Planar Motion Measurement of a Compliant Micro Stage: An Enhanced Microscopic Vision Approach

Su, Gaozhao; Lu, Guoliang; Yan, Peng

doi:10.1109/tim.2019.2924066

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…(b) An example of a feature-based method for tracking a microrobot under an electron scanning microscope [52]. (c) An example of using an enhanced microscopic vision-based approach to support high-precision measurement on planar motions of a micro stage [46]. (d) A CNN-based method for depth and 3D orientation estimation for optical microrobots [53].…”

Section: Traditional Methodsmentioning

confidence: 99%

“…To build intelligent micro-scale robotic systems, vision techniques for tracking micro-objects should be explored. Visionbased tracking normally includes feature-based methods [43], template matching-based methods [44], statistical shape models, the phase correlation-based methods [45], and other multi-modal image processing methods [46]. Among all these traditional approaches, template matching is a simple technique that is commonly used.…”

Section: Traditional Methodsmentioning

confidence: 99%

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Fabrication and optical manipulation of micro-robots for biomedical applications

Zhang

Ren

Barbot

et al. 2022

Matter

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Section: Traditional Methodsmentioning

confidence: 99%

Section: Traditional Methodsmentioning

confidence: 99%

Fabrication and optical manipulation of micro-robots for biomedical applications

Zhang

Ren

Barbot

et al. 2022

Matter

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“…Microscopic vision has been widely employed in medical field, industry, and academia [1,2]. Accurate centroid positioning of micropart is a key issue for automated assembly [3].…”

Section: Introductionmentioning

confidence: 99%

A de-texturing model for enhancing the accuracy of centroid positioning

Wang,

Xu,

Wang

et al. 2024

Meas. Sci. Technol.

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In tasks guided by microvision, extracting edges and centroids is a common method for positioning, which is negatively affected by texture. Here, an attention-related de-texturing model is proposed to eliminate the texture of microparts and preserve accurate edges. A network with an attention module called De-texturing Net is built, in which both the transformer and channel attention modules are included. Considering the importance of texture, the additional factor in loss function is constructed based on the Gram matrix difference between target images and generated images. Results show that De-texturing Net can generate de-texturized images with high PSNR/SSIM, indicating the similarity between de-texturized and target images. Moreover, for the centroid positioning, the error in de-texturized images is significantly lower than the error in original images. This study helps improve the accuracy of centroid positioning due to the de-texturized images with accurate edges.

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“…Moreover, the cascaded structure results in large inertia for the moving stage, which reduces the natural frequency, and hence the maximum attainable speed of the stage. Another attempt was made to decouple the x and y motions in [17] using a decoupling mechanism. However, these motions were still coupled with the θ motion, and the use of the decoupling mechanism resulted in limited rotational motion.…”

Section: Introductionmentioning

confidence: 99%

Antlion Optimized Robust Control Approach for Micropositioning Trajectory Tracking Tasks

et al. 2020

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Planar 3-Degree of Freedom (3-DOF) micropositioning stages are widely adopted in many precision applications for their ability to provide in-plane rotation. However, their motion accuracy is adversely affected by cross-couplings, model uncertainty, and external disturbances. This work proposes an optimized robust control methodology based on disturbance estimation to address these issues. Systematic modeling of a 3-DOF precision micropositioning stage was utilized to develop a nonlinear disturbance observer-based sliding mode control methodology. This methodology can estimate and compensate the unavoidable cross-couplings between the major axes of motion. A stability analysis is conducted to prove the stability of the feedback system when the control approach is combined with the disturbance observer. A maximum bound on the tracking error is derived through finite-time analysis, and the parameters that affect this bound are identified. The antlion optimizer algorithm is used to optimize the control parameter based on the mean square error cost function to eliminate the requirement for manual tuning and to achieve the best attainable performance. The proposed control method is experimentally demonstrated to track complex trajectory with less tracking error than the classical sliding mode approach. The main contribution of this study is the improvement of the trajectory tracking accuracy in multi-DOF micropositioners using an optimized disturbance observer-based robust control technique.

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Planar Motion Measurement of a Compliant Micro Stage: An Enhanced Microscopic Vision Approach

Cited by 11 publications

References 33 publications

Fabrication and optical manipulation of micro-robots for biomedical applications

Fabrication and optical manipulation of micro-robots for biomedical applications

A de-texturing model for enhancing the accuracy of centroid positioning

Antlion Optimized Robust Control Approach for Micropositioning Trajectory Tracking Tasks

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