An idmo-pid-based precise motion controller for magnetically controlled capsule robots

Abstract

This invention discloses a precise motion controller for a magnetically controlled capsule robot based on IDMO-PID, comprising a magnetically controlled capsule robot and an external magnetic source. The magnetically controlled capsule robot is placed in the stomach of a human, and the external magnetic source is located on the outside of the human body. This invention optimizes the motion control system of the capsule robot by rationally analyzing the motion mode and spatial magnetic field of the capsule robot. Based on the linearly decreasing inertia weight factor and the Cauchy-Gaussian mutation strategy, the population position update stage and the optimal solution output stage of the dwarf meerkat optimization algorithm are optimized, which effectively improves and reasonably balances the global search capability and local search capability of the basic dwarf meerkat optimization algorithm at different stages, thereby improving the convergence progress and convergence speed of the algorithm. The improved dwarf meerkat optimization algorithm is used to optimize the motion control system parameters of the capsule robot, realizing the precise motion of the capsule robot under multi-source spatial magnetic fields.

Description

Technical Field

[0001] This invention relates to a robot motion controller, specifically a precision motion controller for a magnetically controlled capsule robot based on IDMO-PID, belonging to the field of capsule robot control technology. Background Technology

[0002] With the continuous development and improvement of global medical standards, early detection and treatment of gastrointestinal diseases are now crucial for effectively improving cure rates. Capsule robots, as intelligent devices for gastrointestinal testing, enable safe and effective non-invasive diagnosis and treatment of gastrointestinal diseases, marking a significant milestone in the development of miniature medical robots.

[0003] However, existing capsule robots mostly rely on the peristalsis of the gastrointestinal tract for movement, which is passive control and makes their detection area random, easily leading to missed or false detections. Therefore, this study investigates an active magnetically controlled capsule robot based on spatial magnetic field control to achieve its posture control within the gastrointestinal tract, facilitating targeted detection of suspicious areas in the stomach by physicians and effectively improving the accuracy and reliability of detection results.

[0004] Current capsule robot motion control technology has the following problems:

[0005] (1) The magnetic control technology of the active magnetically controlled capsule robot is relatively complex. When switching motion modes, the coil current of the external magnetic field needs to be adjusted at multiple angles, resulting in low control accuracy.

[0006] (2) Due to the many folds inside the gastrointestinal tract, there is a lack of adaptive high-precision control methods to accurately control the capsule robot in this unstructured and rugged environment. Summary of the Invention

[0007] The purpose of this invention is to provide a precise motion controller for a magnetically controlled capsule robot based on IDMO-PID in order to solve at least one of the above-mentioned technical problems, establish a motion control system for the capsule robot, and use an improved dwarf mongoose optimization algorithm to optimize the selection of parameters for the control system.

[0008] The present invention achieves the above objectives through the following technical solution: a precision motion controller for a magnetically controlled capsule robot based on IDMO-PID, comprising a magnetically controlled capsule robot and an external magnetic source, wherein the magnetically controlled capsule robot is placed in the stomach of a human body and the external magnetic source is located on the outside of the human body;

[0009] The magnetically controlled capsule robot includes a shell and a sampling component disposed inside the shell. The shell contains a ferromagnetic ring unit and a ferrofluid unit, and the sampling component includes a camera module and a retractable sampling needle.

[0010] Motion control of a magnetically controlled capsule robot includes the following steps:

[0011] Step 1: Optimize the design of the capsule robot's motion control system by rationally analyzing the capsule robot's movement mode and spatial magnetic field;

[0012] Step 2: Optimize the population position update stage and optimal solution output stage of the dwarf meerkat optimization algorithm based on the linearly decreasing inertia weight factor and the Cauchy-Gaussian mutation strategy;

[0013] Step 3: Optimize the motion control system parameters of the capsule robot using the improved dwarf mongoose optimization algorithm to achieve precise movement of the capsule robot under multi-source spatial magnetic fields.

