Development of a Large-Range XY-Compliant Micropositioning Stage with Laser-Based Sensing and Active Disturbance Rejection Control.

This paper presents a novel design and control strategies for a parallel two degrees-of-freedom (DOF) flexure-based micropositioning stage for large-range manipulation applications. The motion-guiding beam utilizes a compound hybrid compliant prismatic joint (CHCPJ) composed of corrugated and leaf flexures, ensuring increased compliance in primary directions and optimal stress distribution with minimal longitudinal length. Additionally, a four-beam parallelogram compliant prismatic joint (4BPCPJ) is used to improve the motion decoupling performance by increasing the off-axis to primary stiffness ratio. The mechanism's output compliance and dynamic characteristics are analyzed using the compliance matrix method and Lagrange approach, respectively. The accuracy of the analysis is verified through finite element analysis (FEA) simulation. In order to examine the mechanism performance, a laser interferometer-based experimental setup is established. In addition, a linear active disturbance rejection control (LADRC) is developed to enhance the motion quality. Experimental results illustrate that the mechanism has the capability to provide a range of 2.5 mm and a resolution of 0.4 µm in both the X and Y axes. Furthermore, the developed stage has improved trajectory tracking and disturbance rejection capabilities.

Detection, Verification and Analysis of Micro Surface Defects in Steel Filament Using Eddy Current Principles, Scanning Electron Microscopy and Energy-Dispersive Spectroscopy.

In the current industrial revolution, advanced technologies and methods can be effectively utilized for the detection and verification of defects in high-speed steel filament production. This paper introduces an innovative methodology for the precise detection and verification of micro surface defects found in steel filaments through the application of the Eddy current principle. Permanent magnets are employed to generate a magnetic field with a high frequency surrounding a coil of sensors positioned at the filament's output end. The sensor's capacity to detect defects is validated through a meticulous rewinding process, followed by a thorough analysis involving scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Artificial defects were intentionally introduced into a sample, and their amplitudes were monitored to establish a threshold value. The amplitude signal of these created defect was identified at approximately 10% FSH, which corresponds to a crack depth of about 20 µm. In the experimental production of 182 samples covering 38 km, the defect ratio was notably high, standing at 26.37%. These defects appeared randomly along the length of the samples. The verification results underscore the exceptional precision achieved in the detection of micro surface defects within steel filaments. These defects were primarily characterized by longitudinal scratches and inclusions containing physical tungsten carbide.

On the Role of Damage Evolution in Finite Element Modeling of the Cutting Process and Sensing Residual Stresses.

This study focuses on the role of the damage evolution when estimating the failure behavior of AISI 1045 steel for sensing and measuring metal cutting parameters. A total of five Lagrangian explicit models are established to investigate the effect of applying damage evolution techniques. The Johnson-Cook failure model is introduced once to fully represent damage behavior, i.e., no damage evolution is considered, and as a damage initiation criterion in the remaining approaches. A fracture energy-based model is included to model damage propagation with different evolution rates. Temperature-dependent and temperature-independent fracture energy models are also investigated. Dry orthogonal cutting and residual stresses measurements of AISI 1045 are conducted for validation. The significance of the damage evolution is investigated using honed-tool and sharp-tool models. Including the damage evolution led to a prediction of higher workpiece temperatures, plastic strains, cutting forces, and residual stresses, with no clear differences between linear and exponential evolution rates. The role of damage evolution is more evident when temperature-dependent evolution models are used.

A Transparent Teleoperated Robotic Surgical System with Predictive Haptic Feedback and Force Modelling.

In recent years, robotic minimally invasive surgery has transformed many types of surgical procedures and improved their outcomes. Implementing effective haptic feedback into a teleoperated robotic surgical system presents a significant challenge due to the trade-off between transparency and stability caused by system communication time delays. In this paper, these time delays are mitigated by implementing an environment estimation and force prediction methodology into an experimental robotic minimally invasive surgical system. At the slave, an exponentially weighted recursive least squares (EWRLS) algorithm estimates the respective parameters of the Kelvin-Voigt (KV) and Hunt-Crossley (HC) force models. The master then provides force feedback by interacting with a virtual environment via the estimated parameters. Palpation experiments were conducted with the slave in contact with polyurethane foam during human-in-the-loop teleoperation. The experimental results indicated that the prediction RMSE of error between predicted master force feedback and measured slave force was reduced to 0.076 N for the Hunt-Crossley virtual environment, compared to 0.356 N for the Kelvin-Voigt virtual environment and 0.560 N for the direct force feedback methodology. The results also demonstrated that the HC force model is well suited to provide accurate haptic feedback, particularly when there is a delay between the master and slave kinematics. Furthermore, a haptic feedback approach that incorporates environment estimation and force prediction improve transparency during teleoperation. In conclusion, the proposed bilateral master-slave robotic system has the potential to provide transparent and stable haptic feedback to the surgeon in surgical robotics procedures.

