Organoid robotic manipulation methods, systems, computer program products, and readable media

Abstract

This application provides a method, system, computer program product, and readable medium for manipulating organoid robots, relating to the field of organoid microrobot actuation, to solve the problems of speed and flexibility in motion control of flexible continuum organoid robots. This invention uses magnetic particles and microbubbles as apron and end effectors, respectively; it monitors the current pose data and obstacle boundary data of the organoid robot in real time; it performs obstacle avoidance and path planning based on the set target position, current pose data, and obstacle boundary data, and generates magnetic and ultrasonic signals with multiple superimposed magnetic fields. These magnetic and ultrasonic signals control the pose of the apron and end effectors, thereby controlling the organoid robot to move to the target position. This invention is mainly used for driving and controlling organoid robots.

Description

Technical Field

[0001] This invention relates to the field of organoid microrobot actuation, and more particularly to organoid robot manipulation methods, systems, computer program products, and readable media. Background Technology

[0002] Regarding propulsion mechanisms for organoid microrobots, existing research primarily focuses on methods using physical fields such as acoustic, magnetic, or optical fields for actuation. While these technologies can achieve remote control of microrobots to some extent, they each have limitations. For example, acoustic and magnetic field actuation requires specific working environments, while optical field actuation may damage biological tissues due to thermal effects. In the tumor microenvironment, organoid robots require precise control over their motion paths and postures to achieve efficient obstacle avoidance and stable movement, ensuring effective drug delivery and targeted release. Existing technologies struggle to meet the demands for such high-precision control, while multimodal actuation faces challenges related to inter-system collaboration and mutual interference. Flexible continuum organoid robots still cannot meet the requirements for speed and flexibility in motion morphology control, exhibiting poor performance in achieving micrometer-level accuracy and multi-degree-of-freedom drug delivery, which requires further improvement. Summary of the Invention

[0003] The purpose of this invention is to solve the problems of speed and flexibility in motion control of flexible continuum organoid robots. Based on this, this invention provides organoid robot manipulation methods, systems, computer program products, and readable media.

[0004] A method for manipulating an organoid robot, the method comprising:

[0005] An injection signal is sent to inject magnetic particles and microbubbles into the head and tail organs of the organoid chain structure, respectively, to obtain an organoid robot; wherein the magnetic particles and microbubbles serve as the head and tail actuators of the organoid robot, respectively; the microbubbles are submicron-sized bubbles;

[0006] Set the target location;

[0007] Monitor the current pose data of the organoid robot;

[0008] Monitor obstacle boundary data of the organoid robot;

[0009] The path of the organoid robot is planned based on the set target position and obstacle boundary data, and magnetic and ultrasonic signals with multiple superimposed magnetic fields are generated by combining the current pose data of the organoid robot. The magnetic and ultrasonic signals control the pose of the first and last actuators, respectively, thereby controlling the organoid robot to move to the target position.

[0010] Preferably, the specific process for planning the path of the organoid robot is as follows:

[0011] The intelligent obstacle avoidance algorithm updates its obstacle avoidance loss function based on obstacle boundary data and the set target position;

[0012] The obstacle avoidance loss function serves as the path constraint for the path optimization algorithm, and velocity / acceleration constraints and start / end point constraints are defined.

[0013] A multi-mode motion loss function is constructed, and slack variables are introduced into it to obtain a loss function with added redundancy.

[0014] By combining all constraints and solving the loss function with added redundancy, the optimal path can be obtained.

[0015] Preferably, the implementation method for constructing the multi-mode motion loss function is as follows:

[0016] The energy consumption, tracking error, path smoothness, and various obstacle constraints of the organoid robot during movement are summarized to form a multi-mode motion loss function.

[0017] Preferably, the methods for generating magnetic force signals and ultrasonic signals with multiple superimposed magnetic fields include:

[0018] Electromagnetic and ultrasonic commands are generated based on the current pose data of the organoid robot and the deviation between the poses of the target nodes at the next moment on the optimal path obtained after path planning.

[0019] Based on electromagnetic and ultrasonic commands, corresponding magnetic and ultrasonic signals with superimposed multiple magnetic fields are generated respectively.

