WO2023142215A1 - Procédé de prise automatique de nanofils par un robot d'opération micro-nano sur la base de primitives de mouvement dynamique - Google Patents

Procédé de prise automatique de nanofils par un robot d'opération micro-nano sur la base de primitives de mouvement dynamique Download PDF

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WO2023142215A1
WO2023142215A1 PCT/CN2022/078316 CN2022078316W WO2023142215A1 WO 2023142215 A1 WO2023142215 A1 WO 2023142215A1 CN 2022078316 W CN2022078316 W CN 2022078316W WO 2023142215 A1 WO2023142215 A1 WO 2023142215A1
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meta
task
micro
motion
trajectory
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Chinese (zh)
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杨湛
韩驰瑞
巢沛栋
房梁
张略
孙立宁
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苏州大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0014—Image feed-back for automatic industrial control, e.g. robot with camera
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G06T5/30—Erosion or dilatation, e.g. thinning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/215—Motion-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G06T2207/10061—Microscopic image from scanning electron microscope
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to the technical field of robot automation, in particular to a method for automatically picking up nanowires by a micro-nano operating robot based on dynamic motion primitives.
  • SEM Scanning Electron Microscope
  • These robots are assembled in the cavity of the SEM, and their overall dimensions are very small, usually on the order of a few centimeters.
  • the operating objects of the micro-nano manipulator are usually at the micron and nanometer level. In order to ensure normal operation, the motion accuracy needs to be guaranteed at the nanometer level.
  • the contact between the probe tip and the substrate can be detected, and the relative vertical position of the probe tip and the nano-object to be manipulated can also be determined, which can be based on the SEM image.
  • the design scheme of macro-robot automation mainly adopts the trajectory-based demonstration learning method, which generally needs to segment the trajectory to improve the learning efficiency of the trajectory.
  • the automatic segmentation trajectory is mainly divided into supervised segmentation and unsupervised methods.
  • the supervised segmentation method It is a segmentation technology based on shape matching, contacting the motion trajectory model trained in advance, and segmenting the teaching trajectory through the geometric shape and other features between the trajectory; one of the segmentation demonstration trajectory in the unsupervised segmentation method is to divide the robot end effector
  • the contact keyframe with the operating object is used as the split point to complete the split of the track.
  • the above-mentioned general demonstration motion trajectory segmentation methods are basically used in the field of macro-robots.
  • the technical problem to be solved by the present invention is to overcome the problems existing in the prior art, and to propose a method for automatically picking up nanowires by a micro-nano manipulating robot based on dynamic motion primitives.
  • the present invention provides a method for automatically picking up nanowires by a micro-nano operating robot based on dynamic motion primitives, including the following steps:
  • S30 Encode the meta-task and establish a meta-task library.
  • the micro-nano operation robot calls the meta-task in the meta-task library, moves according to the trajectory of the meta-task, and completes Nanowire automatic pickup tasks.
  • obtaining the demo track of the micro-nano manipulator includes:
  • a low-pass Gaussian filter is selected to filter each frame of image, and the filtered image is etched and binarized successively to obtain the outlines of the cantilever beam and the nanowire in the image.
  • using the evaluation function to judge whether there is depth motion information on the demo trajectory of the micro-nano manipulator includes:
  • the Tenengrad gradient function is used as the evaluation function, and the Tenengrad gradient function is used to calculate the sharpness changes of the cantilever beam when it is stationary and in depth motion, and the polynomial regression model is used for characterization, and the corresponding slope is obtained to judge whether the cantilever beam is in depth. Motion or stillness.
  • the demonstration trajectory of the micro-nano manipulator robot is divided into a plurality of simple meta-tasks, wherein the division criterion of the meta-tasks includes:
  • Segmenting a segment of the track for deep motion on the demo track defining the motion as an ascending element task, before performing the ascending element task, moving the cantilever beam directly below the nanowire, and defining the motion as a positioning element task;
  • encoding the meta-task includes:
  • the trajectory of the meta-task is filtered, and Platts dynamic time warping is used to perform trajectory alignment processing on the trajectory of the filtered meta-task, and multiple trajectories are normalized to the same time step;
  • a Gaussian mixture model is used to characterize the motion characteristics of the meta-task after trajectory alignment processing, and a mixture of Gaussian regression generalization is used to generate the meta-task demonstration trajectory;
  • Meta-task demonstration trajectories are encoded using dynamic motion primitives.
  • the meta-task includes a divided meta-task and an introduced empty walk demonstration meta-task.
  • the formula of the Platts dynamic time warping is as follows:
  • the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor implements the steps of the above-mentioned method when executing the program.
  • the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above-mentioned method are realized.
