flowchart of the improved Golden Jackal Optimization Algorithm (IGJO) in this invention;

[0094] Figure 5 This is a convergence curve of the first test function in this embodiment of the invention under the improved Golden Jackal optimization algorithm and several other intelligent optimization algorithms;

[0095] Figure 6 This is a convergence curve of the second test function in this embodiment of the invention under the improved Golden Jackal optimization algorithm and several other intelligent optimization algorithms;

[0096] Figure 7 This is a convergence curve of the third test function in this embodiment of the invention under the improved Golden Jackal optimization algorithm and several other intelligent optimization algorithms;

[0097] Figure 8This is a convergence curve of the fourth test function in this embodiment of the invention under the improved Golden Jackal optimization algorithm and several other intelligent optimization algorithms.

[0098] In the figure: 1. Soft capsule shell, 2. First ferromagnetic ring, 3. First ferrofluid, 4. Second ferromagnetic ring, 5. Second ferrofluid, 6. Camera module, 7. Battery, 8. Wireless transmission module, 9. Support component, 10. Extrusion plate, 11. Retractable sampling needle, 12. Vibrating plate, 13. Gasket, 14. Buffer plate. Detailed Implementation

[0099] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0100] Example 1 Figures 1 to 2 As shown, a peanut-shaped magnetorheological capsule robot tissue sampling controller based on IGJO includes a peanut-shaped magnetorheological capsule robot and a permanent magnet. The peanut-shaped magnetorheological capsule robot includes a soft capsule shell 1 and a sampling component disposed inside the soft capsule shell 1. The soft capsule shell 1 is provided with a ferromagnetic ring unit and a ferrofluid unit. The sampling component includes a camera module 6 and a retractable sampling needle 11.

[0101] Motion control of a peanut-shaped magnetorheological capsule robot includes the following steps:

[0102] Step 1: Mechanical analysis was performed on the living tissue sampling module of the peanut-shaped magnetorheological capsule robot to study the driving effect of the external driving permanent magnet on the capsule robot and to establish a living tissue sampling controller for the capsule robot.

[0103] Step 2: Design an improved Golden Jackal Optimization Algorithm (IGJO), introducing three major improvements. In the population initialization stage, Fuchs mapping theory and elite population strategy are introduced to optimize and initialize the Golden Jackal population. In the iterative output stage of the population, adaptive inertia weight ω and the explorer position update strategy from the improved Sparrow Algorithm are introduced to update the solution of the improved Sparrow Algorithm again.

[0104] Step 3: Repeatedly optimize the output solution of the golden jackal population to effectively improve the global search capability of the algorithm. Select the optimal and second-best golden shots as the positions of the male and female shots, and calculate the prey's selection energy E and the random number of levy flight motion.

[0105] Example 2: In addition to all the technical features included in Example 1, this example also includes:

[0106] The soft capsule shell 1 has two cavities. The outer cavity contains a first ferromagnetic ring 2 and a second ferromagnetic ring 4 that constitute a ferromagnetic ring unit. The inner cavity contains a first ferromagnetic fluid 3 and a second ferromagnetic fluid 5 that constitute a ferromagnetic fluid unit.

[0107] The camera module 6 of the sampling component is located at the opening at one end of the soft capsule shell 1, and a wireless transmission module 8 for transmitting the image data captured by the camera module 6 and a battery 7 for powering the camera module 6 are also fixedly connected inside the soft capsule shell 1.

[0108] The retractable sampling needle 11 of the sampling assembly is located at the opening at the other end of the soft capsule shell 1, and the support 9 connected to the tail end of the retractable sampling needle 11 is locked on the extrusion plate 10 which is fixedly connected to the inner wall of the soft capsule shell 1. The front end of the retractable sampling needle 11 is provided with a vibration plate 12 and a buffer plate 14, and a gasket 13 is placed at the connection between the vibration plate 12 and the buffer plate 14.

[0109] Example 3 In addition to all the technical features included in Example 1, this example also includes:

[0110] In step one, during the mechanical analysis, to ensure successful sampling, the retractable sampling needle can penetrate to the specified depth and retract, establishing its needle pressure. Total spring force With permanent magnet magnetic force relation:

[0111] ;

[0112] in, For the stress that damages gastrointestinal tissue, The area of ​​the needle tip. Let be the spring constant. This represents the spring elongation.