[0020] Preferably, the intelligent obstacle avoidance algorithm is implemented using the IL-CBFs algorithm based on integral local control obstacle functions.

[0021] Preferably, the magnetic particles have at least one of a chemical plating layer, a functionalized coating, and a nanolayer.

[0022] An organoid robot control system includes a host computer, an ultrasonic phased array, a multi-magnetic field fusion electromagnetic generator, a micromanipulation actuator, and a sensing and monitoring device.

[0023] The host computer is used to send injection signals to the micromanipulation actuator to inject magnetic particles and microbubbles into the first and last organoids of the organoid chain structure, respectively, and obtain an organoid robot; wherein, the magnetic particles and microbubbles serve as the first and last actuators of the organoid robot, respectively; the microbubbles are submicron-sized bubbles;

[0024] The sensing and monitoring device is used to monitor the current pose data and obstacle boundary data of the organoid robot and send them to the host computer.

[0025] The host computer is also used to plan the path of the organoid robot according to the set target position and obstacle boundary data, and to control the multi-magnetic field fusion electromagnetic generator and ultrasonic phased array to generate magnetic force signals and ultrasonic signals with multiple magnetic fields superimposed, respectively, in combination with the current pose data of the organoid robot. The magnetic force signals and ultrasonic signals control the pose of the first and last actuators, thereby controlling the organoid robot to move to the target position.

[0026] Preferably, the specific process for planning the path of the organoid robot is as follows:

[0027] The intelligent obstacle avoidance algorithm updates its obstacle avoidance loss function based on obstacle boundary data and the set target position;

[0028] The obstacle avoidance loss function serves as the path constraint for the path optimization algorithm, and velocity / acceleration constraints and start / end point constraints are defined.

[0029] A multi-mode motion loss function is constructed, and slack variables are introduced into it to obtain a loss function with added redundancy.

[0030] By combining all constraints and solving the loss function with added redundancy, the optimal path can be obtained.

[0031] Preferably, the implementation method for constructing the multi-mode motion loss function is as follows:

[0032] The energy consumption, tracking error, path smoothness, and various obstacle constraints of the organoid robot during movement are summarized to form a multi-mode motion loss function.

[0033] Preferably, the methods for generating magnetic force signals and ultrasonic signals with multiple superimposed magnetic fields include:

[0034] Electromagnetic and ultrasonic commands are generated based on the current pose data of the organoid robot and the deviation between the poses of the target nodes at the next moment on the optimal path obtained after path planning.

[0035] The electromagnetic and ultrasonic commands respectively control the multi-magnetic field fusion electromagnetic generator and the ultrasonic phased array to generate corresponding magnetic force signals and ultrasonic signals superimposed with multiple magnetic fields.

[0036] Preferably, the intelligent obstacle avoidance algorithm is implemented using the IL-CBFs algorithm based on integral local control obstacle functions.

[0037] Preferably, the magnetic particles have at least one of a chemical plating layer, a functionalized coating, and a nanolayer.

[0038] A computer program product includes a computer program that, when executed, implements the organoid robot manipulation method.

[0039] A computer-readable medium storing processor-executable program code, which, when executed by the processor, causes the processor to perform the organoid robot manipulation method.

[0040] Advantages of this invention:

[0041] This invention presents a method and system for manipulating organoid robots. First, a prototype model of the organoid robot was designed. Second, a magnetic field controls the proximal end of the organoid robot, while an ultrasonic field controls its distal end. This combination of magnetic and ultrasonic actuation effectively meets the speed and flexibility requirements of the flexible continuum organoid robot in morphological control, enabling flexible and efficient movement in complex microenvironments. Furthermore, by employing path planning and obstacle avoidance, precise posture adjustment and efficient obstacle avoidance are achieved in complex microenvironments, creating possibilities for research on flexible obstacle avoidance and efficient drug delivery using organoid robots in complex microenvironments of in vitro experimental platforms. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the structure of an organoid robot;

[0043] Figure 2 This is a schematic diagram of the control system for organoid robots;

[0044] Figure 3 This is a schematic diagram illustrating the principle of obtaining the optimal path;

[0045] In the attached figures, reference numeral 1 represents organoids, reference numeral 2 represents magnetic particles, reference numeral 3 represents microbubbles, and reference numeral 4 represents targeted drugs. Detailed Implementation

[0046] 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.

