Search

The compliance of continuum robot poses difficulty in designing the controller, as the robot does not have position encoder and could be subject to uncertain external force (UEF). The former can be resolved through external sensors, while the latter issue still needs further investigation. This article presents a novel robot design that embeds eGaIn sensors into the soft material, and the elongation in the robot chamber can lead to different changes in resistance values. The sensors act as a proprioceptive mechanism to sense the presence of the UEF. To locate the UEF, the robot surface is divided into multiple regions, and the column and row positions with the force can be solved using Hidden Markov Model and regularization algorithm. Through the virtual work principle, the force magnitude can be also evaluated. Experimental results confirm that, with possible four columns and five rows, the accuracy of finding the actual column and row positions are, respectively, $ 97\%$ and $ 98.5\%$. The mean error in evaluating the tip position and the force magnitude is about $ \text{4}\;\text{mm}$ and $ \text{0.23}\;\text{N}$. This article offers a new approach to obtain the information of the UEF, allowing continuum robots to work in a constrained environment. [ABSTRACT FROM AUTHOR]

In this study, a system of automatic microparticle patterning that could enable the separation, trapping, and translation of single microbeads in liquid suspension using negative dielectrophoresis (DEP) tweezers was presented to form a single-bead pattern. A microchip with integrated electrodes was flipped and placed above the substrate through a micromanipulator. Microparticles laying on the substrate could be displaced to different positions relative to the electrodes on the microchip, and only the selected particles would be trapped by the electric fields generated from electrodes. Vision-based approaches were used to evaluate the necessary information, such as the gap distance and the positions of electrodes and microparticles in the image. A strategy for separating nearby particles was proposed to achieve single-bead patterning with high accuracy. A controller was used to guide the microparticles toward the position for trapping while avoiding flow disturbance. Different strategies were simulated to decrease the patterning time and find the minimum traveling distance and the best route of movement. The optimization problem is NP-hard. Hence, global optimization algorithms, such as genetic algorithm, particle swarm optimization, and ant colony optimization (ACO), were simulated, and the results were compared with those of the local optimization method. The comparison results showed that ACO obtained the best performance among the methods. The strategy for constructing high-quality microparticle patterns was also examined through experiments. Orange fluorescent polystyrene beads suspended in 6-aminohexanoic acid solution were considered and successfully patterned on a glass substrate by using the proposed system. Note to Practitioners—Micropatterning is an effective tool for pharmaceutical research and drug discovery. However, the reliability of results depends on the quality of patterns. Existing approaches, such as microfluidic devices, are limited to create a pattern from one chip for single use, and the entire process is sealed and isolated from the environment. In this study, a multielectrode microchip combined with a vision-based micromanipulator is introduced to create a novel noncontact approach for microparticle patterning, which offers high flexibility and guarantees the quality of the constructed patterns. The electrodes on the chip can be selectively energized to determine the shape of the final pattern. A real-time screening is performed so that the micromanipulator will only guide the particles in good condition for selection. An optimization algorithm is implemented to aid the particle selection with the electrodes, allowing high-quality microparticle patterns to be constructed in a short time for various applications. [ABSTRACT FROM AUTHOR]

Embryo manipulation is a fundamental task in assisted reproductive technology (ART). Nevertheless, conventional pick-place techniques often require proper alignment to avoid causing damage to the embryo and further, the tools have limited capability to orient the embryo being handled. Objective: This paper presents a novel and non-invasive technique that can easily manipulate mouse embryos on a polyvinyl chloride (PVC) Petri dish. Methods: An inverted microchip with quadrupole electrodes was attached to a micromanipulator to become a robotic dielectrophoresis (DEP) tweezers, and a motorized platform provided additional mobility to the embryos lying on a Petri dish. Vision-based algorithms were developed to evaluate relevant information of the embryos from the image, and to provide feedback signals for precise position and orientation control of the embryo. Results: A series of experiments was conducted to examine the system performance, and the embryo can be successfully manipulated to a specified location with the desired orientation for subsequent processing. Conclusion: This system offers a non-contact, low cost, and flexible method for rapid cell handling. Significance: As the DEP tweezers can grasp the embryo without the need for precise alignment, the overall time required to process a large number of embryos can be shortened. [ABSTRACT FROM AUTHOR]

In robot-assisted surgery (RAS), three-dimensional (3-D) position estimation of a surgical suture thread is essential in automating the wound suturing procedure. Nevertheless, accurate suture thread’s segmentation and its 3-D coordinates reconstruction remain challenging because of its flexible property and lacking indistinctive feature points. In this paper, a novel model-free method is proposed to enable the 3-D computation of a suture thread through a calibrated stereo camera system. In this method, only the suture thread’s tip and grasping points need to be indicated on one image with mouse clicks, and their accurate image positions can be detected using computed contour information. Afterward, key points that represent the suture thread are generated on one image, while their matched points on the second camera frame are evaluated through an iterative algorithm for 3-D model reconstruction. Experiments using different backgrounds and environmental conditions were conducted to examine the accuracy and robustness of the 2-D segmentation. The suture threads were then formed in various 3-D shapes, and their respective 3-D models were reconstructed for comparison. The experimental results confirm the feasibility of our method in reducing human intervention in the detection of matched key points on an image pair, thereby guaranteeing an accurate 3-D coordinates computation of a suture thread. [ABSTRACT FROM AUTHOR]

