A method for controlling a plurality of micro robots based on photoelectric driving

Abstract

This application discloses a multi-microrobot control method based on photoelectric drive, belonging to the field of microrobot control technology. This method uses a combination of microrobots and a target object as the processing object, achieving executable global path planning that meets the requirements for static obstacle avoidance and moves the combination from its initial position to the target position. It also achieves local path planning suitable for photoelectric drive execution, meeting the mutual collision avoidance requirements between the multi-microrobot and the target object while ensuring the target object successfully reaches its target position. This improves the control effect of the multi-microrobot and significantly enhances the parallel processing capability and task throughput of the micro-operating system.

Description

Technical Field

[0001] This application belongs to the field of micro-robot control technology, specifically relating to a multi-micro-robot control method based on photoelectric drive. Background Technology

[0002] Microrobot control (such as robot-assisted micromanipulation) has become an emerging field in contemporary scientific research and application. It provides solutions for applications ranging from targeted drug delivery to complex microscale assembly, cell transport, and single-cell injection. The introduction of photoelectric drive technology further provides a technical basis for the control of multiple microrobots, which is conducive to the parallel operation of many small objects.

[0003] Autonomous navigation in complex environments is a key step toward the development of intelligent microrobots. However, most current path planning research focuses on single microrobots and does not consider the requirements for real-time collision avoidance, static obstacle avoidance, and feasibility of multiple microrobots in control scenarios based on photoelectric drives. As a result, the applicability of such scenarios is limited, leading to poor control performance of current photoelectric driven multi-microrobot systems. Summary of the Invention

[0004] The purpose of this application is to provide a control method for multiple micro-robots based on photoelectric drive, which can solve the problem in related technologies of lacking path planning suitable for control scenarios of multiple micro-robots based on photoelectric drive, thus leading to poor control effect.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a method for controlling multiple micro-robots based on photoelectric drive, the method comprising: Based on the location distribution of obstacles, a global reference path is generated for each assembly from its initial position to its target position. The assembly includes a microrobot and a target object. Based on the global reference path of each assembly, the reference velocity of each assembly at different times is determined, and the reference velocity is adjusted according to the speed amplitude constraint of the photoelectric drive. The speed amplitude constraint of the photoelectric drive and the distance between any two combined bodies in the next time step not being less than the preset minimum safe distance are used as safety constraints. The optimization objective is to make the speed of each combined body as close as possible to the reference speed. Based on the actual position and actual speed of each combined body at the current time, the target speed of each combined body at different times after the current time is solved in parallel. The target speed is the optimal speed that satisfies the safety constraints. Based on the target velocities of each of the aforementioned combined bodies at different times after the current time, determine the expected trajectory of each of the aforementioned combined bodies at the current time; Based on the desired trajectory of each of the assembled bodies at the current moment, the microrobots in each of the assembled bodies perform non-contact operations on the target object using photoelectric drive control.

[0006] Secondly, embodiments of this application provide a multi-micro robot control device based on photoelectric drive, the device comprising: A global planning module is used to generate a global reference path from the initial position to the target position for each assembly, which includes a microrobot and a target object, based on the location distribution of obstacles; and to determine the reference velocity of each assembly at different times based on the global reference path of each assembly, and to adjust the reference velocity according to the speed amplitude constraint of photoelectric drive. The local planning module is used to constrain the speed amplitude of the photoelectric drive and the distance between any two combined bodies in the next time step by not being less than a preset minimum safety distance as safety constraints, and to optimize the speed of each combined body as close as possible to the reference speed. Based on the actual position and speed of each combined body at the current time, it solves in parallel the target speed of each combined body at different times after the current time. The target speed is the optimal speed that satisfies the safety constraints. Based on the target speed of each combined body at different times after the current time, it determines the expected trajectory of each combined body at the current time. The trajectory tracking module is used to control the microrobots in each of the assemblies to perform non-contact operations on the target object based on photoelectric drive, according to the expected trajectory of each of the assemblies at the current moment.

[0007] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of the photoelectric-driven multi-microrobot control method described in the first aspect.

[0008] Fourthly, embodiments of this application provide a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the photoelectric-driven multi-microrobot control method described in the first aspect.

[0009] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the photoelectric-driven multi-micro-robot control method as described in the first aspect.

[0010] In this embodiment, a combination of a microrobot and a target object is used as the processing object. First, global path planning is performed. Based on the location distribution of obstacles, a global reference path is generated for each combination from its initial position to its target position. The reference speed is adjusted according to the speed amplitude constraint of the photoelectric drive, thereby achieving executable global path planning that meets the requirements for avoiding static obstacles and can move the combination from its initial position to its target position. Then, local path planning is performed, using the speed amplitude constraint of the photoelectric drive and the requirement that the distance between any two combinations in the next time step is not less than a preset minimum safe distance as safety constraints. This ensures that the local path planning is suitable for photoelectric drive execution and meets the mutual collision avoidance requirements between multiple microrobots and the target object. The speed of each assembly is optimized to be as close as possible to the reference speed, thereby ensuring that the local path planning does not deviate too much from the global path planning, and thus ensuring that the target object can successfully reach the target position. Finally, based on the target speeds of each assembly at different times after the current time, which are solved in parallel by the local path planning, the expected trajectory of each assembly at the current time is determined. Based on this, the micro-robots in each assembly are controlled by photoelectric drive to perform non-contact operations on the target object. This enables real-time collision avoidance without communication between multiple micro-robots and the target object, thus maintaining stable navigation in complex environments with communication interference and obstacles. This improves the control effect and significantly enhances the parallel processing capability and task throughput of the micro-operating system. Attached Figure Description

[0011] Figure 1 A flowchart illustrating an implementation of a photoelectric-driven multi-microrobot control method provided in this application embodiment; Figure 2 A schematic diagram of a non-contact micromanipulation framework provided in an embodiment of this application; Figure 3 This application provides a schematic diagram illustrating the control implementation of photoelectric non-contact operation in an embodiment. Figure 4 This is a schematic diagram illustrating a photoelectric driven robot performing a non-contact operation on a target object, as provided in an embodiment of this application. Figure 5 A schematic diagram of a multi-micro robot control device based on photoelectric drive provided in an embodiment of this application; Figure 6This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0014] First, to facilitate understanding of the technical solutions provided in this application, the main technical concepts involved in the embodiments of this application will be briefly explained below.