[0047] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0048] With the rapid development of nanotechnology and biomedical engineering, microrobots have attracted widespread attention in the field of oncology as novel tools for drug delivery and local treatment. Microrobots are autonomous or semi-autonomous robots with extremely small sizes, typically in the millimeter range or even reaching the micrometer range. They combine advanced technologies from multiple fields, including microelectromechanical systems (MEMS), nanotechnology, materials science, and control theory, enabling them to perform specific tasks within very confined spaces. Microrobots are mainly classified into three categories: bio-driven, chemical / physical driven, and hybrid. Bio-driven microrobots, such as systems constructed using living cells or bacteria, exhibit high biocompatibility after genetic engineering, but face potential biotoxicity issues. Chemical / physical driven microrobots rely on chemical reactions or external physical fields (such as electromagnetic waves and ultrasound) for power, and have limitations in navigation accuracy, runtime, penetration depth, and drug loading. In contrast, hybrid microrobots combine the advantages of both bio- and chemical / physical actuation, offering better adaptability and therapeutic efficacy, particularly suitable for complex in vivo environments, but still require addressing clinical application challenges related to biocompatibility, safety, and stability.

[0049] Given the diverse biological characteristics of tumors in different patients—a phenomenon known as tumor heterogeneity—personalized treatment plans are crucial. In recent years, the development of organoid technology has provided a new avenue for such personalized therapy. Organoids are clusters of cells derived from stem cells, such as adult stem cells and dedifferentiated stem cells. They contain more than one cell type and exhibit certain physiological characteristics of the source organ, mimicking its cell type and structure. Because organoids originate from individual patients, they retain that individual's genetic characteristics, significantly reducing the risk of rejection in vivo, making them ideal carriers for delivering targeted cancer drugs.

[0050] Therefore, this invention constructs a flexible continuum organoid robot. This flexible continuum organoid robot belongs to the category of hybrid microrobots, with organoids as its carrier (biological organisms) and external physical field actuation. Magnetic particles are injected into the proximal organoid of the constructed flexible continuum organoid robot to construct a proximal actuator driven by magnetic signals, while microbubbles that enhance acoustic sensitivity are injected into the distal organoid to construct an end effector driven by ultrasonic signals. The dual-end control structure design is suitable for multi-mode posture and motion control, creating possibilities for organoid robots to achieve flexible obstacle avoidance and efficient drug delivery in the complex microenvironment of in vitro experimental platforms. An in vitro experimental platform refers to a laboratory environment that simulates the complex environment within a living organism to study the behavior of cells, tissues, or microrobots. This environment typically includes various physical (physical obstacles) and chemical (chemical concentration, pH value) factors, aiming to reflect the conditions within a living organism as realistically as possible, providing an intermediate experimental data foundation for the final application of organoid robots in in vivo tumor treatment. Therefore, the following implementation method is provided:

[0051] Specific Implementation Method 1: Combination Figure 1 and Figure 2 This embodiment describes a method for manipulating an organoid robot, the method comprising:

[0052] An injection signal is sent to inject magnetic particles and microbubbles into the first and last organoids of an organoid chain structure, respectively, to obtain an organoid robot; wherein the magnetic particles and microbubbles serve as the first and last actuators of the organoid robot, respectively; the intermediate organoid between the first and last organoids is used to carry targeted drugs; the microbubbles are submicron-sized bubbles;

[0053] Set the target location;

[0054] Monitor the current pose data of the organoid robot;

[0055] Monitor obstacle boundary data of the organoid robot;

[0056] The path of the organoid robot is planned based on the set target position and obstacle boundary data, and magnetic and ultrasonic signals with multiple superimposed magnetic fields are generated by combining the current pose data of the organoid robot. The magnetic and ultrasonic signals control the pose of the first and last actuators, respectively, thereby controlling the organoid robot to move to the target position.