[0014] As a further aspect of the present invention: the outer shell of the magnetically controlled capsule robot is a peanut-shaped soft capsule shell, and the external magnetic source is a permanent magnet.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] As a further aspect of the present invention: In step one, the analysis of the capsule robot's motion includes translational motion and flipping motion, and the analysis of the capsule robot's motion and the spatial magnetic field specifically includes:

[0019] The external permanent magnet drives the capsule robot to perform translational and tumbling movements in the stomach by acting on the ferromagnetic ring and ferrofluid inside the capsule robot.

[0020] Under a spatial magnetic field, the magnetic field dB induced by a current of arbitrary length dl in a current-carrying circuit can be expressed by the following formula:

[0021]

[0022] Where μ0 is the free magnetic permeability, I is the current, and r is the pointing vector of the current element;

[0023] The magnetic flux density generated at a certain point in the entire magnetron unit can be calculated using the following formula:

[0024]

[0025] To determine the magnetic force exerted by an external magnetic field on the capsule robot during its steady motion, the derivative of the magnetic induction intensity of the capsule robot at the point (x,y,z) in magnetic space is solved. First, F1 and F2 are defined as follows:

[0026]

[0027]

[0028] Taking the partial derivatives with respect to x, y, and z, we get:

[0029]

[0030]

[0031] By using the above formula, the magnetic induction intensity is solved in each direction, and the magnetic force is calculated. By decomposing and calculating the spatial magnetic force, the capsule robot can be controlled.

[0032] As a further aspect of the present invention, the motion control system for the capsule robot specifically includes:

[0033] Using the MATLAB identification toolbox, the closed-loop transfer function of the capsule robot's motion velocity and the external magnetic field is established as follows:

[0034]

[0035] Based on the above motion equations and transfer functions, a motion control system for the capsule robot is established, and the parameters of the control system are optimized by using an improved dwarf mongoose optimization algorithm.

[0036] The basic dwarf mongoose optimization algorithm is as follows:

[0037] The Dwarf Meerkat Optimization (DMO) algorithm is a swarm intelligence optimization algorithm based on the group foraging behavior of dwarf meerkat, which simulates the three social functions of dwarf meerkat: foraging, scouting, and babysitting.

[0038] The pygmy meerkat is known for its collective foraging and scouting, with the female leader guiding the herd in the search for food sources. Once the conditions for foster care exchange are met, i.e. when the alpha group fails to find suitable food, members of the alpha group and foster care group will be exchanged, and the alpha group will simultaneously forage and search for sleeping mounds.

[0039] The female leader is generated from the alpha group. The probability of each female individual in the alpha group becoming the leader is α, calculated as follows:

[0040]

[0041] Where fit i is the fitness of the i-th individual, N is the total number of individuals in the dwarf meerkat population; the number of individuals in the alpha group is n′, and bs is the number of caregivers;

[0042] Alpha group members will travel together and be fed, and the candidate locations of food sources are given by the above formula:

[0043] x i+1 =x i +pi×peep×(x i -x rand )

[0044] Where x i+1 It is the new location of the food source that has been found, x i Let pi be the current position of the female leader, and x be a random number uniformly distributed between [-1, 1]. rand It is a random individual in the alpha group;

[0045] The foster parent exchange condition is used to reset the meerkats in the Alpha Group and the foster parent Group. When an Alpha Group member fails to find suitable food, it is considered that the Alpha Group member is insufficient, and a member will be exchanged between the Alpha Group and the foster parent Group. After the exchange condition is met, the Alpha Group will simultaneously engage in feeding and searching for sleep mounds. The calculation formula is as follows:

[0046] x b =lb + rand * (ub - lb)

[0047] Where x b The new positions of the individuals after the swap are defined by ub and lb, which are the upper and lower bounds of the search space, respectively, and rand is a random number between 0 and 1.

[0048] The foraging behavior following the foster parent exchange is determined by the probability formula of each female in the alpha group becoming the leader; the sleeping mound is a resting place for the meerkats, and they do not return to their previous sleeping mounds, a lifestyle that avoids the problem of overexploitation of the search area; the mathematical model for newly discovered sleeping mounds is as follows:

[0049]

[0050] Where x sm For the location of the new sleep mound, It is the direction vector that determines the mongoose's movement to the new sleeping mound. This is the average value of the sleep mound, calculated using the following formula:

[0051]

[0052]

[0053] Among them sm i Represents the sleep hill value:

[0054]

[0055] In the formula, CF represents the mobility parameter of the meerkat population, which decreases linearly with the number of iterations. The calculation formula is as follows:

[0056]

[0057] Where t is the current iteration number and T is the maximum iteration number.