  • the present invention proposes a method for automatically picking up nanowires by a micro-nano operating robot based on dynamic motion primitives. Aiming at the operating environment and motion characteristics of the micro-nano operating robot, it establishes a criterion for dividing element tasks, and divides complex operating trajectories into multiple A simple meta-task trajectory greatly reduces the learning difficulty of micro-nano manipulator robots, and is suitable for robot operations in micro-environment.
  • Fig. 1 is a schematic flowchart of the method for automatically picking up nanowires by a micro-nano manipulating robot based on dynamic motion primitives in the present invention.
  • Fig. 2 is the sharpness change and polynomial regression characterization results of the AFM cantilever beam of the present invention in different states, wherein Fig. 2 (a) is the sharpness change and polynomial regression characterization results of the AFM cantilever beam at rest; Fig. 2 (b) is the AFM Characterization results of sharpness change and polynomial regression during the depth motion of the cantilever beam.
  • Fig. 3 is three contact states of the nanowire of the present invention and the AFM cantilever beam.
  • Fig. 4 is a motion track diagram of the demonstration end effector of the micro-nano manipulation robot picking up nanowires of the present invention.
  • Fig. 5 is a schematic diagram of the meta-tasks of the depth motion divided by the present invention.
  • Fig. 6 is a schematic diagram illustrating the division of a complete nanowire pick-up task according to the present invention.
  • Figure 7(a) is the original trajectory diagram of the positioning meta-task
  • Figure 7(b) is the original trajectory diagram of the positioning meta-task after filtering.
  • Fig. 8 is an effect diagram of aligning multiple positioning unit task trajectories in the present invention, the left side is before alignment, and the right side is after alignment.
  • Fig. 9 is the meta-task demonstration trajectory after learning in the present invention, the upper half of which is GMM characterizing the motion characteristics of positioning meta-task, and the lower half is the most stable trajectory graph generated by GMR generalization.
  • Fig. 10 is a trajectory diagram of the DMP recurring positioning meta-task demonstration in the present invention.
  • present embodiment provides a kind of micro-nano operation robot based on dynamic motion primitive to pick up nanowire method automatically, comprises the following steps:
  • S30 Encode the meta-task and establish a meta-task library.
  • the micro-nano operation robot calls the meta-task in the meta-task library, moves according to the trajectory of the meta-task, and completes Nanowire automatic pickup tasks.
  • the micro-nano operation robot is built in the SEM, and the end effector is an atomic force microscope (Atom Force Microscope). Microscope, AFM) probe, but what can be seen in the SEM image is the cantilever beam of the AFM probe.
  • the operator manipulates the control handle and the controller to control the movement of the micro-nano manipulating robot.
  • the cantilever beam of the AFM probe can be recognized through the image, and the trajectory of the cantilever beam of the probe can be calculated to obtain the demonstration trajectory of the end effector.
  • the main processing process is: obtain the motion video of the operation demonstration, intercept multiple frames of images on the motion video, filter each frame of image with a low-pass Gaussian filter, and use the morphological operation on the filtered image to convert the finer nano
  • the wires are etched away so that the probes can also be identified when they overlap the nanowires.
  • the image was then binarized using automatic histogram thresholding, enabling the profile of the probe cantilever and nanowires to be captured in the image. Due to the disorder of the nanowires, its profile changes greatly, but the profile of the probe cantilever does not change significantly, so the cantilever of the probe can be identified by polygonal approximation.
  • the shape of the cantilever is very similar to an isosceles trapezoid, so choose the X coordinate of the short side of the cantilever as the X coordinate of the cantilever, and the Y coordinate of the centroid of the profile as the Y coordinate of the cantilever.
  • the method adopted in the present invention is to analyze the Z-axis motion of the end effector AFM cantilever by judging the change of the definition of the cantilever beam.
  • the definition change of the AFM cantilever beam area is calculated, so the present invention expands according to the matched AFM tip area, and selects a fixed size area to calculate the definition change of the AFM cantilever beam, that is, the present invention uses the Tenengrad gradient function as the evaluation Clarity evaluation function.
  • the Tenengrad gradient function uses the Sobel operator to extract the gradient value of the horizontal and vertical directions of the image pixel, which is defined as the sum of the squares of the gradient of the pixel, and sets the sensitivity of a threshold adjustment function for the gradient.
  • the expression is:
  • T is a given edge detection threshold
  • G(x, y) is the gradient at the pixel point (x, y)
  • Gx and Gy are the convolution of Soble horizontal and vertical edge detection operators at the pixel point respectively.
  • the SEM image is an image containing two-dimensional information, and the depth information of the Z axis cannot be directly obtained. And researchers can't judge the relative information of the Z axis when manipulating the micro-nano robot, so the plane operation and the depth operation are separated, which is a good segmentation point, and the segmented segments can also be easily given Reasonable semantic interpretation.
