In this article, the fixed-time formation-containment tracking (FXFCT) problem is addressed for heterogeneous multi-agent systems (HMASs) under a directed interaction topology. Agents are divided into a reference leader, providing a trajectory for the entire HMASs; formation-leaders, achieving the desired formation by following this trajectory; and followers. Novel distributed fixed-time observers are developed for the formation-leaders and followers with the directed interaction topology, respectively. Distributed fixed-time control protocols are then proposed for the formation-leaders and followers using the coordinate transformation methods, eliminating the restrictive but commonly adopted full-row rank assumption of agent input matrices. It is shown that under the proposed control protocols, the concerned FXFCT problem can be solved. Simulations verify the effectiveness of the obtained theoretical results. Note to Practitioners - Fixed-time cooperative control of HMASs, e.g., autonomous aerial vehicles (AAVs) and autonomous ground vehicles (AGVs), enables rapid execution of complex collective tasks, efficient resource allocation, and improved operational efficiency. Such capabilities are particularly relevant to applications in logistics, energy management, and smart agriculture, where time-critical coordination delivers significant social and economic benefits. As a unified cooperative control framework, the formation-containment tracking (FCT) problem encompasses several classical problems, such as consensus tracking, formation tracking, and containment control. Solving the FCT problem therefore provides a comprehensive solution that addresses multiple coordination objectives simultaneously, making it particularly relevant for complex real-world missions that require hierarchical coordination in HMASs. This paper addresses the FXFCT problem for HMASs under a directed interaction topology. We develop novel distributed fixed-time protocols that integrate distributed fixed-time observers and controllers for both formation-leaders and followers. For practitioners, a key practical advantage lies in an explicit, a priori computable upper bound on the convergence time that is independent of initial conditions, enabling predictable, deadline-aware mission planning. In addition, directed communication and the removal of restrictive full-row rank assumptions on agent dynamics alleviate communication overhead and improve practical applicability to heterogeneous platforms. © 2026 IEEE.

The magnetic micromanipulation of swarm microparticles has attracted considerable attention because of its advantages of non-invasiveness, high drug-carrying capacity, and easy observation in the targeted delivery in in-vivo environments. This paper presents an automated control scheme for the magnetic micromanipulation of microswarms in a rotating gradient-based field. Different from the rotating uniform magnetic field generated by Helmholtz coils, the rotating gradient-based field is a type of convergent field established by sequentially powering each coil of the electromagnetic coil system. By changing the coil currents, the field can rotate while driving the microswarm to a pre-determined position, facilitating the swarm localization and tracking. According to the preliminary motion characterization of the swarm in the rotating gradient-based field, an intuitive trapping dynamic model which can simplify the analysis of swarm dynamics is established to facilitate controller design. Based on this model, a super-twisting sliding mode estimator is first designed to estimate the position of the microswarm as well as the disturbances caused by parameter variations and unmodeled dynamics. A robust controller is then developed based on the estimator. In this way, closed-loop manipulation of the microswarm to follow a desired trajectory in the rotating gradient-based field is realized, and the system’s behavior has been significantly improved due to the capability to estimate disturbances. The proposed control scheme for the rotating gradient-based field has the potential to avoid volume loss and unexpected drug diffusion of the swarm when facing complex in-vivo environments. The stability of the control scheme is proved by the Lyapunov approach. Experiments are finally performed to demonstrate the effectiveness of the proposed control approach in a collision-free environment and in a simulated channel.Note to Practitioners—The motivation of this study is to realize automated feedback control of the microparticle swarm in a rotating gradient-based magnetic field. The rotating gradient-based magnetic field is a type of convergent field that can rotate while driving the microswarm to a pre-determined position. This magnetic actuation method facilitates microswarm tracking and enables microagents to overcome static friction with the bottom substrate by rotating them, thereby inducing movement. However, existing research on the rotating gradient-based magnetic field concentrates on moving the microswarm to the targeted position without real-time visual guidance in an open-loop manner, which provides less reliability and convenience compared to real-time closed-loop control. Moreover, it will result in unavoidable volume loss when collisions happen in the complicated environments. In addition, the existing swarm dynamics in such a field need to be simplified to apply in controller design. To solve the above problems, an automated point-to-point navigation control scheme is proposed in this study. An intuitive trapping model is first established to simplify the swarm dynamics. Then, a robust controller with a super-twisting algorithm-based estimator is designed based on the model to manipulate the microswarm. Experimental results have shown that the proposed method can successfully control the microparticle swarm in collision-free environments and a simulated channel with considerable accuracy in the rotating gradient-based magnetic field. The proposed control scheme has the potential to avoid volume loss and unexpected drug diffusion of the swarm when facing complex in-vivo vascular environments, particularly those involve fluids with completely different directions in a bifurcation, such as the vascular network of lymphatic vessels and blood vessels.

In recent years, magnetic field-controlled microparticles have demonstrated their superiority in biomedical applications. To improve the operation efficiency and imaging effect of microparticles, increasing numbers of researchers are devoted to driving a swarm composed of a group of microparticles. In this study, a new strategy for autonomously manipulating microparticle swarms in liquids via an electromagnetic coil system was investigated. Compared with the commonly used rotating magnetic field composed of multiple electromagnetic coils, the oscillating magnetic field drive system formed by a single electromagnetic coil reduces the occupied space meanwhile ensures the sufficient working space, making the microparticle movement more flexible while providing greater feasibility for good compatibility with imaging systems. Through designing the parameters of the input current, a vortex-shaped swarm can be formed, and the movement of the entire swarm can be pulled by controlling the coil position. Owing to the rotation of microparticles, the coefficient of the control input is unknown uncertainty. To solve this uncertainty problem, a sliding mode controller is used, and the chattering caused by the sliding mode surface is reduced using a saturation function. Lastly, the effectiveness of the proposed strategy is verified by simulation and experiments. Note to Practitioners—The motivation of this article is mainly to solve the problem of magnetic microparticle aggregation, aiming to form a stable swarm and automatically control the movement of the swarm. Most of the existing methods of aggregating microparticles to form a swarm are based on the creation of a rotating magnetic field by Helmholtz coils, which leads to the problem of spatial limitation. To solve this problem, this research uses one single solenoid to form a stable swarm, which provides more space for imaging equipment and objects requiring operation. The controlled movement of the stable swarm is achieved by manipulating the solenoid with a sliding mode controller. It reduces the impact of system uncertainties, allowing the swarm to automatically track a predefined path accurately. This will provide a new idea for the application of microparticles in the biomedical field.

This research investigates learning-based control of continuum robots in constrained environments without relying on analytical models. We propose a data-efficient stochastic control strategy incorporating online model updates to achieve precise manipulation even when arbitrary robot deformations occur due to environmental interactions. A localized Gaussian process regression approach accounting for state stochasticity is first presented to approximate the forward kinematics. The learned model enables uncertainty-aware stochastic predictions via the proposed scaled unscented transform (SUT)-based method for efficient exploration. Leveraging new data, online model updates are performed in a highly sample-efficient manner. Furthermore, a probabilistic model predictive control approach integrating the learned models and chance constraints based on Chebyshev’s inequality is developed for searching an optimal control sequence. Simulations and experiments are performed to demonstrate the effectiveness of the proposed approach for controlling continuum robots in constrained environments using limited observational data.Note to Practitioners—The motivation of this research is to solve the problem of controlling continuum robots in constraint environment. The flexibility of continuum robots significantly affects the manipulation accuracy, and the interaction between the continuum robot and environmental constraints can also lead to unpredictable behavior. Learning control methods that rely only on sensory data, provide a feasible solution to the aforementioned problem. However, current methods lack sample efficiency and the capability to handle unknown environmental constraints. This research proposes a learning control method which can control a flexible continuum robot in constrained environments with high data-efficiency and robustness even when the robot shape undergoes sudden deformations due to contact with obstacles.

This paper proposes a practical micropipette-image 2-D pixel and 3-D spatial coordination) calibration method indispensable to somatic cell microinjection, leveraging advancements in modern motorized micromanipulators. The method determines the depth information of the micropipette in the microscope field by assessing the contact between the micropipette tip and the bottom of the culture dish. It uses recoverable deformation upon contact as a criterion for precise positioning, ensuring the tip is on the dish’s bottom surface and in the microscope’s focus plane. Additionally, the paper introduces an on-the-spot method for breaking a micropipette tip and a preprocessing technique for somatic cells. The proposed micropipette tip-breaking method, using a low-cost acrylic ring, overcomes previous drawbacks and proves quick and user-friendly. The preprocessing technique converts fully adherent somatic cells into semi-adherent cells, increasing cell thickness for easier puncturing. Combining these techniques, the study validated the approaches through over 900 injections on human dermal fibroblast (HDF) cells, achieving a success rate of 53.3% and a survival rate of 95.8%. Note to Practitioners–This study was initiated to address the challenge of establishing accurate mapping relationships between observed 2-D coordinates of a micropipette tip in microscope vision and 3-D positional data obtained from an external controller of an XYZ micromanipulator. However, its relevance extends beyond somatic cell microinjection to other micromanipulation applications like micro-assembly. Common calibration methods involve motorized objectives or stereo microscopic vision, whereas our paper proposes a practical approach utilizing recoverable deformation upon tip contact with the dish bottom for precise positioning. The paper mathematically characterizes relationships between the pixel coordinate system and spatial coordinate systems, demonstrating problem-solving by ensuring the tip aligns with the dish’s bottom surface and the microscope’s focus plane. Although experiments affirm feasibility, refinement and testing in other micromanipulation applications are pending. Future research aims to enhance system modeling and calibration accuracy.

Accurate estimation of absolute depth from a monocular endoscope is a fundamental task for automatic navigation systems in robotic surgery. Previous works solely rely on uni-modal data (i.e., monocular images), which can only estimate depth values arbitrarily scaled with the real world. In this paper, we present a novel framework, SADER, which explores vision and robot kinematics to estimate the high-quality absolute depth for monocular surgical scenes. To jointly learn the multi-modal data, we introduce a self-distillation based two-stage training policy in the framework. In the first stage, a boosting depth module based on vision transformer is proposed to improve the relative depth estimation network that is trained in a self-supervised method. Then, we develop an algorithm to automatically compute the scale from robot kinematics. By coupling the scale and relative depth data, pseudo absolute depth labels for all images are yielded. In the second stage, we re-train the network with 3D loss supervised by pseudo labels. To make our method generalize to different endoscopes, the learning of endoscopic intrinsics is integrated into the network. In addition, we did cadaver experiments to collect new surgical depth estimation data about robotic laparoscopy for evaluation. Experimental results on public SCARED and cadaver data demonstrate that the SADER outperforms previous state-of-art even stereo-based methods with an accuracy error under 1.90 mm, proving the feasibility of our approach to recover the absolute depth with monocular inputs. Note to Practitioners—This paper aims to solve the problem of absolute monocular depth estimation in automatic surgical navigation by leveraging the multi-modal data from the robot-based endoscopic system. Accurate depth perception with real scales of the monocular scene is essential for the control of surgical robots in automatic navigation. However, current methods can only predict the relative depth of the surgical scene using monocular images. In this article, we propose a self-supervised learning-based method to achieve high-quality absolute depth estimation of monocular endoscopic images. It neither needs manual data annotation, nor other imaging modalities. The experiments extensively validate the feasibility and high performance of our framework for absolute depth estimation on monocular endoscopes. This absolute depth perception framework can be potentially encapsulated into the automatic navigation system in the near future.