[0015] Optoelectronic tweezers (OET) are an advanced micromanipulation technique combining optical tweezers and dielectrophoresis. They utilize optical patterns to illuminate photosensitive materials, inducing a non-uniform electric field in space. This polarizes particles, generating dielectrophoretic (DEP) forces to manipulate microparticles (such as cells, viruses, or macromolecules). Compared to optical tweezers, OET systems exhibit photoinduced dielectrophoresis, producing significant manipulation forces even at lower light intensities. By altering the projection optical pattern, parallel manipulation of many small objects can be achieved.

[0016] In recent years, many methods, including mechanical, fluid, magnetic, acoustic, electrical, and optical approaches, have been used to automate micromanipulation. However, unlike robotic manipulation at the macroscopic scale, the flexibility of these micromanipulation systems is relatively low. This is because manipulation at the microscopic scale presents several challenges: viscous effects are more prevalent than inertia, and sensing and actuation capabilities are limited compared to those of robotic arms. Therefore, micromanipulation tasks are typically simple, involve few degrees of freedom (DoF), and require customized microtools for specific tasks and objects.

[0017] To avoid optical damage, several indirect manipulation methods for robots based on photoelectric actuation have been proposed. For example, micro-clamp structures are designed to hold and transfer objects, and electro-adhesive forces between particles are used for transporting and releasing objects. However, due to limitations in manufacturing methods or physical principles, these indirect manipulation methods cannot achieve precise and dexterous manipulation or avoid obstacles in confined spaces. Furthermore, direct contact between the robot and the target object may lead to solid contamination, which limits the application of these manipulation methods and further promotes the exploration of non-contact technologies.

[0018] Recent research has explored non-contact technologies based on photoelectric drives (such as OET drives) in robotic micromanipulation, aiming to achieve precise control without the need for direct physical contact. This provides solutions for applications such as high-throughput and precise manipulation of cells in microscale environments.

[0019] However, current research on non-contact technology mainly focuses on manipulating a single object. Specifically, most current path planning research focuses on a single micro-robot, without considering the requirements for real-time collision avoidance, static obstacle detour, and feasibility between multiple micro-robots and the target object in the control scenario of multiple micro-robots based on photoelectric drive. As a result, the applicability to this scenario is limited, which leads to the poor control effect of current multi-micro-robots based on photoelectric drive.

[0020] Based on the above analysis, in order to address the problem that the related technologies lack path planning suitable for the control scenarios of multiple micro-robots based on photoelectric drive, which leads to poor control performance, this application provides a control method for multiple micro-robots based on photoelectric drive. This method can realize path planning suitable for the control scenarios of multiple micro-robots based on photoelectric drive, thereby improving the control performance and enabling multiple micro-robots to accurately and efficiently move their respective target objects to the target positions.

[0021] The following description, in conjunction with the accompanying drawings, details a photoelectric-driven multi-microrobot control method provided in this application through specific embodiments and application scenarios.

[0022] See Figure 1 The diagram shown is an implementation flowchart of a photoelectric-driven multi-microrobot control method provided in this application embodiment. The method may include the following steps: Step S101: Based on the location distribution of obstacles, generate a global reference path from the initial position to the target position for each assembly, which includes a micro-robot and a target object.

[0023] The target object can be a microparticle (such as a cell, virus, or macromolecule) or other object that supports the microrobot to perform non-contact operations; the target position of the assembly (i.e. the position that the assembly needs to reach) can be determined based on the target position of the target object (i.e. the position that the target object needs to reach).

[0024] In practical implementation, global path planning is performed using path planning algorithms such as A*. Based on the location distribution of obstacles, a global reference path is generated for each assembly from its initial position to its target position, avoiding all obstacles. For example, the global reference path for the i-th assembly can be represented as follows: ,in, This represents the position of the i-th composite in the global reference path at time k. This indicates the initial position of the i-th composite element. This represents the target position of the i-th composite.

[0025] In some embodiments, the global reference path can also be a path that further satisfies optimization objectives such as the shortest distance and photoelectric drive-related constraints such as being within the effective range of the photoelectric drive (e.g., within the OET AC electric field), thereby further ensuring the optimality and executability of the global reference path.

[0026] In some embodiments, the target position of the assembly can be determined based on the equivalent radius of the assembly, the center position of the assembly, and the target position of the target object in the assembly.

[0027] In some embodiments, the OET chip environment can be modeled to obtain the location distribution of obstacles in the environment.

[0028] In some embodiments, the microrobot can be any microsphere that can be optically captured without special preparation, thereby performing non-contact tasks in an incomprehensible manner, thus enhancing flexibility.

[0029] Step S102: Determine the reference speed of each assembly at different times based on the global reference path of each assembly, and adjust the reference speed according to the speed amplitude constraint of the photoelectric drive.

[0030] The speed amplitude constraint is used to describe the combined movement speed supported by the photoelectric drive in the current scenario. For example, the speed amplitude constraint may include the maximum or minimum movement speed that the combined body can achieve under the photoelectric drive.

[0031] In practical implementation, the reference velocity of the assembly at different times can be calculated based on its position in the global reference path and the time step between two adjacent times. Subsequently, the reference velocity is adjusted according to the velocity amplitude constraint of the photoelectric drive. For example, if the reference velocity exceeds the maximum achievable movement speed of the assembly under photoelectric drive, the reference velocity is adjusted to that maximum movement speed to ensure the executability of the global path planning.