[0113] During sampling, the permanent magnet is radially magnetized, with the direction being along... Axial direction. The field point coordinate vector is... The field source coordinate vector is If expressed in cylindrical coordinates, the coordinates of the source point are: The field point coordinates are The distance between the two points is For ease of understanding and calculation, the origin of the coordinate system is... Set at the geometric center of the robot, that is, the coordinate system The origin is placed at the center of the cylindrical permanent magnet; based on Gauss's law of Maxwell's equations, the magnetic scalar potential at any point in the external space of the permanent magnet can be obtained;

[0114] ;

[0115] in, For volume magnetic charge density, , This refers to the magnetization, which is the volume density of the magnetic dipole moment. The outward normal unit vector of the permanent magnet boundary surface. for and The angle between the two points is L, and the distance between the two points is L.

[0116] When no bulk magnetic charge exists:

[0117] ;

[0118] The magnetic field strength can be obtained by calculating the divergence of the magnetic scalar potential. The vacuum permeability here ;

[0119] Based on the above relationships, the magnetic field strength and magnetic induction intensity can be obtained as follows:

[0120] ;

[0121] in is the vacuum permeability.

[0122] From this, we can obtain the magnetic flux density of a cylindrical permanent magnet at any point in space:

[0123] In the formula, It is the outer radius of the cylindrical permanent magnet; For the bottom surface of the permanent magnet Axis coordinate values; For the top surface of the permanent magnet Axis coordinate values;

[0124] In cylindrical coordinates, the distance between the field source and the field point is... From the formula for the distance between two points, we get:

[0125] ;

[0126] make ,have to:

[0127] ;

[0128] in , , These are the unit vectors in the radial, circumferential, and axial directions, respectively. From the above three equations, the spatial magnetic field induction intensity of the permanent magnet in the radial, circumferential, and axial directions can be obtained.

[0129] 1) Radial magnetic field induction intensity

[0130] ;

[0131] 2) Circumferential magnetic field induction intensity

[0132] ;

[0133] 3) Axial magnetic field induction intensity

[0134] ;

[0135] After vector synthesis, the magnetic flux density at any point outside the permanent magnet can be obtained:

[0136] ;

[0137] The above derivation can be used to obtain the magnetic field strength of the external driving permanent magnet for the sampling module, and the corresponding magnetic field driving force can be derived.

[0138] Through the above process, a mathematical model of the externally driven permanent magnet and the capsule robot sampling is established, a capsule robot sampling control system is built, and the sampling PID method is used for control.

[0139] Example 4 In addition to all the technical features included in Example 1, this example also includes:

[0140] In step two, the improved Golden Jackal Optimization Algorithm (IGJO) optimizes the selection of PID control parameters. The basic Golden Jackal Optimization Algorithm simulates the hunting behavior of a golden jackal, whose hunting process mainly consists of three basic stages:

[0141] (1) Search for prey and approach it;

[0142] (2) Surround the prey and stimulate it until it stops moving;

[0143] (3) Attacking prey;

[0144] The algorithm flow is as follows:

[0145] First, perform population initialization:

[0146] ;

[0147] In the formula: This indicates the location of the initial golden jackal population. It is a random number in the range [0,1]. and These are the upper and lower boundaries of the problem to be solved;

[0148] In the GJO algorithm, the prey matrix is ​​represented as:

[0149] ;

[0150] In the formula: For the prey matrix; For the first The first prey Dimensional position; This refers to the population size of the golden jackal; The dimensions for solving the problem;

[0151] During the optimization process, the fitness value of each golden jackal during the hunting process is calculated using the target fitness function, and its fitness value matrix is ​​represented as follows:

[0152] ;

[0153] In the formula: This is the fitness value matrix of the prey; The fitness function or objective function is defined as follows: the jackal with the best fitness value is designated as the male, and the jackal with the second best fitness value is designated as the female.