The rapid advancement of autonomous driving technology has reshaped the automotive industry, highlighting the need for diverse and high-quality image data. Existing image datasets for training and improving autonomous driving technologies lack rare scenarios like extreme weather, limiting the effectiveness and reliability of autonomous driving technologies. One possible way of expanding the dataset coverage is to augment the existing dataset with artificial ones, which, however, still suffers from various challenges like limited controllability and unclear corner case boundaries. To address these challenges, we design and develop an interactive visual analysis system, HuGe , to achieve efficient and semi-automatic controllable image generation. HuGe incorporates weather transformation models and a novel semi-automatic knowledge-based controllable object insertion method which leverages the controllability of convex optimization and the variability of diffusion models. We formulate the design requirements, propose an effective framework, and design four coordinated views to support controllable image generation, multidimensional dataset analysis, and evaluation of the generated samples. Two case studies, a metric-based evaluation and interviews with domain experts demonstrate the practicality and effectiveness of HuGe in controllable image generation for autonomous driving.

Mitochondrial dysfunction plays a significant role in the development of fatal diseases such as aging, cancer, and Alzheimer’s. Transferring mitochondria to cells is a new and potential treatment for mitochondrial DNA (mtDNA)-related illnesses. This article describes a novel technique to control the quality and quantity of mitochondria injected into single live cells using a robot-aided microneedle and optical tweezers (OTs)-based micromanipulation system. Isolated mitochondria and cells are patterned in a 1-dimensional (1-D) array in a microfluidic device, and a robot-aided microneedle collects the predefined number of functional mitochondria with the help of OTs. Then, the microneedle precisely and non-invasively injects these collected mitochondria into single live cells. Given that the two manipulation tools of OTs and microneedle were used, a switch controller strategy is developed to enable mitochondria trapping and injection with OTs and microneedle-based micromanipulator, respectively. The effectiveness of the developed robotic system is experimentally demonstrated with automated injections of isolated mitochondria into HeLa and mesenchymal stem cells (aMSCs). A precise and efficient quality and quantity control of mitochondria injection was possible by using Ots in conjunction with microneedle and microfluidics technologies. Quality-and quantity-controlled ability is analyzed and compared with the traditional mitochondria transfer method (co-culture). Biological tests are further conducted to assess the viability of mitochondria recipient cells. Experimental results demonstrate that the developed system can non-invasively transfer healthy mitochondria into single live cells while precisely controlling the quantity and quality of injected mitochondria. The proposed mitochondrial transfer method has the potential to advance precision medicine methods, particularly for cellular therapy of (mtDNA)-related diseases.Note to Practitioners—This study was motivated by the problem of inefficient methods of mitochondria injection into single, small, living cells. Due to the issue of microneedle clogging, existing mitochondria injection methods can only inject mitochondria into cells with a 0.3 to 2% efficiency. To address this issue, we have developed an automated method of mitochondria injection into cells that can inject mitochondria into single cells non-invasively with an efficiency of up to 80 %. Existing methods of mitochondria injection also ignored the heterogeneity of mitochondria, such as functionality and the precise number of mitochondria prior to injection. Our methodology can efficiently and precisely control the quality and quantity of mitochondria injection into single cells, which can be useful for the discovery of new information concerning precision medicine, particularly in diseases associated with mtDNA.

Recent advancements toward perception and decision-making of flexible endoscopes have shown great potential in computer-aided surgical interventions. However, owing to modeling uncertainty and inter-patient anatomical variation in flexible endoscopy, the challenge remains for efficient and safe navigation in patient-specific scenarios. This paper presents a novel data-driven framework with self-contained visual-shape fusion for autonomous intelligent navigation of flexible endoscopes requiring no priori knowledge of system models and global environments. A learning-based adaptive visual servoing controller is proposed to online update the eye-in-hand vision-motor configuration and steer the endoscope, which is guided by monocular depth estimation via a vision transformer (ViT). To prevent unnecessary and excessive interactions with surrounding anatomy, an energy-motivated shape planning algorithm is introduced through entire endoscope 3-D proprioception from embedded fiber Bragg grating (FBG) sensors. Furthermore, a model predictive control (MPC) strategy is developed to minimize the elastic potential energy flow and simultaneously optimize the steering policy. Dedicated navigation experiments on a robotic-assisted flexible endoscope with an FBG fiber in several phantom environments demonstrate the effectiveness and adaptability of the proposed framework.

Despite the rapid development in providing precise delivery of extraneous samples to the vast majority of cells, robotic microinjection is still hindered by cumbersome operations and low throughput in practice. This study presents a new automated microinjection system equipped with two micromanipulators and a deep learning algorithm for cell identification. The introduction of two coordinated micromanipulators based on the same cell handling platform results in a large increase in injection throughput. A deep convolutional neural network, Mask R-CNN, is used to detect and segment stain-free adherent cells, leading to a considerable increase in operational efficiency and subsequent throughput. In the three independent experiments, over 10,000 MC3T3 mouse fibroblast cells are injected to evaluate the injection speed, success rate, and survival rate. Experimental results confirm that our system can inject around 4,000 cells in 1 h with an approximately 60.3% success rate and an 82.0% survival rate. This research’s success will make robotic microinjection a competitive tool in many biomedical applications, such as plasmid DNA transfection. Note to Practitioners—The motivation of this study is to improve the throughput of cell microinjection, which has become a bottleneck problem hindering the clinical application of microinjection technology. Existing cell microinjection systems typically use only one micromanipulator and rely on manual identification of cells, resulting in a limited ability to process cells for one experimental cycle. This study proposes a novel method that identifies cells through deep learning automatically and uses two micromanipulators simultaneously to improve cell processing capabilities. The problem of manipulating two micromanipulators for nearby cells is formulated as a route optimization problem, which is also suitable for three or more micromanipulators. A deep learning algorithm is used to identify and select cells to increase the processing speed. Experiments are performed to demonstrate that the proposed method can greatly increase the throughput while maintaining satisfactory performance in microinjection.

The human–rail vehicle transportation system safety design is complicated given that the complexity of the multilevel system with parameter uncertainties propagating from the vehicle structure (primary collision) to the interior human compartment (secondary collision). This article establishes a hybrid framework incorporating a stochastic approach and an integrated optimization strategy to improve train crashworthiness and reduce passenger crash injuries. The stochastic approach utilizes adaptive sparse polynomial chaos expansion models and variance-based sensitivity indices to evaluate the statistic characteristics of system responses and quantify the contribution ranking of uncertain parameters to response variations. The optimization strategy integrating the evolutionary algorithm and the multicriteria decision making is proposed to solve the nonuniqueness of Pareto optimal solutions. In the optimization process, the modified DEMATEL–ANP method with interval type-2 fuzzy sets is developed to deal with vague linguistic judgments for the importance sequence of human injury responses. Theq-rung orthopair trapezoidal fuzzy uncertain linguistic sets–TOPSIS method is established to address hesitant linguistic evaluations for the Pareto front and select the final optimal solution. Compared with the initial design, the driver abbreviated injury scale (AIS) 3 plus joint injury probability is reduced from 67.08% to 14.17% after optimization. Results prove that the proposed framework is a practical tool for improving the passive safety of railway industry.

Soft climbing robots have elicited widespread attention for their potential applications in inspection, maintenance, and search and rescue tasks. However, these robots face challenges in terms of their adaptability and motion capability in various surface environments, such as surfaces with a large gap or nonflat surfaces. In this article, we develop a soft modular climbing robot called Smcbot, which is assembled based on several soft bodies and feet modules. The soft body module provides large-scale telescopic motion and high output load through two soft actuators made of shape memory alloy wires. Meanwhile, the feet modules provide considerable adhesion on different surfaces due to the applied adhesive strategy. A theoretical model is also developed to predict the output performance (e.g., stiffness and output displacement) of the soft body, and the results are verified through experiments. The adaptability and motion capability of Smcbot is demonstrated on flat and nonflat surfaces with different properties, such as surfaces with a large gap and those with smooth and rough surfaces. This article uses the modular design to expand the application scope of soft climbing robots and to facilitate their adaption to various challenging environments with low time consumption and cost.

Estimating precise metric depth and scene reconstruction from monocular endoscopy is a fundamental task for surgical navigation in robotic surgery. However, traditional stereo matching adopts binocular images to perceive the depth information, which is difficult to transfer to the soft robotics-based surgical systems due to the use of monocular endoscopy. In this paper, we present a novel framework that combines robot kinematics and monocular endoscope images with deep unsupervised learning into a single network for metric depth estimation and then achieve 3D reconstruction of complex anatomy. Specifically, we first obtain the relative depth maps of surgical scenes by leveraging a brightness-aware monocular depth estimation method. Then, the corresponding endoscope poses are computed based on non-linear optimization of geo-metric and photometric reprojection residuals. Afterwards, we develop a Depth-driven Sliding Optimization (DDSO) algorithm to extract the scaling coefficient from kinematics and calculated poses offline. By coupling the metric scale and relative depth data, we form a robust ensemble that represents the metric and consistent depth. Next, we treat the ensemble as supervisory labels to train a metric depth estimation network for surgeries (i.e., MetricDepthS-Net) that distills the embeddings from the robot kinematics, endoscopic videos, and poses. With accurate metric depth estimation, we utilize a dense visual reconstruction method to recover the 3D structure of the whole surgical site. We have extensively evaluated the proposed framework on public SCARED and achieved comparable performance with stereo-based depth estimation methods. Our results demon-strate the feasibility of the proposed approach to recover the metric depth and 3D structure with monocular inputs.

Navigation control of microrobots in vivo has great potential in precision medicine and has attracted considerable attention in recent years. The control performance of the existing methods is considerably affected by hysteresis nonlinearity. This article presents a robust control method that can overcome hysteresis influence in navigating a microrobot actuated by an electromagnetic coil system. A motion planner that combines the breadth-first search (BFS) method and genetic algorithm (GA) is used to plan a reliable and flexible trajectory for the microrobot navigation. To compensate for hysteresis nonlinearity existing in the system, the Prandtl–Ishlinskii (PI) model is introduced. A robust controller that integrates adaptive sliding mode control (ASMC) and nonlinear disturbance observer is designed to guarantee the stability and accuracy of the microrobot in motion. Experiments have been performed to demonstrate the effectiveness of the proposed approach. The success of this research will advance the microrobot navigation for in vivo applications. Note to Practitioners—The motivation of this article is to eliminate the effect of hysteresis caused by the electromagnetic system on microrobot motion. Existing research on hysteresis compensation mostly uses inverse model or online model identification, which is not suitable for real-time control of the microrobot. In addition, the disturbance and uncertainty caused by the microfluidic environment at different flow rates will also affect the control performance of the microrobot. To solve the above problems, an adaptive robust control method is developed in this article. Experimental results have shown that the proposed method can successfully control the microrobot in the in vitro and in vivo environment.

Automatic 3D navigation control of microrobot in large microenvironment is one of the primary challenges hindering its applications. In this article, we present a systematic approach to use an electromagnetic manipulation system to deal with this challenge. Two movable orthogonal microscopic cameras scan the microenvironment on the top view and side view to build a stereo occupancy map automatically. The initial position of microrobot can be located on the basis of the similarity curves generated by the scanning sequences. In accordance with the distribution characteristic of electromagnetic fields in workspace, an enhanced rapidly exploring random tree algorithm is proposed to avoid obstacles in the complex environment. To ensure continuous visual servo in wide range, a field-of-view tracking method is developed using a specific image definition evaluation algorithm. A prescribed performance controller with disturbance observer is designed, which guarantees that the microrobot can remain in the view of microscopic cameras, and the transient and steady-state performance of the system can satisfy the expected requirements. A set of experiments are performed to verify the effectiveness of the proposed automatic 3D navigation strategy. Experimental results show that the microrobot can navigate automatically in a$\boldsymbol{12}\,\times 10\,\times \text{10-mm}$microenvironment containing obstacles, and achieve reliable field-of-view tracking and path following at high speed.