[0032] For example, the reference velocity of the i-th assembly at time k. It can be calculated using the following formula:

[0033] in, and This represents the position of the i-th composite element in the global reference path at time (k+1) and time (k). It represents the discrete time step (i.e., the time step between two adjacent moments).

[0034] Step S103: Using the speed amplitude constraint of the photoelectric drive and the distance between any two combined bodies in the next time step not less than the preset minimum safe distance as safety constraints, and taking the speed of each combined body as close as possible to the reference speed as the optimization objective, according to the actual position and actual speed of each combined body at the current time, the target speed of each combined body at different times after the current time is solved in parallel. The target speed is: the optimal speed that satisfies the safety constraints.

[0035] In practical implementation, local path planning is performed. The speed amplitude constraint of the photoelectric drive and the distance between any two combined bodies in the next time step (i.e., the distance between the centers of any two combined bodies) are not less than a preset minimum safe distance are used as safety constraints. The optimization goal is to make the speed of each combined body as close as possible to the reference speed. Based on the actual position and actual speed of each combined body at the current time, the optimal speed of each combined body at different times after the current time that satisfies the safety constraints and is as close as possible to the corresponding reference speed is solved in parallel as the target speed. In this way, local path planning suitable for photoelectric drive execution is achieved, which meets the mutual collision avoidance requirements between multiple micro-robots and the target object (i.e., between multiple combined bodies) and ensures that the target object can successfully reach the target position.

[0036] Step S104: Determine the expected trajectory of each of the combined bodies at the current moment based on their target velocities at different times after the current moment.

[0037] In practice, the desired trajectory can be obtained by integrating the target velocity at different times or by inputting the target velocity at different times into a pre-trained machine learning model.

[0038] Step S105: Based on the desired trajectory of each of the assembled bodies at the current moment, the micro-robots in each of the assembled bodies perform non-contact operations on the target object using photoelectric drive control.

[0039] In practical implementation, after obtaining the desired trajectory of each of the aforementioned assemblies at the current moment, each microrobot can be controlled to push the target object in its own assembly without contact based on photoelectric induced dielectric force. For example, a microrobot driven by projection light can be used to indirectly manipulate the target object by using dielectric repulsion force, so that the assembly formed by the microrobot and the target object can move along the desired trajectory at the current moment.

[0040] Therefore, this application introduces an optimization-based centralized smoothing method. By combining the above-mentioned safety constraints and optimization objectives, the global reference path of each assembly is uniformly optimized to obtain the trajectory that is closest to the global reference path (which is convenient to obtain the trajectory with characteristics such as reachable target position and local shortest path) and suitable for photoelectric drive execution as the desired trajectory. This allows multiple robots to move independently and simultaneously along the desired trajectory obtained in real time to the target object until they reach the target position. Moreover, multiple micro-robots and the target object can automatically coordinate and yield to achieve real-time collision avoidance without communication. This can effectively improve the control effect, enabling multiple micro-robots to accurately and efficiently move their respective target objects to the target position.

[0041] As can be seen from the above technical solution, this application takes the combination of a micro-robot and a target object as the processing object. First, global path planning is performed. Based on the location distribution of obstacles, a global reference path from the initial position to the target position is generated for each combination. The reference speed is adjusted according to the speed amplitude constraint of photoelectric drive, thereby achieving executable global path planning that meets the requirements of static obstacle avoidance and can move the combination from the initial position to the target position. Then, local path planning is performed. The speed amplitude constraint of photoelectric drive and the distance between any two combinations in the next time step not being less than a preset minimum safe distance are used as safety constraints. This ensures that the local path planning is suitable for photoelectric drive execution and meets the mutual collision avoidance requirements between multiple micro-robots and the target object. The optimization objective is to make the speed of each assembly as close as possible to the reference speed, thereby ensuring that the local path planning does not deviate too much from the global path planning, and thus ensuring that the target object can successfully reach the target position. Finally, based on the target speeds of each assembly at different times after the current time obtained by parallel solving of the local path planning, the expected trajectory of each assembly at the current time is determined. Based on this, the micro-robots in each assembly are controlled by photoelectric drive to perform non-contact operations on the target object. This enables real-time collision avoidance between multiple micro-robots and the target object without communication, thus maintaining stable navigation in complex environments with communication interference and obstacles. This improves the control effect and significantly enhances the parallel processing capability and task throughput of the micro-operating system.

[0042] In some embodiments, the speed amplitude constraint of the photoelectric drive includes an upper limit for translational speed; the target speed of the i-th assembly at time k is obtained through the following steps: A constrained quadratic optimization problem is established for the i-th assembly, and the constrained quadratic optimization problem is solved:

[0043]

[0044]

[0045] in, Let represent the optimal speed of the i-th assembly at time k that satisfies the safety constraints. This represents the reference velocity of the i-th composite body at time k. This represents the candidate velocity of the i-th composite at time k. Indicates the search for When the minimum value is obtained The operation of retrieving values, and This represents the actual positions of the i-th and j-th combined entities at time k. This represents the actual velocity of the j-th composite body at time k. Indicates the discrete time step. This represents the preset minimum safe distance associated with the i-th and j-th composite entities. Indicates the upper limit of translation speed. This represents the operation for calculating the L2 norm.