[0154] Golden jackal populations rely on their own senses to search for prey, and the amount of energy the prey uses to escape directly affects the population behavior of golden jackals. The escape energy of prey can be calculated using the following formula:

[0155] ;

[0156] in, This indicates the initial energy state of the prey. This indicates the process of the prey's energy decreasing;

[0157] ;

[0158] In the formula: This represents the maximum number of iterations. It is a constant with a value of 1.5; This represents the current iteration number; throughout the entire iteration process, It decreases linearly from 1.5 to 0;

[0159] The formula mentioned above It is based on A vector of random numbers distributed as follows:

[0160] ;

[0161] in The Lévy flight function is represented and its calculation method is as follows:

[0162] ;

[0163] ;

[0164] In the formula: and A random number within the range (0,1); To use the default constant, the value is 1.5;

[0165] Once the desired location is found, the golden jackal population enters the search and exploration phase, with male jackals leading the hunt and female jackals following behind. Their group behavior is as follows:

[0166] ;

[0167] In the formula: This represents the current iteration number; For the first The location of the prey in the next iteration; , The first The positions of the male and female golden jackals in the next iteration; , The first The positions of the male and female golden jackals after the first iteration are updated; thus, the updated positions of the golden jackals after the first iteration are as follows:

[0168] ;

[0169] When the desired result is obtained At this stage, the golden jackal population enters the encirclement and attack phase, where the jackals surround and prey on the prey they found in the previous phase. The behavioral patterns of male and female golden jackals in this phase are as follows:

[0170] ;

[0171] The above method is used to update and obtain the first... The positions of male and female golden jackals after each iteration are determined, and the positions of the golden jackals are updated according to formula (26).

[0172] In step two, the population initialization of the golden jackal is optimized by introducing Fuch mapping, and an elite population strategy is used to optimize the selection of the population.

[0173] The Fuch mapping is a novel one-dimensional discrete mapping, and its expression is:

[0174] ;

[0175] in, ,

[0176] The Fuch mapping population is obtained using the above method. This invention employs an elite population strategy, merging the Fuch mapping population and the regular initialization population, calculating the fitness of each golden jackal, ranking them, and selecting the top-performing jackals. There are 1 elite individual, and the sequence of elite individuals is as follows:

[0177] ;

[0178] in, , , The number of individual golden jackals. , The first The positions of the male and female golden jackals in the next iteration.

[0179] In step two, an adaptive inertia weight ω is introduced to update the optimal solution obtained by the golden jackal population in each iteration;

[0180] To further balance the global and local search capabilities of the Golden Jackal algorithm at different stages, an adaptive inertia weight ω is proposed. In the early stage of iteration, the Golden Jackal moves at a faster speed to quickly reach the vicinity of the target value. In the later stage of iteration, the Golden Jackal moves at a slower speed to avoid getting trapped in local optima.

[0181] The formula for calculating the adaptive weighting factor is:

[0182] ;

[0183] in, This represents the maximum number of iterations. This represents the current iteration number; The optimized solution output after adaptive adjustment. The original output solution for this iteration is, i.e. , .

[0184] In step two, the explorer position update strategy from the sparrow algorithm is introduced and improved. The sparrow search probability ST is pre-set to 0.8, and the improved formula is as follows:

[0185] ;

[0186] This allows for further optimization of the output solution for the golden jackal population, effectively improving the algorithm's global search capability.

[0187] Example 5 presents a living tissue sampling controller for a peanut-shaped magnetorheological capsule robot based on IGJO. A basic test function is used to verify the performance of the improved algorithm; the theoretical optimal value of this function is 0. The improved Golden Jackal algorithm is compared with the basic Golden Jackal algorithm, the Gray Wolf optimization algorithm, the Northern Eagle optimization algorithm, the Whale optimization algorithm, the Sparrow Search algorithm, and the Harris Eagle optimization algorithm to verify the performance of the improved Golden Jackal optimization algorithm. To ensure fairness in the testing, the population size for each algorithm is set to 50, and the maximum number of iterations is set to 300.

[0188] like Figure 5 As shown, the improved Golden Jackal algorithm has significant advantages in both convergence speed and convergence accuracy compared to other algorithms.