A Force-Feedback Methodology for Teleoperated Suturing Task in Robotic-Assisted Minimally Invasive Surgery.

With robotic-assisted minimally invasive surgery (RAMIS), patients and surgeons benefit from a reduced incision size and dexterous instruments. However, current robotic surgery platforms lack haptic feedback, which is an essential element of safe operation. Moreover, teleportation control challenges make complex surgical tasks like suturing more time-consuming than those that use manual tools. This paper presents a new force-sensing instrument that semi-automates the suturing task and facilitates teleoperated robotic manipulation. In order to generate the ideal needle insertion trajectory and pass the needle through its curvature, the end-effector mechanism has a rotating degree of freedom. Impedance control was used to provide sensory information about needle-tissue interaction forces to the operator using an indirect force estimation approach based on data-based models. The operator's motion commands were then regulated using a hyperplanar virtual fixture (VF) designed to maintain the desired distance between the end-effector and tissue surface while avoiding unwanted contact. To construct the geometry of the VF, an optoelectronic sensor-based approach was developed. Based on the experimental investigation of the hyperplane VF methodology, improved needle-tissue interaction force, manipulation accuracy, and task completion times were demonstrated. Finally, experimental validation of the trained force estimation models and the perceived interaction forces by the user was conducted using online data, demonstrating the potential of the developed approach in improving task performance.

On the Sensing and Calibration of Residual Stresses Measurements in the Incremental Hole-Drilling Method.

The current study presents three calibration approaches for the hole-drilling method (HDM). A total of 72 finite element models and 144 simulations were established to calibrate the measurements of the strain sensors. The first approach assumed the stresses acted on the boundaries of the drilled hole and thus analyzed the surrounding displacements field. The second analysis considered the loads on the outer surfaces of the specimen while measuring the strains' differences between the model with and without the drilled hole. The third approach was more comprehensive as it considered the mechanical and thermal effects of the drilling operations. The proposed approaches were applied to two different materials (AISI 1045 and CFRP). The steel specimens were machined using a CNC lathe while the composite laminates were manufactured using the robotic fiber placement (RFP) process. Subsequently, the residual stresses (RSs) were measured using the HDM. The obtained data were compared with X-ray diffraction measurements for validation. The results showed better estimation of the RSs when utilizing the third approach and clear underestimation of the stresses using the second approach. A divergence in RSs values between the three approaches was also detected when measuring the stresses in the internal layers of the composite laminates.

Enhancing Solid State LiDAR Mapping with a 2D Spinning LiDAR in Urban Scenario SLAM on Ground Vehicles.

Solid-State LiDAR (SSL) takes an increasing share of the LiDAR market. Compared with traditional spinning LiDAR, SSLs are more compact, energy-efficient and cost-effective. Generally, the current study of SSL mapping is limited to adapting existing SLAM algorithms to an SSL sensor. However, compared with spinning LiDARs, SSLs are different in terms of their irregular scan patterns and limited FOV. Directly applying existing SLAM approaches on them often increase the instability of a mapping process. This study proposes a systematic design, which consists of a dual-LiDAR mapping system and a three DOF interpolated six DOF odometry. For dual-LiDAR mapping, this work uses a 2D LiDAR to enhance a 3D SSL performance on a ground vehicle platform. The proposed system takes a 2D LiDAR to preprocess the scanning field into a number of feature sections according to the curvatures on the 2D fraction. Subsequently, this section information is passed to 3D SSL for direction feature selection. Additionally, this work proposes an odometry interpolation method which uses both LiDARs to generate two separated odometries. The proposed odometry interpolation method selectively determines the appropriate odometry information to update the system state under challenging conditions. Experiments are conducted in different scenarios. The results proves that the proposed approach is able to utilise 12 times more corner features from the environment than the comparied method, thus results in a demonstrable improvement in its absolute position error.

Sensing and Modelling Mechanical Response in Large Deformation Indentation of Adherent Cell Using Atomic Force Microscopy.

