Robot control methods, robots, electronic devices and computer program products

Abstract

This disclosure provides a robot control method, a robot, an electronic device, and a computer program product, relating to the field of computer technology. The active layer on the robot surface is composed of a spin-crossing material. The method includes: inversely solving a kinematic model and a thermodynamic model based on target coordinates to determine the excitation energy; projecting a light spot generated based on the excitation energy onto the active layer on the robot surface, and solving the spin-crossing phase transition occurring in the active layer using the thermodynamic model to determine the mole fraction of the high-spin state; calculating the volume expansion of the high-spin state mole fraction to determine the linear strain; inputting the linear strain into a structural mechanics model to determine the instantaneous bending curvature; inputting the instantaneous bending curvature into a kinematic model to determine the theoretical coordinates; and calculating the laser pulse width modulation duty cycle using a feedback control algorithm based on the pose deviation between the theoretical coordinates and the target coordinates. The laser pulse width modulation duty cycle is used to adjust the excitation energy to achieve robot control.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a robot control method, a robot, an electronic device, and a computer program product. Background Technology

[0002] Existing photothermal or thermally driven soft microrobots mainly rely on shape memory polymers (SMPs) or liquid crystal elastomers (LCEs) as driving materials. These materials have small driving strain and slow response speed, making it difficult to achieve large deflection and high-precision operation of robots at the microscale, and they also have poor environmental interference resistance. In non-constant temperature environments, small fluctuations in background temperature can easily induce spontaneous deformation of the materials, causing robot motion distortion and control instability. Summary of the Invention

[0003] This disclosure provides a robot control method, a robot, an electronic device, and a computer program product.

[0004] According to one aspect of this disclosure, a robot control method is provided, wherein an active layer on the surface of the robot is composed of a spin-crossing material. The method includes: performing inverse kinematic and thermodynamic model calculations on the robot based on acquired target coordinates to determine the excitation energy required for the target driving region; projecting a light spot generated based on the excitation energy onto the active layer on the robot surface, and calculating the spin-crossing phase transition occurring in the active layer using the thermodynamic model to determine the high-spin mole fraction, wherein the high-spin mole fraction represents the proportion of molecules in the high-spin state in the spin-crossing material to the total number of molecules; performing volume expansion calculations on the high-spin mole fraction to determine the linear strain of the active layer; inputting the linear strain into a structural mechanics model to determine the instantaneous bending curvature of the active layer; inputting the instantaneous bending curvature into the kinematic model to determine the theoretical coordinates of the end effector; and calculating a laser pulse width modulation duty cycle using a feedback control algorithm based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates, wherein the laser pulse width modulation duty cycle is used to adjust the excitation energy to achieve robot control.

[0005] According to the robot control method disclosed herein, by coupling the photothermal phase transition mechanism of spin-crossed materials with structural mechanics and kinematic models through multi-physics fields, a reverse calculation channel from target coordinates to excitation energy is established, enabling quantitative prediction and precise delivery of photothermal energy for the actuation process of the giant volume expansion of the microrobot's active layer. Simultaneously, by comparing the pose deviation between the theoretical coordinates of the end effector and the target coordinates in real time and adjusting the laser pulse width modulation duty cycle accordingly, a closed-loop control circuit is constructed, thereby improving the driving strain amplitude and motion control determinism of the microrobot in microscale operations.

[0006] According to at least one embodiment of the robot control method of this disclosure, a laser pulse width modulation duty cycle is calculated using a feedback control algorithm based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates. The method includes: calculating the laser intensity based on the instantaneous bending curvature of the target and a state identifier, where the state identifier represents the phase transition state of the spin-crossing material; calculating the temperature deviation based on the current ambient temperature and a reference ambient temperature, and determining the ambient temperature compensation using a temperature compensation coefficient; and determining the laser pulse width modulation duty cycle using a feedback control algorithm based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates, the laser intensity, and the ambient temperature compensation.

[0007] According to at least one embodiment of the robot control method of this disclosure, the laser intensity is calculated based on the target instantaneous bending curvature and a state identifier, including: calculating, based on the target instantaneous bending curvature, a first linear strain required to achieve the target instantaneous bending curvature using the structural mechanics model; converting the first linear strain into a first high-spin state mole fraction according to the strain-phase transition relationship of the spin-crossing material; selecting a corresponding inverse mapping curve according to the state identifier, and calculating the laser intensity required to achieve the first high-spin state mole fraction using the inverse mapping curve.

[0008] According to at least one embodiment of the robot control method of this disclosure, selecting a corresponding inverse mapping curve based on a state identifier includes: performing a first-order difference calculation on the instantaneous curvature of the target to determine the rate of change of curvature; when the rate of change of curvature is greater than a curvature threshold, the state identifier is a first identifier, and a first inverse mapping curve for a heating phase transition process is selected; when the rate of change of curvature is less than the curvature threshold, the state identifier is a second identifier, and a second inverse mapping curve for a cooling phase transition process is selected; when the absolute value of the rate of change of curvature is equal to the curvature threshold, the state identifier is a third identifier, and a third inverse mapping curve is determined by interpolating the first inverse mapping curve and the second inverse mapping curve.

[0009] According to at least one embodiment of the robot control method of this disclosure, based on the acquired target coordinates, the kinematic model and thermodynamic model of the robot are inversely solved to determine the excitation energy required for the target driving region. This includes: based on the acquired target coordinates and the initial coordinates of the robot's end effector, performing inverse calculation through the kinematic model to determine the second instantaneous bending curvature required for the robot's end effector to reach the target coordinates; inputting the second instantaneous bending curvature into a structural mechanics model for inverse calculation to determine the second linear strain required to achieve the second instantaneous bending curvature; converting the second linear strain into a second high-spin state mole fraction according to the strain-phase transition relationship of the spin-crossing material; and inputting the second high-spin state mole fraction into a thermodynamic model for inverse calculation to determine the excitation energy required for the target driving region.

[0010] According to at least one embodiment of the robot control method of this disclosure, before projecting a light spot generated based on the excitation energy onto an active layer on the surface of the robot, and before determining the high-spin state mole fraction by solving the spin-crossing phase transition occurring in the active layer using the thermodynamic model, the method includes: obtaining the two-dimensional geometric contour coordinates of the joints or tentacle regions where the robot needs to be bent, and mapping the global coordinate system of the robot to the target pixel coordinate system using an affine transformation matrix; generating a corresponding target image based on the two-dimensional geometric contour coordinates in the target pixel coordinate system; and generating a light spot projected onto the active layer on the surface of the robot based on the spatial distribution of the light field determined by the target image and the laser intensity determined by the excitation energy.