[0189] Example 6: A bio-tissue sampling controller for a peanut-shaped magnetorheological capsule robot based on IGJO, using a basic test function. To verify the performance of the improved algorithm, the theoretical optimal value of the function is 0. The improved Golden Jackal algorithm is compared with the basic Golden Jackal algorithm, the Gray Wolf optimization algorithm, the Northern Eagle optimization algorithm, the Whale optimization algorithm, the Sparrow Search algorithm, and the Harris Eagle optimization algorithm to verify the performance of the improved Golden Jackal optimization algorithm. To ensure fairness in the testing, the population size for each algorithm is set to 50, and the maximum number of iterations is set to 300.

[0190] like Figure 6 As shown, the improved Golden Jackal algorithm has significant advantages in both convergence speed and convergence accuracy compared to other algorithms.

[0191] Example 7

[0192] A bio-tissue sampling controller for a peanut-shaped magnetorheological capsule robot based on IGJO, employing fundamental test functions. To verify the performance of the improved algorithm, the theoretical optimal value of the function is 0. The improved Golden Jackal algorithm is compared with the basic Golden Jackal algorithm, the Gray Wolf optimization algorithm, the Northern Eagle optimization algorithm, the Whale optimization algorithm, the Sparrow Search algorithm, and the Harris Eagle optimization algorithm to verify the performance of the improved Golden Jackal optimization algorithm. To ensure fairness in the testing, the population size for each algorithm is set to 50, and the maximum number of iterations is set to 300.

[0193] like Figure 7 As shown, the improved Golden Jackal algorithm has significant advantages in both convergence speed and convergence accuracy compared to other algorithms.

[0194] Example 8

[0195] A bio-tissue sampling controller for a peanut-shaped magnetorheological capsule robot based on IGJO, employing fundamental test functions.

[0196] To verify the performance of the improved algorithm, the theoretical optimal value of the function is 0. The improved Golden Jackal algorithm is compared with the basic Golden Jackal algorithm, the Gray Wolf optimization algorithm, the Northern Eagle optimization algorithm, the Whale optimization algorithm, the Sparrow Search algorithm, and the Harris Eagle optimization algorithm to verify the performance of the improved Golden Jackal optimization algorithm. To ensure fairness in the testing, the population size for each algorithm is set to 50, and the maximum number of iterations is set to 300.

[0197] like Figure 8 As shown, the improved Golden Jackal algorithm has significant advantages in both convergence speed and convergence accuracy compared to other algorithms.

[0198] Working principle: Taking a peanut-shaped magnetorheological capsule robot as the research object, its center of mass is located at the geometric center of the peanut-shaped capsule. It mainly relies on an external permanent magnet to drive the live tissue sampling module. An external magnetic field is applied by the external permanent magnet, which drives an oscillating plate and extends the retractable sampling needle in the module to sample the surface tissue of the gastrointestinal tract. After collection, the external magnetic field is removed, allowing the sampling module to reset, completing the sampling process.

[0199] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0200] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A peanut-shaped magnetorheological capsule robot tissue sampling controller based on IGJO, comprising a peanut-shaped magnetorheological capsule robot and a permanent magnet, characterized in that: The peanut-shaped magnetorheological capsule robot includes a soft capsule shell (1) and a sampling component disposed inside the soft capsule shell (1). The soft capsule shell (1) is provided with a ferromagnetic ring unit and a ferrofluid unit. The sampling component includes a camera module (6) and a retractable sampling needle (11). The motion control of the peanut-shaped magnetorheological capsule robot includes the following steps: Step 1: Perform mechanical analysis on the living tissue sampling module of the peanut-shaped magnetorheological capsule robot, study the driving effect of the external driving permanent magnet on the capsule robot, and establish a living tissue sampling controller for the capsule robot. Step 2: Design an improved Golden Jackal optimization algorithm, introducing three major improvements. In the population initialization stage, Fuchs mapping theory and elite population strategy are introduced to optimize and initialize the Golden Jackal population. In the iterative output stage of the population, adaptive inertia weight ω and the explorer position update strategy from the improved Sparrow algorithm are introduced. The explorer position update strategy from the Sparrow algorithm is introduced to update the solution of the improved Sparrow algorithm again. Step 3: Repeatedly optimize the output solution of the golden jackal population to improve the global search capability of the algorithm. Select the optimal and second-best golden shots as the positions of the male and female shots, and calculate the prey's selection energy E and the random number of levy flight motion.