[0058] As a further aspect of this invention: during the dwarf meerkat's search for the sleep mound, a linearly decreasing inertia weight factor is introduced to optimize the algorithm, and its formula is as follows:

[0059]

[0060] Where: T represents the maximum number of iterations; t represents the current number of iterations; w max and w min These represent the maximum and minimum inertia weights, respectively.

[0061] As a further aspect of this invention: for the mathematical model of the sleep mound search stage of the dwarf meerkat, optimization is performed based on a linearly decreasing inertia weight factor, and the optimized mathematical model is as follows:

[0062]

[0063] In the later stages of the DWO algorithm iteration, multiple dwarf meerkats tend to cluster together, easily getting trapped in local optima. To prevent the algorithm from stagnating, a Cauchy-Gaussian mutation strategy is introduced. At the end of each iteration, the individual with the best fitness is selected for Cauchy-Gaussian mutation. If the mutated position is better than the current position, the individual with the better position is selected for the next iteration. The Cauchy-Gaussian perturbation formula is:

[0064]

[0065] in: This represents the position before mutation in the t-th iteration; Let represent the position after mutation in the t-th iteration; Cauchy(0,1) and Gauss(0,1) are random variables that satisfy Cauchy distribution and Gaussian distribution, respectively; As the iterations gradually decrease; Gradually increase.

[0066] The beneficial effects of this invention are:

[0067] By rationally analyzing the motion mode and spatial magnetic field of the capsule robot, the motion control system of the capsule robot is optimized. At the same time, based on the linearly decreasing inertia weight factor and the Cauchy-Gaussian mutation strategy, the population position update stage and the optimal solution output stage of the dwarf meerkat optimization algorithm are optimized, which effectively improves and reasonably balances the global search capability and local search capability of the basic dwarf meerkat optimization algorithm at different stages, and improves the convergence progress and convergence speed of the algorithm. The motion control system parameters of the capsule robot are optimized through the improved dwarf meerkat optimization algorithm, so as to realize the precise movement of the capsule robot under multi-source spatial magnetic field. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of the structure of the peanut-shaped active capsule robot based on magnetorheological fluid, which is the subject of this invention.

[0069] Figure 2 This is a schematic diagram of the movement of the peanut-shaped active capsule robot under the control of an external magnetic source in this invention;

[0070] Figure 3 This is a flowchart of the capsule robot motion controller in this invention;

[0071] Figure 4 The flowchart of the improved dwarf mongoose optimization algorithm in this invention is shown.

[0072] Figure 5 This is a convergence curve of the first test function in this embodiment of the invention under IDMO and DMO.

[0073] Figure 6 This is a convergence curve of the second test function in this embodiment of the invention under IDMO and DMO.

[0074] 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

[0075] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Example 1

[0077] like Figures 1 to 2 As shown, a magnetically controlled capsule robot precision motion controller based on IDMO-PID includes a magnetically controlled capsule robot and an external magnetic source. The magnetically controlled capsule robot is placed in the human stomach, and the external magnetic source is set on the outside of the human body.

[0078] The magnetically controlled capsule robot includes a shell and a sampling component disposed inside the shell. The shell contains a ferromagnetic ring unit and a ferrofluid unit. The sampling component includes a camera module 6 and a retractable sampling needle 11.

[0079] Motion control of a magnetically controlled capsule robot includes the following steps:

[0080] Step 1: Optimize the design of the capsule robot's motion control system by rationally analyzing the capsule robot's movement mode and spatial magnetic field;

[0081] Step 2: Optimize the population position update stage and optimal solution output stage of the dwarf meerkat optimization algorithm based on the linearly decreasing inertia weight factor and the Cauchy-Gaussian mutation strategy;

[0082] Step 3: Optimize the motion control system parameters of the capsule robot using the improved dwarf mongoose optimization algorithm to achieve precise movement of the capsule robot under multi-source spatial magnetic fields.