[0011] According to at least one embodiment of the robot control method of this disclosure, the preparation process of the spin-crossed material includes: dissolving 1H-1,2,4-triazole in a mixed solvent of water and methanol to prepare a first ligand solution; adding an aqueous solution of ferrous sulfate dropwise to the first ligand solution under stirring conditions to obtain a second ligand solution; reacting the second ligand solution under target conditions to obtain a Fe-triazole complex precipitate having a one-dimensional chain polymer structure; precipitating the Fe-triazole complex precipitate by cooling or vacuum evaporation, filtering, washing with water and ethanol sequentially, and drying to obtain spin-crossed nanorods; performing silanization surface modification on the spin-crossed nanorods, coating MXene nanosheets by electrostatic self-assembly, dispersing them in a thermoplastic polyurethane solution, coating them into a film under shear flow field assistance, and curing them to obtain the spin-crossed material.

[0012] According to at least one embodiment of the robot control method of this disclosure, the preparation process of the spin-crossing material includes: dissolving Fe(BF4)2 and ascorbic acid in water, adding a surfactant and an oil phase solvent, and stirring to form a transparent first microemulsion; dissolving 1,2,4-triazole in water, similarly adding the surfactant and the oil phase solvent, and stirring to form a second microemulsion; adding the second microemulsion dropwise to the first microemulsion to undergo a coordination reaction, generating purple-red nanoparticles; stirring the nanoparticles, adding ethanol or acetone to disrupt the microemulsion, centrifuging to collect the precipitate, and washing with ethanol to remove surface oil and excess surfactant to obtain the spin-crossing material.

[0013] According to at least one embodiment of the robot control method of this disclosure, the active layer of the robot is composed of spin-crossed nanorods with surface electrostatically adsorbed MXene nanosheets.

[0014] According to another aspect of this disclosure, a robot is provided that applies the robot control method of any of the above embodiments.

[0015] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, causing the processor to perform a robot control method according to any embodiment of this disclosure.

[0016] According to another aspect of this disclosure, a readable storage medium is provided, wherein execution instructions are stored therein, which, when executed by a processor, are used to implement a robot control method according to any embodiment of this disclosure.

[0017] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements a robot control method according to any embodiment of this disclosure.

[0018] The beneficial technical effects of the robot control method disclosed herein include at least the following: By coupling the photothermal phase transition mechanism of the spin-crossing material with the structural mechanics model and kinematic model through multi-physics fields, a reverse calculation channel from target coordinates to excitation energy is established, enabling quantitative prediction and precise delivery of photothermal energy for the actuation process of the giant volume expansion of the microrobot's active layer. Simultaneously, by comparing the pose deviation between the theoretical coordinates of the end effector and the target coordinates in real time and adjusting the laser pulse width modulation duty cycle accordingly, a closed-loop control loop is constructed, thereby improving the driving strain amplitude and motion control determinism of the microrobot in microscale operations. Attached Figure Description

[0019] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0020] Figure 1 This is a schematic diagram of the overall process of a robot control method according to one embodiment of the present disclosure.

[0021] Figure 2 This is a flowchart illustrating the process of determining the excitation energy required for a target driving region in a robot control method according to one embodiment of the present disclosure.

[0022] Figure 3 This is a schematic diagram of the process of generating a light spot projected onto the active layer of a robot surface based on a laser intensity determined in a robot control method according to one embodiment of the present disclosure.

[0023] Figure 4 This is a schematic flowchart illustrating the calculation of the laser pulse width modulation duty cycle in a robot control method according to one embodiment of the present disclosure.

[0024] Figure 5 This is a schematic flowchart illustrating the calculation of laser intensity in a robot control method according to one embodiment of the present disclosure.

[0025] Figure 6 This is a flowchart illustrating the determination of the inverse mapping curve in a robot control method according to one embodiment of the present disclosure.

[0026] Figure 7 This is a schematic structural block diagram of a robot control device according to one embodiment of the present disclosure.

[0027] Figure 8 This is a schematic structural block diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

[0028] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0029] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0030] In microscale precision manipulation scenarios (such as minimally invasive grasping of living cells, assembly of micro / nano parts, or targeted delivery within the body), existing photothermal-driven soft microrobots struggle to achieve large-angle bending and strong clamping due to insufficient material-driven strain (typically <5%). Furthermore, existing microrobots rely on Joule heating with embedded resistance wires, resulting in external wiring constraints that cannot meet the demands of multi-degree-of-freedom wireless manipulation within confined spaces. Moreover, in complex thermal environments such as biological tissues or industrial microfabrication, even minute fluctuations in background temperature can trigger spontaneous material deformation (thermal-actuated drift), leading to cumulative errors and oscillating instability in the microrobot's end effector during repeated actuation.

[0031] To address this, this disclosure proposes a robot control method that utilizes a spin-crossing material as the active layer. Leveraging its massive volume expansion effect exceeding 10% during phase transition, this method overcomes the physical bottleneck of insufficient strain in traditional soft materials, enabling the microrobot to achieve large-angle bending and strong gripping capabilities. Simultaneously, a photothermal actuation method based on light spot projection replaces Joule heating with embedded resistance wires, completely eliminating external wiring constraints and enabling wireless multi-degree-of-freedom manipulation within confined spaces. By performing multi-physics coupling and inverse calculation of kinematic, thermodynamic, and structural mechanics models, a precise mapping from target coordinates to excitation energy is established. Furthermore, a feedback control algorithm based on the deviation between the end effector's theoretical coordinates and the target's pose is used to adjust the laser pulse width modulation duty cycle in real time. This effectively suppresses thermal actuation drift caused by background temperature fluctuations in complex thermal environments, avoiding accumulated errors and oscillation instability during repeated actuation. This improves the driving strain amplitude, wireless manipulation degrees of freedom, and closed-loop control accuracy of the microrobot in scenarios such as minimally invasive grasping of living cells, assembly of micro / nano parts, and targeted delivery within the body.

[0032] In the biomedical field, the robot control method disclosed herein can serve as the core execution unit of an in vitro high-precision cell micromanipulation platform. An edge computing terminal (such as an embedded industrial computer equipped with a real-time control system) independently completes light field modulation and visual loop closure, enabling microrobot single-cell grasping and drug microinjection. It can also serve as a wireless end effector for minimally invasive in vivo diagnosis and treatment, using miniature optical and sensing modules integrated into catheters or capsule endoscopes to perform targeted tissue clamping and sampling within narrow body cavities.