This study investigates the use of a tendon-driven continuum robot to deform a soft object, whereas the robot body is deformed into an arbitrary shape to adapt to a constrained environment. A dynamic estimator (DE) is developed to approximate the Jacobian matrix that associates the actuator input with the deformed output of the soft object. This helps solve the singularity problem and reduce the effects of noise. Then a visual predictive controller (VPC) with a reference trajectory is developed to ensure a smooth operation. A linear extended-state observer (ESO) is further designed to measure the robot states, such that the controller can compensate for the estimation error. Simulations and experiments are performed to verify the proposed control approach.Note to Practitioners—The motivation of this article is to solve the problem of automatic deformation control of soft objects in restricted environments. The existing soft object deformation control is achieved using rigid robots in an open environment, but rigid robots are difficult to use in specific applications where the environment is restricted (e.g., natural orifice surgery). Flexible continuum robots with mechanical compliance can manipulate soft objects in narrow spaces. However, due to environmental constraints, the robot body may be deformed into any shape regardless of the input of the actuator. To solve the problem, this research provides a new visual servo control strategy that deforms soft objects using a continuum robot in a restricted environment. The proposed method can control a flexible robot to manipulate soft objects while taking into account the change in the robot configuration in a restricted environment.

Soft grippers can be used to grasp objects with various geometric surface structures or stiffness but typically encounter difficulty in providing high grasping force. Although the combination of soft grippers with adhesive technology can increase their load capacity, this approach has disadvantages of compatibility and limited application range. This article has reported a bioinspired design of a soft gripper that integrates flat dry adhesive, soft actuator, and microspine to improve the comprehensive grasping ability of the soft gripper on smooth or rough surfaces. The adhesive strength of flat dry adhesives with different thickness or cross-linking ratios was investigated to ensure that a large grasping force can be provided. Soft actuators with uniform and nonuniform cross-sectional heights were compared, and results indicated that the soft actuator with a nonuniform cross-sectional height exhibited remarkable advantages for designing the integrated gripper. A microspine-spring-shape memory alloy coil structure was designed to control the retraction and protrusion of microspine and distribute the load on rough surfaces evenly. The proposed integrated gripper with the aforementioned design can lift regularly or irregularly shaped objects with smooth or rough surfaces and provide a higher adhesive force than the nonadhesive gripper. After inserting a flexible pressure film sensor into the soft gripper, the surface property of the grasped object can be measured, which is beneficial for the selection of a suitable grasping strategy in unknown environments. The designed gripper can be used in many applications, such as in unmanned aerial vehicles, industrial manipulators, and climbing robots.

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.

In vivo manipulation of microparticles, such as biological cells and drugs, has attracted considerable attention in recent years. This paper presents the development of robot-aided manipulation technology that can control targeted microparticles to move a relatively long distance in an in vivo environment. The field of view can be updated online, such that the controlled microparticle can be tracked automatically in transportation. Simultaneous localization and mapping for in vivo applications are first investigated. Based on the in vivo map, an artificial potential field-based controller with disturbance compensation is developed to navigate microparticles in vivo. Experiments on navigating single cells in living zebrafish embryos by using optical tweezers manipulator are performed to demonstrate the effectiveness of the proposed control approach in a dynamic in vivo environment.

Microrobots show great targeted-delivery potential in precision medicine. This article presents the use of a model-free approach to the navigation control of a microrobot in the cardiovascular environment. With the proposed approach, the microrobot can adapt to the non-Newton behavior of blood and environmental disturbances when it moves in blood vessels without knowledge of blood-velocity distribution. The referred trajectory of the navigated microrobot is generated by using a breadth-first search and genetic algorithm, aiming to minimize the energy consumption. The proposed navigation controller combines sliding mode control, backstepping control, and disturbance compensation. A high-gain extended state observer is designed to estimate and reject the uncertainties of model parameters and environmental disturbances. Simulations are performed to demonstrate the effectiveness of the proposed approach in a small artery compared with other control methods.

Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide summarized results and fail to show details for comparative analysis of production plans. Besides, the rapid adjustment to the plan in the case of an unanticipated incident is also not supported. In this paper, we propose PlanningVis, a visual analytics system to support the exploration and comparison of production plans with three levels of details: a plan overview presenting the overall difference between plans, a product view visualizing various properties of individual products, and a production detail view displaying the product dependency and the daily production details in related factories. By integrating an automatic planning algorithm with interactive visual explorations, PlanningVis can facilitate the efficient optimization of daily production planning as well as support a quick response to unanticipated incidents in manufacturing. Two case studies with real-world data and carefully designed interviews with domain experts demonstrate the effectiveness and usability of PlanningVis.

Animals in nature, such as geckos, inchworms, and felines can climb on various surfaces using different mechanisms and serve as references for the study of bio-inspired robots. This paper presents an inchworm-inspired climbing robot that consists of soft body and feet. The soft robot is actuated by shape memory alloy wires and utilizes microspine arrays to attach its feet to rough or soft surfaces. A series of experiments to test the functionality of the feet and torso of the designed robot have verified the theoretical feasibility of the robot. Results have shown that the designed bio-inspired robot can climb on inclined or vertical curved surfaces and flat surfaces. The robot can also adapt to the underwater environment. Thus, this robot has great potential for various applications such as pipeline inspection.

Introducing alterations in the mtDNA sequence is challenging but needed for potential therapies and basic studies. Direct microinjection of mitochondria into small cells has been considered inefficient and impractical. To address this issue, we present a highly efficient and precise robotic approach for automatically transferring mitochondria from one single cell to another. A microfluidic cell positioning device is used to pattern two different types of cells in one dimensional array, and an image processing algorithm is applied to identify the location of the mitochondria and cell. A visual feedback control mechanism is developed to enhance the mitochondrial extraction efficiency. A robust adaptive sliding control algorithm is developed to precisely control an X-Y stage to accomplish the extraction of mitochondria from A type cell followed by injection of the mitochondria into B type cell automatically. The system can transfer mitochondria from one cell to another with an average duration of 15 s/mitochondria. Experiments of mitochondrial transfer from THPI and NB4 cells to THPI cells and fibroblasts are conducted to show the effectiveness of the developed approach.

Cell migration plays an essential role in cancer cell study. Investigation of a novel method for controlling cell migration movement can help develop new therapeutic strategies. In this paper, a chemoattractant-loaded microbead, which is controlled by optical tweezers, is used to stimulate a target cell to accomplish automated migration along a desired path while avoiding obstacles. Models of both tweezers-bead and bead-cell interactions are investigated. A dual closed-loop control strategy is proposed, which includes an inner tweezers-bead control loop and an outer bead-cell control loop. A proportional-integral feedback plus feedforward controller is used to control the inner loop, and an active disturbance rejection controller is used for the outer loop, which can address the cell migration modeling errors and unknown external disturbances. A traffic rule based on interference-clearing mechanism is also proposed to reduce external disturbances on the system by preventing other particles from interfering with the migration process. The effectiveness of the proposed control approach is verified by simulations and experiments on migrating leukemia cancer cells.

This paper presents the development of a fish-like magnetically propelled microswimmer fabricated by 3D laser lithography. The microswimmer consists of a head and a caudal fin, just like a natural fish. There is a joint between the head and the fin so that the caudal fin can oscillate around the head to generate thrust, and the oscillation of the fin hardly transfers to the head, which benefits the stable motion of the microswimmer. The caudal fin of the microswimmer is deposited with a layer of 50 nm nickel (Ni) for magnetic actuation. Through applying an oscillating uniform magnetic field, the microswimmer can move along with the direction guided by the external magnetic field. A magnetic control system with permanent magnets is designed to provide such an oscillating uniform magnetic field, where the oscillating frequency and amplitude are controllable. A micro probe operation platform is used to detach the fabricated microswimmers from glass substrate in manufacturing. The proposed magnetically propelled microswimmer can be potentially used as powerful detoxification and biosensing tools for medical diagnosis and treatment in precision medicine.

Flexible continuum robots have exhibited unique advantages in working in an unstructured environment. Many applications require robots to actively control the deformation of soft objects, such as soft tissues in surgery. Thus, this study presents a robust model-predictive deformation control of a soft object using a flexible continuum robot. A linear approximation model for mapping from actuation space of a continuum robot to deformation space of a soft object is established. Jacobian matrix is estimated online by using a robust Geman-McClure estimator. Then, the deformation of the soft object is regulated by using a prediction horizon-based controller with exponential weighting for model uncertainty. The proposed control approach is effective in manipulating a soft object with a flexible continuum robot that is in contact with obstacles.

Electromagnetically actuated microparticles can be widely applied in the field of biomedicine, for its advantages of minimally invasive feature and approachability to complex microenvironments. In this paper, we propose a robust feedback control approach for precise 3D manipulation of a microparticle actuated by a self-constructed electromagnetic coil system. Model uncertainties, environmental disturbances as well as actuator energy loss problem are all taken into account in the controller design. It is shown that this proposed control scheme can enable the entire system to maintain the input-to-state stability in presence of various perturbations. Experimental results have demonstrated the effectiveness of the proposed control approach. Success of the current study will benefit the precise motion control with high throughput in applications of the targeted material delivery.

In this paper, we present a precise robot-aided single-cell surgery system to perform single-cell biopsy for cells <25 μm in diameter. A microfluidic chip is designed to arrange upto 100 individual cells in an array. A micropipette mounted onto a 3-DOF micromanipulator and a computer mouse-operated high-precision XY stage is developed to perform high-precision and high-throughput single-cell biopsy. The system is evaluated experimentally by extracting two organelles from adherent cells patterned in a microfluidic chip. The fluorescent-labeled nucleus and mitochondria of human foreskin fibroblast cells are biopsied to demonstrate the capability of the proposed system. The survival rate of the semi-automated biopsy is 73% and 45% for mitochondrial and nucleus biopsies, respectively.

As the rapid development of precision medicine, in-vivo manipulation of micro/nano-scaled particles has attracted increasing attention in recent years. The collision is one of the main reasons that falls the in-vivo particle transportation fail. In this paper, we develop an in-vivo cell transportation control approach using a robotically controlled optical tweezers manipulation system, where a so-called wide-area image gradient algorithm is used to avoid collision of the transported cell with other obstacles. The controller exhibits advantages of the reduced online calculation for collision avoidance, fast response, high accuracy, as well as an ability to compensate the environmental disturbance caused by blood flow. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed controller.

Most studies on automated cell transportation are single-task oriented. Results from these investigations hardly meet the increasing demand for emerging cell surgery operations that usually require a series of manipulation tasks with multiple processing steps. In this paper, automated cell transportation to accomplish a multistep process in cell surgery was investigated. A novel control system that can manipulate grouped cells to move into different task regions sequentially and continuously without interruption was developed based on a robot-aided optical tweezers manipulation system. A potential field-based controller was designed to achieve multistep processing control, where the new concepts of contractive coalition and switching region were incorporated into tweezers-cell coalition. The success of this controller lies in simultaneously controlling the positions of the optical tweezers, trapping multiple cells effectively, and avoiding collisions in a unified manner. Simulations and experiments of transferring a group of cells to a number of task regions were performed to demonstrate the effectiveness of the proposed approach.

In vivo manipulation of biological cells has attracted considerable attention in recent years. This process is particularly useful for precision medicine, such as cancer target therapy. Robotics technology is becoming necessary to stably and effectively manipulate and control single target cells in a complex in vivo environment. This paper presents a robot-aided optical tweezers-based manipulation technology that serves a function in the transport of single biological cells in vivo. An enhanced disturbance compensation controller is developed to minimize the effect of fluids (e.g., blood flow) on the cell. The method has exhibited advantages of flexibility in adjusting cell tracking trajectory online and the capability to minimize steady-state error and eliminate overshoot. Simulations and experiments of tracking single target cells in living zebrafish embryos have demonstrated the effectiveness of the proposed approach in a dynamic in vivo environment.