2. The peanut-shaped magnetorheological capsule robot tissue sampling controller according to claim 1, characterized in that: The soft capsule shell (1) has two cavities. The outer cavity contains a first ferromagnetic ring (2) and a second ferromagnetic ring (4) that constitute a ferromagnetic ring unit. The inner cavity contains a first ferromagnetic fluid (3) and a second ferromagnetic fluid (5) that constitute a ferromagnetic fluid unit.

3. The peanut-shaped magnetorheological capsule robot tissue sampling controller according to claim 1, characterized in that: The camera module (6) of the sampling component is located at the opening at one end of the soft capsule shell (1), and a wireless transmission module (8) for transmitting the image data captured by the camera module (6) and a battery (7) for powering the camera module (6) are also fixedly connected inside the soft capsule shell (1).

4. The peanut-shaped magnetorheological capsule robot tissue sampling controller according to claim 3, characterized in that: The retractable sampling needle (11) of the sampling assembly is located at the opening at the other end of the soft capsule shell (1), and the support (9) connected to the tail end of the retractable sampling needle (11) is placed on the extrusion plate (10) which is fixedly connected to the inner wall of the soft capsule shell (1). The front end of the retractable sampling needle (11) is provided with a vibration plate (12) and a buffer plate (14), and a gasket (13) is placed at the connection between the vibration plate (12) and the buffer plate (14).

5. The peanut-shaped magnetorheological capsule robot tissue sampling controller according to claim 1, characterized in that: In step one, during the mechanical analysis, to ensure successful sampling, the retractable sampling needle can puncture and retract to a specified depth, establishing its needle pressure. Total spring force With permanent magnet magnetic force relation: ； in, For the stress that damages gastrointestinal tissue, The area of ​​the needle tip. Let be the spring constant. This is the spring elongation; During sampling, the permanent magnet is radially magnetized, with the direction being along... Axial direction; Take the field point coordinate vector as The field source coordinate vector is If expressed in cylindrical coordinates, the coordinates of the source point are: The field point coordinates are The distance between the two points is ; For ease of understanding and calculation, the origin of the coordinate system is... Set at the geometric center of the robot, that is, the coordinate system The origin is placed at the center of the cylindrical permanent magnet; Gauss's law, based on Maxwell's equations, yields the magnetic scalar potential at any point in the external space of the permanent magnet. ； in, For volume magnetic charge density, , This refers to the magnetization, which is the volume density of the magnetic dipole moment. The outward normal unit vector of the permanent magnet boundary surface. for and The angle between the two points is L, and L is the distance between the two points. When no bulk magnetic charge exists: ； The magnetic field strength can be obtained by calculating the divergence of the magnetic scalar potential. The vacuum permeability here ; Based on the above relationships, the magnetic field strength and magnetic induction intensity are respectively: ； in The vacuum permeability; Therefore, the magnetic flux density of a cylindrical permanent magnet at any point in space is: ； In the formula, It is the outer radius of the cylindrical permanent magnet; For the bottom surface of the permanent magnet Axis coordinate values; For the top surface of the permanent magnet Axial coordinate values; In cylindrical coordinates, the distance between the field source and the field point is... From the formula for the distance between two points, we get: ； make ,have to: ； in , , These are the unit vectors in the radial, circumferential, and axial directions, respectively. The spatial magnetic field induction intensity of the permanent magnet in the radial, circumferential, and axial directions can be obtained from the above three equations. 1) Radial magnetic field induction intensity : ; 2) Circumferential magnetic field induction intensity : ; 3) Axial magnetic field induction intensity : ; After vector synthesis, the magnetic flux density at any point outside the permanent magnet can be obtained: ； Based on the above derivation, the magnetic field strength of the external driving permanent magnet for the sampling module is obtained, and the corresponding magnetic field driving force is derived. Through the above process, a mathematical model of the externally driven permanent magnet and the capsule robot sampling is established, a capsule robot sampling control system is built, and the sampling PID method is used for control.