[0033] In micro-nano manufacturing and precision assembly scenarios, multiple microrobot workstations can be centrally scheduled by a cloud server to achieve collaborative assembly and defect detection of batch micro-parts through remote reverse engineering and thermodynamic model prediction. In the fields of consumer electronics and laboratory automation, it can also be applied to terminal devices (such as smartphones or tablets equipped with miniature near-infrared light sources and thermal imaging modules) as a portable micro-manipulation terminal to perform rapid thermally actuated deformation detection and local repair of micro-samples (such as semiconductor chip micro-solder joints and microfluidic chips).

[0034] Figure 1 A schematic flowchart illustrating the overall process of a robot control method according to one embodiment of this disclosure is shown. Figure 1 The method M100 shown includes steps S110 to S160. The active layer on the robot surface is composed of a spin-crossing material. This method can be executed by electronic devices such as a central controller, processor, industrial computer, server, smart terminal, or embedded microcontroller.

[0035] In step S110, based on the acquired target coordinates, the kinematic and thermodynamic models of the robot are inversely solved to determine the excitation energy required for the target driving region.

[0036] The aforementioned target driving area is a local area on the robot's surface that needs to be precisely irradiated by a laser to achieve a specific deformation, such as a joint or tentacle that needs to be bent.

[0037] The aforementioned excitation energy is the laser energy value that needs to be projected onto the active layer to achieve the robot's specific deformation.

[0038] Preferably, the active layer of the robot is composed of spin-crossed nanorods with MXene nanosheets electrostatically adsorbed on the surface.

[0039] Preferably, high-resolution images of the microrobot in a relaxed state (without laser excitation) are acquired and then denoised and edge-enhanced. Significant feature points of the microrobot's end effector are identified, and a two-dimensional workspace coordinate system is established based on the image center. The feature point coordinates are converted into physical space coordinates to accurately calibrate the initial null-state coordinates of the microrobot's end effector. Simultaneously, the target object's position is converted into target grasping coordinates (i.e., target coordinates).

[0040] For example, when grasping a cell with a diameter of 10 μm, the precise target coordinates to which the end effector needs to move are first determined. Then, the laser energy required to bend the microrobot's tentacle to the target coordinates is precisely calculated through inverse calculation. Because cells are extremely fragile, excessive energy may cause cell membrane rupture, while insufficient energy will prevent grasping. Through precise modeling, the minimum laser energy required to reach the target position can be calculated, ensuring a high success rate of grasping while reducing the risk of thermal damage to the cell.

[0041] In step S120, a light spot generated based on excitation energy is projected onto the active layer on the robot surface. The spin-crossing phase transition that occurs in the active layer is solved by a thermodynamic model to determine the mole fraction of high-spin states. The mole fraction of high-spin states represents the proportion of molecules in high-spin states in the total number of molecules in the spin-crossing material.

[0042] The light spot generated by the excitation energy is projected onto the active layer on the robot's surface. The active layer absorbs photon energy, generating localized heat through photothermal conversion, leading to an increase in the active layer's temperature. This temperature change triggers a phase transition process in the spin-crossed (SCO) material from a low-spin (LS) state to a high-spin (HS) state. Using a thermodynamic model, based on the current temperature field distribution and material property parameters, the proportion of molecules in the high-spin state in the active layer (i.e., the high-spin mole fraction) is calculated.

[0043] Preferably, the thermodynamic model is the Slichter-Drickamer model.

[0044] Preferably, the thermodynamic model is as follows: in, For high-spin mole fraction, and The enthalpy change and entropy change are related to the phase transition. These are parameters representing intermolecular cooperative interactions. Let be the ideal gas constant. Let t be the local temperature of the material, and t be the time variable.

[0045] In step S130, the volume expansion of the high spin state mole fraction is calculated to determine the linear strain of the active layer.

[0046] Using the high spin state mole fraction as input, volume expansion is calculated based on the strain-phase transition relationship unique to spin-crossed materials. The dynamic macroscopic linear strain of the active layer in the long axis direction is determined. The linear strain reflects the degree of volume change of the active layer due to the spin-crossing phase transition, thus transforming the microscopic phase transition state of the spin-crossed material into macroscopic mechanical parameters.

[0047] Preferably, the dynamic macroscopic linear strain along the long axis of the active layer is: in, For linear strain, This represents the maximum volume fraction expansion rate.

[0048] In step S140, the linear strain is input into the structural mechanics model to determine the instantaneous bending curvature of the active layer.

[0049] Linear strain is input into the structural mechanics model as an input parameter to calculate the instantaneous bending curvature of the robot under this linear strain state. The instantaneous bending curvature represents the degree of instantaneous bending of the robot's end effector (i.e., tentacle or joint) under the current driving state. Specifically, when the active layer expands in volume while the passive layer remains unchanged, the double-layer structure bends due to interlayer strain mismatch. The magnitude of its curvature is related to the strain difference, material stiffness, and layer thickness ratio. The robot's passive layer is a passive pure TPU film, hot-pressed together with the active layer.

[0050] Preferably, the structural mechanics model is constructed based on the modified Timoshenko double-layer plate theory.

[0051] Preferably, the instantaneous bending curvature follows a modified Timoshenko double-layer plate theory: in, For instantaneous bending curvature, and The Young's modulus and thickness of the active layer and passive layer are respectively.

[0052] In step S150, the instantaneous bending curvature is input into the kinematic model to determine the theoretical coordinates of the end effector.

[0053] The instantaneous bending curvature is input into the kinematic model as an input parameter to calculate the theoretical coordinates of the robot end effector in the current bending state. The theoretical coordinates represent the expected position of the robot end effector in the workspace.

[0054] For example, when a robot tentacle bends with a constant curvature, its end effector trajectory forms an arc, and the spatial coordinates of the end effector can be calculated by the relationship between the arc length and the chord length.

[0055] Preferably, the kinematic model is constructed based on the constant curvature assumption.

[0056] Preferably, the kinematic model is as follows: in, and This indicates the position of the tip of a single driving tentacle in a planar coordinate system. The effective length of a single driving tentacle.

[0057] In step S160, based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates, the laser pulse width modulation duty cycle is calculated through a feedback control algorithm. The laser pulse width modulation duty cycle is used to adjust the excitation energy to achieve robot control.

[0058] The pose deviation of the end effector is calculated by comparing its theoretical coordinates with the target coordinates. Simultaneously, the ambient temperature signal is received, and the deviation between the current ambient temperature and the reference ambient temperature is calculated. Using a feedback control algorithm, considering the pose deviation, temperature deviation, and material phase transition state indicators, the laser pulse width modulation (PWM) duty cycle is calculated. This calculated PWM duty cycle is sent to the laser generator to adjust the laser energy output, achieving precise control of the robot's motion.

