Motion control of magnetized <i>Tetrahymena pyriformis</i> cells by a magnetic field with Model Predictive Control

Ou, Yan; Kim, Dal Hyung; Kim, Paul; Kim, Min Jun; Julius, A. Agung

doi:10.1177/0278364912464669

Cited by 34 publications

(19 citation statements)

References 29 publications

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“…In this context, sophisticated and advanced control algorithms, based especially on a model-based predictive control, have been demonstrated to enable very accurate autonomous navigation of magnetic microrobots [47]- [49]. Ou et al, for example, developed a predictive control of magnetized Tetrahymena pyriformis for changing the orientation of the cell, whereas the speed of the cell remained constant regardless of the strength of the external magnetic field [48]. Making a comparison with more classic PID feedback control, although the model predictive control algorithms bring advantages in terms of time delays, accuracy, and high-order dynamics, they need a long time to solve optimization problems due to hardware limitations in the high-computational cost.…”

Section: Navigation Of Magnetic Microrobots With Different User Intermentioning

confidence: 99%

Navigation of Magnetic Microrobots With Different User Interaction Levels

Lucarini

Palagi

Levi

et al. 2014

IEEE Trans. Automat. Sci. Eng.

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Micro-technologies based on wirelessly powered and manoeuvred submillimeter device, i.e.,microrobots, are attracting growing attention. Their application in lab-on-a-chip systems, such as micromanipulation and in vitro cell sorting, is expected to steeply increase. However, the actuation, powering and control of microrobots are challenges that still need concrete solutions. Magnetic fields generally enable wireless navigation of microrobots, but proper control architectures and magnetic navigation systems are needed, depending on the specific task and on the level of interaction required to the user. Here we present a magnetic navigation platform intended for lab-on-a-chip applications and we address its usability with different levels of human involvement by using two control architectures: teleoperated and autonomous. We perform an experimental analysis to demonstrate that both architectures, enrolling different levels of interaction by the user, lead to reliable execution of the microrobotic task. First, we validate the open-loop response of the microrobotic system, and second, we evaluate the performance of the system by testing both control architectures with a standard mobility task. The results show that users can teleoperate the microrobot with 100% success rate, in with a normalized spatial mean error of . Moreover, results show a fast decaying learning curve for the users involved in the study. Compared to this, when the navigation task is performed by the autonomous control, 100% success rate, a time of and a normalized spatial mean error of are obtained. Finally, we quantitatively demonstrate how both control methodologies enable very smooth movements of the microrobot, suggesting application for any task where repeatable and dexterous movements in liquid microenvironments are key requirements. Note to Practitioners-In life sciences, the need for advanced automation methods to sample preparation and analysis at the mi- Manuscript

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Section: Navigation Of Magnetic Microrobots With Different User Intermentioning

confidence: 99%

Navigation of Magnetic Microrobots With Different User Interaction Levels

Lucarini

Palagi

Levi

et al. 2014

IEEE Trans. Automat. Sci. Eng.

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“…We record the discrete-time cell orientation information as 0 4 (Ο), 0<(1),..., O^k),..., 0 4 (π), and the magnetic field orientation as ψ(0), ψ(1), · · ·, i>(k), ... ,ψ(η). The following equation is derived from (9).…”

Section: B System Identificationmentioning

confidence: 99%

“…Y. Ou Using two sets of orthogonal electromagnets (left), Ou et al demonstrated steering a living magnetized T. pyriformis cell [9](middle). In this paper we exploit differences in magnetism between cells to steer multiple cells to arbitrary x, y locations and stabilize them in limit cycles at these locations (six stabilized cells shown at right).…”

Section: Introductionmentioning

confidence: 99%

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Feedback control of many magnetized: Tetrahymena pyriformis cells by exploiting phase inhomogeneity

Becker

Kim

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Biological robots can be produced in large num bers, but are often controlled by uniform inputs. This makes position control of multiple robots inherently challenging.This paper uses magnetically-steered ciliate eukaryon {Tetrahymena pyriformis) as a case study. These cells swim at a constant speed, and can be turned by changing the orientation of an external magnetic field. We show that it is possible to steer multiple T. pyriformis to independent goals if their turningmodeled as a first-order system-has unique time constants. We provide system identification tools to parameterize multiple cells in parallel.We construct feedback control-Lyapunov methods that ex ploit differing phase-lags under a rotating magnetic field to steer multiple cells to independent target positions. We prove that these techniques scale to any number of cells with unique first-order responses to the global magnetic field. We provide simulations steering hundreds of cells and validate our proce dure in hardware experiments with multiple cells.

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“…Kim et al [9] use realtime feedback control and the Rapidly-exploring Random Tree (RRT) for path planning to control the movement of magnetotactic T. pyriformis as micro-robots. Ou et al [10] investigate the motion control of T. pyriformis cells using the Model Predictive Control (MPC) algorithm. These control Fig.…”

Section: Introductionmentioning

confidence: 99%

Steering micro-robotic swarm by dynamic actuating fields

Chao

Dai

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We present a general solution for steering microrobotic swarm by dynamic actuating fields. In our approach, the motion of micro-robots is controlled by changing the actuating direction of a field applied to them. The time-series sequence of actuating field's directions can be computed automatically. Given a target position in the domain of swarm, a governing field is first constructed to provide optimal moving directions at every points. Following these directions, a robot can be driven to the target efficiently. However, when working with a crowd of micro-robots, the optimal moving directions on different agents can contradict with each other. To overcome this difficulty, we develop a novel steering algorithm to compute a statistically optimal actuating direction at each time frame. Following a sequence of these actuating directions, a crowd of micro-robots can be transported to the target region effectively. Our steering strategy of swarm has been verified on a platform that generates magnetic fields with unique actuating directions. Experimental tests taken on aggregated magnetic micro-particles are quite encouraging.

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Motion control of magnetized Tetrahymena pyriformis cells by a magnetic field with Model Predictive Control

Cited by 34 publications

References 29 publications

Navigation of Magnetic Microrobots With Different User Interaction Levels

Navigation of Magnetic Microrobots With Different User Interaction Levels

Feedback control of many magnetized: Tetrahymena pyriformis cells by exploiting phase inhomogeneity

Steering micro-robotic swarm by dynamic actuating fields

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