The mechanical behaviour of adherent cells when subjected to the local indentation can be modelled via various approaches. Specifically, the tensegrity structure has been widely used in describing the organization of discrete intracellular cytoskeletal components, including microtubules (MTs) and microfilaments. The establishment of a tensegrity model for adherent cells has generally been done empirically, without a mathematically demonstrated methodology. In this study, a rotationally symmetric prism-shaped tensegrity structure is introduced, and it forms the basis of the proposed multi-level tensegrity model. The modelling approach utilizes the force density method to mathematically assure self-equilibrium. The proposed multi-level tensegrity model was developed by densely distributing the fundamental tensegrity structure in the intracellular space. In order to characterize the mechanical behaviour of the adherent cell during the atomic force microscopy (AFM) indentation with large deformation, an integrated model coupling the multi-level tensegrity model with a hyperelastic model was also established and applied. The coefficient of determination between the computational force-distance (F-D) curve and the experimental F-D curve was found to be at 0.977 in the integrated model on average. In the simulation range, along with the increase in the overall deformation, the local stiffness contributed by the cytoskeletal components decreased from 75% to 45%, while the contribution from the hyperelastic components increased correspondingly.

Effects of realistic force feedback in a robotic assisted minimally invasive surgery system.

BACKGROUND: Robotic assisted minimally invasive surgery systems not only have the advantages of traditional laparoscopic procedures but also restore the surgeon's hand-eye coordination and improve the surgeon's precision by filtering hand tremors. Unfortunately, these benefits have come at the expense of the surgeon's ability to feel. Several research efforts have already attempted to restore this feature and study the effects of force feedback in robotic systems. The proposed methods and studies have some shortcomings. The main focus of this research is to overcome some of these limitations and to study the effects of force feedback in palpation in a more realistic fashion. MATERIAL AND METHODS: A parallel robot assisted minimally invasive surgery system (PRAMiSS) with force feedback capabilities was employed to study the effects of realistic force feedback in palpation of artificial tissue samples. PRAMiSS is capable of actually measuring the tip/tissue interaction forces directly from the surgery site. Four sets of experiments using only vision feedback, only force feedback, simultaneous force and vision feedback and direct manipulation were conducted to evaluate the role of sensory feedback from sideways tip/tissue interaction forces with a scale factor of 100% in characterising tissues of varying stiffness. Twenty human subjects were involved in the experiments for at least 1440 trials. Friedman and Wilcoxon signed-rank tests were employed to statistically analyse the experimental results. RESULTS: Providing realistic force feedback in robotic assisted surgery systems improves the quality of tissue characterization procedures. Force feedback capability also increases the certainty of characterizing soft tissues compared with direct palpation using the lateral sides of index fingers. CONCLUSION: The force feedback capability can improve the quality of palpation and characterization of soft tissues of varying stiffness by restoring sense of touch in robotic assisted minimally invasive surgery operations.

Predicting and Improving Interlaminar Bonding Uniformity during the Robotic Fiber Steering Process.

With their high specific stiffness, corrosion resistance and other characteristics, especially their outstanding performance in product weight loss, fiber-reinforced resin matrix composites are widely used in the aviation, shipbuilding and automotive fields. The difficulties in minimizing defects are an important factor in the high cost of composite material component fabrication. Fiber steering is one of the typical means of producing composite parts with increased strength or stiffness. However, fiber waviness is an important defect induced by fiber steering during the fiber placement process. Meanwhile, the laying speeds of the inner and outer tows along the path width direction are different during the fiber steering process, resulting in different interlaminar bond strengths. Therefore, the fiber waviness and uneven interlaminar bonding strength during fiber steering not only affect the dimensions of a composite product, but also influence the mechanical properties of the part. This study aims to reduce fiber waviness and improve interlaminar bonding uniformity along the path width direction using a multi-piece compaction roller. By analyzing the mechanism of the generation of fiber waviness, the interlaminar bonding strength for each tow during fiber steering is investigated. Through analyzing and optimizing the compaction force, laying temperature and laying velocity during fiber steering experiments, the optimization approach is verified.

Modeling of soft tissue thermal damage based on GPU acceleration.

