Category: Publications

Fabrication of Patient-Specific Intracranial Aneurysm Models for Burst Testing

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Abstract

A cerebral or intracranial aneurysm (ICA) is a condition that is defined as a local dilation of an artery in the brain due to locally weakened blood vessel walls. This creates a balloon-shaped bulge in the thin artery wall that can rupture, and the ensuing subarachnoid hemorrhage can cause a stroke, coma, or even death. Therefore, it is of interest to understand how ICAs grow and eventually rupture in order to develop earlier diagnosis or treatment techniques. Current imaging technologies include computed tomography and magnetic resonance imaging, which can be used to generate three-dimensional computer-assisted design models. However, these 3D models only provide the shape of the ICA and monitory macroscopic growth of aneurysms, but are too low resolution to determine the specific wall thickness of vasculature. Aneurysms tend to rupture at the thinnest point in the vessel wall, but it is difficult to predict rupture location from just 3D geometry alone using a CT scan reconstruction.

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A Skull-Mounted Robot with a Compact and Lightweight Parallel Mechanism for Positioning in Minimally Invasive Neurosurgery

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Robotic systems play an increasingly important role in improving feasibility and effectiveness of minimally invasive neurosurgery (MIN). However, large footprint, bulky size, and complex mechanisms limit the clinical application of existing robotic neurosurgery solutions. This paper proposes a novel skull-mounted robot with a compact and lightweight parallel mechanism for positioning of surgical tools in MIN. The system serves as a mechanical guide for automatic positioning of needles, catheters, probes, or electrodes. A parallel mechanism with 4 degrees of freedom (DOFs) is adopted, with the aim of providing sufficient accuracy and load capacity. The volume of the robot is only 50 mm × 50 mm × 40 mm and the weight is 73 g. The miniature design allows the robot to be mounted on the skull easily without consuming space in the operating room while avoiding the patient’s immobilization, simplifying the registration operation, and increasing patient comfort and tolerability. The mechanical design, kinematics and workspace are analyzed and described in detail. Three experiments on the prototype are conducted to test the stiffness, accuracy and performance. Results show that the deflection is less than 0.1 mm for holding common surgical tools and the tracking errors are less than 1.2 mm and 1.9° which is acceptable for MIN. The robot can be easily and firmly mounted on the skull model and cadaver head, and flexibly manipulated on the skull model.
 

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Finite Time Fault Tolerant Control for Robot Manipulators Using Time Delay Estimation and Continuous Nonsingular Fast Terminal Sliding Mode Control

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In this paper, a novel finite time fault tolerant control (FTC) is proposed for uncertain robot manipulators with actuator faults. First, a finite time passive FTC (PFTC) based on a robust nonsingular fast terminal sliding mode control (NFTSMC) is investigated. Be analyzed for addressing the disadvantages of the PFTC, an AFTC are then investigated by combining NFTSMC with a simple fault diagnosis scheme. In this scheme, an online fault estimation algorithm based on time delay estimation (TDE) is proposed to approximate actuator faults. The estimated fault information is used to detect, isolate, and accommodate the effect of the faults in the system. Then, a robust AFTC law is established by combining the obtained fault information and a robust NFTSMC. Finally, a high-order sliding mode (HOSM) control based on super-twisting algorithm is employed to eliminate the chattering. In comparison to the PFTC and other state-of-the-art approaches, the proposed AFTC scheme possess several advantages such as high precision, strong robustness, no singularity, less chattering, and fast finite-time convergence due to the combined NFTSMC and HOSM control, and requires no prior knowledge of the fault due to TDE-based fault estimation. Finally, simulation results are obtained to verify the effectiveness of the proposed strategy. Index Terms—Fault diagnosis (FD), fault tolerant control (FTC), robot manipulators, terminal sliding mode, time delay estimation (TDE).
 