[0083] Example 2

[0084] In addition to all the technical features included in Embodiment 1, this embodiment also includes:

[0085] The outer shell of the magnetically controlled capsule robot is a peanut-shaped soft capsule shell 1, and the external magnetic source is a permanent magnet.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] Step one includes analyzing the capsule robot's motion, which includes translational and tumbling motions. The analysis of the capsule robot's motion and the spatial magnetic field specifically includes:

[0090] The external permanent magnet drives the capsule robot to perform translational and tumbling movements in the stomach by acting on the ferromagnetic ring and ferrofluid inside the capsule robot.

[0091] Under a spatial magnetic field, the magnetic field dB induced by a current of arbitrary length dl in a current-carrying circuit can be expressed by the following formula:

[0092]

[0093] Where μ0 is the free magnetic permeability, I is the current, and r is the pointing vector of the current element;

[0094] The magnetic flux density generated at a certain point in the entire magnetron unit can be calculated using the following formula:

[0095]

[0096] To determine the magnetic force exerted by an external magnetic field on the capsule robot during its steady motion, the derivative of the magnetic induction intensity of the capsule robot at the point (x,y,z) in magnetic space is solved. First, F1 and F2 are defined as follows:

[0097]

[0098]

[0099] Taking the partial derivatives with respect to x, y, and z, we get:

[0100]

[0101]

[0102] By using the above formula, the magnetic induction intensity is solved in each direction, and the magnetic force is calculated. By decomposing and calculating the spatial magnetic force, the capsule robot can be controlled.

[0103] Example 3

[0104] like Figures 3 to 4 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes:

[0105] The motion control system for the capsule robot specifically includes:

[0106] Using the MATLAB identification toolbox, the closed-loop transfer function of the capsule robot's motion velocity and the external magnetic field is established as follows:

[0107]

[0108] Based on the above motion equations and transfer functions, a motion control system for the capsule robot is established, and the parameters of the control system are optimized by using an improved dwarf mongoose optimization algorithm.

[0109] The basic dwarf mongoose optimization algorithm is as follows:

[0110] The Dwarf Meerkat Optimization (DMO) algorithm is a swarm intelligence optimization algorithm based on the group foraging behavior of dwarf meerkat, which simulates the three social functions of dwarf meerkat: foraging, scouting, and babysitting.

[0111] The pygmy meerkat is known for its collective foraging and scouting, with the female leader guiding the herd in the search for food sources. Once the conditions for foster care exchange are met, i.e. when the alpha group fails to find suitable food, members of the alpha group and foster care group will be exchanged, and the alpha group will simultaneously forage and search for sleeping mounds.

[0112] The female leader is generated from the alpha group. The probability of each female individual in the alpha group becoming the leader is α, calculated as follows:

[0113]

[0114] Where fit i is the fitness of the i-th individual, N is the total number of individuals in the dwarf meerkat population; the number of individuals in the alpha group is n′, and bs is the number of caregivers;

[0115] Alpha group members will travel together and be fed, and the candidate locations of food sources are given by the above formula:

[0116] x i+1 =x i +phi×peep×(x i -x rand )

[0117] Where x i+1 It is the new location of the food source that has been found, x i The current position of the female leader is given by pi, which is a random number uniformly distributed between [-1, 1]. In this paper, peep is chosen to be 2, and x... rand It is a random individual in the alpha group;

[0118] The foster parent exchange condition is used to reset the meerkats in the Alpha Group and the foster parent Group. When an Alpha Group member fails to find suitable food, it is considered that the Alpha Group member is insufficient, and a member will be exchanged between the Alpha Group and the foster parent Group. After the exchange condition is met, the Alpha Group will simultaneously engage in feeding and searching for sleep mounds. The calculation formula is as follows:

[0119] x b =lb + rand * (ub - lb)

[0120] Where x b The new positions of the individuals after the swap are defined by ub and lb, which are the upper and lower bounds of the search space, respectively, and rand is a random number between 0 and 1.