Hyperthermia treatments require precise control of thermal energy to form the coagulation zones which sufficiently cover the tumor without affecting surrounding healthy tissues. This has led modeling of soft tissue thermal damage to become important in hyperthermia treatments to completely eradicate tumors without inducing tissue damage to surrounding healthy tissues. This paper presents a methodology based on GPU acceleration for modeling and analysis of bio-heat conduction and associated thermal-induced tissue damage for prediction of soft tissue damage in thermal ablation, which is a typical hyperthermia therapy. The proposed methodology combines the Arrhenius Burn integration with Pennes' bio-heat transfer for prediction of temperature field and thermal damage in soft tissues. The problem domain is spatially discretized on 3-D linear tetrahedral meshes by the Galerkin finite element method and temporally discretized by the explicit forward finite difference method. To address the expensive computation load involved in the finite element method, GPU acceleration is implemented using the High-Level Shader Language and achieved via a sequential execution of compute shaders in the GPU rendering pipeline. Simulations on a cube-shape specimen and comparison analysis with standalone CPU execution were conducted, demonstrating the proposed GPU-accelerated finite element method can effectively predict the temperature distribution and associated thermal damage in real time. Results show that the peak temperature is achieved at the heat source point and the variation of temperature is mainly dominated in its direct neighbourhood. It is also found that by the continuous application of point-source heat energy, the tissue at the heat source point is quickly necrotized in a matter of seconds, while the entire neighbouring tissues are fully necrotized in several minutes. Further, the proposed GPU acceleration significantly improves the computational performance for soft tissue thermal damage prediction, leading to a maximum reduction of 55.3 times in computation time comparing to standalone CPU execution.

Medical nanorobotics for diabetes control.

This work presents an innovative nanorobot architecture based on nanobioelectronics for diabetes. The progressive development toward the therapeutic use of nanorobots should be observed as the natural result from some ongoing and future achievements in biomedical instrumentation, wireless communication, remote power transmission, nanoelectronics, new materials engineering, chemistry, proteomics, and photonics. To illustrate the nanorobot integrated circuit architecture and layout described here, a computational approach with the application of medical nanorobotics for diabetes is simulated using clinical data. Integrated simulation can provide interactive tools for addressing nanorobot choices on sensing, hardware design specification, manufacturing analysis, and methodology for control investigation. In the proposed 3D prototyping, a physician can help the patient to avoid hyperglycemia by means of a handheld device, like a cell phone enclosed with cloth, that is used as a smart portable device to communicate with nanorobots. Therefore, this architecture provides a suitable choice to establish a practical medical nanorobotics platform for in vivo health monitoring.

Nanorobot Hardware Architecture for Medical Defense.

This work presents a new approach with details on the integrated platform and hardware architecture for nanorobots application in epidemic control, which should enable real time in vivo prognosis of biohazard infection. The recent developments in the field of nanoelectronics, with transducers progressively shrinking down to smaller sizes through nanotechnology and carbon nanotubes, are expected to result in innovative biomedical instrumentation possibilities, with new therapies and efficient diagnosis methodologies. The use of integrated systems, smart biosensors, and programmable nanodevices are advancing nanoelectronics, enabling the progressive research and development of molecular machines. It should provide high precision pervasive biomedical monitoring with real time data transmission. The use of nanobioelectronics as embedded systems is the natural pathway towards manufacturing methodology to achieve nanorobot applications out of laboratories sooner as possible. To demonstrate the practical application of medical nanorobotics, a 3D simulation based on clinical data addresses how to integrate communication with nanorobots using RFID, mobile phones, and satellites, applied to long distance ubiquitous surveillance and health monitoring for troops in conflict zones. Therefore, the current model can also be used to prevent and save a population against the case of some targeted epidemic disease.

Development of Piezo-Driven Compliant Bridge Mechanisms: General Analytical Equations and Optimization of Displacement Amplification.

Compliant bridge mechanisms are frequently utilized to scale micrometer order motions of piezoelectric actuators to levels suitable for desired applications. Analytical equations have previously been specifically developed for two configurations of bridge mechanisms: parallel and rhombic type. Based on elastic beam theory, a kinematic analysis of compliant bridge mechanisms in general configurations is presented. General equations of input displacement, output displacement, displacement amplification, input stiffness, output stiffness and stress are presented. Using the established equations, a piezo-driven compliant bridge mechanism has been optimized to maximize displacement amplification. The presented equations were verified using both computational finite element analysis and through experimentation. Finally, comparison with previous studies further validates the versatility and accuracy of the proposed models. The formulations of the new analytical method are simplified and efficient, which help to achieve sufficient estimation and optimization of compliant bridge mechanisms for nano-positioning systems.

Soft tissue modelling through autowaves for surgery simulation.