[0059] Preferably, the laser pulse width modulation duty cycle is: in, The duty cycle of the laser pulse width modulation. This is the nominal light intensity mapping function for thermal hysteresis sensing. For the instantaneous bending curvature of the target, For status indicators, This is a temperature feedforward suppression term. The current ambient temperature. For reference to ambient temperature, This is the temperature feedforward compensation coefficient. , and For closed-loop control gain. This refers to visual pose error (i.e., pose deviation).

[0060] Therefore, the robot control method disclosed herein establishes a multi-physics coupled model of kinematics, thermodynamics, and structural mechanics to achieve accurate inverse calculation of target coordinates and forward deformation prediction of photothermal excitation energy. This enables the microrobot to generate large driving strain by utilizing the large volume expansion effect during the spin-crossing material phase transition process. Simultaneously, through a feedback control algorithm based on the deviation between the theoretical coordinates of the end effector and the target coordinates, combined with ambient temperature feedforward compensation and thermal hysteresis state perception, the laser pulse width modulation duty cycle is adjusted in real time. This effectively suppresses thermal actuation drift caused by background temperature fluctuations in complex thermal environments, as well as accumulated errors and oscillation instability during repeated actuation processes. This improves the microrobot's driving strain amplitude, wireless control degrees of freedom, closed-loop control accuracy, and thermal environment robustness in various application scenarios.

[0061] Regarding step S110, based on the acquired target coordinates, the kinematic and thermodynamic models of the robot are inversely calculated to determine the excitation energy required for the target driving region. In some embodiments of this disclosure, it may include, for example... Figure 2 Steps S1101 to S1104 are shown.

[0062] In step S1101, based on the acquired target coordinates and the initial coordinates of the robot end effector, the second instantaneous bending curvature required for the robot end effector to reach the target coordinates is determined by inverse calculation using a kinematic model.

[0063] Optionally, the target displacement is calculated based on the acquired target coordinates and the initial coordinates of the robot's end effector, and then input into the inverse solution equation established based on the forward kinematics model. Since the inverse solution equation is a transcendental equation and cannot be solved analytically, Newton's iteration method is used for numerical solution until the convergence condition is met. The converged value obtained is the second instantaneous curvature that realizes the target coordinates.

[0064] In step S1102, the second instantaneous bending curvature is input into the structural mechanics model for inverse calculation to determine the second linear strain required to achieve the second instantaneous bending curvature.

[0065] The second instantaneous bending curvature is input into the structural mechanics model as a known quantity. The structural mechanics model quantitatively describes the physical laws governing the bending of the double-layer structure due to interlayer strain mismatch. The bending curvature is related to the linear strain, the Young's modulus of the active layer, and the thickness of the passive layer. In the reverse calculation process, the second instantaneous bending curvature, along with the pre-determined Young's modulus and thickness parameters of the active and passive layers, are used as known conditions. The second linear strain term is then derived from the mapping relationship between curvature and strain through algebraic transformations.

[0066] In step S1103, the second linear strain is converted into the mole fraction of the second high-spin state according to the strain-phase transition relationship of the spin-crossed material.

[0067] Based on the quantitative relationship between volume expansion and spin state transition during the phase transition of spin-crossed materials, the second linear strain is substituted into the dynamic macroscopic linear strain formula along the long axis of the active layer. The dynamic macroscopic linear strain formula establishes the proportional relationship between linear strain and the mole fraction of high-spin state and the maximum volume fraction expansion rate.

[0068] In the reverse calculation process, the second linear strain and the pre-calibrated maximum volume fraction expansion rate are used as known conditions, and the mole fraction of the second high spin state is solved from the linear strain formula through algebraic transformation.

[0069] In step S1104, the mole fraction of the second high-spin state is input into the thermodynamic model for inverse calculation to determine the excitation energy required for the target driving region.

[0070] Using the mole fraction of the second high-spin state, along with pre-calibrated phase transition enthalpy change, entropy change, cooperative interaction parameters, and the ideal gas constant as known conditions, the local temperature is inversely solved from the thermodynamic equations through algebraic transformation. Substituting the local temperature into an unsteady photothermal conversion model, and combining it with the light absorption characteristics, thermal conductivity, and convective heat dissipation conditions of the target driving region, the laser energy value required to bring the active layer to this local temperature is inversely solved; this laser energy value is then converted into the excitation energy required for the target driving region.

[0071] Therefore, by establishing a multi-physics field coupling inverse solution link involving kinematics, structural mechanics, thermodynamics, and photothermal conversion, a deterministic white-box mapping from the end effector target coordinates to the photothermal excitation energy is achieved. This directly converts the desired pose into precise laser energy input, avoiding energy overshoot and response lag in traditional experience-based trial-and-error driven systems.

[0072] Regarding step S120, before projecting the light spot generated based on the excitation energy onto the active layer on the robot surface, and calculating the spin-crossing phase transition occurring in the active layer using a thermodynamic model to determine the mole fraction of high-spin states, some embodiments of this disclosure may include, for example... Figure 3 Steps S310 to S330 are shown.

[0073] In step S310, the two-dimensional geometric contour coordinates of the joints or tentacle regions that the robot needs to bend are obtained, and the global coordinate system of the robot is mapped to the target pixel coordinate system using an affine transformation matrix.

[0074] The edge contours of the robot's joints or tentacles to be driven are identified and extracted from images acquired by visual recognition algorithms, obtaining a set of two-dimensional geometric contour coordinate points of the robot in the global physical coordinate system. Based on the correspondence between the pre-calibrated global coordinate system and the target pixel coordinate system (such as the micromirror array coordinate system of a digital micromirror device), an affine transformation matrix is ​​constructed. The extracted contour coordinate point set is substituted into this affine transformation matrix to perform coordinate mapping operations, converting the geometric contours in physical space into pixel coordinates in the target pixel coordinate system, thereby determining the spatial distribution range of the light field modulation.

[0075] In step S320, the corresponding target image is generated based on the two-dimensional geometric contour coordinates in the target pixel coordinate system.

[0076] In the target pixel coordinate system, the boundary range of the driving region enclosed by the contour is determined based on the mapped two-dimensional geometric contour coordinates. According to the spatial distribution requirements of the excitation energy, the pixels inside the region are grayscale assigned or binarized, so that the pixel values ​​within the driving region correspond to the laser projection intensity, and the pixel values ​​outside the region are set to zero, thereby generating a light field mask image that matches the target driving contour, i.e., the target image.

[0077] In step S330, the spatial distribution of the light field is determined based on the target image, and the laser intensity is determined based on the excitation energy to generate a light spot projected onto the active layer of the robot surface.