A vision-based system presents one of the most reliable methods for achieving an automated robot-assisted manipulation associated with surgical knot tying. However, some challenges in suture thread detection and automated suture thread grasping significantly hinder the realization of a fully automated surgical knot tying. In this article, we propose a novel algorithm that can be used for computing the 3-D coordinates of a suture thread in knot tying. After proper training with our data set, we built a deep-learning model for accurately locating the suture’s tip. By applying a Hessian-based filter with multiscale parameters, the environmental noises can be eliminated while preserving the suture thread information. A multistencils fast marching method was then employed to segment the suture thread, and a precise stereomatching algorithm was implemented to compute the 3-D coordinates of this thread. Experiments associated with the precision of the deep-learning model, the robustness of the 2-D segmentation approach, and the overall accuracy of 3-D coordinate computation of the suture thread were conducted in various scenarios, and the results quantitatively validate the feasibility and reliability of the entire scheme for automated 3-D shape reconstruction. Note to Practitioners—This article was motivated by the challenges of suture thread detection and 3-D coordinate evaluation in a calibrated stereovision system. To precisely detect the suture thread with no distinctive feature in an image, additional information, such as the two ends of the suture thread or its total length, is usually required. This article suggests a new method utilizing a deep-learning model to automate the tip detection process, eliminating the need of manual click in the initial stage. After feature enhancements with image filters, a multistencils fast marching method was incorporated to compute the arrival time from the detected tip to other points on the suture contour. By finding the point that takes the maximal time to travel in a closed contour, the other end of the suture thread can be identified, thereby allowing suture threads of any length to be segmented out from an image. A precise stereomatching method was then proposed to generate matched key points of the suture thread on the image pair, thereby enabling the reconstruction of its 3-D coordinates. The accuracy and robustness of the entire suture detection scheme were validated through experiments with different backgrounds and lengths. This proposed scheme offers a new solution for detecting curvilinear objects and their 3-D coordinates, which shows potential in realizing automated suture grasping with robot manipulators. [ABSTRACT FROM AUTHOR]

Robot-assisted surgery has revolutionized the field of surgery over the past few decades. Despite many successes, achieving full automation in these surgeries remains a challenging task. In this paper, a dynamic approach is proposed to automate knot tying with an in-house robot vision system. Through efficient path planning and coordination between two grippers, the workspace required for constructing the suture loop can be reduced while issues such as suture slippage and collisions between instruments can be eliminated. Visual images were employed to monitor the two grippers in real time and their positions were evaluated using transformation matrices obtained experimentally. A linear quadratic control scheme was applied to optimize the tracking performance of the two grippers. From the experiments, this visual evaluation method can achieve a position accuracy of 1 mm in the workspace. The proposed algorithm was evaluated and automatic suture looping operation was successfully performed in all six trials. Different parameters in the control scheme were also examined by introducing external impulse disturbances during the knot-tying process. This proposed knot-tying approach demonstrates a simple and efficient way to construct a suture knot in a minimal workspace. Note to Practitioners—A surgical operation usually takes several hours to complete, which is a test of surgeon’s endurance. To better assist surgeons, several repetitive surgical tasks can be performed automatically with a robotic system. This paper presents a new method for robot-assisted surgical knot tying in a confined environment. Through simultaneous manipulation and trajectory planning of the two surgical grippers, suture loops were constructed while keeping the suture in tension. Images were employed to guide the gripers in completing the knot-tying process with high precision. Experiments were conducted to demonstrate the automated suture looping operation with the system and the proposed method. The robustness of the method was also examined by introducing impulse disturbances and occlusions during the experiments. This proposed method does not require complicated hardware setup, allowing easy implementations on various surgical systems. [ABSTRACT FROM AUTHOR]

Objective: Seeding and patterning of cells with an engineered scaffold is a critical process in artificial tissue construction and regeneration. To date, many engineered scaffolds exhibit simple intrinsic designs, which fail to mimic the geometrical complexity of native tissues. In this study, a novel scaffold that can automatically seed cells into multilayer honeycomb patterns for bone tissue engineering application was designed and examined. Methods: The scaffold incorporated dielectrophoresis for noncontact manipulation of cells and intrinsic honeycomb architectures were integrated in each scaffold layer. When a voltage was supplied to the stacked scaffold layers, three-dimensional electric fields were generated, thereby manipulating cells to form into honeycomb-like cellular patterns for subsequent culture. Results: The biocompatibility of the scaffold material was confirmed through the cell viability test. Experiments were conducted to evaluate the cell viability during DEP patterning at different voltage amplitudes, frequencies, and manipulating time. Three different mammalian cells were examined and the effects of the cell size and the cell concentration on the resultant cellular patterns were evaluated. Conclusion: Results showed that the proposed scaffold structure was able to construct multilayer honeycomb cellular patterns in a manner similar to the natural tissue. Significance: This honeycomb-like scaffold and the dielectrophoresis-based patterning technique examined in this study could provide the field with a promising tool to enhance seeding and patterning of a wide range of cells for the development of high-quality artificial tissues. [ABSTRACT FROM PUBLISHER]