Modelling of soft tissue deformation is of great importance to virtual reality based surgery simulation. This paper presents a new methodology for simulation of soft tissue deformation by drawing an analogy between autowaves and soft tissue deformation. The potential energy stored in a soft tissue as a result of a deformation caused by an external force is propagated among mass points of the soft tissue by non-linear autowaves. The novelty of the methodology is that (i) autowave techniques are established to describe the potential energy distribution of a deformation for extrapolating internal forces, and (ii) non-linear materials are modelled with non-linear autowaves other than geometric non-linearity. Integration with a haptic device has been achieved to simulate soft tissue deformation with force feedback. The proposed methodology not only deals with large-range deformations, but also accommodates isotropic, anisotropic and inhomogeneous materials by simply changing diffusion coefficients.

A cellular neural network methodology for deformable object simulation.

This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by a nonlinear CNN. The novelty of the methodology is that: 1) CNN techniques are established to describe the potential energy distribution of the deformation for extrapolating internal forces and 2) nonlinear materials are modeled with nonlinear CNNs rather than geometric nonlinearity. Integration with a haptic device has been achieved for deformable object simulation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but it also accommodates isotropic, anisotropic, and inhomogeneous materials, as well as local and large-range deformation.

Simulation of deformable models with the Poisson equation.

In this paper, we present a new methodology for the deformation of soft objects by drawing an analogy between the Poisson equation and elastic deformation from the viewpoint of energy propagation. The potential energy stored due to a deformation caused by an external force is calculated and treated as the source injected into the Poisson system, as described by the law of conservation of energy. An improved Poisson model is developed for propagating the energy generated by the external force in a natural manner. An autonomous cellular neural network (CNN) model is established by using the analogy between the Poisson equation and CNN to solve the Poisson model for the real-time requirement of soft object deformation. A method is presented to derive the internal forces from the potential energy distribution. The proposed methodology models non-linear materials with the non-linear Poisson equation and thus non-linear CNN, rather than geometric non-linearity. It not only deals with large-range deformations, but also accommodates isotropic, anisotropic and inhomogeneous materials by simply modifying constitutive coefficients. A haptic virtual reality system has been developed for deformation simulation with force feedback. Examples are presented to demonstrate the efficiency of the proposed methodology.

A Simple Weighing Method for Spherical Cells.

This article presents a simple weighing method for spherical cells to avoid the high cost of correlated devices in traditional cell-weighing methods. In this method, the constant falling speeds of the spherical objects in liquid are derived to estimate their masses online. Using this method, the detected density of one type of microbead is highly in accordance with the known value. This method is proved to be capable of detecting tiny variations of the cell mass (at least within 1% of the cell mass). Finally, the proposed method is applied in nuclear transplantation operations, and, for the first time, the proper amount of the removed cytoplasm in porcine enucleation is estimated. The proposed method is able to weigh cells with a success rate of 92% at an average speed of 22 s/cell, and it can be performed on traditional microoperation systems, which makes it easily applicable in biological applications.

Soft tissue modelling with conical springs.

This paper presents a new method for real-time modelling soft tissue deformation. It improves the traditional mass-spring model with conical springs to deal with nonlinear mechanical behaviours of soft tissues. A conical spring model is developed to predict soft tissue deformation with reference to deformation patterns. The model parameters are formulated according to tissue deformation patterns and the nonlinear behaviours of soft tissues are modelled with the stiffness variation of conical spring. Experimental results show that the proposed method can describe different tissue deformation patterns using one single equation and also exhibit the typical mechanical behaviours of soft tissues.

Vision-based force measurement using neural networks for biological cell microinjection.

This paper presents a vision-based force measurement method using an artificial neural network model. The proposed model is used for measuring the applied load to a spherical biological cell during micromanipulation process. The devised vision-based method is most useful when force measurement capability is required, but it is very challenging or even infeasible to use a force sensor. Artificial neural networks in conjunction with image processing techniques have been used to estimate the applied load to a cell. A bio-micromanipulation system capable of force measurement has also been established in order to collect the training data required for the proposed neural network model. The geometric characterization of zebrafish embryos membranes has been performed during the penetration of the micropipette prior to piercing. The geometric features are extracted from images using image processing techniques. These features have been used to describe the shape and quantify the deformation of the cell at different indentation depths. The neural network is trained by taking the visual data as the input and the measured corresponding force as the output. Once the neural network is trained with sufficient number of data, it can be used as a precise sensor in bio-micromanipulation setups. However, the proposed neural network model is applicable for indentation of any other spherical elastic object. The results demonstrate the capability of the proposed method. The outcomes of this study could be useful for measuring force in biological cell micromanipulation processes such as injection of the mouse oocyte/embryo.