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Ultrasound-Assisted Guidance With Force Cues for Intravascular Interventions

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Image guidance during minimally invasive intravascular interventions is primarily achieved based on X-ray fluoroscopy, which has several limitations including limited 3-D imaging capability, significant doses of radiation to operators, and lack of contact force measurement between the cardiovascular tissue and interventional tools. Ultrasound imaging can be adopted to complement or possibly replace 2-D fluoroscopy for intravascular interventions due to its portability, safety to use, and the ability of providing depth information. However, it is challenging to precisely visualize catheters and guidewires in the ultrasound images. In this paper, we propose a novel method to figure out both the position and orientation of the catheter tip in 2-D ultrasound images in real time by detecting and tracking a passive marker attached to the catheter tip. Moreover, the contact force can be estimated simultaneously as well via measuring the length variation of the marker. A geometrical model-based method is introduced to detect the initial position of the marker, and a Kanade-Lucas-Tomasi-based algorithm is developed to track the position, orientation, and length of the marker. The ex vivo experiment results validate the effectiveness of the proposed approach in automatically locating the catheter tip in ultrasound images and its capability of sensing the contact force. Therefore, it can be concluded that the presented method can be utilized to better facilitate operators during intravascular interventions.

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Multi-objective parameter optimization design of a magnetically actuated intravitreal injection device

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Abstract
Aiming at intravitreal injection procedures for eye diseases, needless injectors are emerging to puncture complications, save time and improve the safety of the process. In this paper, an injection device based on electromagnetic E-core actuation is selected for its better position control and improved controllability over current solenoid designs. The multi-objective optimization model of the E-core device is derived. Then, an integrated NSGA-II and TOPSIS based on combinatorial weighting approach is proposed for the parameter optimization design of the device and the selection of a final compromise solution or optimal solution. The combination weighting method combines the advantages of the objective and subjective weighting method, making the results more reasonable.
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Simultaneous Hand–Eye, Tool–Flange, and Robot–Robot Calibration for Comanipulation by Solving the Problem

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Abstract
Multirobot comanipulation shows great potential in surpassing the limitations of single-robot manipulation in complicated tasks such as robotic surgeries. However, a dynamic multirobot setup in unstructured environments poses great uncertainties in robot configurations. Therefore, the coordination relationships between the end-effectors and other devices, such as cameras (hand–eye calibration) and tools (tool–flange calibration), as well as the relationships among the base frames (robot–robot calibration) have to be determined timely to enable accurate robotic cooperation for the constantly changing configuration of the systems. We formulated the problem of hand–eye, tool–flange, and robot–robot calibration to a matrix equation AXB=YCZ. A series of generic geometric properties and lemmas were presented, leading to the derivation of the final simultaneous algorithm. In addition to the accurate iterative solution, a closed-form solution was also introduced based on quaternions to give an initial value. To show the feasibility and superiority of the simultaneous method, two nonsimultaneous methods were compared through thorough simulations under various robot movements and noise levels. Comprehensive experiments on real robots were also performed to further validate the proposed methods. The comparison results from both simulations and experiments demonstrated the superior accuracy and efficiency of the proposed simultaneous calibration method.
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Self-Triggered Output Feedback Control for Consensus of Multi-Agent Systems

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This paper studies the self-triggered control consensus problem of general linear multi-agent systems(MASs). A novel self-triggered control strategy based on output feedback is proposed for centralized and distributed cases, respectively. In consideration of the states of agents are not available, a state observer is adopted. A dynamic observer-based control law is employed to improve the transient response. Under this triggering strategy, both the estimated states of MASs and the states of controller are updated at triggering time. The next triggering time is predetermined at the last triggering instant. Moreover, the asymptotic consensus of MASs can be guaranteed. Finally, the effectiveness of the proposed control strategy is illustrated by a numerical example.
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Low-Cost Pyrometry System With Nonlinear Multisense Partial Least Squares