[0046] In this embodiment, distance constraints and velocity amplitude constraints are introduced to construct constraint conditions as safety constraints to achieve collision avoidance between multiple microrobots and target objects. These constraint conditions characterize the situation at discrete time steps. Below, for any candidate velocity of the composite... ,Require The speed must not exceed the upper limit of the translational speed, and ensure that the distance (e.g., the center distance) between the combined body and any other combined body in the next time step is not less than the preset minimum safe distance. By solving the above-mentioned constrained quadratic optimization problem, the collision-free safe speed (i.e., the candidate speed that meets the constraints and optimization objectives) that is as close as possible to the reference speed at the corresponding time can be obtained as the target speed. It can be understood that by solving the above-mentioned constrained quadratic optimization problem in parallel for all combined bodies, the multi-agent safe speed command at the corresponding time (i.e., the relevant time step) can be obtained without the need for communication between multiple micro-robots and the target object, thereby achieving better real-time collision avoidance between multiple micro-robots and the target object.

[0047] Optionally, a preset minimum safety distance is defined for the i-th assembly and the j-th assembly. Determined by the following formula:

[0048] in, and This represents the equivalent radius of the i-th and j-th composites. This indicates that additional safety redundancy is preset.

[0049] In this embodiment, by defining a preset minimum safe distance between two combined bodies based on the equivalent radius between them and a preset additional safety redundancy, a complex geometric collision avoidance problem is simplified into a "ball-ball" distance maintenance problem. This simplifies the calculation and improves real-time performance while preventing collisions (i.e. maintaining a non-contact state between multiple microrobots and any two of multiple target objects) and dealing with uncertainties (such as buffering disturbances and control errors).

[0050] In some embodiments, the speed amplitude constraint of the photoelectric drive includes an upper limit for translational speed; adjusting the reference speed according to the speed amplitude constraint of the photoelectric drive includes: The reference speed is adjusted using the following formula:

[0051] in, This represents the reference velocity of the i-th composite body at time k. This indicates the upper limit of translation speed, and 'min' indicates the operation of taking the minimum value. This represents the operation for calculating the L2 norm.

[0052] In this embodiment, the reference speed is trimmed according to the upper limit of translation speed (i.e., the maximum translation speed that the assembly can achieve under photoelectric drive) to suit photoelectric drive execution.

[0053] In some embodiments, the step of controlling the microrobots in each of the assemblies to perform non-contact operations on the target object based on photoelectric drive, according to the desired trajectory of each of the assemblies at the current moment, includes: Based on the desired trajectory of each assembly at the current moment (e.g., using a linear MPC controller), solve the following quadratic optimization problem to obtain the optimal control input at the current moment, and then use a photoelectric tweezers projection system to control the microrobot in each assembly to perform non-contact operations on the target object according to the optimal control input:

[0054] Where u represents the optimal control input, which describes the displacement between the position of the light spot to be generated by the photoelectric tweezers projection system and the position of the microrobot in each assembly; This represents the predictive control input at time k, where H represents the number of times the predictive control input is required; min represents the operation of taking the minimum value. This represents the actual position of the target object in each assembly at time k. This represents the expected position of the target object in each assembly at time k, determined based on the expected trajectory of each assembly. Operations that represent norms; and Indicates the weighting coefficient; Indicates the discrete time step; , , , This represents the equivalent control input at the k-th moment, determined based on the desired trajectory and position tracking error. Representing the dynamic state equation, This represents the nominal state of each assembly at time k. This represents the actual state of each assembly at time k. This represents the actual state of each assembly at time k+1; This indicates the upper limit of the input limit.

[0055] In this embodiment, considering the underactuated and self-driven characteristics of the microoperating system, a balance needs to be struck between forward prediction and computation speed. Therefore, this application introduces Model Predictive Control (MPC). Furthermore, considering that directly using nonlinear MPC would result in a long solution time, failing to meet real-time requirements, and that initial biases and disturbances are typically present, refer to... Figure 3 The diagram shown illustrates the control implementation of photoelectric non-contact operation. Figure 3 middle These represent the nominal state trajectory and the nominal control input, respectively. To represent the nominal state, this application first generates a nominal trajectory (containing the nominal control input and nominal state at different times) based on the desired trajectory, position tracking error (used to correct initial deviations and disturbances), and dynamic state equations (such as the fourth-order Runge-Kutta discrete state transition function). For example, this nominal trajectory can be represented as: ,in, This represents the equivalent control input (also known as the nominal control input) at time k. This represents the nominal state at time k (e.g., the position reached by the composite at time k), and the initial nominal state. Compared with the initial state of the actual system (i.e., the initial actual state) (For example, the actual positions of the combined objects at the same time are the same.)

[0056] Then, a linear model (also known as a local linear model, used to linearize the micro-operation model at the nominal trajectory) is generated based on this nominal trajectory. This linear model can be represented as follows:

[0057] in, u represents the actual control input or the predictive control input. Indicates the equivalent control input. Representing the dynamic state equation, Indicates the nominal state. Indicates the actual state.

[0058] It is understandable that, given initial biases and disturbances, the linearized model obtained through the above process is closer to the actual model than the model obtained by linearizing directly from the desired trajectory.

[0059] Therefore, based on the above-mentioned quadratic optimization problem, this application reasonably transforms the desired velocity generated in the velocity space that satisfies the multi-agent collision avoidance constraint into the control input. Furthermore, MPC based on linearized dynamics is introduced into this quadratic optimization problem. In each control cycle, the desired velocity is rolled for optimization and constraint projection to ensure that the generated trajectory can be tracked. This achieves complete consistency from planning to control, ensuring that all generated desired trajectories can be stably executed by the actual system, and significantly improving the success rate and robustness of multi-objective micro-operation tasks.

[0060] In some embodiments, the nominal state at the next time step can be obtained from the nominal control input, the nominal state, and the Runge-Kutta simulation of the complete model. That is, the nonlinear iterative model can be expressed as:

[0061] in, Indicates the discrete time step. This represents the fourth-order Runge-Kutta discrete state transition function. This represents the nominal state at time k+1. and This represents the nominal state and nominal control input at time k.