As rapid development of precision medicine, in vivo manipulation of micro/nano-scaled particles have attracted increasing attention in recent years. To accommodate complex in-vivo environment, robot-aided automated manipulation technology is highly demanded in trapping and controlling micro/nano-particles stably and effectively. This paper presents an in-vivo cell manipulation system, where a disturbance compensation controller is utilized to minimize the effect of fluid (e.g., blood flow) on the cell. The controller has exhibited advantages in adjusting cell tracking trajectory online, minimizing the steady-state error, and eliminating overshoot. Simulation and experimental results verify the performance of the controller.

Research and biomedical applications in cell surgery require transportation and rotation of biological cells. In these cell manipulation tasks, the cell of interest must be translated and oriented properly such that the desired component, such as the polar body or other organelles, can be imaged with optical microscopy. This paper presents a holographic optical tweezers (HOT) based system to carry out automated translational control in the plane, and rotational control about one rotational axes of a suspended cell. Based on the proposed general equations of motion of the cell, held in an optical trap, two controllers, one for cell translational and one for rotational control, are developed to translate and orient the cells to the desired position and orientation in a sequential manner. Experiments are performed to demonstrate the effectiveness of the proposed approach.

Transportation manipulation of biological cells, where cells are required to move into a fixed or moving region, has recently attracted increasing attention in bioscience and nanomedicine. Currently, the multicell transportation in practical applications is implemented manually, with low precision and efficiency. This paper presents a swarm-inspired approach to automated transportation of multiple cells using robotically controlled optical tweezers. A swarming controller, where holographic optical tweezers function as end-effectors to manipulate the cells, was developed. To ensure that the cells do not escape from the optical traps, the controller was designed by incorporating a saturation control of the cell offset to the laser center. Because the optical tweezers can only be position-controlled, oscillation may easily occur. This problem was solved by integrating artificial first-order kinematics of the optical tweezers into the controller design. Experiments of transporting multiple yeast cells were performed to verify the effectiveness of the proposed approach.

Precise manipulation of microparticles has received considerable attention for its great potential applications to clinical medicine. Among the existing manipulation techniques, the method of magnetic force based manipulation exhibits great advantages for its minimally-invasive feature and insensitivity to biological substance, making it ideally suitable to in vivo environment. On the other hand, increasing demand for accurate and high throughput magnetic manipulation highlights the need of incorporating automation technology in the manipulation. In this paper, we propose an automated control approach to manipulating a magnetic microparticle (bead) with a home-designed electromagnetic coil system. A simplified two-order dynamic model for a microparticle suspended in fluidic environment is established first. A closed-loop controller with utilizing visual feedback is then developed based on input-to-state stability and backstepping methodology. The proposed controller guarantees that the microparticle follows the desired trajectory even in presence of environmental uncertainties and disturbances. Experiments are performed to demonstrate the effectiveness of the proposed approach.

Optical tweezers has become a powerful tool in automated cell transportation control and has been used in a variety of biological applications. The use of optical tweezers for cell surgery has great potential for various biomedical applications such as microinjection, organelle extraction and modification, and preimplantation genetic diagnosis (PGD). In these cell surgical manipulation tasks, the cell of interest must be oriented properly such that the desired component, e.g., the polar-body or organelles, can be visualized by optical microscopy; thus cell rotation becomes a necessary procedure. Currently, cell rotational control can be carried out by laser tools that are usually handled by skilled people. The open-loop manual operation cannot be readily used for applications requiring precise and high throughput cell rotational control. This highlights the need of developing an automated controlled robot manipulator to rotate biological cells more accurately and efficiently. In this paper, we propose a cell surgery system that utilizes two optical traps, generated by robotically controlled holographic optical tweezers (HOT), to manipulate the cell for rotation, where the optical tweezers functions as special robot manipulators. Through dynamic modeling using T-matrix approach, the relationship between the applied torques and the spherical coordinates of the optical tweezers is characterized. A rotational controller is further developed to rotate the cell to the pre-desired orientation accurately. Experiments are performed to demonstrate the effectiveness of the proposed approach.

Rapid separation and transfer of target cells from heterogeneous mixture to desired region have become a key issue in many biomedical applications. In this paper, we propose an approach to high speed cell transfer for sorting rare cells from a small population of samples accurately using a robotic manipulation system equipped with optical tweezers. A novel switching geometrical model is established first for modeling high speed cell transfer, optical laser trapping, and obstacle avoidance based on the dynamics analysis. A normalized constraint function is designed to formulate how the trapped cell is maintained within the optical trap while avoiding obstacles. Then, a potential field based switching controller is developed, which can achieve automated cell trapping, high speed cell transfer, optical trap maintenance, and obstacle avoidance simultaneously. Finally, experiments of transferring cancer cells are performed to demonstrate the effectiveness of the proposed approach.

Few of the current injection technologies can be applied to those human cells whose diameters are ranged about 10-25 (im only. This paper reports our most recent effort in developing a robot-aided microinjection system to solve the challenging problem of automated injection on human cells. A unique microfluidic cell holding chip is designed and fabricated to trap the single cells in the predefined docking area. Imaging processing technique is used to recognize automatically the target cells to be injected. A microrobot system equipped with a micropipette is used to perform the injection tasks on these target cells. Injection experiments on human embryonic stem cells (hESCs) (ranged about 17-25μm) are performed to demonstrate the effectiveness of the proposed microinjection system.

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.

In cell manipulation with optical tweezers, it is required that the cell must be located within the optical trap. Due to the lack of a control technique that can automatically locate the cell within the optical trap while controlling the cell motion, the cell will easily escape from the optical trap and hence the manipulation task is failed. Therefore, development of a unified controller that can control both cell trapping and cell motion simultaneously has received increased attention in optical cell manipulations. In this paper, we addressed this challenging problem by developing a novel visual based control method that controls both cell positioning and cell trapping simultaneously. We first established a new geometric model aiming for confining the cell within a local region near the optical trap, and then formulated a so-called Cell-Tweezers Coalition (C-T Coalition). We secondly developed a potential field function based controller to drive the C-T Coalition to the desired position while avoiding collisions with any other obstacles in environment. Finally, we performed experiments of transferring yeast cells to demonstrate the effectiveness of the proposed approach.

Automatic manipulation and patterning of biological cells into an artificial scaffold is an imperative step in the production of high-quality tissue for tissue transplantation. This paper examines the incorporation of dielectrophoresis into a three-dimensional (3D) scaffold body for batch manipulation and patterning of cells. To facilitate dielectrophoresis-based manipulation, a multi-layer biocompatible scaffold structure utilizing its body as the integrated micro-electrodes was designed and fabricated using soft lithography. Voltage of opposite polarity was applied to the scaffold structure and the resultant electric field from the scaffold body polarized the cells in the culture medium and attracted them to migrate towards the scaffold body. Experiments were conducted and the results confirm that the proposed multi-layer scaffold is capable of generating dielectrophoretic forces to manipulate the cells to the scaffold surface, forming a three-dimensional cellular pattern automatically.

This paper investigates the problem of steering a nonholonomic mobile robot to achieve a circular motion around a target. We propose control schemes that require only bearing measurements and deal with two types of targets: point target and disk target. Circumnavigation schemes are developed to achieve efficient encirclement of the target. We show that using the proposed control schemes, the robot can circle the target from a prescribed radius without distance measurement and avoid collision with disk target as well. The validity of the proposed control schemes is supported by experiments on an e-puck robot.

In numerous cellular applications, cells are transported to specific positions or extracted from complex cell solutions. Therefore, an efficient cell transportation path planner for these applications is important for avoiding collisions with other cells or obstacles. In this paper, a path planning approach to transporting cells using a robot-aided optical manipulation system is presented. Optical tweezers functions as a special end-effector in transporting a target cell to the desired position along the generated path. The path planner is designed based on the rapidly exploring random trees (RRT) algorithm for calculating a collision-free path for cell transportation. Both static and dynamic path planners are developed. For the dynamic path planner, an online monitoring strategy is employed to dynamically avoid collisions with randomly appeared obstacles caused by environmental influence such as the Brownian movement of microparticles. Experiments of transporting yeast cells are performed to demonstrate the effectiveness of the proposed approach. Note to Practitioners - Manipulations of cells and other microparticles represent an essential process for most cell-based bioengineering applications, such as cytopathology, cell sociology, and cytotaxonomy. Cell transportation, which is treated as a typical cell manipulation task, has recently received considerable attention because of its wide applications. This paper presents a novel approach to applying RRT-based path planner to cell transportation with a robot-aided optical manipulation system. The research outcome provides a unique solution to achieving cell transportation automatically and efficiently.

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.

Optical manipulation of biological cells has recently attracted increasing attention in bioscience and nanotechnology, where optical tweezers are used as end-effectors to manipulate the cells with high precision and flexibility. Analysis of the dynamics of the optically trapped cells plays a critical role in many cell manipulation tasks such as the automatic cell transportation and force transducer. This paper presents a novel approach to calibrating the cell dynamics with the adaptive control technology. According to different measurements, two adaptive tracking controllers are designed, based on which the estimated parameters of the cell trapping dynamics (i.e., the rate of viscous coefficient and trapping stiffness) can automatically converge to the true values. Stability of the adaptive controllers and convergence of the estimated parameters are analyzed by using Lyapunov approach. Simulations and experiments of manipulating yeast cells are performed to verify the effectiveness of the proposed approach.

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.

Engineering induced cell fusion is becoming a promising tool in novel therapeutic studies for treating various diseases. The majority of current in vitro cell fusion methods are based on random cell pairing with loose contact, which also needs large amounts of cells. In this paper, we present a robotically controlled laser-induced cell fusion approach based on the microwell array, which exhibits advantages of high selectivity and controllability. Optical tweezers and optical scissors are employed to achieve cell pairing and fusion, respectively. The specific cells are characterized and preselected with an on-chip isolation method prior to pairing. The paired cells are then transported, deposited, fused, and released at a predefined location with high spatiotemporal resolution. Experiments of fusion on individual pairs of human embryonic stem cells are performed to evaluate the performance of the proposed cell fusion tool.

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.

Bevel-tip steerable needles are widely used in modern minimally invasive percutaneous procedures to reach specific positions inside the body. In this paper, we propose a dynamic path planning approach to insert a steerable needle into soft tissue. Based on mathematic modeling of soft tissue deformation during the insertion process, the proposed approach can drive the needle to reach a goal position in deformable environment. As the goal position changes in the deformable environment, a dynamic path planner is proposed to re-plan the path until the needle tip approaches the target successfully. Simulation and experiment are performed to demonstrate the effectiveness of the proposed approach to insert a flexible needle in deformable environment.

Flocking of micro-scaled particles, attracts increasing attention especially in cell engineering and drug industry, due to its potential application for particle manipulation with high throughput and productivity. This paper presents an efficient approach to flocking micro particles with robotics and optical tweezers technologies. All particles trapped by optical tweezers can be gradually moved towards a pre-defined region. The main contribution of this paper lies in a solution to achieve the flocking manipulation of particles in micro environments. A local potential function is proposed to avoid collision amongst particles and obstacles. Based on the relationship amongst laser power, particle movement velocity, and trapping force, saturation of velocities is employed to bound particle velocities. In this way, the flocking manipulation can be operated with efficiency and safety. Experiments on yeast cells with a robot-tweezers system are finally performed to verify 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 presents a path planning approach to the transportation of biological cells with combined robotics and optical tweezers technologies. A rapid path planner based on RRT (Rapidly-exploring random trees) algorithm is applied to find a collision-free path for automatic cell transportation. The optical tweezers are employed to trap and move the cell along the generated path toward a pre-specified goal position. Extending our early reported work on static path planning, a new dynamic path planner that considers the environmental change due to the Brownian movement of the cells is developed. This dynamic path planner can successfully enable the trapped cell to avoid collisions with other cells during transportation in a dynamic environment. Experiments on transporting yeast cells are performed to demonstrate the effectiveness of the proposed approach.