  • the depth motion is segmented first. The plane movement occurs on the XY plane, and the XY trajectory changes drastically at this time, while the depth movement occurs on the Z axis.
  • the AFM cantilever beam moves in the Z axis, the distance between its upper surface and the SEM objective lens will change, resulting in a change in the definition of the cantilever beam in the SEM image. Therefore, by evaluating the clarity through the above-mentioned Tenengrad gradient function, it is possible to calculate the changes in the clarity of the AFM cantilever when it is stationary and in deep motion, and use polynomial regression to characterize the two changes, such as As shown in Fig. 2, for example, the
  • the present invention divides a segment of the track for deep motion on the demonstration track, and defines the motion as an ascending meta-task.
  • the cantilever beam is moved directly below the nanowire , define this movement as a positioning element task; after controlling the cantilever beam to rise, what you can see from the image is the top view of the nanowire and the cantilever beam, and there is an overlapping state between them, but their positional relationship in the Z-axis direction we If it cannot be known, it is impossible to accurately judge whether it is in contact with the nanowire.
  • the present invention increases the short-distance swing motion of the cantilever beam in the Y direction, and defines this motion as a contact judgment meta-task (a priori meta-task).
  • the contact judgment meta-task judges the contact state between the cantilever beam and the nanowire. After the contact judgment, the contact state between the nanowire and the cantilever beam can be obtained, which is divided into three contact states: line contact, point contact a, point contact Contact b, as shown in Figure 3.
  • the present invention defines these two contact repairs as two priori meta-tasks according to the difference between the two types of line contacts, which are respectively contact repair meta-task a and contact repair unit Task b.
  • contact repair is an action that occurs between two contact judgments.
  • the present invention can further divide these two meta-tasks, so as to obtain multiple relatively simple meta-tasks, which greatly reduces the difficulty for robots to learn these meta-tasks.
  • the present invention encodes the trajectories of these meta-tasks, so that the micro-nano Manipulating the robot learns and reproduces the motion of these meta-tasks.
  • the divided meta-tasks may not be representative enough, so the present invention also introduces the empty-walk demonstration meta-task of the micro-nano manipulator to increase the teaching ability of the meta-task demonstration.
  • GMM Gaussian Mix Model
  • BIC Bayesian Information Criterion
  • GMR Gaussian Mix Regression
  • the dynamic motion primitive transforms an easy-to-understand simple attractor system into a desired attractor system using a learnable forcing function term, which can work for point attractors and limit cycle attractors of almost any complexity, Has good coordination and stability.
  • DMP is used to encode the optimal meta-task trajectory, and a meta-task library is established.
  • the subsequent automatic execution task is that the micro-nano operation robot calls the meta-task DMP corresponding to the meta-task library, which is generated according to the DMP plan trajectory for movement.
  • One of the one-dimensional DMP equations is:
  • the first half of the first formula is the derivation equation of the spring damping model.
  • f is the interference item.
  • the third formula is the canonical system, x will decay to 0 before the end of the motion, so as not to affect the position of the target, so DMP can reproduce the generalized GMR very accurately track.
  • the present invention establishes a criterion for dividing meta-tasks, and divides complex operation trajectories into a plurality of simple meta-task trajectories, which greatly reduces the learning difficulty of micro-nano manipulating robots, and is applicable to micro-manipulators. environment for robot operation.
  • the present invention uses the sharpness evaluation function to calculate the change of the sharpness of the end effector of the micro-nano manipulator robot during the deep motion, to analyze the movement of the micro-nano manipulator robot during the deep motion, and can distinguish between motion and static states.
  • the present invention clusters different trajectories of the same divided meta-task, and performs code learning for each type of trajectories, and uses different meta-task trajectories in different scenarios to better complete the operation task.
  • the present invention uses the segmented simple meta-task trajectory as teaching, obtains the most stable teaching trajectory through a series of optimization processes, and then uses DMP to encode and learn this trajectory, greatly reducing The error between the trajectory generated by the DMP planning and the demonstration trajectory is minimized.
  • the operator manually controls the handle to complete the task of picking up the nanowires, and obtains a demonstration motion video. For each frame of image intercepted from the motion video, there are many noise points. After comparison and verification, the low-pass Gaussian filter is used for processing, and then a circular nucleus with a size of 1 is selected to corrode the nanowires in the SEM image. Then calculate the pixel values of all pixels in the image. Since the background accounts for most of the area in the image, the pixel value with the largest ratio is counted as the threshold, and the binary segmentation is performed, and the AFM cantilever beam and the nanowire substrate are identified.
