Toward automated formation of microsphere arrangements using multiplexed optical tweezers

Rajasekaran, Keshav; Bollavaram, Manasa; Banerjee, Ashis G.

doi:10.1117/12.2239084

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The dynamics of this system is stochastic due to the Langevin force that gives rise to Brownian motion-based diffusion. Rajasekaran et al [48] describe the details of the dynamics model, which also includes optical forces, viscous drag, and buoyancy, using a tensor stochastic differential equation. We directly use the OTGO toolbox [11] to generate look-up tables of the optical trapping forces F o .…”

Section: Stochastic Double Inverted Pendulum On a Cartmentioning

confidence: 99%

A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation

Parsa¹,

Rajasekaran²,

Meier³

et al. 2018

Preprint

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One of the challenges with model-based control of stochastic dynamical systems is that the state transition dynamics are involved, making it difficult and inefficient to make good-quality predictions of the states. Moreover, there are not many representational models for the majority of autonomous systems, as it is not easy to build a compact model that captures all the subtleties and uncertainties in the system dynamics. In this work, we present a hierarchical Bayesian linear regression model with local features to learn the dynamics of such systems. The model is hierarchical since we consider non-stationary priors for the model parameters which increases its flexibility. To solve the maximum likelihood (ML) estimation problem for this hierarchical model, we use the variational expectation maximization (EM) algorithm, and enhance the procedure by introducing hidden target variables. The algorithm is guaranteed to converge to the optimal log-likelihood values under certain reasonable assumptions. It also yields parsimonious model structures, and consistently provides fast and accurate predictions for all our examples, including two illustrative systems and a challenging micro-robotic system, involving large training and test sets. These results demonstrate the effectiveness of the method in approximating stochastic dynamics, which make it suitable for future use in a paradigm, such as model-based reinforcement learning, to compute optimal control policies in real time.

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Section: Stochastic Double Inverted Pendulum On a Cartmentioning

confidence: 99%

A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation

Parsa¹,

Rajasekaran²,

Meier³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…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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A Step Toward Learning to Control Tens of Optically Actuated Microrobots in Three Dimensions

Banerjee

Rajasekaran

Parsa

2018

2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)

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Toward automated formation of microsphere arrangements using multiplexed optical tweezers

Cited by 4 publications

References 17 publications

A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation

A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation

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

A Step Toward Learning to Control Tens of Optically Actuated Microrobots in Three Dimensions

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