We propose a coalition-based approach to solve the task allocation problem of multiple robots with resource constraints. The resources required by task execution characterize the robots and tasks. Robots must form coalitions to accomplish the assigned tasks because individually, each robot may not complete the task independently due to resource limitation. We consider both online and offline assignment manners of the task allocation problem. For online assignment, a sequential coalition method is proposed to select efficiently the suitable robots to form coalitions for the assigned task. For offline assignment, a holistic coalition method is proposed for global optimization of all the assigned tasks. Both sequential and holistic coalition methods are compared with existing approaches. Numerous simulations and experiments performed on heterogeneous multiple mobile robots demonstrate the effectiveness of the proposed coalition-based task allocation methods. Note to Practitioners - Task allocation into a group of heterogeneous mobile robots for implementing multiple tasks is a challenge in multirobot applications. To find and organize the most suitable coalition for each task, we need to well organize the coalition for each task to maximize the robot group utility and optimize the task allocation solution. The sequential and holistic coalition methods presented in this paper provide both online and offline solutions for optimal multirobot task allocation. The advantage of the sequential coalition method lies in its efficiency in selecting best-fitted robots during coalition forming, and the advantage of the holistic coalition method lies in its effectiveness in finding the global optimal solution for all tasks. We illustrate the effectiveness of the proposed methods through numerous case studies with comparisons in this paper.

Significant demand for both accuracy and productivity in batch manipulation of microparticles highlights the need to develop an automatic arraying approach to placing groups of particles into a predefined array with right pairs. This paper presents our latest effort to achieve this objective using integrated robotics and holographic optical tweezers technologies, where holographic optical tweezers function as special robot end-effectors to manipulate the microparticles. Based on the physical dynamics of trapping, a potential-field-based controller is developed to drive every pair of particles to the assigned array, while preventing collisions between particles. The significance of the proposed controller lies in the capability of driving two groups of particles into a common array in right pair and controlling the interdistances between the particles in pairs. Experiments are performed to demonstrate the effectiveness of the proposed approach.

This paper presents a novel formation framework for the manipulation of micro-scaled particles with robotics and optical tweezers technologies. An allocation-based formation algorithm is used to calculate particles' trajectories. Along the trajectories, particles are trapped and moved by optical tweezers. Particles can be gradually moved into a pre-defined formation array. The main contribution of this paper lies in the proposal of using multi-agent solution to address the formation problem of particles in micro environment. The proposed framework can be applied to many bio-applications, such as cell sorting, cell transportation, cell-to-cell interaction study, etc., with high throughput and precision. Experiments on micro-scaled particles, with a robot-tweezer manipulation system, are performed to demonstrate the effectiveness of the proposed approach.

Increasing demand for both accuracy and productivity in cell positioning highlights the need for automation process that integrates robotics and micro/nano manipulation technologies. Optical tweezers, which use low power laser beams to trap and manipulate particles at micro/nano scale, can be treated as special robot "end-effectors" to position biological objects in a noninvasive way. In this paper, we propose to use a robot-tweezer manipulation system for automatic transportation of biological cells. Computer vision is utilized to supply real-time positions of target cells. Dynamics equation of the trapped cell during the movement is analyzed. Closed-loop controllers are designed for transporting single cell as well as multiple cells. Experiments are performed on transporting live cells to verify the effectiveness of the proposed approach.

This work presents a novel panoramic stereoscopic system consisting of a fisheye lens camera and a hyperbolic mirror with co-axis installation. From the overlapping field of view captured through the fisheye lens and the reflection of the mirror, position of an object point in 3D Euclidean space can be reconstructed once the system geometry is calibrated. To deal with the non-single viewpoint issue in the catadioptric image, a generic radial distortion model is used to describe the imaging process with a series of viewing cones. The parameters of the viewing cones are estimated using a homography based method with observations of an LCD panel at a few unknown positions. Following this, a closed form solution for 3D reconstruction is used with a non-linear optimization to obtain an optimal calibration. A prototype of the proposed design is constructed. Quantitative experiments are conducted to evaluate the calibration result in terms of 3D reconstruction precision. With the calibration result, we also present potential robotic applications of the proposed system such as 3D environment reconstruction in a 360 degree horizontal field of view.

In this paper, we demonstrate the effectiveness of robotic cell stretching with optical tweezers for biomechanical characterization. Optical traps serve as end-effectors to manipulate micro-beads attached to the cell surface. The dynamics of the cell-bead mixture during cell stretching is investigated for the first time. Based on our previous work, cell stiffness is extracted and biomechanical properties of cells can be characterized. Our study shows that the modeling results agree with the experimental data. Further, the area compressibility moduli of two types of cells, human embryonic stem cells (hESC) and hESC-derived cardiomyocytes (hESC-CM), are characterized. The results indicate that undifferentiated stem cells are much softer than differentiated ones, which provides an important insight into the cell mechanics during hESC differentiation. In summary, this paper successfully demonstrates that the robot-tweezer system can manipulate biological cells effectively to characterize biomechanical properties of living cells.

This paper presents a robot-assisted methodology that integrates optical tweezer and microfluidic chip technologies to realize automatic cell sorting from small sample population. The microfluidic chip used for cell sorter is designed and fabricated, and the flow environment within the microfluidic channel is investigated with simulation. Two image processing methods, depending on size and fluorescence label respectively, are used to recognize the target cells. With robotic manipulation of optical tweezers, the target cells can be moved to the desired area. Motions of optical tweezers are further analyzed for improvement of transportation efficiency. The relationship between the laser power and the cell maximum moving velocity is analyzed such that the robust cell sorting with as low power as possible can be achieved. Experiments on sorting yeast cells are performed to demonstrate the effectiveness of the proposed cell sorting approach.

Batch manipulation of micro particles attracts increasing attention among researchers in bio-medical fields such as cellular engineering and drug discovery. Significant demand for both accuracy and productivity highlights the need of developing an automatic arraying approach to moving and pairing a swarm of particles to a pre-defined array. This paper presents our latest effort to achieve this objective by using integrated robotics and holographic optical tweezers technologies, where holographic optical tweezers function as special robot end-effectors. A controller is proposed to drive pairs of particles to the assigned regions which are centered at array points. The potential field method is utilized to avoid collisions between particles. Experiments on colloidal particles are performed to demonstrate the effectiveness of the proposed approach.

Sorting of specific target cells is an important process in biotechnological research and clinical medicine. This paper proposes a methodology that integrates optical tweezers and microfluidic chip technologies to realize the automatic cell sorting in a continuous flow environment. In the proposed system, cells are driven through the region of interest, and the digital image processing technique is utilized to recognize the target cells. The optical tweezers are used to move the cells selected by image processing to the desired area. In order to move the target cells to the collection reservoir more quickly and reduce the cell-cell interactions (e.g., clustering or jamming), the motion of optical tweezers is further investigated. The relationship between the laser power and the cell maximal escape velocity has been studied to achieve the robust cell sorting with lower power. The idea of multi-trap parallel processing is proposed to achieve high throughput without losing the purity. Utilizing the proposed cell sorter, we can collect rare cells from a sample of primary tissue without expansion, with less damage and higher accuracy.

Laser trapping in the near infrared regime is a noninvasive and convenient manipulation tool, which can be utilized as micromanipulator for a large number of biological applications. Increasing demands for both accuracy and efficiency in cell manipulation highlight the need for automation process that integrates robotics and tweezers technologies. In this paper, we propose a robotic manipulation system with optical tweezers, and analyze the force applied on the trapped cell for design of an optimal trapping strategy. The dynamic motion of the cell with consideration of both the trapping and the viscous forces is analyzed, based on which the motion profile of the motorized stage is designed to ensure both safety and efficiency of the cell delivery. A modified A-star algorithm is used for path planning in transporting cells. Experiments are performed on manipulating the yeast cells to demonstrate the effectiveness of the proposed approach.

This paper presents a novel flocking framework for the manipulation of micro-scale particles with robotics and optical tweezers technologies. A region-based flocking algorithm is used to calculate the particles' trajectories. The optical tweezers are used to trap and move the particles along generated trajectories. All particles can be gradually moved into a pre-defined region. The main contribution of this paper lies in the proposal of using multi-agent solution to address the flocking problem of particles in micro environment. The proposed framework can be applied to many bio-applications such as cell sorting, cell property characterization, and so on, with high throughput and precision. Experiments on micro-scale particles with a robot-tweezers system are finally performed to verify the effectiveness of the proposed 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.

In the existing literatures of multirobots, it is usually assumed that the networked robots remain connected in topology during the task execution. In practice, however, it is not easy to guarantee connectivity of the networked robots in a clustered environment. Failure to maintain connectivity may decrease the performance of the networked robots or even fail the task. In this paper, we propose a multirobot motion coordination strategy that can maintain multirobot connectivity as well as guarantee obstacle avoidance. A potential function is proposed to generate bounded control inputs for networked robots. The efficiency of the proposed approach is demonstrated in both simulation and experiment performed on multirobot consensus tasks.

This paper presents a motion planning approach to coordinating multiple mobile robots in moving along specified paths. The robots are required to fulfill formation requirements while meeting velocity/acceleration constraints and avoiding collisions. Coordination is achieved by planning robot velocities along the paths through a velocity optimization process. An objective function for minimizing formation errors is established and solved by a linear interactive and general optimizer. Motion planning can be further adjusted online to address emergent demands such as avoiding suddenly-appearing obstacles. Simulations and experiments are performed on a group of mobile robots to demonstrate the effectiveness of the proposed coordinated motion planning in multirobot formations.

Cell manipulation has received considerable attentions in recent years. Most of cell manipulations are performed manually without guarantee of high precision and high throughput. This paper reports our latest research on integrating robotics technologies into optical tweezers system for manipulation and biomechanical characterization of human red blood cells (RBCs). We first demonstrate the effectiveness of the robot-tweezers system in manipulation of micro-beads, which is followed by stretching RBCs to different levels of deformations. The whole manipulation process is conducted with visual guidance and position feedback control, where the cell stretching direction is determined automatically through image analysis. The relationship between the stretching force and the induced deformation is obtained through force calibration and image processing. To characterize the mechanical properties of RBCs from the obtained experimental results, a mechanical model based cell property characterization strategy is introduced. Comparing the modeling results to the experimental data, the mechanical properties of human RBCs are characterized. In conclusion, this study demonstrates that the robotic manipulation technology with optical tweezers can be used to manipulate biological cells, and further, to characterize the biomechanical properties based on the cell mechanical model.

Robotic cell injection is a technique that employs automated device to insert substances into a single living cell with a fine needle. Most existing microinjection methods are based on position control without explicit regulation of the injection force. The injection force, if not controlled properly, may destroy the cell and lead to death of the cells. In this paper, we propose a force control based cell injection technology to explicitly regulate the injection force to follow the desired force trajectory during the injection. Any desired force trajectory that is twice continuously differentiable can be realized by the proposed approach. The convergence of the force tracking algorithm is provably guaranteed. Experiments performed on a laboratorial robotic cell injection system demonstrate the effectiveness of the proposed approach.

This paper presents a new approach to formation forming of multiple mobile robots with decentralized motion planning. When the robots enter the required formation, there exists the formation-structure constraint, which causes disorder or even deadlock of the formation. A dynamic priority strategy is developed to solve the problem of the formation-structure constraint, and coordinate the robots to form the formation in a proper order. Simulations are performed on a group of mobile robots to demonstrate the validity of the proposed strategy to the formation system.

Mechanical properties of biological cells play an important role in regulating cellular functions. Some micromanipulation methods have been reported in the literature to measure cell mechanics, but they are either high-costly or difficultly-operated. This paper presents our approach to use microrobotic cell injection technology as the test bed to characterize the mechanical properties of biological cells, by virtue of low cost and easy operation. By extending our previous work [41], we develop a mechanical model to interpret the mechanical responses during microinjection and extract the cells properties. Both finite element analysis and microinjection experiments are performed to verify the mechanical model. It is shown that the results obtained from the proposed mechanical model agree well with that obtained from finite element analysis and the experiments. Elastic moduli of zebrafish embryos at different developmental stages are characterized. This demonstrates not only the validity of the proposed model but also the fact that the microrobotic cell injection technology combining with the mechanical model can be used to characterize the mechanical properties of biological cells.