6. A bio-tissue sampling controller for a peanut-shaped magnetorheological capsule robot based on IGJO according to claim 1, characterized in that: In step two, the improved golden jackal optimization algorithm optimizes the selection of PID control parameters. The basic golden jackal optimization algorithm simulates the hunting behavior of a golden jackal, whose hunting process is mainly divided into three basic stages: (1) Search for prey and approach it; (2) Surround the prey and stimulate it until it stops moving; (3) Attacking prey; The algorithm flow is as follows: First, perform population initialization: ； In the formula: This indicates the location of the initial golden jackal population. It is a random number in the range [0,1]. and These are the upper and lower boundaries of the problem to be solved; In the GJO algorithm, the prey matrix is ​​represented as: ； In the formula: For the prey matrix; For the first The first prey Dimensional position; This refers to the population size of the golden jackal; The dimensions for solving the problem; During the optimization process, the fitness value of each golden jackal during the hunting process is calculated using the target fitness function, and its fitness value matrix is ​​represented as follows: ； In the formula: This is the fitness value matrix of the prey; The fitness function or objective function is defined as follows: the jackal with the best fitness value is designated as the male, and the jackal with the second best fitness value is designated as the female. Golden jackal populations rely on their own senses to search for prey, and the amount of energy the prey uses to escape directly affects the population behavior of golden jackals. The escape energy of the prey is calculated using the following formula: ； in, This indicates the initial energy state of the prey. This indicates the process of the prey's energy decreasing; ； In the formula: This represents the maximum number of iterations. It is a constant with a value of 1.5; This represents the current iteration number; throughout the entire iteration process, It decreases linearly from 1.5 to 0; The formula mentioned above It is based on A vector of randomly distributed numbers is represented as: ； in The Lévy flight function is represented and its calculation method is as follows: ； In the formula: and A random number within the range (0,1); To use the default constant, the value is 1.5; Once the desired location is found, the golden jackal population enters the search and exploration phase, with male jackals leading the hunt and female jackals following behind. Their group behavior is as follows: In the formula: This represents the current iteration number; For the first The location of the prey in the next iteration; , The first The positions of the male and female golden jackals in the next iteration; , The first The locations of the male and female golden jackals after the next iteration update; Therefore, the updated position of the golden jackal after the i-th iteration is as follows: ； When the desired result is obtained At this time, the golden jackal population enters the encirclement and attack phase, where the golden jackals surround and prey on the prey they have searched for in the previous phase. The behavioral patterns of male and female golden jackals at this stage are as follows: ； The above method is used to update and obtain the first... The positions of male and female golden jackals after each iteration are determined, and the positions of the golden jackals are updated according to formula (27).

7. The peanut-shaped magnetorheological capsule robot tissue sampling controller according to claim 6, characterized in that: In step two, the population initialization of the golden jackal is optimized by introducing Fuch mapping, and an elite population strategy is used to optimize the selection of the population. The Fuch mapping is a novel one-dimensional discrete mapping, and its expression is: ;in, , ; The Fuch mapping population was obtained using the above method. An elite population strategy was adopted, merging the Fuch mapping population with the regular initialization population. The fitness of each golden jackal was calculated, sorted, and the top-performing jackals were selected. There are 1 elite individual, and the sequence of elite individuals is as follows: ； in, , , This represents the number of individual golden jackals.

8. The peanut-shaped magnetorheological capsule robot tissue sampling controller according to claim 7, characterized in that: In step two, an adaptive inertia weight ω is introduced to update the optimal solution obtained by the golden jackal population in each iteration. To further balance the global and local search capabilities of the Golden Cow algorithm at different stages, an adaptive inertial weight ω is proposed. In the early stages of the iteration, make the golden jackal move faster to quickly reach the vicinity of the target value; In the later stages of the iteration, allowing the golden jackal to move at a slower speed reduces the risk of getting trapped in local optima. The formula for calculating the adaptive weighting factor is: ;in, This represents the maximum number of iterations. This represents the current iteration number; The optimized solution output after adaptive adjustment. This is the original output solution for this iteration, which is... .

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