[0062] Finally, based on the above linear model, a linear MPC is designed, which yields the quadratic optimization problem used to solve for the optimal control input. Among these, the weight coefficients... , The corresponding parameter terms are used to penalize the deviation of the target object from the desired trajectory (e.g., deviation distance) and the relative control input magnitude between the nominal control input and the predicted control input. Based on this, considering the local effectiveness of the photoelectric force, this application further constrains the maximum value of the predicted control input in the aforementioned quadratic optimization problem, and uses a multi-shot approach to construct dynamic constraints based on the aforementioned linear model, extracting the first control input obtained from the solution. The optimal control input at the current moment is used for subsequent photoelectric-driven control processes (i.e., sent to the photoelectric tweezers projection system) so that the new state at the next moment can be used as the starting point for the next optimization and the optimization solution can be performed again.

[0063] Optionally, the equivalent control input is determined by the following calculation formula (this part of the calculation can be handled by a feedforward-feedback controller, whose input includes the position tracking error (or the position of the microrobot relative to the target object) and the reference trajectory, and whose output is the equivalent control input):

[0064] in, Indicates the equivalent control input. Indicates position tracking error. R represents the desired velocity determined based on the desired trajectory of the combined body, and R represents the rotation matrix. Represents the stiffness coefficient matrix. The pseudo-inverse matrix representing the kinematic Jacobian matrix. This represents the proportional gain matrix, and -1 indicates the operation of calculating the inverse matrix.

[0065] In this embodiment, a precise tracking strategy combining feedforward-feedback and model predictive control (MPC) is implemented, enabling the target object to achieve high-precision trajectory following under underactuated and strongly nonlinear conditions, significantly improving the safety and control accuracy of microscale operations.

[0066] It should be noted that, considering the scenario of using a photoelectric tweezers projection system (also known as a photoelectric robot platform) to achieve photoelectric drive, the micro-robot and the target object in the OET alternating electric field will be polarized to form an induced electric dipole.

[0067] For example, see Figure 2 The schematic diagram of the non-contact micromanipulation framework shown is as follows ( Figure 2 In the diagram, F represents the photoelectric driving force generated by the microrobot on the target object, and K... e (where f represents the stiffness coefficient and x represents the distance between the microrobot and the light beam). The photoelectric tweezers projection system projects the light spot onto the bottom of the OET chip to generate a strong electric field region at the center of the photoelectrode. This causes the microrobot to be non-uniformly polarized and subjected to a positive bidirectional dielectrophoresis (p-DEP) force that attracts it to the high electric field gradient. d It can be represented as:

[0068] in, The stiffness coefficient represents the isotropic stiffness. Indicates the position of the light spot. This represents the position of the microrobot. It can be understood that the control input can be represented by the displacement between the position of the light spot and the position of the microrobot it drives, such as control inputs (including optimal control input, nominal control input, and other types of control inputs). It can be defined as .

[0069] Simultaneously, the homogeneous microrobots and the target object, which are close to each other, exhibit dielectric interactions. Since the electric field is perpendicular to the line connecting their centers of mass, the magnitude of the electrostatic force is inversely proportional to the fourth power of the distance between them, and its direction is along the line connecting their centers of mass; that is, the electrostatic force f... e It can be represented as:

[0070] in, Indicates the electrostatic coefficient. Indicates the position of the target object. This indicates the location of the micro-robot.

[0071] Therefore, microrobots can indirectly control target objects through dielectric electric field driven by light spot, and the light spot does not need to directly irradiate the target object (that is, the target object experiences electric dipole repulsion from the microrobot under the electric field, so that indirect operation can be performed without physical contact). This non-contact characteristic can minimize the risk of potential damage, contamination or adhesion.

[0072] Because inertia is neglected at low Reynolds coefficients, the dynamics of the system... (in Represents state variables. Indicates control input, The dynamic state equations can be represented as the following input affine model (which is a nonlinear model used to model repulsive forces):

[0073] in, is a diagonal positive definite matrix used to represent the damping of the microrobot and the target object, respectively; I represents the identity matrix.

[0074] The above formula reflects three characteristics of the system: nonlinearity, self-propulsion, and underactuation, which pose challenges to tracking control and trajectory generation. Specifically, the interaction force between the robot and the object is inversely proportional to the fourth power of their distance, which introduces nonlinearity into the system; and there is always a repulsive force between the robot and the object, meaning the system's autonomous term is greater than zero, which gives the system self-propulsion capability, requiring a high-frequency controller to overcome the system's self-propulsion force and keep the object on the desired trajectory; furthermore, the robot can only apply a positive repulsive force to the object along the direction of the line connecting them, and can only "push" the robot forward, not "pull," which requires the controller to consider the effects of control actions over long sequences.

[0075] To address the aforementioned issues, this application introduces a simplified model based on virtual links (i.e., a virtual link model) to reduce system nonlinearity. For example, see [link to example]. Figure 4 The diagram shown illustrates a photoelectric driven robot performing a non-contact operation on a target object. Figure 4 In this context, L represents the local coordinate system, and G represents the global coordinate system. Represents the tangential unit vector. Represents the normal unit vector. Let d represent the basis vector of the global coordinate system and d represent the distance between the microrobot and the target object. Considering that the interaction force increases rapidly as the distance decreases, we can assume that there is a link with infinite stiffness and fixed length between the robot and the object. This application introduces a local coordinate system fixed at the center of the microrobot, with its normal pointing towards the target object. In the local coordinate system, the normal velocities of the microrobot and the target object are... The same, but the target object has no tangential velocity. The simplified kinematics (also known as the virtual link model, which can be seen as a simplification of the nonlinear model) can then be expressed as:

[0076] in, The time derivative of the state variables in simplified kinematics. This represents the velocity of the target object in the global x-direction. This represents the velocity of the target object in the global y-direction. This represents the angular velocity of the target object. Represents the kinematic Jacobian matrix, and the normal velocity of the target object. tangential velocity of the target object , Indicates the normal control input, Indicates tangential control input. b represents the stiffness coefficient for isotropic properties. r and b o This indicates the damping of the microrobot and the target object.