[0057] In this embodiment, multi-mode attitude and motion control is implemented. Multi-mode attitude and motion control refers to the use of a magnetic field and an acoustic field to form a multi-physics field to drive the organoid robot. The two fields work together to control the movement and attitude of the organoid robot. The magnetic field controls the head end of the organoid robot, and the ultrasonic field controls the tail end of the organoid robot. The combination of magnetic and ultrasonic driving methods can well meet the speed and flexibility requirements of the flexible continuum organoid robot in morphological control, and provide technical support for achieving micron-level precision and multi-degree-of-freedom drug delivery.

[0058] Detailed effect analysis:

[0059] Biocompatibility: Among the existing microrobots for targeted therapy, biorobots that use bacteria as drug carriers face potential toxicity risks, while organoid robots, whose drug carriers are derived from the patient's own body, have higher biocompatibility and lower risk of immune response.

[0060] Drug loading capacity: Existing microrobots have limited drug loading capacity, while organoid robots use microinjection technology to encapsulate a large amount of drug inside a single organoid. Multiple drug-loaded organoids are spliced ​​together to form a chain structure, which significantly improves drug loading capacity.

[0061] Driving method: The multi-mode physical field driving of this invention can combine the advantages of each mode and work together to achieve attitude adjustment and motion control.

[0062] Obstacle avoidance capability: Utilizing the magnetoacoustic cooperative controllers at both ends and advanced control algorithms, it can perform more complex and flexible obstacle avoidance maneuvers.

[0063] In practical applications, based on the structural and functional characteristics of the magnetic field acoustic field generator, the structural layout of the magnetic-acoustic hybrid physical field generator is designed to meet the requirements of the organoid robot driving theory system, ensuring that they do not interfere with each other and give full play to the advantages of each subsystem.

[0064] See Figure 3 The specific process of planning the path for the organoid robot is as follows:

[0065] The intelligent obstacle avoidance algorithm updates its obstacle avoidance loss function based on obstacle boundary data and the set target position;

[0066] The obstacle avoidance loss function serves as the path constraint for the path optimization algorithm, and velocity / acceleration constraints and start / end point constraints are defined.

[0067] A multi-mode motion loss function is constructed, and slack variables are introduced into it to obtain a loss function with added redundancy.

[0068] By combining all constraints and solving the loss function with added redundancy, the optimal path can be obtained.

[0069] Specifically, the implementation method for constructing the multi-mode motion loss function is as follows:

[0070] The energy consumption, tracking error, path smoothness, and various obstacle constraints of the organoid robot during movement are summarized to form a multi-mode motion loss function.

[0071] Specifically, the methods for generating magnetic and ultrasonic signals from multiple superimposed magnetic fields include:

[0072] Electromagnetic and ultrasonic commands are generated based on the current pose data of the organoid robot and the deviation between the poses of the target nodes at the next moment on the optimal path obtained after path planning.

[0073] Based on electromagnetic and ultrasonic commands, corresponding magnetic and ultrasonic signals with superimposed multiple magnetic fields are generated respectively.

[0074] In this preferred embodiment, the obstacle avoidance loss function reflects the specific boundaries of the obstacle. If the organoid robot's pose satisfies the obstacle function's constraints during operation, it can be assumed that it will not collide with the obstacle in its current state, meaning it can pass normally. In practical applications, if, during the organoid robot's movement towards the target, some organoid cell clusters have passed the obstacle but the tail organoid cell clusters cannot, ultrasonic control can be used to change the robot's posture (especially the tail posture), and the path can be replanned to ensure its movement satisfies the obstacle avoidance loss function and path constraints, preventing collisions. Adding slack variables results in a loss function with increased redundancy, allowing for a certain degree of path deviation to more flexibly bypass obstacles.

[0075] Taking into account the various requirements and indicators in the multi-mode motion loss function, the system ultimately achieves precise attitude adjustment and obstacle avoidance. Through intelligent obstacle avoidance algorithm, it can avoid encountering obstacles; through path optimization algorithm, it can obtain a path with better overall performance without encountering obstacles.