  • the contour of the AFM cantilever can be identified by the difference between the contours, and the position of the AFM cantilever can be calculated.
  • the continuous motion track of the XY plane of the micro-nano manipulator robot can be obtained, as shown in Figure 4.
  • the demonstration trajectory of the operator manually picking up the nanowire is divided: firstly, it is divided according to whether the depth movement is performed, and it can be found that the XY axis trajectory during the period from 32s to 252s is basically unchanged , and the jitter does not exceed 15 pixels, and the Teneggrad function is used to calculate the sharpness change during this period, and then the polynomial regression model is used to characterize it, and the slope is 0.036, which is determined to be a deep movement during this period.
  • This section is segmented out and named as rising meta-task according to its motion characteristics, as shown in Figure 5.
  • the present invention names this movement as the positioning meta-task, as shown in FIG. 6 .
  • the positioning meta-task After the ascent meta task, it is necessary to judge the contact state of the nanowire and the AFM cantilever beam.
  • the secondary electron image collected from the SEM shows the top view of the nanowire and the AFM cantilever beam. There is an overlapping state between them, but we cannot know their positional relationship in the Z-axis direction. Therefore, the short-distance swing motion of the cantilever beam in the Y direction can be increased.
  • the present invention defines the motion as a contact judgment meta-task. It is a back-and-forth motion, and the trajectory is also special. Using the particularity of the contact judgment trajectory, it is divided from the demonstration trajectory. As shown in Figure 6, a total of three contact judgment meta-tasks are divided. Between the two contact judgment meta-tasks is the contact repair meta-task, which is divided according to this temporal feature, as shown in Figure 6. The result of the last contact judgment must be determined as wire contact. At this time, it is necessary to control the AFM cantilever beam to pull the nanowire from the substrate.
  • This movement is defined as the separation element task in the present invention, as shown in FIG. 6 .
  • the present invention divides a complete nanowire picking task demonstration into six simple meta-tasks, so that learning these six meta-tasks for the micro-nano manipulation robot is definitely much easier than learning a complex complete demonstration task.
  • the embodiment of the present invention also provides a computer device, including:
  • a processor which is used to implement the steps of the above-mentioned method for automatically picking up nanowires by a micro-nano manipulating robot based on dynamic motion primitives when executing a computer program.
  • the processor may be a central processing unit (Central Processing Unit, CPU), an application-specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices.
  • CPU Central Processing Unit
  • application-specific integrated circuit e.g., an application-specific integrated circuit
  • digital signal processor e.g., a field programmable gate array
  • field programmable gate array e.g., a field programmable gate array
  • the processor can call the program stored in the memory. Specifically, the processor can execute the operations in the embodiment of the method for automatically picking up nanowires by a micro-nano manipulation robot based on dynamic motion primitives.
  • the memory is used to store one or more programs, the programs may include program codes, and the program codes include computer operation instructions.
  • the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device or other volatile solid-state storage devices.
  • the embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned dynamic motion primitive-based micro Steps in the method for automatically picking up nanowires by a nanomanipulator robot.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may 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.) having computer-usable program code embodied therein.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

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Abstract

La présente invention concerne un procédé de prise automatique de nanofils par un robot d'opération micro-nano sur la base de primitives de mouvement dynamique. Le procédé consiste : à acquérir une trajectoire de démonstration d'un robot d'opération micro-nano ; à déterminer s'il existe des informations de mouvement en profondeur sur la trajectoire de démonstration du robot d'opération micro-nano, si le résultat de détermination est " oui ", la trajectoire étant un mouvement en profondeur, et si le résultat de détermination est " non ", la trajectoire étant un mouvement dans le plan, et à diviser la trajectoire de démonstration du robot d'opération micro-nano en une pluralité de méta-tâches simples, un critère de division pour les méta-tâches étant qu'une division est effectuée selon que le mouvement en profondeur est effectué ou non ; et à coder les méta-tâches et établir une bibliothèque de méta-tâches, et le robot d'opération micro-nano appelant les méta-tâches dans la bibliothèque de méta-tâches et se déplaçant selon des trajectoires des méta-tâches. Selon la présente invention, en ce qui concerne l'environnement de fonctionnement et les caractéristiques de mouvement d'un robot d'opération micro-nano, une trajectoire d'opération complexe est divisée en une pluralité de trajectoires de méta-tâche simples, ce qui permet de réduire considérablement la difficulté d'apprentissage pour le robot d'opération micro-nano. Le procédé est applicable à une opération de robot dans un micro-environnement.
PCT/CN2022/078316 2022-01-27 2022-02-28 Procédé de prise automatique de nanofils par un robot d'opération micro-nano sur la base de primitives de mouvement dynamique WO2023142215A1 (fr)

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