[0077] Therefore, by introducing nonlinear models, simplified models based on virtual links, and local linear models into the control process, the non-contact interaction between the microrobot and the target object can be accurately described, which is beneficial to ensuring controllable propulsion under underactuated conditions.

[0078] Understandably, based on the above expressions for normal and tangential velocities, the simplified dynamics can be further described as follows:

[0079] Among them, the stiffness coefficient matrix , Represents the rotation matrix. Indicates control input, This represents the kinematic Jacobian matrix.

[0080] With the dynamics of the system In comparison, the simplified dynamics described above eliminate autonomous terms and reduce state dimension and nonlinearity. Therefore, constructing the calculation formula for the equivalent control input based on the simplified dynamics described above can effectively improve computational efficiency.

[0081] In some embodiments, the kinematic Jacobian matrix It can be defined as:

[0082] in, The directional angle represents the direction from the local to the global coordinate system, and d represents the distance between the microrobot and the target object.

[0083] In some embodiments, position tracking error It can be ,in, Indicates the actual position of the combined object (determined based on the actual positions of the microrobot and the target object). This indicates the desired location (i.e., the location where the combined object is expected to reach, determined based on the desired trajectory).

[0084] In some embodiments, considering It is an identity matrix, therefore It can be simplified to .

[0085] In some embodiments, with simplified dynamics, the desired trajectory It can be represented as ,in, express The trajectory mapped in the global x-direction. express The trajectory mapped in the global y-direction.

[0086] For example, the photoelectric-driven multi-microrobot control method described in this application can employ... Figure 2 The non-contact micromanipulation framework shown is implemented, and the non-contact micromanipulation framework mainly includes: The trajectory tracking controller is used to ensure that the target object can stably track the desired trajectory and maintain a non-contact control relationship with the micro-robot during the tracking process. Its inputs mainly include the desired trajectory and the current positions of the micro-robot and the target object, and the output is the position of the light spot.

[0087] The global planner is used to generate a globally feasible reference path for a swarm of microrobots and their respective target objects, given the known location distribution of obstacles, thus avoiding getting trapped in local optima. Its inputs mainly include the initial position of the swarm, the target position, and the location distribution of obstacles, and the output is the global reference path.

[0088] The local planner is used to optimize the global reference path generated by the global planner. Its input is the global reference path, and its output is the expected trajectory of each assembly (or the expected velocity of each target object).

[0089] Optionally, both the global planner and the local planner can perform corresponding path planning based on the virtual link model.

[0090] Therefore, this application provides a non-contact micromanipulation framework that supports multi-target synchronous transportation, static obstacle avoidance, and global observability and real-time requirements, which can stably complete multi-microrobot cooperative navigation and non-contact micromanipulation tasks in the limited space of a chip.

[0091] It should be noted that the multi-micro robot control method based on photoelectric drive provided in this application embodiment can be executed by a multi-micro robot control device based on photoelectric drive, or by a control module in the device for executing the multi-micro robot control method based on photoelectric drive. This application embodiment uses the execution of the multi-micro robot control method based on photoelectric drive by a multi-micro robot control device as an example to illustrate the multi-micro robot control method based on photoelectric drive provided in this application embodiment.

[0092] This application also provides a multi-micro robot control device based on photoelectric drive, such as... Figure 5 As shown, the device includes: A global planning module is used to generate a global reference path from the initial position to the target position for each assembly, which includes a microrobot and a target object, based on the location distribution of obstacles; and to determine the reference velocity of each assembly at different times based on the global reference path of each assembly, and to adjust the reference velocity according to the speed amplitude constraint of photoelectric drive. The local planning module is used to constrain the speed amplitude of the photoelectric drive and the distance between any two combined bodies in the next time step by not being less than a preset minimum safety distance as safety constraints, and to optimize the speed of each combined body as close as possible to the reference speed. Based on the actual position and speed of each combined body at the current time, it solves in parallel the target speed of each combined body at different times after the current time. The target speed is the optimal speed that satisfies the safety constraints. Based on the target speed of each combined body at different times after the current time, it determines the expected trajectory of each combined body at the current time. The trajectory tracking module is used to control the microrobots in each of the assemblies to perform non-contact operations on the target object based on photoelectric drive, according to the expected trajectory of each of the assemblies at the current moment.

[0093] Optionally, the speed amplitude constraint of the photoelectric drive includes an upper limit for translational speed; The local planning module is also used to establish a constrained quadratic optimization problem for the i-th assembly, and to solve the constrained quadratic optimization problem:

[0094]

[0095]

[0096] in, Let represent the optimal speed of the i-th assembly at time k that satisfies the safety constraints. This represents the reference velocity of the i-th composite body at time k. This represents the candidate velocity of the i-th composite at time k. Indicates the search for When the minimum value is obtained The operation of retrieving values, and This represents the actual positions of the i-th and j-th combined entities at time k. This represents the actual velocity of the j-th composite body at time k. Indicates the discrete time step. This represents the preset minimum safe distance associated with the i-th and j-th composite entities. Indicates the upper limit of translation speed. This represents the operation for calculating the L2 norm.

[0097] Optionally, a preset minimum safety distance is set between the i-th and j-th combined entities. Determined by the following formula:

[0098] in, and This represents the equivalent radius of the i-th and j-th composites. This indicates that additional safety redundancy is preset.

[0099] Optionally, the speed amplitude constraint of the photoelectric drive includes an upper limit for translational speed; The global planning module is also used to adjust the reference speed using the following formula:

[0100] in, This represents the reference velocity of the i-th composite body at time k. This indicates the upper limit of translation speed, and 'min' indicates the operation of taking the minimum value. This represents the operation for calculating the L2 norm.