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Accurate high-temperature measurement is very important for process monitoring of an industrial system. Infrared thermometers usually can handle no more than 1000 °C and should use some expensive accessories for higher temperature measurements. This paper proposes a low-cost pyrometry system with nonlinear multisense partial least squares (NMSPLS). The ordinary camera with different filters is designed to collect the images of hot object at different wavelengths, and the NMSPLS is presented for predicting the temperature of the hot object from the obtained images. For the proposed method, the obtained images are represented by the multisense tensor, where red, green, and blue are regarded as three different dimensions in a sense of the tensor, respectively. The proposed method integrates an outer model and a nonlinear inner model. For the outer model, the independent variables and the dependent variables are projected into a low-dimensional common latent subspace. The weight matrices are calculated from the independent variables by the tucker decomposition, and the single value decomposition is adopted for extracting the latent variables (Lvs) based on the covariance between the independent variables and the dependent variables. For the nonlinear inner model, the neural network is adopted and the extracted Lvs are used as the input and the output of the neural network, respectively. Two real experiments are performed for estimating the proposed method. The experimental results verify that the proposed method can be applied for pyrometry and have higher effectiveness.

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Electromagnetically Enhanced Soft & Flexible Bend Sensor: A Quantitative Analysis with Different Cores

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Advantages of soft, flexible materials with developments in refined magnetic actuation can be intertwined for a promising platform to work on a resilient, adaptable manipulator aimed to meet ever-increasing demands in safe regulated medical environments. Taking advantages of these soft magnetic polymers, we propose a novel, soft-squishy and flexible bend sensor by determining the relationship between inductance changes with bending angle. This bend sensor employs flexible wire embedded in a silicone elastomer with the different permeable core. The principle notion is to have a comprehensive analysis of the change in morphology of the sensor with bending angle which can be translated to inductance generated therein. The performance of the sensor is evaluated with various experimental trials while analytical modelling elucidates that the bend angle is linearly proportional to the sensor signal citing R-square value up to 0.9204. The proposed sensor produces the desired output in the EM frequency range of 8 MHz – 10 MHz with a tunable sensitivity of 0.418 mV/rad. The sensor is robust enough to stretch up to twice of its original length. The main advantage of this bend sensor is its simple fabrication technique, flexibility, robustness and economical. Conclusively, this work on induction based tactile bending sensor is proved to produce robust output and can be extrapolated to sense bending angle using induction principle for the rehabilitative device, wearable robots and related biomedical applications requiring low cost, soft and flexible operations.

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Crumpling and Unfolding of Montmorillonite Hybrid Nanocoatings as Stretchable Flame‐Retardant Skin

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Flame‐retardant coatings are widely used in a variety of personnel or product protection, and many applications would benefit from film stretchability if suitable materials are available. It is challenging to develop flame‐retardant coatings that are stretchable, eco‐friendly, and capable of being integrated on mechanically dynamic devices. Here, a concept is reported that uses pretextured montmorillonite (MMT) hybrid nanocoatings that can undergo programed unfolding to mimic the stretchability of elastomeric materials. These textured MMT coatings can be transferred onto an elastomeric substrate to achieve an MMT/elastomer bilayer device with high stretchability (225% areal strain) and effective flame retardancy. The bilayer composite is utilized as flame‐retardant skin for a soft robotic gripper, and it is demonstrated that the actuated response can manipulate and rescue irregularly shaped objects from a fire scene. Furthermore, by depositing the conformal MMT nanocoatings on nitrile gloves, the firefree gloves can endure direct flame contact without ignition. Montmorillonite–elastomer bilayer architectures with high stretchability and effective flame retardancy can be applied as flame‐retardant protective skins for soft robotic grippers and nitrile gloves. With the stretchable and flame‐retardant barriers, the soft pneumatic actuator is capable of continuous inflation/deflation within flames and can act as a compliant gripper for manipulating and rescuing irregular objects from a fire scene.

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