[0078] The target image is loaded into the controller of the digital micromirror device. Millions of micromirrors inside the device deflect at angles based on the grayscale or binarized values ​​of each pixel in the image. This causes the laser beam emitted from the laser source to be selectively reflected by the micromirror array, forming a spatial light field distribution in the target driving region that matches the two-dimensional geometric contour. Simultaneously, the output power and pulse width modulation duty cycle of the laser are set according to the excitation energy calculation results. This ensures that the light spot projected onto the active layer of the robot surface has a precise spatial shape while possessing the laser intensity required to drive the phase transition, achieving region-selective photothermal excitation.

[0079] Therefore, by establishing an affine mapping from the global physical coordinate system to the target pixel coordinate system, and combining it with high-resolution spatial light field modulation of digital micromirror devices, precise regional selective projection of laser energy onto the active layer of the robot surface was achieved. This allows the shape of the laser spot to accurately match the geometric contour of the joint or tentacle to be driven, avoiding thermal damage and energy waste in non-target areas, and improving the spatial resolution, energy utilization efficiency, and accuracy of microscale manipulation in photothermal drive.

[0080] Regarding step S160, based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates, the laser pulse width modulation duty cycle is calculated using a feedback control algorithm. In some embodiments of this disclosure, it may include, for example... Figure 4 Steps S1601 to S1603 are shown.

[0081] In step S1601, the laser intensity is calculated based on the target instantaneous bending curvature and the state identifier, which is used to represent the phase transition state of the spin-crossing material.

[0082] In step S1602, the temperature deviation is calculated based on the current ambient temperature and the reference ambient temperature, and the ambient temperature compensation is determined in combination with the temperature compensation coefficient.

[0083] The current ambient temperature of the robot's microenvironment is collected in real time at a set frequency, and the difference is calculated with the reference ambient temperature calibrated during the system initialization phase to obtain the temperature deviation. Combined with a preset temperature compensation coefficient, an ambient temperature compensation amount is generated.

[0084] Preferably, when the current ambient temperature is detected to be higher than the reference ambient temperature, the ambient temperature compensation is negative, which is used to actively reduce the laser duty cycle to offset ambient thermal interference; conversely, the duty cycle is increased to compensate for ambient thermal loss, thereby achieving real-time feedforward suppression of background temperature fluctuations.

[0085] In step S1603, the laser pulse width modulation duty cycle is determined by a feedback control algorithm based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates, the laser intensity, and the ambient temperature compensation.

[0086] The spatial distance between the theoretical coordinates of the end effector and the target coordinates is calculated to obtain the visual pose deviation. The pose deviation, along with the ambient temperature compensation and laser intensity, are simultaneously input into the feedback control algorithm to determine the laser pulse width modulation duty cycle.

[0087] Therefore, by improving the control accuracy and motion determinism of microrobots under complex thermal environments and repeated actuation, high-precision closed-loop operations at the micrometer level have been achieved.

[0088] Regarding step S1601, the laser intensity is calculated based on the instantaneous bending curvature of the target and the state indicator. In some embodiments of this disclosure, it may include, for example... Figure 5 Steps S510 to S530 are shown.

[0089] In step S510, based on the target instantaneous bending curvature, the first line strain required to achieve the target instantaneous bending curvature is calculated in reverse using the structural mechanics model.

[0090] In the reverse calculation process, the instantaneous bending curvature of the target and the pre-determined Young's modulus and thickness parameters of the active and passive layers are used as known conditions. The first line strain is solved from the mapping relationship between curvature and strain through algebraic transformation.

[0091] In step S520, the first linear strain is converted into the first high-spin state mole fraction based on the strain-phase transition relationship of the spin-crossed material.

[0092] Using the first linear strain and the pre-calibrated maximum volume fraction expansion rate as known conditions, the mole fraction term of the first high-spin state is obtained from the linear strain formula through algebraic transformation.

[0093] In step S530, the corresponding inverse mapping curve is selected according to the state identifier, and the laser intensity required to achieve the first high spin state mole fraction is calculated through the inverse mapping curve.

[0094] Therefore, it overcomes the phase transition hysteresis caused by the inconsistent heating and cooling paths of spin-crossed materials, and avoids oscillation and fatigue drift during repeated actuation.

[0095] Regarding step S530, the corresponding inverse mapping curve is selected based on the status identifier. In some embodiments of this disclosure, it may include, for example... Figure 6 Steps S610 to S620 are shown.

[0096] In step S610, the instantaneous bending curvature of the target is calculated using a first-order difference to determine the rate of change of curvature.

[0097] The time-series data of the instantaneous bending curvature of the target is continuously acquired at a fixed sampling period. The difference between the target curvature value at the current moment and the target curvature value at the previous sampling moment is calculated, and the difference is divided by the sampling period to obtain the first-order difference value of the target curvature, i.e., the rate of change of curvature. The rate of change of curvature is used as a criterion for phase transition state. When it is greater than zero, it is marked as the heating bending activation stage; when it is less than zero, it is marked as the cooling recovery stage; and when it approaches zero, it is marked as the steady-state maintenance stage.

[0098] In step S620, when the rate of change of curvature is greater than the curvature threshold, the state is identified as the first identifier, and the first inverse mapping curve of the heating phase transition process is selected; when the rate of change of curvature is less than the curvature threshold, the state is identified as the second identifier, and the second inverse mapping curve of the cooling phase transition process is selected; when the absolute value of the rate of change of curvature is equal to the curvature threshold, the state is identified as the third identifier, and the third inverse mapping curve is determined by interpolating the first inverse mapping curve and the second inverse mapping curve.

[0099] Therefore, by performing first-order difference calculations on the instantaneous bending curvature of the target, the microrobot can be identified in real time as being in a phase transition stage: heating-up bending activation, cooling-up recovery, or steady-state maintenance. Then, based on a comparison of the rate of curvature change with a threshold, the inverse mapping curve corresponding to the heating or cooling branch is dynamically invoked, and hyperbolic interpolation is used to achieve smooth switching of light intensity at the critical transition state. This effectively overcomes the phase transition hysteresis and light intensity jumps caused by the thermal hysteresis effect of spin-crossing materials, improving control stability and trajectory tracking accuracy during repeated actuation processes.

[0100] In one specific embodiment, the preparation process of the spin-crossing material includes: The first ligand solution was prepared by dissolving 1H-1,2,4-triazole in a mixed solvent of water and methanol.

[0101] A ferrous sulfate aqueous solution was added dropwise to the first ligand solution under stirring to obtain the second ligand solution.

[0102] The second ligand solution was reacted under target conditions to obtain a Fetriazole complex precipitate with a one-dimensional chain polymer structure.