[0101] Optionally, the trajectory tracking module is further configured to solve a quadratic optimization problem based on the expected trajectory of each assembly at the current moment to obtain the optimal control input at the current moment, and use the photoelectric tweezers projection system to control the microrobot in each assembly to perform non-contact operations on the target object according to the optimal control input:

[0102] Where u represents the optimal control input, which describes the displacement between the position of the light spot to be generated by the photoelectric tweezers projection system and the position of the microrobot in each assembly; This represents the predictive control input at time k, where H represents the number of times the predictive control input is required; min represents the operation of taking the minimum value. This represents the actual position of the target object in each assembly at time k. This represents the expected position of the target object in each assembly at time k, determined based on the expected trajectory of each assembly. Operations that represent norms; and Indicates the weighting coefficient; Indicates the discrete time step; , , , This represents the equivalent control input at the k-th moment, determined based on the desired trajectory and position tracking error. Representing the dynamic state equation, This represents the nominal state of each assembly at time k. This represents the actual state of each assembly at time k. This represents the actual state of each assembly at time k+1; This indicates the upper limit of the input limit.

[0103] Optionally, the equivalent control input is determined by the following calculation formula:

[0104] in, Indicates the equivalent control input. Indicates position tracking error. R represents the desired velocity determined based on the desired trajectory of the combined body, and R represents the rotation matrix. Represents the stiffness coefficient matrix. The pseudo-inverse matrix representing the kinematic Jacobian matrix. This represents the proportional gain matrix, and -1 indicates the operation of calculating the inverse matrix.

[0105] Optionally, the calculation formula for the equivalent control input is constructed based on the following simplified dynamics:

[0106] in, The time derivative of the state variables in simplified kinematics. This represents the velocity of the target object in the global x-direction. This represents the velocity of the target object in the global y-direction. This represents the angular velocity of the target object. Represents the kinematic Jacobian matrix, and the normal velocity of the target object. tangential velocity of the target object , Indicates the normal control input, Indicates tangential control input. b represents the stiffness coefficient for isotropic properties. r and b o This indicates the damping of the microrobot and the target object.

[0107] As can be seen from the above technical solution, this application takes the combination of a micro-robot and a target object as the processing object. First, global path planning is performed. Based on the location distribution of obstacles, a global reference path from the initial position to the target position is generated for each combination. The reference speed is adjusted according to the speed amplitude constraint of photoelectric drive, thereby achieving executable global path planning that meets the requirements of static obstacle avoidance and can move the combination from the initial position to the target position. Then, local path planning is performed. The speed amplitude constraint of photoelectric drive and the distance between any two combinations in the next time step not being less than a preset minimum safe distance are used as safety constraints. This ensures that the local path planning is suitable for photoelectric drive execution and meets the mutual collision avoidance requirements between multiple micro-robots and the target object. The optimization objective is to make the speed of each assembly as close as possible to the reference speed, thereby ensuring that the local path planning does not deviate too much from the global path planning, and thus ensuring that the target object can successfully reach the target position. Finally, based on the target speeds of each assembly at different times after the current time obtained by parallel solving of the local path planning, the expected trajectory of each assembly at the current time is determined. Based on this, the micro-robots in each assembly are controlled by photoelectric drive to perform non-contact operations on the target object. This enables real-time collision avoidance between multiple micro-robots and the target object without communication, thus maintaining stable navigation in complex environments with communication interference and obstacles. This improves the control effect and significantly enhances the parallel processing capability and task throughput of the micro-operating system.

[0108] The photoelectric-driven multi-microrobot control device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0109] The photoelectric-driven multi-micro robot control device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0110] The photoelectric-driven multi-micro robot control device provided in this application embodiment can achieve… Figure 1 The various processes implemented in the embodiment of the photoelectric-driven multi-microrobot control method shown will not be repeated here to avoid repetition.

[0111] Optionally, this application embodiment also provides an electronic device, including a processor 110, a memory 109, and a program or instructions stored in the memory 109 and executable on the processor 110. When the program or instructions are executed by the processor 110, they implement the various processes of the above-described embodiments of the photoelectric-driven multi-micro-robot control method and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0112] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0113] Figure 6 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0114] The electronic device 100 includes, but is not limited to, components such as: radio frequency unit 101, network module 102, audio output unit 103, input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, and processor 110.

[0115] Those skilled in the art will understand that the electronic device 100 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 110 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 6 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0116] The processor 110 is used to perform the following steps: Based on the location distribution of obstacles, a global reference path is generated for each assembly from its initial position to its target position. The assembly includes a microrobot and a target object. Based on the global reference path of each assembly, the reference velocity of each assembly at different times is determined, and the reference velocity is adjusted according to the speed amplitude constraint of the photoelectric drive. The speed amplitude constraint of the photoelectric drive and the distance between any two combined bodies in the next time step not being less than the preset minimum safe distance are used as safety constraints. The optimization objective is to make the speed of each combined body as close as possible to the reference speed. Based on the actual position and actual speed of each combined body at the current time, the target speed of each combined body at different times after the current time is solved in parallel. The target speed is the optimal speed that satisfies the safety constraints. Based on the target velocities of each of the aforementioned combined bodies at different times after the current time, determine the expected trajectory of each of the aforementioned combined bodies at the current time; Based on the desired trajectory of each of the assembled bodies at the current moment, the microrobots in each of the assembled bodies perform non-contact operations on the target object using photoelectric drive control.