In a robotic cell injection system, the penetration force applied on the cell reflects the changes of the physical behavior of the cell. The force, if not controlled properly, may damage to the cells or even lead to death of the cells. The current cellular force measurement is limited by the inherent cantilever structure of the sensor, which may not be applicable to a practical cell injection system. In this paper, a simply supported beam structure based PVDF force sensor is first presented. The proportion relation is established between the penetration force and the sensor output after compensation. Using the designed force sensor, the force applied on the cell can be measured, and a force control based cell injection system is constructed. The experimental results performed on zebrafish embryos demonstrate the effectiveness of the micro force sensor and the force based control framework.

In this paper, a vision-based impedance control algorithm is proposed to regulate the cell injection force, based on dynamic modeling conducted on a laboratory test-bed cell injection system. The injection force is initially calibrated to derive the relationship between the force and the cell deformation utilizing a cell membrane point-load model. To increase the success rate of injection, the injector is positioned out of the focal plane of the camera, used to obtain visual feedback for the injection process. In this out-of-plane injection process, the total cell membrane deformation is estimated, based on the$X-Y$coordinate frame deformation of the cell, as measured with a microscope, and the known angle between the injector and the$X-Y$plane. Further, a relationship between the injection force and the injector displacement of the cell membrane, as observed with the camera, is derived. Based on this visual force estimation scheme, an impedance control algorithm is developed. Experimental results of the proposed injection method are given which validate the approach.

This paper presents a synchronization control approach for the minimization of contouring errors of multi-axis CNC machine tools. The contouring errors are presented by the position synchronization errors that are defined as differential position errors between each axis and its adjacent ones. Using cross-coupling concept, a decentralized tracking controller is developed with feedback of both position and synchronization errors, formed with a combination of feedforward, feedback and a saturation control. It is proven that this controller can asymptotically stabilize both position and synchronization errors to zero. The proposed controller does not require significant use of the system dynamic models. Experiments performed on a multi-axis machine tool demonstrate improved performance especially in the contouring error minimization.

Biological cell injection is laborious work that requires lengthy training and suffers from a low success rate. In this paper, a robotic cell-injection system for automatic injection of batch-suspended cells is proposed. To facilitate the process, these suspended cells are held and fixed to a cell array by a specially designed cell-holding device, and injected one by one through an ldquoout-of-planerdquo cell-injection process. A micropipette equipped with a polyvinylidene fluoride microforce sensor to measure real-time injection force is integrated in the proposed system. Through calibration, an empirical relationship between the cell-injection force and the desired injector pipette trajectory is obtained in advance. Then, after decoupling the out-of-plane cell injection into a position control in theX-Yhorizontal plane and an impedance control in theZ-axis, a position and force control algorithm is developed to control the injection pipette. The depth motion of the injector pipette, which cannot be observed by microscope, is indirectly controlled via the impedance control, and the desired force is determined from the onlineX-Yposition control and cell calibration results. Finally, experimental results demonstrate the effectiveness of the proposed approach.

In this paper, we present a synchronization approach to trajectory tracking of multiple mobile robots while maintaining time-varying formations. The main idea is to control each robot to track its desired trajectory while synchronizing its motion with those of other robots to keep relative kinematics relationships, as required by the formation. First, we pose the formation-control problem as a synchronization control problem and identify the synchronization control goal according to the formation requirement. The formation error is measured by the position synchronization error, which is defined based on the established robot network. Second, we develop a synchronous controller for each robot's translation to guarantee that both position and synchronization errors approach zero asymptotically. The rotary controller is also designed to ensure that the robot is always oriented toward its desired position. Both translational and rotary controls are supported by a centralized high-level planer for task monitoring and robot global localization. Finally, we perform simulations and experiments to demonstrate the effectiveness of the proposed synchronization control approach in the formation control tasks.

Automatic cell injection has been the focus of many researches and commercial development for several years. In this paper, a robotic cell injection system for automatic batch injection of suspended cells is developed. To facilitate the process, these suspended cells are held and fixed to a cell array by a specially designed cell holding device, and injected one by one through an “out-of-plane” cell injection process. A micropipette equipped with a PVDF micro force sensor is integrated in the proposed system. The force sensor is utilized to measure real time injection force applied to the cells during injection process. Through calibration of the relationship between the cell injection force and the desired injector pipette trajectory, a position (vision) and force control algorithm is proposed and applied to the motion control of the injection pipette in three-coordinate directions during an injection process. The out-of-plane cell injection task is decoupled into a position control in X-Y horizontal plane and an impedance force control in Z-axis. The depth motion of the injector pipette, a common problem of three-dimensional micromanipulation, is indirectly controlled by the force control. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach.

This paper presents a synchronization control strategy for formation control of swarm of mobile robots. The formation control problem is successfully posed as a synchronization control problem, and the concept of synchronization error is created for measuring the formation control effect. A decentralized trajectory tracking controller is developed with feedback of both position and synchronization errors, formed with a combination of feedforward, feedback and a saturation control, without significant use of robot models. It is proven that this synchronization controller guarantees both position and synchronization errors to converge to zero. The rotary control, in the form of a computed torque control, ensures that the robot is always oriented towards its desired position to assist the formation control in translation. Experiments were performed on three mobile robots in a formation switch task. The experimental results demonstrate the effectiveness of the proposed synchronous formation control approach.

Robotic cell microinjection is a technique that employs an automatic method to insert substances into a single living cell with a fine needle. Most available microinjection methods are based on position/velocity tracking, which make it incapable of controlling the injection force. The uncontrolled injection force, however, may destroy the cell and lead to the death of the cell. In this paper, a new adaptive force tracking algorithm within the impedance control framework is proposed to control the injection force applied on the cell. A target impedance is specified in the inner-loop and a trajectory modifying controller is design in the outer-loop for time varying ramp force tracking. The adaptive technique is also used to compensate for the uncertainty of the cell membranepsilas stiffness, the convergence of the system is provably guaranteed. Experiments performed on a laboratorial robotic cell injection system demonstrate the effectiveness of the approach.

This paper presents a new control mechanism based on a novel distinguished node (DN) model, for network topology and analysis in small-agent networks such as urban traffic network. The proposed DN model is represented by an extended direct graph that contains congregation and dispersing nodes, and main and connecting links. Both static and dynamic properties of the DN model are analyzed. With this new network model, a control mechanism is developed, which is governed by two heuristic rules of conflict avoidance and total delay minimization. A case study on a real urban traffic network is performed to demonstrate feasibility of the proposed DN model applied to the empirical networks, and verify the effectiveness of the proposed control mechanism.

This paper presents two filter design methods for discrete time fuzzy dynamic systems based on a piecewise quadratic Lyapunov function. It is shown that the resulting filtering error system is globally stable with guaranteed Hinfinor generalized H2performance and the filter gains can be obtained by solving a set of linear matrix inequalities. Two simulation examples are also given to illustrate the performance of the proposed approaches.

Biological cell injection is laborious work which requires lengthy training and suffers from a low success rate. Even a tiny excessive manipulation force can destroy the membrane or tissue of the biological cell. This makes the control of the injection force an important factor in the cell injection process. In this paper, a vision-based impedance force control algorithm is proposed based on dynamic modeling of a laboratory test-bed injection system. The injection force is calibrated in a cell injection task to derive the relationship between the force and the cell deformation. A cell biomembrane point-load model is utilized in this force calibration. In three-dimensional cell injection task, the total cell membrane deformation is estimated, based on the X - Y coordinate frame deformation of the cell, as measured with a microscope, and the known angle between the injector and the X Y plane. Further, a relationship between the injection force and the injector visual displacement of the cell membrane is derived. Based on this force visual estimation scheme, an impedance force control algorithm is developed. Finally, experimental results are given which demonstrate the effectiveness of the proposed approach.

This paper presents a synchronous control approach to swarms of mobile robots in switching between formations. According to the desired formation, a synchronization control goal is derived, based on which the position synchronization error is defined as differential position errors between very pair of two neighboring robots. A decentralized trajectory tracking controller is then developed with feedback of both position and synchronization errors, formed with a combination of feedforward and feedback controls. It is proven that this tracking controller can asymptotically converge both position and synchronization errors to zero. Simulations are performed on a group of twenty fully-actuated mobile robots in a switching task between different ellipse curves. The simulation results demonstrate the effectiveness of the proposed synchronous control design for the formation control.

In this paper, we propose a vision control strategy to perform automatic microassembly tasks in threedimension (3-D), and develop relevant control software. Specifically, using a 6 degree-of-freedom (DOF) robotic workstation to control a passive microgripper to automatically grasp a designated micropart from the chip, pivot the micropart, and then move the micropart to vertically insert into a designated slot on the chip. In the proposed control strategy, the whole microassembly task is divided into two subtasks, micro-grasping and micro-joining, in sequence. To guarantee the success of microassembly and manipulation accuracy, two different two-stage feedback motion strategies, the pattern matching and auto-focus method are employed, with the use of vision-based control system and the vision control software developed. Experiments conducted demonstrate the efficiency and validity of the proposed control strategy.

In this paper, a new robot controller architecture is proposed to implement various complex control algorithms for improved high-speed performance. The main thrust of the research is to remove the servo control loop from the digital signal processor (DSP) and implement the high-speed servo loop in a field programmable gate array (FPGA). The main objective of this architecture is to utilize the ultra-high-speed hardwired logic of the FPGA to enhance the overall computational capability and relieve the computational load of the DSP for other tasks. The control algorithm is partitioned into a linear portion and a nonlinear portion. The linear portion with position/velocity feedback represents the major control loop and is implemented in the FPGA. The nonlinear portion acts as dynamic compensation to the linear portion to calculate model-related control gains/parameters, and it is implemented in the DSP. In tandem, with the newly developed control hardware architecture, an FPGA-based motion control integrated circuit (IC) is designed. Experiments are conducted on an industrial robot manipulator to compare the closed-loop performance with this new control architecture and the traditional one, when the same control algorithm is used. The experimental results demonstrate that the proposed new control architecture exhibits much improved motion performance indeed, especially in high-speed motions.