[0103] The Fe triazole complex precipitate was cooled or evaporated under reduced pressure, filtered, washed successively with water and ethanol, and dried to obtain spin-crossed nanorods.

[0104] Spin-crossed nanorods were silanized for surface modification, then coated with MXene nanosheets via electrostatic self-assembly, dispersed in a thermoplastic polyurethane solution, and coated and cured under shear flow field assistance to obtain spin-crossed materials.

[0105] Therefore, by synthesizing Fetriazole complexes with one-dimensional chain polymer structures from 1H-1,2,4-triazole and ferrous sulfate under specific conditions, and then obtaining spin-crossed nanorods after cooling or vacuum evaporation, washing, and drying, the controllable preparation of spin-crossed materials was achieved. Subsequently, the nanorods were silanized and surface-modified, and then coated with MXene nanosheets through electrostatic self-assembly to enhance photothermal conversion efficiency and interfacial adhesion strength. The composite powder was dispersed in a thermoplastic polyurethane solution, coated into a film under shear flow field assistance, and cured to achieve highly oriented alignment of the spin-crossed nanorods, thereby obtaining an active layer material with both large-volume phase transition effect, high photothermal response characteristics, and excellent mechanical properties.

[0106] In one specific embodiment, the preparation process of the spin-crossing material includes: Fe(BF4)2 and ascorbic acid were dissolved in water, and a surfactant and an oil phase solvent were added. The mixture was stirred to form a transparent first microemulsion. 1,2,4-triazole was dissolved in water, and the surfactant and the oil phase solvent were added. The mixture was stirred to form a second microemulsion.

[0107] When the second microemulsion is added dropwise to the first microemulsion, a coordination reaction occurs, generating purple-red nanoparticles.

[0108] After stirring the nanoparticles, ethanol or acetone is added to disrupt the microemulsion, and the precipitate is collected by centrifugation. The precipitate is then washed with ethanol to remove surface oil and excess surfactant, thus obtaining spin-crossed materials.

[0109] Therefore, Fe(BF4)2 and 1,2,4-triazole were encapsulated in water-in-oil microemulsion droplets using a microemulsion method. The microemulsion droplets served as nanoscale reactors for confined synthesis of coordination reactions, yielding uniform, well-dispersed, purplish-red spin-crossed nanoparticles. Demulsification with ethanol or acetone followed by centrifugation effectively removed the oil phase and excess surfactant, resulting in pure spin-crossed materials. Precise control of nanoparticle size was achieved by adjusting the microemulsion droplet size, improving the phase transition uniformity and photothermal response consistency of the material.

[0110] The technical solution of this disclosure will be further explained below with a specific implementation example.

[0111] The microrobot system using the robot control method disclosed herein includes: a microrobot body, an optical modulation module, an environmental perception module, and a central controller.

[0112] The microrobot body adopts an asymmetric double-layer thin film structure, consisting of an active layer and a passive layer.

[0113] Preferably, the active layer is formed by surface electrostatic adsorption of MXene ( The nanosheets are composed of SCO nanorods, which are highly oriented and uniformly embedded in a TPU (thermoplastic polyurethane) elastomer matrix under the action of a shear flow field.

[0114] Preferably, the passive layer is a passive pure TPU film, which is hot-pressed together with the active layer.

[0115] The optical modulation module includes an 808nm near-infrared laser generator and a digital micromirror device (DMD). The laser beam is modulated into a spatial optical field by the DMD before being projected onto the surface of the microrobot.

[0116] The environmental perception module includes a thin-film thermocouple (used to acquire background temperature signals of the microenvironment in which the microrobot is located in real time) and a high frame rate CCD camera (used to acquire pose images of the microrobot's end effector in real time) integrated on the back of the workbench.

[0117] The optical modulation module, thin-film thermocouple, and CCD camera are all electrically connected to the central controller. The central controller receives temperature and image signals, runs the robot control method disclosed herein, and sends laser pulse width modulation (PWM) duty cycle and micromirror deflection commands to the laser generator and digital micromirror device.

[0118] The microrobot system disclosed herein is based on a rigorous thermodynamic and kinematic model, and achieves precise actuation and control through the following methods: Upon system startup, a physical baseline of the microenvironment is first established. Thin-film thermocouples operate at a frequency... Continuously collect ambient temperature signals from the workbench surface The average value during the system initialization phase was taken as the reference ambient baseline temperature. The CCD camera extracts feature points from the microrobot, establishes a two-dimensional workspace coordinate system, and calibrates the initial null-state coordinates of the microrobot's end effector. And lock the input target capture coordinates That is, the target coordinates.

[0119] Based on the target's coordinates, the central controller calculates the initial nominal excitation energy required for the target's driving region by inversely solving the kinematic and thermodynamic models. It then controls the digital micromirror device to shape the 808 nm laser into a spatially distributed spot.

[0120] Preferably, the specific shaping steps are as follows: (1) Extract the two-dimensional geometric contours of the joints or tentacle regions that the microrobot needs to bend; (2) Using an affine transformation matrix, the physical coordinate system of the worktable is mapped to the pixel coordinate system of the micromirror array of the digital micromirror device; (3) Generate a binarized or grayscale light field mask image that matches the driving contour; (4) By loading a mask image, the deflection angle of millions of micromirrors inside the digital micromirror device is controlled (flipped to +12° or -12°) to achieve high spatial resolution light field projection.

[0121] After the light spot is projected onto the active layer, MXene absorbs photons and generates local heat. The transient temperature evolution of the active layer follows an unsteady heat conduction equation: in, These are equivalent density, specific heat capacity, and thermal conductivity, respectively. Instantaneous laser surface radiation intensity modulated for digital micromirror devices; To achieve effective photothermal conversion efficiency; The thickness of the active layer; The convective heat transfer coefficient is... For the local temperature of the material, This represents the current ambient temperature.

[0122] Local temperature Increasing the trigger value of the spin-crossed nanorods causes a phase transition from a low-spin (LS) to a high-spin (HS) state. The central controller incorporates a Slichter-Drickamer model to calculate the mole fraction of the high-spin state. : in, For high-spin mole fraction, and The enthalpy change and entropy change are related to the phase transition. These are parameters representing intermolecular cooperative interactions. Let be the ideal gas constant. Let t be the local temperature of the material, and t be the time variable.

[0123] Dynamic macroscopic linear strain along the long axis of the active layer The calculation is as follows: in, For linear strain, This represents the maximum volume fraction expansion rate.

