This paper investigates the low-complexity predefined-time (PT) optimal formation control (FC) of multi-nonholonomic mobile robots (MNMRs) with collision avoidance safety requirements. Firstly, by applying the funnel technique, the collision avoidance and communication range constraints between adjacent robots are uniformly transformed into formation error constraints, thereby eliminating the complex barrier Lyapunov functions in existing methods and reducing the complexity of controller design. Then, to mitigate the complexity explosion problem, an enhanced nonlinear filter with adaptive parameters is designed, which reduces the computational load while improving the convergence performance of the system. On this basis, the low-complexity safe FC scheme is constructed using broad fuzzy neural networks (BFNNs) and reinforcement learning techniques, achieving PT stability, optimality, avoiding collisions, and no overshoot behavior in formation errors. Finally, the effectiveness and feasibility of the proposed control method are verified on the Speedgoat real-time target machine.

This paper investigates the predefined-time (PT) formation control for heterogeneous vehicle systems with general narrow tunnel constraints and non-affine actuator faults. Unlike conventional barrier Lyapunov function-based approaches that rely on specific constraint forms, a novel funnel-based control framework is developed to directly accommodate general curvilinear constraint boundaries. To address the explosion of complexity issue, an enhanced nonlinear filter is proposed, which reduces parameter dependence while improving filtering accuracy. By combining funnel control, coordinate transformations, and Butterworth low-pass filter, an adaptive broad fuzzy neural network control strategy is constructed to ensure PT stability of the closed-loop system. Besides, the proposed approach guarantees safe vehicle operation under narrow-tunnel constraints while keeping formation errors within prescribed performance funnels. Real-time experiments on a Speedgoat platform validate the effectiveness and feasibility of the proposed method.

Most mammalian cells (e.g., cancer cells and cardiomyocytes) adhere to a culturing surface. Compared to robotic injection of suspended cells (e.g., embryos and oocytes), fewer attempts were made to automate the injection of adherent cells due to their smaller size, highly irregular morphology, small thickness (a few micrometers thick), and large variations in thickness across cells. This paper presents a recently developed robotic system for automated microinjection of adherent cells. The system is embedded with several new capabilities: automatically locating micropipette tips; robustly detecting the contact of micropipette tip with cell culturing surface and directly with cell membrane; and precisely compensating for accumulative positioning errors. These new capabilities make it practical to perform adherent cell microinjection truly via computer mouse clicking in front of a computer monitor, on hundreds and thousands of cells per experiment (vs. a few to tens of cells as state-of-the-art). System operation speed, success rate, and cell viability rate were quantitatively evaluated based on robotic microinjection of over 4,000 cells. This paper also reports the use of the new robotic system to perform cell-cell communication studies using large sample sizes. The gap junction function in a cardiac muscle cell line (HL-1 cells), for the first time, was quantified with the system.

In robotic micromanipulation, end-effector tips must be first located under microscopy imaging before manipulation is performed. The tip of micromanipulation tools is typically a few micrometers in size and highly delicate. In all existing micromanipulation systems, the process of locating the end-effector tip is conducted by a skilled operator, and the automation of this task has not been attempted. This paper presents a technique to automatically locate end-effector tips. The technique consists of programmed sweeping patterns, motion history image end-effector detection, active contour to estimate end-effector positions, autofocusing and quad-tree search to locate an end-effector tip, and, finally, visual servoing to position the tip to the center of the field of view. Two types of micromanipulation tools (micropipette that represents single-ended tools and microgripper that represents multiended tools) were used in experiments for testing. Quantitative results are reported in the speed and success rate of the autolocating technique, based on over 500 trials. Furthermore, the effect of factors such as imaging mode and image processing parameter selections was also quantitatively discussed. Guidelines are provided for the implementation of the technique in order to achieve high efficiency and success rates.