[0121] The foraging behavior following the foster parent exchange is determined by the probability formula of each female in the alpha group becoming the leader; the sleeping mound is a resting place for the meerkats, and they do not return to their previous sleeping mounds, a lifestyle that avoids the problem of overexploitation of the search area; the mathematical model for newly discovered sleeping mounds is as follows:

[0122]

[0123] Where x sm For the location of the new sleep mound, It is the direction vector that determines the mongoose's movement to the new sleeping mound. This is the average value of the sleep mound, calculated using the following formula:

[0124]

[0125]

[0126] Among them sm i Represents the sleep hill value:

[0127]

[0128] In the formula, CF represents the mobility parameter of the meerkat population, which decreases linearly with the number of iterations. The calculation formula is as follows:

[0129]

[0130] Where t is the current iteration number and T is the maximum iteration number.

[0131] Example 4

[0132] In addition to all the technical features included in Embodiment 3, this embodiment also includes:

[0133] During the sleep mound search phase of the dwarf meerkats, a linearly decreasing inertia weight factor is introduced to optimize the algorithm, and its formula is as follows:

[0134]

[0135] Where: T represents the maximum number of iterations; t represents the current number of iterations; w max and w min These represent the maximum and minimum inertia weights, respectively. In this invention, w is set... max =0.9, w min =0.4, which greatly improves the search performance of the algorithm.

[0136] For the mathematical model of the sleep mound search stage of the dwarf meerkat, based on the linearly decreasing inertia weight factor optimization, the optimized mathematical model is as follows:

[0137]

[0138] In the later stages of the DWO algorithm iteration, multiple dwarf meerkats tend to cluster together, easily getting trapped in local optima. To prevent the algorithm from stagnating, a Cauchy-Gaussian mutation strategy is introduced. At the end of each iteration, the individual with the best fitness is selected for Cauchy-Gaussian mutation. If the mutated position is better than the current position, the individual with the better position is selected for the next iteration. The Cauchy-Gaussian perturbation formula is:

[0139]

[0140] in: This represents the position before mutation in the t-th iteration; Let represent the position after mutation in the t-th iteration; Cauchy(0,1) and Gauss(0,1) are random variables that satisfy Cauchy distribution and Gaussian distribution, respectively; As the iterations gradually decrease; Gradually increase.

[0141] Example 5

[0142] like Figure 5 As shown, a precise motion controller for a magnetically controlled capsule robot based on IDMO-PID is presented. Commonly used test functions are selected in this embodiment as follows:

[0143]

[0144] The performance of the IDMO algorithm was verified using the aforementioned test function, whose theoretical optimal value is 0, and whose search region is defined as [-10, 10]. The IDMO optimization algorithm was compared with the basic DMO algorithm to test the performance of the improved dwarf mongoose optimization algorithm, IDMO. To ensure fairness in the testing, the population size for each algorithm was set to 40, and the maximum number of iterations was set to 400.

[0145] like Figure 5 As shown, the ID,O algorithm has a faster optimization speed than the basic D,O algorithm and can find the optimal value of the function very well.

[0146] Example 6

[0147] like Figure 6 As shown, a precise motion controller for a magnetically controlled capsule robot based on IDMO-PID is presented. Commonly used test functions are selected in this embodiment as follows:

[0148] F2(x)=max i {|x i |,1≤i≤n}

[0149] The performance of the IDMO algorithm was verified using the aforementioned test function, whose theoretical optimal value is 0, and whose search region is defined as [-100, 100]. The IDMO optimization algorithm was compared with the basic DMO algorithm to test the performance of the improved dwarf mongoose optimization algorithm, IDMO. To ensure fairness in the testing, the population size for each algorithm was set to 40, and the maximum number of iterations was set to 400.

[0150] like Figure 6 As shown, the IDMO algorithm has a faster optimization speed than the basic DMO algorithm and can find the optimal value of the function very well.