[0124] Interlaminar strain mismatch drives the bending of a microrobot. Instantaneous bending curvature. Following the modified Timoshenko double-layer plate theory: in, For instantaneous bending curvature, and The Young's modulus and thickness of the active layer and passive layer are respectively.

[0125] Based on the constant curvature assumption, the end effector coordinates The correct kinematic solution is: in, and This indicates the position of the tip of a single driving tentacle in a planar coordinate system. The effective length of a single driving tentacle.

[0126] Given the significant nonlinear thermal hysteresis effect (the heating and cooling paths do not coincide) during the spin-crossed material's spin phase transition, conventional linear control is prone to oscillation and instability. The central controller employs a composite adaptive PID demodulation algorithm based on hysteresis state observation to output the laser generator's pulse width modulation (PWM) duty cycle in real time. : in, The duty cycle of the laser pulse width modulation. This is the nominal light intensity mapping function for thermal hysteresis sensing. For the instantaneous bending curvature of the target, For status indicators, This is a temperature feedforward suppression term. The current ambient temperature. For reference to ambient temperature, This is the temperature feedforward compensation coefficient. , and For closed-loop control gain. This refers to visual pose error (i.e., pose deviation).

[0127] Among them, the nominal light intensity mapping function for thermal hysteresis sensing is not simply preset, but is solved in reverse by solving a series of equations (target curvature). Target strain The target temperature is determined by combining it with an offline calibrated spin-crossed thermal hysteresis loop multidimensional lookup table (LUT). The system identifies whether the material is currently in the heating or cooling phase. The basic light intensity is calculated by dynamically calling different inverse mapping curves.

[0128] When the thin-film thermocouple detects a rise in ambient temperature, the system actively reduces the duty cycle to offset thermal interference. Furthermore, by introducing integral and differential terms, it further eliminates creep caused by material fatigue and extremely small nonlinear steady-state errors.

[0129] Preferably, the system responds to the input target curvature command. Perform real-time first-order difference calculations. When the derivative... When the microrobot is determined to be in the activation phase of tending to bend, the state flag is set to [value]. (Calling the heating phase transition LUT branch); when the derivative At that time, it is determined that the microrobot is in the recovery phase where it tends to stretch, and the status flag is set to [value missing]. (Calling the cooling phase transition LUT branch); when When the state is determined to be in a steady-state maintenance phase, a hysteresis loop intermediate interpolation algorithm is introduced to maintain the output light intensity.

[0130] Preferably, the spin-crossing material of this disclosure achieves a lattice transformation of >8% using a one-dimensional chain-like complex (1D-triazole iron complex), and the specific steps are as follows: Ligand solution preparation: 1H-1,2,4-triazole (Htrz) was dissolved in a mixed solvent of water and methanol, with the concentration controlled at 0.1~0.5 M.

[0131] Metal salt reaction: Fe(BF4)2·6H2O aqueous solution was slowly added dropwise, and the molar ratio of Fe to ligand was strictly controlled at 1:3. The mixture was stirred at room temperature or 50-80°C for 12-24 hours to synthesize Fe triazole complexes with a one-dimensional chain polymer structure.

[0132] Precipitation and purification: The product was precipitated by cooling or vacuum evaporation, filtered, washed with water and ethanol, and dried to obtain SCO nanorod powder.

[0133] Surface modification and composite molding: SCO nanorods were surface-silanized and then self-assembled with MXene nanosheets using electrostatic interactions (ensuring an adhesion shear strength >1 MPa at the TPU interface). The composite powder was dispersed in a TPU solution and coated into a film under a shear flow field to ensure the high orientation of the SCO nanorods. After curing, it was hot-pressed with a TPU passive layer and finally laser-cut into the desired microrobot shapes such as multi-tentacle grippers.

[0134] In one specific embodiment, the spin-crossing material is synthesized using an inverse micelle / microemulsion method, which allows for controllable adjustment of the nanoscale size. The specific steps are as follows: Preparation of microemulsion A: Fe(BF4)2 and a small amount of ascorbic acid were dissolved in a small amount of water, and surfactant and oil phase solvent were added. The mixture was stirred vigorously to form a transparent microemulsion.

[0135] Preparation of microemulsion B: Dissolve 1,2,4-triazole (Htrz) in a small amount of water, add surfactant and oil phase solvent, and stir to form microemulsion.

[0136] Mixing and Reaction: Microemulsion B is slowly added dropwise to microemulsion A. Upon collision, the water droplets fuse, and iron ions and ligands undergo coordination reactions within the droplets, generating purplish-red nanoparticles.

[0137] Demulsification and washing: After stirring for several hours, add excess ethanol or acetone to break up the microemulsion, and collect the precipitate by centrifugation. Finally, wash repeatedly with ethanol to remove surface oil and excess surfactant.

[0138] Based on any of the above embodiments, this disclosure also provides a robot control device.

[0139] Figure 7 This is a schematic block diagram of the structure of a robot control device according to one embodiment of the present disclosure.

[0140] like Figure 7 As shown, the robot control device includes: The energy calculation module 7002, based on the acquired target coordinates, performs inverse calculations on the robot's kinematic and thermodynamic models to determine the excitation energy required for the target driving region. The phase transition calculation module 7004 projects a light spot generated based on excitation energy onto the active layer on the robot surface. It then uses a thermodynamic model to calculate the spin-crossing phase transition that occurs in the active layer and determines the mole fraction of high-spin states. The mole fraction of high-spin states represents the proportion of molecules in high-spin states in the total number of molecules in the spin-crossing material. The strain calculation module 7006 performs volume expansion calculations on the mole fraction of high-spin states to determine the linear strain of the active layer. The curvature calculation module 7008 inputs the linear strain into the structural mechanics model to determine the instantaneous bending curvature of the active layer; The coordinate calculation module 7010 inputs the instantaneous bending curvature into the kinematic model to determine the theoretical coordinates of the end effector. The robot control module 7012 calculates the laser pulse width modulation duty cycle based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates through a feedback control algorithm. The laser pulse width modulation duty cycle is used to adjust the excitation energy to achieve robot control.

[0141] The robot control device described above can be in the form of computer software, and each module of the robot control device can be implemented through computer software modules.

[0142] The specific implementation process of the functions and roles of each module in the above-mentioned robot control device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0143] Therefore, based on any of the above embodiments, this disclosure also provides an electronic device that can execute the robot control method of any of the embodiments described above.

[0144] Figure 8 This is a schematic block diagram of an electronic device 1000 according to one embodiment of the present disclosure.

[0145] The hardware architecture of the electronic device 1000 can be implemented using a bus architecture. The bus architecture can include any number of interconnect buses and bridges, depending on the specific application of the hardware and overall design constraints. Bus 1100 connects various circuits, including one or more processors 1200, memory 1300, and / or hardware modules. Bus 1100 can also connect various other circuits 1400, such as peripheral devices, voltage regulators, power management circuits, external antennas, etc.