Locating end-effector tips is a prerequisite step in micromanipulation. The tip of micromanipulation tools is typically a few micrometers in size and highly delicate. In all existing automated micromanipulation systems, the process of locating the end-effector tip is conducted by a skilled operator, and the automation of this task has not been attempted. This paper presents a technique for automatically locating end-effector tips. The technique consists of programmed sweeping patterns, MHI (motion history image) end-effector detection, active contour for estimating end-effector positions, autofocusing and quad-tree search for locating end-effector tip, and finally visual servoing to position the tip to the center of the field of view. Two types of micromanipulation tools (micropipette representing single-ended tools and microgripper representing multi-ended tools) were used in experiments for testing. Quantitative results were reported in the speed and success rate of the auto-locating technique, based on over 500 trials. Furthermore, the effect of factors such as imaging mode and image processing parameter selections was also quantitatively discussed. Guidelines are provided for the implementation of the technique in order to achieve high efficiency and success rates.

Manipulating single cells with a micropipette is the oldest, yet still a widely used technique. This paper discusses the positioning of a single cell to a target position inside the micropipette after the cell is aspirated into the micropipette. Due to the small volume of a single cell (pico-liter) and nonlinear dynamics involved, this task has high skill requirements and is labor intensive in manual operation that is solely based on trial and error and has high failure rates. We present automated techniques in this paper for achieving this task. Computer vision algorithm was developed to track a single cell inside a micropipette for automated single-cell positioning. A closed-loop robust controller integrating the dynamics of cell motion was designed to accurately and efficiently position the cell to a target position inside the micropipette. The system achieved high success rates of 97% for cell tracking (n=100) and demonstrated its capability of accurately positioning a cell inside the micropipette within 8 seconds (vs. 25 seconds by highly skilled operators).

This paper is the first report of robotic ICSI (intracytoplasmic sperm injection). ICSI is a clinical procedure performed worldwide in fertility clinics, requiring pick-up of a single sperm and insert it into oocyte (i.e., an egg cell). Since its invention 20 years ago, ICSI has been conducted manually by a handful of highly skilled embryologists; however, success rates vary significantly among clinics due to poor reproducibility and inconsistency across operators. We leverage our work in robotic cell injection to realize robotic ICSI and aim ultimately, to standardize how clinical ICSI is performed. This paper presents some of the technical aspects of our robotic ICSI system, including a cell holding device and motion control and computer vision algorithms. The system performs visual tracking of single sperm, robotic immobilization of sperm, aspiration of sperm with pico-liter volume, and insertion of sperm into an oocyte with a high degree of reproducibility. The system requires minimal human involvement (requiring only a few computer mouse clicking), and is human operator skill independent. Using the hamster oocyte-human sperm model in preliminary trials, the robotic system demonstrated a high success rate of 90.0% and survival rate of 90.7% (n=120).

Biological experiments and drug screen require the transfer of individual zebrafish embryos into standard multi-well microplates. Manually pipetting embryos into wells is tedious and time consuming. This paper reports a prototype cooperative robotic system capable of transferring zebrafish embryos in parallel and depositing a single embryo per well in a 96-well microplate. A cell holding device was developed to trap multiple embryos in a regular pattern. The cell holding device and a microplate are positioned and aligned along multiple axes by the system. Embryo release strategies were systematically studied and compared. Experiments demonstrated that out of the 1,056 zebrafish embryos used in experiments (i.e., 44 times parallel transfer into 11 96-well plates), 996 wells were successfully filled with one and only one zebrafish embryo, representing a success rate of 94.3%. Further experiments confirmed that the transferred embryos were able to develop into zebrafish with 100% survival rate.

This paper reports a prototype cooperative robotic system capable of transferring zebrafish embryos in parallel and depositing a single embryo per well in a 96-well microplate. A cell holding device was developed to trap multiple embryos in a regular pattern. The cell holding device and a microplate were positioned and aligned along multiple axes by the system. Embryo release strategies were systematically studied and compared. Experiments demonstrated that out of the 1056 zebrafish embryos used in experiments (i.e., 44 times parallel transfer into 11 96-well plates), 996 wells were successfully filled with one and only one zebrafish embryo, representing a success rate of 94.3%. Further experiments confirmed that the transferred embryos were able to develop into zebrafish with 100% survival rate.

