Self-optimizing Visual Servoing Control for Microassembly Robotic Depth Motion

Wang, Min; Lv, Xiadong; Huang, Xinhan

doi:10.1109/icia.2007.4295781

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…It incorporates motion control and vision in micromanipulation under a microscope. This can provide dexterous and visual support during cell manipulation the way it has contributed in the field of microassembly and fabrication [1,2]. In the field of cell manipulation, a user-operated robot micromanipulator, coupled with microscopic vision, means that operators can move and see at microscale in an easier and more effective manner.…”

Section: Introductionmentioning

confidence: 99%

Detect-Focus-Track-Servo (DFTS): A vision-based workflow algorithm for robotic image-guided micromanipulation

Yang

Tan

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Robotic image-guided micromanipulation contributes towards the ease of operation, speed, accuracy, and repeatability in cell manipulation. However, such technology is not fully exploited because of the challenges in the integration of robotic modules with existing microscope systems, and the difficulty in incorporating robot assistance seamlessly into the workflow. In this paper, we propose a vision-based workflow algorithm termed Detect-Focus-Track-Servo (DFTS). It facilitates easy integration of robotic modules. It also supports user interactions while minimizing the need for manual intervention and disruption to workflow through automatic detection, focusing, tracking and servoing. Experimental results suggest satisfactory detection accuracy of 99.0 % at 70 µm tolerance. The robustness test suggests no difference in the accuracy under blurred and cluttered images. The self-focus algorithm is also demonstrated to bring the tip into focus consistently. The track-servo algorithm achieves low sub-pixel uncertainty. By proposing the DFTS workflow algorithm, we hope that the level of autonomy and ease of deployment in robot and vision modules for micromanipulation can be improved so as to open up new possibilities in the development of robotic image-guided cell manipulation.

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Section: Introductionmentioning

confidence: 99%

Detect-Focus-Track-Servo (DFTS): A vision-based workflow algorithm for robotic image-guided micromanipulation

Yang

Tan

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…T HE importance of robotic micromanipulation system is well evidenced in its contribution toward the advancement of micromanipulation technology. Apart from more common industrial applications in microassembly and fabrication [1]- [3], the field of cell manipulation also benefits from the development of robotic micromanipulation system [4], [5]. Robotic micromanipulation benefited cell manipulation applications with speed, repeatability, and ease of the operation.…”

Section: Introductionmentioning

confidence: 99%

Confidence-Based Hybrid Tracking to Overcome Visual Tracking Failures in Calibration-Less Vision-Guided Micromanipulation

Yang

Paranawithana

Tan

2020

IEEE Trans. Automat. Sci. Eng.

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This article proposes a confidence-based approach for combining two visual tracking techniques to minimize the influence of unforeseen visual tracking failures to achieve uninterrupted vision-based control. Despite research efforts in vision-guided micromanipulation, existing systems are not designed to overcome visual tracking failures, such as inconsistent illumination condition, regional occlusion, unknown structures, and nonhomogenous background scene. There remains a gap in expanding current procedures beyond the laboratory environment for practical deployment of vision-guided micromanipulation system. A hybrid tracking method, which combines motion-cue feature detection and score-based template matching, is incorporated in an uncalibrated vision-guided workflow capable of self-initializing and recovery during the micromanipulation. Weighted average, based on the respective confidence indices of the motion-cue feature localization and template-based trackers, is inferred from the statistical accuracy of feature locations and the similarity score-based template matches. Results suggest improvement of the tracking performance using hybrid tracking under the conditions. The mean errors of hybrid tracking are maintained at subpixel level under adverse experimental conditions while the original template matching approach has mean errors of 1.53, 1.73, and 2.08 pixels. The method is also demonstrated to be robust in the nonhomogeneous scene with an array of plant cells. By proposing a self-contained fusion method that overcomes unforeseen visual tracking failures using pure vision approach, we demonstrated the robustness in our developed low-cost micromanipulation platform.

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Defocus-Based Direct Visual Servoing

Caron

2021

IEEE Robot. Autom. Lett.

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Direct Visual Servoing (DVS) considers pixel brightness directly as input of robot control. Recent DVS variants consider image processing as smoothing or frequency domain transforms, resulting in large convergence domains.This paper proposes to consider defocus to optically smooth images without processing. The resulting Defocus-based DVS shows convergence domains competing with the state-of-theart, larger in some challenging cases, for lower complexity.

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Self-optimizing Visual Servoing Control for Microassembly Robotic Depth Motion

Cited by 3 publications

References 7 publications

Detect-Focus-Track-Servo (DFTS): A vision-based workflow algorithm for robotic image-guided micromanipulation

Detect-Focus-Track-Servo (DFTS): A vision-based workflow algorithm for robotic image-guided micromanipulation

Confidence-Based Hybrid Tracking to Overcome Visual Tracking Failures in Calibration-Less Vision-Guided Micromanipulation

Defocus-Based Direct Visual Servoing

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