[0076] Furthermore, the magnetic particles possess at least one of chemical plating, functionalized coatings, and nanolayers. Specifically, chemical plating is applied to the surface of the magnetic particles by using chemical deposition or electrodeposition to cover the surface of the magnetic particles with a layer of metal, such as gold or silver. Functionalized coatings enhance magnetism and conductivity by grafting functional molecules, such as polymers or antibodies, onto the surface of the magnetic particles through chemical reactions to improve stability and selectivity in the environment. Nanolayer modification involves coating the surface of the magnetic particles with a layer of nano-magnetic materials, such as cobalt, to further enhance magnetic responsiveness. All of the above chemical plating, functionalized coatings, or nanolayers can significantly enhance their response to magnetic fields.

[0077] Furthermore, the intelligent obstacle avoidance algorithm is implemented using the IL-CBFs algorithm based on integral local control obstacle functions.

[0078] In this preferred embodiment, the IL-CBFs algorithm based on integral local control obstacle function includes an integral term. When certain conditions are met, the robot is considered to be in a safe area. When the function satisfies the constraint conditions, a safety set corresponding to the safe area is generated.

[0079] Detailed implementation method two, see below. Figure 1 and Figure 2 This embodiment describes an organoid robot control system, which includes a host computer, an ultrasonic phased array, a multi-magnetic field fusion electromagnetic generator, a micromanipulation actuator, and a sensing and monitoring device.

[0080] The host computer is used to send injection signals to the micromanipulation actuator to inject magnetic particles and microbubbles into the first and last organoids of the organoid chain structure, respectively, to obtain an organoid robot; wherein, the magnetic particles and microbubbles serve as the first and last actuators of the organoid robot, respectively; the intermediate organoid between the first and last organoids is used to carry the targeted drug; the microbubbles are submicron-sized bubbles;

[0081] The sensing and monitoring device is used to monitor the current pose data and obstacle boundary data of the organoid robot and send them to the host computer.

[0082] The host computer is also used to plan the path of the organoid robot according to the set target position and obstacle boundary data, and to control the multi-magnetic field fusion electromagnetic generator and ultrasonic phased array to generate magnetic force signals and ultrasonic signals with multiple magnetic fields superimposed, respectively, in combination with the current pose data of the organoid robot. The magnetic force signals and ultrasonic signals control the pose of the first and last actuators, thereby controlling the organoid robot to move to the target position.

[0083] Figure 2 The paper presents the specific structure of the organoid robot control system. In this embodiment, multi-mode attitude and motion control is performed. Multi-mode attitude and motion control refers to the use of a magnetic field and an acoustic field to form a multi-physics field to drive the organoid robot. The two fields work together to control the movement and attitude of the organoid robot. The magnetic field controls the head end of the organoid robot, and the ultrasonic field controls the tail end of the organoid robot. The combination of magnetic and ultrasonic driving methods can well meet the requirements of speed and flexibility in morphological control of flexible continuum organoid robots, and provide technical support for achieving micron-level precision and multi-degree-of-freedom drug delivery.

[0084] Current research on organoid microrobots is relatively limited, but their potential in personalized and precision medicine cannot be ignored. The fabrication of organoid microrobots typically involves the culture, assembly, and implantation of functional modules. Traditionally, this process relies heavily on manual manipulation, leading to inconsistent product quality and a lack of standardized practices. Micromanipulation techniques enable precise control of tiny biological objects, while high-precision micromanipulators can perform delicate manipulations without damaging biological samples. These technological advancements promise to improve the consistency and reliability of organoid microrobot manufacturing.

[0085] See Figure 3 The specific process of planning the path for the organoid robot is as follows:

[0086] The host computer is embedded with intelligent obstacle avoidance algorithms and path optimization algorithms;

[0087] The intelligent obstacle avoidance algorithm updates its obstacle avoidance loss function based on obstacle boundary data and the set target position;

[0088] The obstacle avoidance loss function serves as the path constraint for the path optimization algorithm, and velocity / acceleration constraints and start / end point constraints are defined.

[0089] Construct a multi-mode motion loss function and introduce slack variables into it to obtain a loss function with added redundancy;

[0090] By combining all constraints and solving the loss function with added redundancy, the optimal path can be obtained.

[0091] Specifically, the implementation method for constructing the multi-mode motion loss function is as follows:

[0092] The energy consumption, tracking error, path smoothness, and various obstacle constraints of the organoid robot during movement are summarized to form a multi-mode motion loss function.