[0151] By rationally analyzing the motion mode and spatial magnetic field of the capsule robot, the motion control system of the capsule robot is optimized. At the same time, based on the linearly decreasing inertia weight factor and the Cauchy-Gaussian mutation strategy, the population position update stage and the optimal solution output stage of the dwarf meerkat optimization algorithm are optimized, which effectively improves and reasonably balances the global search capability and local search capability of the basic dwarf meerkat optimization algorithm at different stages, and improves the convergence progress and convergence speed of the algorithm. The motion control system parameters of the capsule robot are optimized through the improved dwarf meerkat optimization algorithm, so as to realize the precise movement of the capsule robot under multi-source spatial magnetic field.

[0152] 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.

[0153] 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. An IDMO-PID based magnetic controlled capsule robot precise motion controller, comprising a magnetic controlled capsule robot and an external magnetic source, characterized in that: The magnetic capsule robot is placed in the stomach of a human body, and the external magnetic source is arranged outside the human body; The magnetic capsule robot comprises a shell and a sampling assembly arranged in the shell, the shell is internally provided with a ferromagnetic ring unit and a ferromagnetic fluid unit, and the sampling assembly comprises a camera module (6) and a retractable sampling needle (11); The motion control method of the magnetic capsule robot comprises the following steps: Step one, by analyzing the motion mode of the capsule robot and the spatial magnetic field, the motion control system of the capsule robot is optimized; Step two, based on the linearly decreasing inertia weight factor and the Cauchy-Gauss mutation strategy, the population position updating stage and the optimal solution output stage of the lemur cat optimization algorithm are optimized; The basic lemur cat optimization algorithm is as follows: The lemur cat optimization algorithm is a kind of swarm intelligence optimization algorithm based on the foraging behavior of lemur cats, which simulates the three social functions of lemur cats, namely foraging, reconnaissance and babysitting; The lemur cats search for food sources under the guidance of the female leader; once the babysitting condition is met, that is, when the alpha group fails to find suitable food, the members of the alpha group and the babysitting group are exchanged, and the alpha group simultaneously forages and searches for sleep hills; The female leader is generated in the alpha group, and the probability of each female individual in the alpha group becoming a leader is alpha, and the calculation formula is as follows: where fit i is the fitness of the ith individual, N is the total number of individuals in the dwarfed quoll population, n' is the number of individuals in the alpha group, and bs is the number of babysitters. The alpha group members will move together and forage, and the candidate position of the food source is given by formula (8): x i+1 = x i + phi x peep x (x i - x rand ) (9) where x i+1 is the new location of the food source found, x i is the current location of the female leader, phi is a random number uniformly distributed between [-1, 1], and x rand is a random individual from the alpha group; The babysitting exchange condition is used to reset the cat individuals in the alpha group and the babysitting group; when the alpha group members fail to search for suitable food, it is considered that the alpha group members are insufficient in ability, and the members of the alpha group and the babysitting group are exchanged; after the exchange condition is met, the alpha group will simultaneously forage and search for sleep hills, and the calculation formula is as follows: x b = lb + rand * (ub - lb) (10) where x b is the new position of the exchanged individual, ub and lb are the upper and lower bounds of the search space, respectively, and rand is a random number between 0 and 1; The foraging behavior after the babysitting exchange is realized by the probability formula that each female individual in the alpha group becomes a leader; The sleep hill is the place where the cat rests, and the cat will not return to the previous sleep hill, and this life mode can avoid the problem of over-exploitation of the search area; The mathematical model of the newly searched sleep hill is as follows: where x sm is the position of the new sleep hill, is the direction vector that determines the movement of the ferret to the new sleep hill, is the average value of the sleep hills, and the calculation formula is as follows: where sm i representing the sleep hill value: In the formula, CF represents the parameter of the cat population movement ability, which will linearly decrease with the number of iterations, and the calculation formula is as follows: Where t is the current iteration number, and T is the maximum iteration number; In the lemur cat search sleep hill stage, the linearly decreasing inertia weight factor is introduced to optimize the algorithm, and the formula is as follows: wherein: T represents the maximum number of iterations; t represents the current number of iterations; w max and w min respectively represent the set maximum and minimum inertia weight values; For the mathematical model of the lemur cat search sleep hill stage, based on the linearly decreasing inertia weight factor optimization, the optimized mathematical model is as follows: In the later stage of the DWO algorithm iteration, multiple lemur cat individuals are easy to gather and easy to fall into local optimum; in order to prevent the algorithm from stagnating, the Cauchy-Gaussian mutation strategy is introduced; at the end of each iteration, the individual with the best fitness is selected for Cauchy-Gaussian mutation, and if the position after mutation is better than the current position, the individual with better position is selected for next iteration; the Cauchy-Gaussian disturbance formula is as follows: wherein: is the position before mutation in the tth iteration; is the position after mutation in the tth iteration; Cauchy(0, 1) and Gauss(0, 1) are random variables satisfying Cauchy distribution and Gaussian distribution, respectively; is gradually reduced with iteration; is gradually increased. Step three, the motion control system parameters of the capsule robot are optimized by the improved lemur cat optimization algorithm, and the accurate motion of the capsule robot in the multi-source spatial magnetic field is realized.

