Automated Translational and Rotational Control of Biological Cells With a Robot-Aided Optical Tweezers Manipulation System

Xie, Mingyang; Mills, James K.; Wang, Yong; Mahmoodi, Masih; Sun, Dong

doi:10.1109/tase.2015.2411271

Cited by 55 publications

(15 citation statements)

References 42 publications

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“…where the term T ′ d is introduced to cancel the lumped uncertainties and will be presented later. Substituting (11) into (10) yields̈=…”

Section: Ude-based Feedback Linearized Controllermentioning

confidence: 99%

“…A few studies have been performed for the achievement of automated cell reorientation control. Xie et al have developed a simple PD control framework for cell in‐plane and out‐of‐plane rotational control utilizing dual optical tweezers, among which optical flow field and projection approach were employed to extract cell orientation angle, respectively. Furthermore, a PD control strategy was proposed in the work of Ta and Cheah for the realization of cell orientation control within the microscope image plane using multiple optical traps‐driven fingertips.…”

Section: Introductionmentioning

confidence: 99%

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Robust orientation control of multi‐DOF cell based on uncertainty and disturbance estimation

Xie

Shakoor

et al. 2019

Intl J Robust & Nonlinear

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Summary Multiple degrees‐of‐freedom (multi‐DOF) cell orientation control is a vital important technique involved in single cell surgery applications. Currently, few studies have been performed toward automation of multi‐DOF cell orientation control using robotically controlled optical tweezers. In this paper, a robust control framework is developed to perform multi‐DOF cell rotational control with consideration of model uncertainties and external disturbances. Both simulation and experimental studies are presented to illustrate the performance of the proposed control strategy. The main contributions of this work lie in that this is the first time to develop a unified framework to achieve multi‐DOF cell orientation control without the need for accurate dynamic model parameters and/or any knowledge about uncertainty characteristic, which greatly enhances the robustness of the overall system.

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“…where the term T ′ d is introduced to cancel the lumped uncertainties and will be presented later. Substituting (11) into (10) yields̈=…”

Section: Ude-based Feedback Linearized Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust orientation control of multi‐DOF cell based on uncertainty and disturbance estimation

Xie

Shakoor

et al. 2019

Intl J Robust & Nonlinear

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“…For path or trajectory planning, representative examples include a partially observable Markov decision process algorithm for single object [8] and multiple object [9] transport, a rapidly-exploring random tree method for cell transport [10], and an A* algorithm for indirect manipulation of cells using optically-trapped microspheres [11]. With respect to motion control, recent representative works include a vision-based observer method for multiple cell transport [12], a proportional-derivative (PD) control strategy for single cell transport [13], a saturation controller for swarming motions of cells [14], a combined translational and rotational controller for cells [15], and a motorized stage-optical tweezers cooperative controller for multi-cell manipulation [16]. Other representative methods include a disturbance compensation controller for in vivo cell manipulation [17], a potential field controller for multi-stage cell transport [18], a neural network controller for object manipulation in the presence of unknown optical trapping stiffness [19], a model predictive controller for microsphere pattern formation [20,21], and independent actuation of fifty microrobots using optically generated thermal gradients [22].…”

Section: Introductionmentioning

confidence: 99%

An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments

et al. 2017

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Automated optical tweezers-based robotic manipulation of microscale objects requires real-time visual perception for estimating the states, i.e., positions and orientations, of the objects. Such visual perception is particularly challenging in heterogeneous environments comprising mixtures of biological and colloidal objects, such as cells and microspheres, when the popular imaging modality of low contrast bright field microscopy is used. In this paper, we present an accurate method to address this challenge. Our method combines many well-established image processing techniques such as blob detection, histogram equalization, erosion, and dilation with a convolutional neural network in a novel manner. We demonstrate the effectiveness of our processing pipeline in perceiving objects of both regular and irregular shapes in heterogeneous microenvironments of varying compositions. The neural network, in particular, helps in distinguishing the individual microspheres present in dense clusters.

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“…Sci. 2019, 9, 2883 2 of 15 focusing the laser on the cell [20][21][22], in which laser beams act as special end-effectors. Using the direct technique, a large number of cells can be manipulated individually or simultaneously in a series or parallel manner with high precision and high efficiency [23,24].…”

mentioning

confidence: 99%

Automated Indirect Transportation of Biological Cells with Optical Tweezers and a 3D Printed Microtool

Xie

Wei

et al. 2019

Applied Sciences

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Optical tweezers are widely used for noninvasive and precise micromanipulation of living cells to understand biological processes. By focusing laser beams on cells, direct cell manipulation with optical tweezers can achieve high precision and flexibility. However, direct exposure to the laser beam can lead to negative effects on the cells. These phenomena are also known as photobleaching and photodamage. In this study, we proposed a new indirect cell micromanipulation approach combined with a robot-aided holographic optical tweezer system and 3D nano-printed microtool. The microtool was designed with a V-shaped head and an optical handle part. The V-shaped head can push and trap different sizes of cells as the microtool moves forward by optical trapping of the handle part. In this way, cell exposure to the laser beam can be effectively reduced. The microtool was fabricated with a laser direct writing system by two-photon photopolymerization. A control strategy combined with an imaging processing algorithm was introduced for automated manipulation of the microtool and cells. Experiments were performed to verify the effectiveness of our approach. First, automated microtool transportation and rotation were demonstrated with high precision. Second, indirect optical transportations of cells, with and without an obstacle, were performed to demonstrate the effectiveness of the proposed approach. Third, experiments of fluorescent cell manipulation were performed to confirm that, indicated by the photobleaching effect, indirect manipulation with the microtool could induce less laser exposure compared with direct optical manipulation. The proposed method could be useful in complex biomedical applications where precise cell manipulation and less laser exposure are required.

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Automated Translational and Rotational Control of Biological Cells With a Robot-Aided Optical Tweezers Manipulation System

Cited by 55 publications

References 42 publications

Robust orientation control of multi‐DOF cell based on uncertainty and disturbance estimation

Robust orientation control of multi‐DOF cell based on uncertainty and disturbance estimation

An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments

Automated Indirect Transportation of Biological Cells with Optical Tweezers and a 3D Printed Microtool

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