This paper presents an FPGA based motion control IC by integrating most of the motion control functions into one FPGA chip. The hardware system of the FPGA executes quickly in dedicated parallel hardware without timing overhead penalty of a serial processor. The update rates of the current control loop and position/velocity control loop can be up to 120 kHz and 40 kHz, respectively. The new designed motion control IC can be incorporated with a general-purpose microcontroller or DSP to provide a simple, compact, low-cost, and effective solution for high sampling rate motion control system. A fully digital 3-axis motion controller/amplifier is designed by using the proposed motion control IC. In order to increase the sampling frequency of the system when using dynamic control algorithm for robotic control, the dynamic control algorithm can be partitioned into a linear portion and a nonlinear portion. The linear portion with position/velocity feedback represents the major control loop and is implemented in the FPGA based IC. The nonlinear portion acts as dynamic compensation to the linear portion to perform complex modeling related calculations, and is implemented in the DSP. Experimental results demonstrate that the new proposed system is successful and it exhibits much improved motion performance especially during high-speed motions

In this paper, in order to improve tracking accuracy and satisfy multiple closed-loop performance specifications simultaneously during high-speed, high-acceleration trajectory tracking for a 3 degree-of-freedom (DOF) planar parallel manipulator, we propose a new control approach, termed convex synchronized (C-S) control. The C-S control is base on the so-called convex combination method and synchronized control. To implement the synchronized control scheme, a feedback signal, termed the synchronization error, is employed, which represents the degree of coordination of the active joints in the parallel manipulator based on the kinematics of the parallel manipulator, and thus tracking accuracy is improved. On the other hand, the convex combination method utilizes the convex property of the required closed-loop performance specifications. Through combining multiple linear controllers, so-called sample controllers, each of which satisfies at least one closed-loop performance specification, a C-S controller is algebraically calculated, which satisfies all closed-loop performance specifications simultaneously. Compared with traditional trial-and-error method, the convex combination method is more straightforward and efficient. Hence, possessing advantages of the synchronized control and the convex combination method, the proposed C-S control method can both improve tracking accuracy and satisfy multiple closed-loop performance specifications simultaneously (MSS). Experiments conducted on a 3-DOF P-R-R type planar parallel manipulator demonstrate the above claims

In this paper, a new motion control hardware architecture is proposed for improved motion performance of robotic manipulators during high speed motion. The main idea is to remove the servo control loop from the DSP (digital signal processor) to a FPGA (field programmable gate array), and utilize the high speed hardwired logic of the FPGA to enhance the computation capability and relieve the computing load on the DSP. The control algorithm is partitioned into a linear portion and a nonlinear portion. The linear portion with position/velocity feedback represents the major control loop and is implemented in the FPGA. The nonlinear portion acts as dynamic compensation to the linear portion to perform complex modeling related calculations, and is implemented in the DSP. A new FPGA-based motion control IC is designed to realize this new control hardware structure. Experiments were conducted on a Yamaha robot manipulator to compare new control architecture and the existing one, when the same control algorithm was used. Experimental results demonstrate that the proposed new control architecture exhibits much improved motion performance especially during high-speed motions

In order to improve trajectory tracking accuracy for a three degree-of-freedom (DOF) planar parallel robot, in this paper, we develop a new control approach based on adaptive control with the use of the so-called synchronization error. Similar to the contour error proposed for machine tools, the defined synchronization error represents the degree of coordination amongst the active joints in the parallel robot, which is substantially different from the traditional tracking errors. By using the synchronization error, all active joints in the parallel robot are controlled to move in a synchronous manner so that the trajectory tracking accuracy of the robot end-effector is substantially improved. In addition, with the use of adaptive control, the synchronization error and the pose error of the platform are guaranteed to converge to zero simultaneously, while uncertain parameters in the system dynamic model are guaranteed to converge to their true values. Experiments conducted on the planar parallel robot verify the above claims and evaluate performance of the proposed control approach, compared with conventional PID control

As a probabilistic localization algorithm, Monte Carlo localization (MCL) method has been widely used for mobile robot localization over the past decade. In this paper, an extended MCL method (EMCL) is developed by incorporating two different resampling processes, namely importance resampling and sensor-based resampling, to conventional MCL for improvement of localization performance. Different resampling processes are utilized based on a matching of sample distribution and observations. Two additional processes for validating over-convergence and uniformity are introduced for examination of such matching. A visual based EMCL is further implemented using a triangulation-based resampling from visual features recognized by Bayesian networks. Experiments are conducted to demonstrate the validity of the proposed approach

This paper presents a simple and globally stable nonlinear proportional-integral-derivative (N-PID) controller incorporated with a saturated function design. The proposed controller is formulated by using a new class of saturated function derived from quasi-natural potential function to shape the position and velocity errors. The global asymptotic stability of the controlled system for the setpoint position control is proven. Experiments are performed on an industrial robot manipulator to demonstrate the improved performance of the proposed approach over conventional linear PID controller

This paper presents an optimal algorithm for finding collision-free path and trajectory for microair vehicles (MAVs) in urban terrain where multiple ground obstacles are distributed. Regarding vertices of polygonal obstacles as input points of the Delaunay triangulations, a Delaunay-based path planning is proposed to determine an optimal waypoint path while considering criterions of minimum fuel expenditure and collision risk. A trajectory generation method with corresponding optimal constraints is further developed to yield feasible fly trajectory along the planned path. The off-line pre-planning and on-line updated planning are integrated to enhance autonomy of vehicles flying in dangerous situations, with tight maneuvering and operational capability. Simulation results of a mission scenario are finally presented to demonstrate effectiveness of the proposed approach

High-precision motion of parallel manipulators depends not only on the position accuracy of each actuator, but also on the position synchronization of all actuators. This paper presents a simple synchronized control algorithm for the setpoint position control of parallel manipulators, by incorporating cross-coupling technology into a common proportional-derivative (PD) control architecture. An integrated controller is developed, consisting of a PD control and a saturated proportional-integral (S-PI) control with feedback of the differential position errors amongst actuators (defined as the synchronization errors). The controller can stabilize the motion of each actuator, and meanwhile synchronize all actuators' motions so that both position and synchronization errors converge to zero. The control algorithm does not use the modeling parameters in the controller formulation, and thus permits easy implementation in practice. It is proved that the proposed method can guarantee global asymptotical stability of the system. Experiments conducted on a planar three-degree-of-freedom parallel manipulator demonstrate the effectiveness of the proposed approach.

In this paper, we develop a new control method for a P-R-R type planar parallel manipulator, termed adaptive synchronized (A-S) control. The novelty of the proposed A-S control, a combination of synchronized control and adaptive control, is in the application of synchronized control to a planar parallel manipulator. To improve trajectory control, based on the kinematics of the planar parallel manipulator, we design a synchronization feedback control signal, termed the synchronization error, which represents the degree of coordination amongst the actuated joints. Employment of the synchronization error is shown to substantially reduce the pose error of the moving platform of the planar parallel manipulator during trajectory tracking. An adaptive controller is also used to estimate uncertain dynamic parameters of the manipulator. Under the assumption of persistent excitation, the proposed A-S control algorithm is theoretically proved to simultaneously guarantee the convergence of tracking errors and the synchronization error. Moreover, the estimated unknown parameters are guaranteed to converge to their true values as well. Finally, experiments are conducted to verify these claims and evaluate the performance of the proposed controller. Experimental results show that A-S control yields good trajectory tracking performance.

A simple synchronized control algorithm is proposed, by incorporating cross-coupling technology into a common PD control architecture, for control of parallel manipulators. A saturated proportional (S-P) control and a linear proportional derivative (PD) control plus gravity force compensation is implemented for synchronization and position control, respectively. The proposed control law is easy to implement and is able to stabilize motion of each actuator while synchronizing all actuators’ motions, so that differential position errors amongst actuators converge to zero. It is shown that the proposed method guarantees global asymptotical stability of the closed-loop system. Experiments performed on a three-DOF parallel manipulator demonstrate the effectiveness of the proposed approach.

This paper presents development of an electrically powered Micro Air Vehicle (MAV) prototype based on aerodynamic analysis in wind tunnel and water tunnel tests. Two airframe models in triangle and square wing planforms, each with a wingspan of 100mm, are analyzed with a small-scale low speed wind tunnel to examine the practical characteristics at the low Reynolds number. The test in a water tunnel is further carried out to investigate the relationship between the vortex aerofoil design and turbulences created. The testing results show that the triangle wing aerofoil appears greater maximum lift coefficient and little occurrence of vortex and turbulences. Based on the verified wing planform in the tunnel tests, a MAV prototype was further designed and fabricated, with successful tests in the real flight.

This paper presents an observer based H/sub /spl infin// output feedback synthesis method for discrete time fuzzy dynamic systems based on a piecewise Lyapunov function. The basic idea of the approach is to design an observer based piecewise linear output feedback control law to guarantee the global stability with H/sub /spl infin// performance of the resulting closed-loop fuzzy control systems. It is shown that the controller parameters can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible with commercially available software. Application to control chaotic systems is given to illustrate the effectiveness and advantages of the proposed method.

This work presents an H/sub /spl infin// controller design method for fuzzy dynamic systems based on techniques of piecewise smooth Lyapunov functions and bilinear matrix inequalities. It is shown that a piecewise continuous Lyapunov function can be used to establish the global stability with H/sub /spl infin// performance of the resulting closed-loop fuzzy control systems and the control laws can be obtained by solving a set of bilinear matrix inequalities (BMIs). Two examples are given to illustrate the application of the proposed methods.

A high-speed, high-accuracy linear positioning system with application to electronic manufacturing is designed to satisfy four simultaneous closed-loop specifications by integrated design approach. Utilizing the three-stage convex integrated design (CID) method proposed here, the mechanical structure parameters of the linear positioning system, both the control gains and controller structure of a closed-loop controller, are uniquely determined, so that improved closed-loop system performance beyond that required by the n pre-specified closed-loop performance specifications, is acquired. The CID method is especially useful when the design problem has a small set of design variables. Experimental results show that all four performance specifications are simultaneously satisfied, which verify the effectiveness of this design.

This paper presents a development of component synthesis vibration suppression (CSVS) method for control of spacecrafts with large flexible appendages. The proposed method eliminates unwanted flexible modes of vibrations while achieving the desired rigid body motion. Unlike traditional input shaping in which a numerical optimization is utilized, design for CSVS commands is based on analytic methodology and is relatively easy to implement. The robustness to uncertainties of dynamic modeling parameters is analyzed. A case study is performed on a time-fuel optimal control strategy using constant amplitude reaction jet thrusters. Both simulation and experimental results validate the effectiveness of the CSVS approach.

In this paper, an enhanced hybrid control algorithm is proposed to control the rotation of a flexible beam while suppressing the beam's vibration. The control law combines an enhanced PD feedback with nonlinear differentiator to derive high-quality velocity signal to control gross motion of the beam, and a vibration control by PZT actuators bonded on the surface of the beam. The significance of the proposed method are threefold: i) The enhanced PD control is a non-model based control, and appears to be more robust against the noise; ii) The linear velocity in contrast the angular velocity is used in the PZT actuator control, a signal which is easily available; iii) A unique solution is provided for examination of actuator placement, based on the analysis of mode shape functions. Experimental results validate these theoretical analyses.

This paper aims to investigate theoretically and experimentally the dynamic behaviors of the pitch and roll motions of a small-scale unmanned air vehicle in loitering flight. Two fourth-order ARX (AutoRegressive with eXogenous input) models are successfully identified, and the performance analysis is carried out based on the flight test data. The validity of the identified model is verified by both time domain model prediction and frequency domain spectral analysis. With the proposed ARX models, two compensators are further designed using a frequency technique to improve the transient performance of the pitch and roll control channels. Simulations and experiments demonstrate that the proposed ARX model-based compensation control design strategy can improve the flight performance.

This paper presents a development of component synthesis vibration suppression (CSVS) method for control of flexible structures. The proposed method eliminates unwanted flexible modes of vibration while achieving the desired rigid body motion. The robustness to uncertainties of dynamic modeling parameters is analyzed. Unlike traditional input shaping, in which a numerical optimization is used, design for CSVS commands is based on analytic methodology and is relatively easy to implement. A case study is performed on a time-fuel optimal control strategy using constant amplitude reaction jet thrusters. Both simulation and experimental results validate the effectiveness of the proposed approach.

This paper presents a model identification of a micro air vehicle in loitering flight, based on the input-output data collected from flight experiments on a homemade 1-m-sized aircraft. A miniature flight-control system, which consists of the onboard and the ground sections, is equipped with a multichannel data logger associated with the data acquisition software. Modeling and performance analysis are carried out, based on the flight-test data using a system identification technique. Two fourth-order autoregressive with exogenous input (ARX) models are identified to present the attitude characteristics of the longitudinal (pitch) and the lateral (roll) control channels, respectively. The validity of the identified model is verified by both time-domain model prediction and frequency-domain spectral analysis. Based on the proposed ARX models, two compensators are further designed using a frequency-domain method, and then added to the closed-loop control systems to improve the transient performance of the pitch- and roll-control channels. Simulations and experiments demonstrate that the flight performance obtained by the proposed ARX model-based compensation control can be improved.