[0146] Bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, this diagram uses only one connection line, but this does not imply that there is only one bus or one type of bus.

[0147] According to an embodiment of this application, a robot is also provided, which applies a robot control method according to any of the above embodiments.

[0148] This disclosure also provides a readable storage medium storing a computer program that, when executed by a processor, is used to implement the methods described above. A "readable storage medium" can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples of a readable storage medium include: an electrical connection with one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM), etc.

[0149] This disclosure also provides a computer program product, the methods of which can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, all or part of the processes or functions of this disclosure are performed.

[0150] Computer programs or instructions can be stored in a readable storage medium or transferred from one readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The readable storage medium can be any available medium capable of access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or it can include both volatile and non-volatile types of storage media.

[0151] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0153] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0154] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0155] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., refer to specific features, structures, or characteristics described in connection with that embodiment / mode or example, which are included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0156] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0157] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A robot control method, characterized in that, The active layer on the surface of the robot is composed of a spin-crossing material, and the method includes: Based on the acquired target coordinates, the kinematic and thermodynamic models of the robot are inversely solved to determine the excitation energy required for the target driving region. The light spot generated based on the excitation energy is projected onto the active layer on the surface of the robot. The spin-crossing phase transition that occurs in the active layer is solved by the thermodynamic model to determine the mole fraction of high spin states. The mole fraction of high spin states represents the proportion of molecules in high spin states in the total number of molecules in the spin-crossing material. The volume expansion of the high-spin mole fraction is calculated to determine the linear strain of the active layer; The linear strain is input into the structural mechanics model to determine the instantaneous bending curvature of the active layer; The instantaneous bending curvature is input into the kinematic model to determine the theoretical coordinates of the end effector; and Based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates, the laser pulse width modulation duty cycle is calculated through a feedback control algorithm. The laser pulse width modulation duty cycle is used to adjust the excitation energy to achieve robot control.

2. The robot control method as described in claim 1, characterized in that, Based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates, the laser pulse width modulation duty cycle is calculated using a feedback control algorithm, including: The laser intensity is calculated based on the instantaneous bending curvature of the target and the state identifier, wherein the state identifier is used to represent the phase transition state of the spin-crossing material; Based on the current ambient temperature and the reference ambient temperature, the temperature deviation is calculated, and the ambient temperature compensation is determined in combination with the temperature compensation coefficient. Based on the pose deviation between the theoretical coordinates of the end effector and the target coordinates, as well as compensation for laser intensity and ambient temperature, the laser pulse width modulation duty cycle is determined through a feedback control algorithm.

3. The robot control method as described in claim 2, characterized in that, The laser intensity is calculated based on the instantaneous curvature of the target and its status indicators, including: Based on the target instantaneous bending curvature, the first line strain required to achieve the target instantaneous bending curvature is calculated in reverse using the structural mechanics model. Based on the strain-phase transition relationship of the spin-crossing material, the first linear strain is converted into the mole fraction of the first high-spin state; Select the corresponding inverse mapping curve based on the state identifier, and calculate the laser intensity required to achieve the first high spin state mole fraction using the inverse mapping curve.

4. The robot control method as described in claim 3, characterized in that, Select the corresponding inverse mapping curve based on the status identifier, including: The rate of change of curvature is determined by performing a first-order difference calculation on the instantaneous bending curvature of the target. When the rate of change of curvature is greater than the curvature threshold, the state identifier is the first identifier, and the first inverse mapping curve of the heating phase transition process is selected; when the rate of change of curvature is less than the curvature threshold, the state identifier is the second identifier, and the second inverse mapping curve of the cooling phase transition process is selected; when the absolute value of the rate of change of curvature is equal to the curvature threshold, the state identifier is the third identifier, and the third inverse mapping curve is determined by interpolating the first inverse mapping curve and the second inverse mapping curve.

5. The robot control method as described in claim 1, characterized in that, Based on the acquired target coordinates, the kinematic and thermodynamic models of the robot are inversely solved to determine the excitation energy required for the target driving region, including: Based on the acquired target coordinates and the initial coordinates of the robot end effector, the second instantaneous bending curvature required for the robot end effector to reach the target coordinates is determined by inverse calculation using a kinematic model. The second instantaneous bending curvature is input into the structural mechanics model for inverse calculation to determine the second linear strain required to achieve the second instantaneous bending curvature. Based on the strain-phase transition relationship of the spin-crossing material, the second linear strain is converted into the mole fraction of the second high-spin state; The mole fraction of the second high-spin state is input into the thermodynamic model for inverse calculation to determine the excitation energy required for the target driving region.

6. The robot control method as described in claim 1, characterized in that, Before projecting a light spot generated based on the excitation energy onto the active layer on the robot's surface, and calculating the spin-crossing phase transition occurring in the active layer using the thermodynamic model to determine the mole fraction of high-spin states, the process includes: Obtain the two-dimensional geometric contour coordinates of the joints or tentacle regions where the robot needs to bend, and use an affine transformation matrix to map the global coordinate system of the robot to the target pixel coordinate system; In the target pixel coordinate system, a corresponding target image is generated based on the two-dimensional geometric contour coordinates; Based on the target image, the spatial distribution of the light field is determined, and the laser intensity is determined by the excitation energy, generating a light spot projected onto the active layer of the robot surface.

7. The robot control method as described in claim 1, characterized in that, The preparation process of the spin-crossing material includes: The first ligand solution was prepared by dissolving 1H-1,2,4-triazole in a mixed solvent of water and methanol. A second ligand solution is obtained by adding an aqueous solution of ferrous sulfate dropwise to the first ligand solution under stirring. The second ligand solution was reacted under target conditions to obtain a Fe triazole complex precipitate with a one-dimensional chain polymer structure. The Fe triazole complex precipitate was cooled or evaporated under reduced pressure, filtered, washed with water and ethanol in sequence, and dried to obtain spin-crossed nanorods. The spin-crossed nanorods were silanized and surface-modified, then coated with MXene nanosheets via electrostatic self-assembly and dispersed in a thermoplastic polyurethane solution. The mixture was then coated and cured under the assistance of a shear flow field to obtain the spin-crossed material.

8. A robot, characterized in that, The robot uses any one of the robot control methods described in 1 to 7 above.

9. An electronic device, characterized in that, include: The memory stores execution instructions; as well as A processor that executes the execution instructions stored in the memory, causing the processor to perform the robot control method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the robot control method according to any one of claims 1 to 7.

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