[0093] Specifically, the methods for generating magnetic and ultrasonic signals from multiple superimposed magnetic fields include:

[0094] Electromagnetic and ultrasonic commands are generated based on the current pose data of the organoid robot and the deviation between the poses of the target nodes at the next moment on the optimal path obtained after path planning.

[0095] The electromagnetic and ultrasonic commands respectively control the multi-magnetic field fusion electromagnetic generator and the ultrasonic phased array to generate corresponding magnetic force signals and ultrasonic signals superimposed with multiple magnetic fields.

[0096] In this preferred embodiment, the obstacle avoidance loss function reflects the specific boundaries of the obstacle. When the organoid robot's pose satisfies the constraints of the obstacle function during its operation, it can be considered that it will not collide with the obstacle in its current state, i.e., it can pass normally. In specific applications, when the organoid robot is moving towards the target, if some organoid cell clusters have passed the obstacle but the tail organoid cell clusters cannot pass successfully, ultrasonic control can be used to change the robot's posture (especially the tail posture) and replan the path so that its movement satisfies the obstacle avoidance loss function and path constraints, thus avoiding collisions with the obstacle. By adding slack variables, a loss function with increased redundancy is obtained, allowing the path to deviate to a certain extent, so as to more flexibly bypass obstacles.

[0097] Taking into account the various requirements and indicators in the multi-mode motion loss function, the system ultimately achieves precise attitude adjustment and obstacle avoidance. Through intelligent obstacle avoidance algorithm, it can avoid encountering obstacles; through path optimization algorithm, it can obtain a path with better overall performance without encountering obstacles.

[0098] Furthermore, the intelligent obstacle avoidance algorithm is implemented using the IL-CBFs algorithm based on integral local control obstacle functions.

[0099] In this preferred embodiment, the IL-CBFs algorithm based on integral local control obstacle function includes an integral term. When certain conditions are met, the robot is considered to be in a safe area. When the function satisfies the constraint conditions, a safety set corresponding to the safe area is generated.

[0100] Furthermore, the magnetic particles possess at least one of chemical plating, functionalized coatings, and nanolayers. Specifically, chemical plating is applied to the surface of the magnetic particles by using chemical deposition or electrodeposition to cover the surface of the magnetic particles with a layer of metal, such as gold or silver. Functionalized coatings enhance magnetism and conductivity by grafting functional molecules, such as polymers or antibodies, onto the surface of the magnetic particles through chemical reactions to improve stability and selectivity in the environment. Nanolayer modification involves coating the surface of the magnetic particles with a layer of nano-magnetic materials, such as cobalt, to further enhance magnetic responsiveness. All of the above chemical plating, functionalized coatings, or nanolayers can significantly enhance their response to magnetic fields.

[0101] Specific Implementation Method 3: A computer program product, comprising a computer program, wherein when the computer program is executed, it implements an organoid robot manipulation method as described in Specific Implementation Method 1.

[0102] Specific Embodiment Four: A computer-readable medium storing processor-executable program code, wherein when the processor executes the program code, the processor performs the organoid robot manipulation method described in Specific Embodiment One.

[0103] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for manipulating an organoid robot, characterized in that, The manipulation method includes: An injection signal is sent to inject magnetic particles and microbubbles into the head and tail organs of the organoid chain structure, respectively, to obtain an organoid robot; wherein the magnetic particles and microbubbles serve as the head and tail actuators of the organoid robot, respectively; the microbubbles are submicron-sized bubbles; Set the target location; Monitor the current pose data of the organoid robot; Monitor obstacle boundary data of the organoid robot; The path of the organoid robot is planned based on the set target position and obstacle boundary data, and magnetic and ultrasonic signals with multiple superimposed magnetic fields are generated by combining the current pose data of the organoid robot. The magnetic and ultrasonic signals control the pose of the first and last actuators, respectively, thereby controlling the organoid robot to move to the target position.