2. The magnetic capsule robot precision motion controller of claim 1, wherein: The shell of the magnetic capsule robot is a peanut-shaped soft capsule shell (1), and the external magnetic source is a permanent magnet.

3. The magnetic capsule robot precise motion controller of claim 2, wherein: The soft capsule shell (1) is provided with two cavities, and the first ferromagnetic ring (2) and the second ferromagnetic ring (4) constituting a ferromagnetic ring unit are symmetrically arranged in the cavity on the outer side, and the first ferromagnetic fluid (3) and the second ferromagnetic fluid (5) constituting a ferromagnetic fluid unit are symmetrically arranged in the cavity on the inner side.

4. The magnetic capsule robot precision motion controller of claim 1 or 2, wherein: The camera module (6) of the sampling assembly is located at the opening of one end of the soft capsule shell (1), and the soft capsule shell (1) is further fixedly connected with a wireless transmission module (8) for transmitting the picture data shot by the camera module (6) and a battery (7) for supplying power to the camera module (6).

5. The magnetic capsule robot precise motion controller of claim 4, wherein: The retractable sampling needle (11) of the sampling assembly is located at the opening of 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 clamped on the extrusion plate (10) fixedly connected to the inner wall of the soft capsule shell (1), and the front end of the retractable sampling needle (11) is provided with a shock plate (12) and a buffer plate (14), and the connection between the shock plate (12) and the buffer plate (14) is provided with a gasket (13).

6. The magnetic capsule robot precision motion controller of claim 1, wherein: In step one, the analysis of the movement mode of the capsule robot includes translation movement and overturning movement, and the analysis of the movement mode of the capsule robot and the spatial magnetic field specifically includes: The external permanent magnet drives the capsule robot to perform translation movement and overturning movement in the stomach by acting on the ferromagnetic ring and the ferromagnetic fluid inside the capsule robot; Under the spatial magnetic field, take a current with an arbitrary length dl, and the magnetic field dB excited in the current-carrying loop can be represented by the following formula: Where μ0 is the vacuum permeability, I is the current, and r is the director vector of the current element; Then, the magnetic induction intensity excited by a certain point of the whole magnetic control unit can be calculated by the following formula: In order to determine the magnetic field force of the external magnetic field on the capsule robot when the capsule robot moves stably, the derivative of the magnetic induction intensity of the capsule robot at the point (x, y, z) in the magnetic space is solved, and F1 and F2 are defined as follows: The partial derivatives of x, y and z are as follows: Through the above formula, the magnetic induction intensity in each direction is solved, the magnetic field force is calculated, and the control of the capsule robot is completed through the decomposition and calculation of the spatial magnetic field force.

7. The magnetic capsule robot precise motion controller of claim 6, wherein: The motion control system of the capsule robot specifically includes: The closed-loop transfer function of the motion speed of the capsule robot and the external spatial magnetic field is established by using the matlab identification toolbox as follows: Based on the above motion equation and transfer function, the motion control system of the capsule robot is established, and the improved weasel optimization algorithm is used to optimize and select the parameters of the control system.

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