In this paper, we report our investigation on the force-induced responses of zebrafish chorion before and after being perturbed by an externally applied prescribed force. Our experimental results show that application of an external force leads to a reduction in the stiffness of the zebrafish chorion, and that variation in such a reduction is strongly influenced by the perturbation time. Our results provide evidence supporting the hypothesis that the stiffness of some cellular organism may be modified by the controlled application of an appropriate mechanical force, and demonstrate the potential of force-feedback control in micro/nano-manipulation as a promising engineering technique for studying disease properties and manipulating cellular biological functions.

This paper presents an approach to optimize the zebrafish-embryo microinjection process by selecting suitable speed trajectories for the motion of the micropipette, with the objective of minimizing the risk of damaging the embryo during the microinjection process. A viscoelastic model of a zebrafish embryo is developed based on experimental data. The speed optimization problem is formulated based on this viscoelastic model. Simulation results indicate that there exists an optimal speed trajectory for the micropipette under certain conditions. The key benefit of this speed optimization approach is that the turgor pressure inside the membrane is prevented from increasing significantly during the microinjection process, thus avoiding damaging the biological components in the embryo.

Many microfabricated devices have been developed to quantify cellular response to a multitude of stimuli at a single-cell level in a high throughput manner. These single-cell studies require cells to be individually positioned at defined locations on a microdevice. This paper presents a micromanipulation system for automated pick-place of single cells. Integrating computer vision and motion control algorithms, the system visually tracks a cell in real time and controls multiple motion devices coordinately. Via fine manipulation of picoliter fluids and pressure of a few Pascals, the system accurately picks up a single cell, transfers the cell, and deposits it at a target location at a speed of 15-30 sec/cell. The micromanipulation system has the advantages of non-invasiveness, high specificity, and high precision. It is suitable to pick-place both non-labeled and labeled cells and applicable to standard cell culture substrates and microdevices with an open top.

The microinjection is a common technique in genetic engineering for transferring genetic material into a cell. It is normally performed on a micromanipulation system that usually consists of an inverted microscope, a micromanipulator, a micropipette and an injector. Manual microinjection is a conventional and widespread practice. However, this approach suffers from low success rate, poor efficiency, and high probability of contamination. We have developed a micromanipulation system for automatic batch microinjection by automating the key process of penetration in a microinjection. The microinjection system automatically identifies and penetrates each embryo/cell with a constant and fast speed, and in doing so, eliminates the possibility of contamination caused by manual operations. The effectiveness of this force-controlled system has been experimentally demonstrated in the tasks of automatic identification and penetration zebrafish embryos, which are widely used as a model for studying vertebrate development and genetics.

Power system planning is a complex multi-objective optimization problem. It aims at locating the minimum cost of additional transmission lines that must be installed to satisfy the forecasted load in a power system. A number of different methods for power system planning have been investigated over the past decades. In this paper, a differential evolution (DE) based approach is proposed as an optimization tool to solve the power system planning problem. A comparison between genetic algorithms, evolutionary strategy (ES), and five different DE schemes are carried out on two benchmark power systems. The results shown that, as a relatively new heuristic optimization method, DE is able to provide robust and efficient solution to power system planning problems.

In micromanipulation, the size of the manipulated object is usually much less than 1 mm in a single dimension, in which case gravitational and inertial forces are no longer dominant. This leads to problems (for manipulation through force) that are not evident in the macroworld, and for which the macroworld techniques alone may not be adequate to provide solutions. This paper surveys critical issues and their available solutions related to force control in micromanipulation. It focuses on: 1) techniques for dealing with adhesion forces and 2) methods for force sensing and control