2. The method for manipulating an organoid robot according to claim 1, characterized in that, The specific process of planning the path for the organoid robot is as follows: The intelligent obstacle avoidance algorithm updates its obstacle avoidance loss function based on obstacle boundary data and the set target position; The obstacle avoidance loss function serves as the path constraint for the path optimization algorithm, and velocity / acceleration constraints and start / end point constraints are defined. Construct a multi-mode motion loss function and introduce slack variables into it to obtain a loss function with added redundancy; By combining all constraints and solving the loss function with added redundancy, the optimal path can be obtained.

3. The method for manipulating an organoid robot according to claim 2, characterized in that, The implementation method for constructing the multi-mode motion loss function is as follows: The energy consumption, tracking error, path smoothness, and various obstacle constraints of the organoid robot during movement are summarized to form a multi-mode motion loss function.

4. The method for manipulating an organoid robot according to claim 1, characterized in that, Methods for generating magnetic and ultrasonic signals from multiple superimposed magnetic fields include: Electromagnetic and ultrasonic commands are generated based on the current pose data of the organoid robot and the deviation between the poses of the target nodes at the next moment on the optimal path obtained after path planning. Based on electromagnetic and ultrasonic commands, corresponding magnetic and ultrasonic signals with superimposed multiple magnetic fields are generated respectively.

5. The method for manipulating an organoid robot according to claim 2, characterized in that, The intelligent obstacle avoidance algorithm is implemented using the IL-CBFs algorithm based on integral local control obstacle functions.

6. The method for manipulating an organoid robot according to claim 1, characterized in that, The magnetic particles have at least one of chemical plating, functionalized coating and nanolayer.

7. A control system for an organoid robot, characterized in that, It includes a host computer, an ultrasonic phased array, a multi-magnetic field fusion electromagnetic generator, a micromanipulation actuator, and a sensing and monitoring device; The host computer is used to send injection signals to the micromanipulation actuator to inject magnetic particles and microbubbles into the first and last organoids of the organoid chain structure, respectively, and obtain an organoid robot; wherein, the magnetic particles and microbubbles serve as the first and last actuators of the organoid robot, respectively; the microbubbles are submicron-sized bubbles; The sensing and monitoring device is used to monitor the current pose data and obstacle boundary data of the organoid robot and send them to the host computer. The host computer is also used to plan the path of the organoid robot according to the set target position and obstacle boundary data, and to control the multi-magnetic field fusion electromagnetic generator and ultrasonic phased array to generate magnetic force signals and ultrasonic signals with multiple magnetic fields superimposed, respectively, in combination with the current pose data of the organoid robot. The magnetic force signals and ultrasonic signals control the pose of the first and last actuators, thereby controlling the organoid robot to move to the target position.

8. The organoid robot control system according to claim 7, characterized in that, The specific process of planning the path for the organoid robot is as follows: The intelligent obstacle avoidance algorithm updates its obstacle avoidance loss function based on obstacle boundary data and the set target position; The obstacle avoidance loss function serves as the path constraint for the path optimization algorithm, and velocity / acceleration constraints and start / end point constraints are defined. Construct a multi-mode motion loss function and introduce slack variables into it to obtain a loss function with added redundancy; By combining all constraints and solving the loss function with added redundancy, the optimal path can be obtained.

9. The organoid robot control system according to claim 8, characterized in that, The implementation method for constructing the multi-mode motion loss function is as follows: The energy consumption, tracking error, path smoothness, and various obstacle constraints of the organoid robot during movement are summarized to form a multi-mode motion loss function.

10. The organoid robot control system according to claim 7, characterized in that, Methods for generating magnetic and ultrasonic signals from multiple superimposed magnetic fields include: Electromagnetic and ultrasonic commands are generated based on the current pose data of the organoid robot and the deviation between the poses of the target nodes at the next moment on the optimal path obtained after path planning. The electromagnetic and ultrasonic commands respectively control the multi-magnetic field fusion electromagnetic generator and the ultrasonic phased array to generate corresponding magnetic force signals and ultrasonic signals superimposed with multiple magnetic fields.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it implements the organoid robot manipulation method as described in any one of claims 1 to 6.

12. A computer-readable medium, characterized in that, The computer-readable medium stores processor-executable program code, which, when executed by the processor, causes the processor to perform the organoid robot manipulation method according to any one of claims 1 to 6.

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