[0117] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiments of the photoelectric-driven multi-microrobot control method and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0118] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0119] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the photoelectric-driven multi-microrobot control method, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0120] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0121] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0123] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A control method for multiple micro-robots based on photoelectric drive, characterized in that, The method includes: Based on the location distribution of obstacles, a global reference path is generated for each assembly from its initial position to its target position. The assembly includes a microrobot and a target object. Based on the global reference path of each assembly, the reference velocity of each assembly at different times is determined, and the reference velocity is adjusted according to the speed amplitude constraint of the photoelectric drive. The speed amplitude constraint of the photoelectric drive and the distance between any two combined bodies in the next time step not being less than the preset minimum safe distance are used as safety constraints. The optimization objective is to make the speed of each combined body as close as possible to the reference speed. Based on the actual position and actual speed of each combined body at the current time, the target speed of each combined body at different times after the current time is solved in parallel. The target speed is the optimal speed that satisfies the safety constraints. Based on the target velocities of each of the aforementioned combined bodies at different times after the current time, determine the expected trajectory of each of the aforementioned combined bodies at the current time; Based on the desired trajectory of each of the assembled bodies at the current moment, the microrobots in each of the assembled bodies perform non-contact operations on the target object using photoelectric drive control.

2. The method according to claim 1, characterized in that, The speed amplitude constraint of the photoelectric drive includes an upper limit for translational speed; the target speed of the i-th assembly at time k is obtained through the following steps: A constrained quadratic optimization problem is established for the i-th assembly, and the constrained quadratic optimization problem is solved: in, Let represent the optimal speed of the i-th assembly at time k that satisfies the safety constraints. This represents the reference velocity of the i-th composite body at time k. This represents the candidate velocity of the i-th composite at time k. Indicates the search for When the minimum value is obtained The operation of retrieving values, and This represents the actual positions of the i-th and j-th combined entities at time k. This represents the actual velocity of the j-th composite body at time k. Indicates the discrete time step. This represents the preset minimum safe distance associated with the i-th and j-th combined entities. Indicates the upper limit of translation speed. This represents the operation for calculating the L2 norm.

3. The method according to claim 2, characterized in that, The preset minimum safe distance associated with the i-th and j-th combined entities Determined by the following formula: in, and This represents the equivalent radius of the i-th and j-th composites. This indicates that additional safety redundancy is preset.

4. The method according to claim 1, characterized in that, The speed amplitude constraint of the photoelectric drive includes an upper limit for translational speed; The adjustment of the reference speed based on the speed amplitude constraint of the photoelectric drive includes: The reference speed is adjusted using the following formula: in, This represents the reference velocity of the i-th composite body at time k. This indicates the upper limit of translation speed, and "min" indicates the operation of taking the minimum value. This represents the operation for calculating the L2 norm.

5. The method according to claim 1, characterized in that, The step of controlling the microrobots in each of the assembled bodies to perform non-contact operations on the target object based on photoelectric drive, according to the desired trajectory of each of the assembled bodies at the current moment, includes: Based on the desired trajectory of each assembly at the current moment, the following quadratic optimization problem is solved to obtain the optimal control input at the current moment. Then, using a photoelectric tweezers projection system, the microrobots in each assembly are controlled to perform non-contact operations on the target object based on the optimal control input: Where u represents the optimal control input, which describes the displacement between the position of the light spot to be generated by the photoelectric tweezers projection system and the position of the microrobot in each assembly; This represents the predictive control input at time k, where H represents the number of times the predictive control input is required; min represents the operation of taking the minimum value. This represents the actual position of the target object in each assembly at time k. This represents the expected position of the target object in each assembly at time k, determined based on the expected trajectory of each assembly. Operations that represent norms; and Indicates the weighting coefficient; Indicates the discrete time step; , , , This represents the equivalent control input at the k-th moment, determined based on the desired trajectory and position tracking error. Representing the dynamic state equation, This represents the nominal state of each assembly at time k. This represents the actual state of each assembly at time k. This represents the actual state of each assembly at time k+1; This indicates the upper limit of the input limit.

6. The method according to claim 5, characterized in that, The equivalent control input is determined by the following calculation formula: in, Indicates the equivalent control input. Indicates position tracking error. R represents the desired velocity determined based on the desired trajectory of the combined body, and R represents the rotation matrix. Represents the stiffness coefficient matrix. The pseudo-inverse matrix representing the kinematic Jacobian matrix. This represents the proportional gain matrix, and -1 indicates the operation of calculating the inverse matrix.

7. The method according to claim 6, characterized in that, The formula for calculating the equivalent control input is based on the following simplified dynamics: in, The time derivative of the state variables in simplified kinematics. This represents the velocity of the target object in the global x-direction. This represents the velocity of the target object in the global y-direction. This represents the angular velocity of the target object. Represents the kinematic Jacobian matrix, and the normal velocity of the target object. tangential velocity of the target object , Indicates the normal control input, Indicates tangential control input. b represents the stiffness coefficient for isotropic properties. r and b o This indicates the damping of the microrobot and the target object.

8. A multi-micro robot control device based on photoelectric drive, characterized in that, The device includes: A global planning module is used to generate a global reference path from the initial position to the target position for each assembly, which includes a microrobot and a target object, based on the location distribution of obstacles; and to determine the reference velocity of each assembly at different times based on the global reference path of each assembly, and to adjust the reference velocity according to the speed amplitude constraint of photoelectric drive. The local planning module is used to constrain the speed amplitude of the photoelectric drive and the distance between any two combined bodies in the next time step by not being less than a preset minimum safety distance as safety constraints, and to optimize the speed of each combined body as close as possible to the reference speed. Based on the actual position and speed of each combined body at the current time, it solves in parallel the target speed of each combined body at different times after the current time. The target speed is the optimal speed that satisfies the safety constraints. Based on the target speed of each combined body at different times after the current time, it determines the expected trajectory of each combined body at the current time. The trajectory tracking module is used to control the microrobots in each of the assemblies to perform non-contact operations on the target object based on photoelectric drive, according to the expected trajectory of each of the assemblies at the current moment.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the photoelectric-driven multi-microrobot control method as described in any one of claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the photoelectric-driven multi-microrobot control method as described in any one of claims 1-7.

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