Precise tracking control of a mobile manipulator is a challenging problem in which tracking errors of the vehicle and the manipulator merge and jointly affect the endpoint trajectory performance. A synchronization approach to minimize the mutual errors between the vehicle and the manipulator is reported in this paper. The basic idea is to utilize the cross-coupling concept to cooperate motions of the vehicle and the manipulator so that the both tracking errors are compensated each other. An adaptive synchronized controller is proposed to guarantee asymptotic convergence to zero of the position tracking error and the synchronization error of the mobile manipulator. The controller is in a decentralized architecture for easy implementation, and is able to address model uncertainty problem. Simulation results verify the effectiveness of the proposed approach.

This paper presents the development of an electrically powered micro air vehicle (MAV) with a wingspan of 360 mm. A miniature flight control system including a self-made micro video image system especially suitable for MAV is developed. The aerodynamic performance of several airfoil sections at low chords Reynolds number is analyzed in order to find an optimum airfoil section for the MAV prototype. A small-sized propulsion testing setup is built to measure the performance of the motor-gear-propeller-battery combination so that an efficient propulsion system can be obtained. The TH360 MAV with a payload of a self-made micro color video image system has been successfully tested in the real-time flight, where the real-time images of the ground target can be transmitted from the onboard video camera to the ground.

This paper presents an investigation of autonomous hovering flight control of micro air vehicles (MAVs). The control system consists of an onboard section with a self-made micro azimuth gradienter sensor and a ground station. An open-loop control strategy named teaching-by showing based control is proposed by stimulating a skilled human operator's manipulation of the aircraft, with the objective of learning operator's manipulation and then generating a set of command data to control MAV's hovering. A feedforward plus a PD feedback control is further employed to control the aircraft using the command data generated in the open-loop control. The PD control gains are tuned automatically according to the attitude of the vehicle by fuzzy logic theory. The effectiveness of the proposed approach is validated in autonomous hovering flight experiments conducted on a one-meter sized aircraft.

This paper presents a generalized H/sub 2/ controller synthesis method for the Takagi-Sugeno fuzzy dynamic systems based on a piecewise smooth Lyapunov function. The basic idea of the proposed approach is to construct a controller for the fuzzy dynamic systems in such a way that a piecewise continuous Lyapunov function can be used to establish the global stability with generalized H/sub 2/ performance of the resulting closed loop fuzzy control systems. It is shown that the control law can be obtained by solving a set of linear matrix inequalities. An example is given to illustrate the application of the proposed method.

In this paper, a new adaptive coupling control algorithm is developed to synchronize motions of two driving wheels of a differential mobile robot so that the robot maintains in the desired trajectory path. Unlike considerable approaches in the literature, in this study motion of driving wheels instead. of the robot configuration are directly controlled in a synchronous manner. This strategy avoids the involvement of nonholonomic constraint in the control design, and therefore simplifies the practical implementation significantly. The proposed controller incorporates the cross-coupling technology into adaptive control architecture, and guarantees asymptotic convergence to zero of both position tracking error and the synchronization error. Experiments have been conducted to verify the effectiveness of the proposed control approach.

A new control algorithm for tracking stabilization of a differential mobile robot is developed by incorporating the cross-coupling technology into an adaptive control architecture. Unlike the majority of previous work in the literature, coordinates of robot wheels instead of the robot configuration, are directly controlled in a synchronous manner. The synchronization effort is aimed to regulate the differential position error of two driving wheels to zero and thus regulate the heading angle of the robot, so that the robot maintains in the desired trajectory path. The proposed adaptive synchronized controller guarantees an asymptotic convergence to zero of wheel displacement errors and the synchronization error between them. Under the condition that the initial error of the robot configuration is zero, the robot configuration converges to the desired state asymptotically. Experimental results demonstrate the effectiveness of the proposed approach.

A coordination scheme for two-manipulator systems is developed by maintaining a certain kinematic relationship between manipulators using motion synchronization. The coordination strategy is to let each manipulator track its desired trajectory while synchronizing its motion with the other manipulator's motion so that the differential position error between two manipulators converge to zero. The proposed synchronized controller for each manipulator incorporates the cross-coupling technology into an adaptive control architecture, by feeding back the position error of each manipulator and the differential position error between two manipulators. The proposed algorithm guarantees asymptotic convergence to zero of both position errors and synchronization error. Implementation of this new coordination scheme, which is in a decentralized architecture, is more straightforward. An experiment on two industrial manipulators demonstrates the effectiveness of the proposed approach.

Coordination of multirobot systems has received extensive studies in the past decade. The majority of previous approaches require a complex setup of the hybrid position/force-control architecture, and have not fully addressed the coordination problem when the robots are not kinematically constrained but perform a common task. In this paper, we propose to use a new coordination scheme that is more straightforward and easier to implement and is applicable to a wider area. The basic idea of the new coordination strategy is to use the concept of motion synchronization, since the problem of coordinating multiple manipulators is basically the problem of maintaining certain kinematic relationships amongst robots. The key to the success of the new method is to ensure that each manipulator tracks its desired trajectory while synchronizing its motion with other manipulators' motions, so that differential (or synchronization) position errors amongst manipulators converge to zero. The controller, designed by incorporating the cross-coupling technology into an adaptive-control architecture, successfully guarantees asymptotic convergence to zero of both position tracking and synchronization errors simultaneously. Experiments and simulations on multirobot assembly systems demonstrate the effectiveness of the approach.

Addresses the application of a fuzzy logic control system to trajectory tracking control of robot manipulators. In the proposed application, the fuzzy logic control system plays the role of a compensator for the robot system, together with the computed torque control method, to improve trajectory tracking performance of an industrial robot. The proposed control scheme is used to adjust weight parameters of a self-tuning fuzzy logic compensator (SFLC). Experimental results demonstrate the effectiveness of the computed torque and the SFLC scheme to control an industrial CRS Robotics Corporation A460 robot.

Discusses the issue of hybrid position and force control of a two-manipulator system manipulating a flexible beam in trajectory tracking. Unlike our previous approach of set-point position control, (Sun and Liu, 1997, and Liu and Sun, 2000), in the trajectory tracking, the system coordinates are hard to regulate to the desired states with non-zero tracking velocities under continuous feedback control. In this study, we design a hybrid position and force tracking controller while using saturation control to compensate the effect of beam vibration dynamics to the tracking performance. All parameters and states used in the controller are readily available so that the proposed method is feasible in implementation. Under the proposed controller, the tracking error asymptotically converges to a predetermined boundary. Simulation results demonstrate the validity of the proposed approach.

A partial model-based torque control algorithm is developed for AC induction motors. The motor is regarded as a torque source, and a desired torque signal is designed based on the desired position trajectory. A reference quadrature axis current input is further developed to cause the torque to track the designed torque signal, utilizing a torque feedforward plus a PI-type torque feedback control. The method utilizes partial knowledge of the electromechanical dynamic model of the induction motor. Experiments conducted on a commercial AC induction servo system demonstrate the validity of the proposed approach.

A control architecture called "advanced torque control" is developed for robot manipulators driven by induction motors. The motor is regarded as a torque source, and a desired torque signal is designed based on the desired position trajectory. A commanded input to the torque loop is further developed to cause the torque to track the designed torque signal, utilizing a torque feedforward plus a PI-type torque feedback control. The method utilizes partial knowledge of the electromechanical dynamic model of the induction motor. Both position and current signals are controlled in the generation of the input to the torque loop. Experimental results conducted on a commercial AC induction servo system demonstrates that this method exhibits good motion performance.

Describes an approach for the use of smart materials, piezoelectric materials of PVDF and PZT, for control of a single-link flexible manipulator. A combined scheme is developed, which consists of a PD feedback for rigid motion control, and a command voltage applied to the PVDF layer or segmented PZT actuator(s) those are bonded to the surface of the flexible link for vibration damping. The command voltage employs linear velocity feedback (L-type), which makes the scheme easy to implement. Global stability of the system is investigated using a Lyapunov approach. It is the first time system stability under the L-type piezoelectric actuator control, utilizing the concept of a virtual joint model, is shown. Simulation results illustrate that the PZT actuator, considering actuator placement, exhibits better performance in vibration damping over the PVDF actuator.

An adaptive-learning (AL) control scheme is developed for control of robotic systems with model uncertainties. When robots perform repetitive tasks, their operations are decomposed into two modes: the single operational mode and the repetitive operational mode. In the single operational mode, the control is a learning based adaptive control where the parameters of the system are updated by using the information of the previous operation. In the repetitive operational mode, the control is a model-based iterative learning control. The advantage of the AL scheme lies in the ability to improve the transient performance at a high rate of learning convergence as robots repeat their operations. Experimental and simulation results ascertain the effectiveness of the AL scheme in controlling a single and multiple robots with model uncertainties.

This paper verifies that the widely used PD plus a force control scheme is also suitable for controlling a system of two industrial robots manipulating a flexible sheet. It is proven by LaSalle's theorem that under the proposed control law, the desired rigid body motion can be achieved and the vibrations of the sheet at each contact are suppressed simultaneously. The offsets of all static deformations of the sheet with reference to the original positions decay to zero. The internal forces between the payload and the robots are well controlled to avoid any damage to the system. The investigation is based on the decomposition of the payload dynamics into two distinct dynamic subsystems, using a "clamped-free" model. The experiments on two CRS A460 robots manipulating a flexible aluminum sheet confirm these theoretical predictions.

This paper describes a new approach to control a single-link flexible manipulator by using smart actuators. A combined PD feedback for rigid motion control and the distributed piezoelectric polymer (PVDF) actuator for vibration damping is investigated using a Lyapunov approach. The PVDF actuator is designed independently from joint velocity of the manipulator, which allows high speed motions. Most of the current PVDF actuators are A-type schemes depending on measurement of the tip angular velocity of the beam, a signal not readily available. This paper is the first to show that the available linear velocity (at the tip) feedback control, L-type scheme, can also guarantee the stability of the system. The L-type scheme does not lead to control problems caused by mode truncation, and is more efficient to suppress the dominant mode vibration of the beam. Simulation results confirm these theoretical predictions.

In this paper, a hybrid impedance control algorithm is proposed to stabilize a flexible beam handled by two manipulators to a desired position/orientation while suppressing its vibration, and simultaneously control the internal forces between the manipulators and beam. The algorithm combines impedance control and an I-type force feedback by designing a proper response of the interaction force including external and internal forces. No information about the vibration is used in the controller. The asymptotic stability is analyzed based on the vibration dynamics of the beam approximated by m assumed modes, where the number m can be as large as necessary. Three particular cases of using different mode functions are discussed under free-free, clamped-free and pinned-pinned boundary conditions. The validity of the proposed scheme is demonstrated by simulations.

Robotic manipulation of a general flexible object is an extremely difficult and challenging control problem. This paper shows that under a simple PD position feedback, the position/orientation of a general flexible object handled by two manipulators is able to approach the desired one and at the same time the vibration of each contact is suppressed. We use the "clamped-free" model to decompose the motion of the object into two components, a rigid and a flexible one, which allows us to treat them separately and achieve desired motions with a simple PD scheme. This is proved to work theoretically.

A new approach for modeling and cooperating two manipulators handling a deformable object is presented. Based on the decomposition of a deformable body into a reference component and a deformation component, a general deformable model is developed and the complex control task is divided into two subtask, i.e., the control of the reference motion and the control of the deformations. The position/force controllers and a null-space control law are proposed and demonstrated in the simulations.

In this paper, a new dynamic control algorithm is proposed for control of a two-arm robotic system sliding a flat object on a smooth supporting surface. Two manipulators press down on the object and manipulate it by applying tangential forces and rotational moments at the contacts. Assuming the contacts are axisymmetric, the article discusses two cases: non-sliding constraints and sliding constraints between the manipulators and the object. Based on this analysis, the kinematics and dynamics of the complete system is studied and a control law is proposed. In the case of sliding constraints, sensors are required to provide information of the contact position and force of the manipulators. The effectiveness of the control algorithm is verified by simulations.