Several control schemes have been proposed for optical tweezers, but most existing methods assume that the optical trapping is maintained throughout the manipulation, and Brownian motion is ignored for the simplification of stability analysis. However, the optical trapping is not effective when a biological cell is initially outside the optical trap, and even if the cell is initially trapped, it may escape from the optical trap due to random Brownian perturbations and kinetic energy gained during manipulation. This paper presents a new robotic control technique to address the trapping and manipulation problem of biological cell in presence of random Brownian perturbations. By using the proposed method, the cell can be automatically trapped when it is not inside the optical trap and the optical manipulation of cell is enhanced by saturating the position feedback. The stability of the closed-loop system is analysed from stochastic perspectives. Experimental results are presented to illustrate the performance of the proposed control method.

Various automatic manipulation techniques have been developed for manipulating micro-objects using optical tweezers. Because of the small trapping force of optical traps and increase in kinetic energy during manipulation, a trapped object may not remain trappable, especially in the presence of random Brownian perturbation. However, there is no theoretical analysis so far to help understand the effects of dynamic motion and Brownian forces on the trappability problem of optical tweezers. This paper investigates the optical manipulation of micro-objects under random perturbations. Here, we provide for the first time a theoretical and experimental analysis of the dynamic trapping problem from stochastic perspectives. We derive the relationship between trapping probability and maximum manipulation velocity. A controller with appropriate velocity bound is then proposed to ensure that the system is bound and stable. The experimental results confirm the accuracy of our theoretical analysis and illustrate the necessity and usefulness of the proposed controller.

Various control approaches have been developed for micro/nanomanipulations using optical tweezers. Most existing methods assume that the micro/nanoparticles stay trapped during manipulations, and stochastic perturbations (Brownian motion) are usually ignored for the simplification of model dynamics. However, the trapped particles could escape from the optical traps especially in motion due to several possible reasons: small trapping stiffness, stochastic perturbations, and kinetic energy gained during manipulation. This paper investigates the conditions under which micro/nanoparticles will stay trapped while in motion. The dynamics of the trapped particles subject to stochastic perturbations is analyzed. Dynamic trapping is considered and the maximum manipulation velocity is determined from a probabilistic perspective. A controller with certain velocity bound is proposed, the stability of the system is analysed in presence of stochastic perturbation. Experimental results are presented to show the effectiveness of the proposed control approach.

Microscopic optics and cameras are commonly used in micromanipulation or biomanipulation workstations since they provide a large spectrum of visual details and information. The visual feedback information also improves robustness to uncertainty and accuracy of micromanipulation. Among various micromanipulation systems, optical tweezers are one of the most useful instruments that utilize a focused beam of light to manipulate biological cell or nanoparticles without physical contact. However, current optical manipulation techniques fail if the laser beam is not within the field of view (FOV) of the microscope. To solve this problem, we present a robotic control technique for optical manipulation with limited FOV of microscope. The proposed control strategy consists of a vision based control that manipulates the trapped cell to move to a desired position inside the FOV and a Cartesian-space feedback control that drives the laser beam back when it is outside the FOV. Thus, the proposed method allows the laser beam to leave the FOV during the course of manipulation and the transition from one feedback to another is smooth. The stability of the closed-loop system is analysed by using Lyapunov-like methods, with consideration of the dynamic interaction between the cell and the manipulator of the laser source. Experimental results are presented to illustrate the performance of the proposed method.

While several automatic manipulation techniques have recently been developed for optical tweezer systems, the measurement of the velocity of cell is required and the interaction between the cell and the manipulator of laser source is usually ignored in these formulations. Although the position of cell can be measured by using a camera, the velocity of cell is not measurable and usually estimated by differentiating the position of cell, which amplifies noises and may induce chattering of the system. In addition, it is also assumed in existing methods that the image Jacobian matrix from the Cartesian space to image space of the camera is exactly known. In the presence of estimation errors or variations of depth information between the camera and the cell, it is not certain whether the stability of the system could still be ensured. In this paper, vision-based observer techniques are proposed for optical manipulation to estimate the velocity of cell. Using the proposed observer techniques, tracking control strategies are developed to manipulate biological cells with different Reynolds numbers, which do not require camera calibration and measurement of the velocity of cell. The control methods are based on the dynamic formulation where the laser source is controlled by the closed-loop robotic manipulation technique. The stability is analyzed using Lyapunov-like analysis. Simulation and experimental results are presented to illustrate the performance of the proposed cell manipulation methods.

Chemotaxis is an event in which cells spatially sense their extro-environment and move along a chemokine gradient. Rapid advances in various cell- and molecule-based biomedical applications have led to increasing demands for the precise and proactive control of cell chemotaxis movement. This paper presents the development of a robot-aided optical manipulation tool for dynamics analysis and automated control of cell chemotaxis movement. Cell locomotion model is developed to characterize the relationship between the chemoattractant and the forces that govern the cell migration. The microparticles, releasing a chemokine while in motion, are optically trapped and robotically controlled to induce cell polarization and migration. Experiments performed on migrating leukemia cancer cells demonstrate the effectiveness of the proposed approach. The arising research income of this paper will benefit the development of an automated control strategy for cell migration, targeting at the novel therapies in the future medicine.

This paper presents the use of robotically controlled optical tweezers to manipulate a group of cells into a region of interest to form the required pattern. A novel multilevel-based topology is designed to present different cell patterns in the region of interest. A potential function-based controller is developed to control the cells to form the required pattern. A pattern regulatory control force is developed which particularly addresses the special case when cells stop at undesired positions. The system stability is analyzed using Lyapunov approach. Experiment is performed with robotically controller optical tweezers to demonstrate the effectiveness of the proposed approach.

Isolation and deposition of specific live cells with the high spatio-temporal resolution from the heterogeneous mixtures are of critical importance to a wide range of biomedical applications. In this paper, we report a robot-assisted cell manipulation tool with optical tweezers based on a uniquely designed microwell array. The whole automatic manipulation includes the target cell recognition, isolation, transportation and deposition. The microwell array is designed based on microfluidics technology, which allows the passive hydrodynamic docking of cells. Image processing technique is used to recognize the target cells based on the cell size or fluorescence label. After recognition, the target cells can be levitated from the microwell, and then assembled by multiple optical traps in parallel. The optically trapped target cells are then transported and deposited to the desired location precisely. Experiments are performed to demonstrate the effectiveness of the proposed cell manipulation approach.

This paper investigates the multirobot task allocation (MRTA) problem for a group of heterogeneous mobile robots. The robots and tasks are characterized by resources as required by task execution. The robots are required to generate optimal solutions for the MRTA problem while forming coalitions to meet the resource constraints imposed by tasks. A leader-follower based coalition methodology is developed, with detailed discussions on leader selection, coalition forming and refinement algorithms. It is shown that the resource constrained task allocation problem can be well resolved by the proposed leader-follower coalition algorithms. Simulations performed on a mobile robot group demonstrate the effectiveness of the proposed approach.