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.

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.

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 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.

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.

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.

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.

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 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.

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.

A wearable and highly sensitive pressure sensor with ultrathin gold nanowires.

Ultrathin gold nanowires are mechanically flexible yet robust, which are novel building blocks with potential applications in future wearable optoelectronic devices. Here we report an efficient, low-cost fabrication strategy to construct a highly sensitive, flexible pressure sensor by sandwiching ultrathin gold nanowire-impregnated tissue paper between two thin polydimethylsiloxane sheets. The entire device fabrication process is scalable, enabling facile large-area integration and patterning for mapping spatial pressure distribution. Our gold nanowires-based pressure sensors can be operated at a battery voltage of 1.5 V with low energy consumption (<30 µW), and are able to detect pressing forces as low as 13 Pa with fast response time (<17 ms), high sensitivity (>1.14 kPa(-1)) and high stability (>50,000 loading-unloading cycles). In addition, our sensor can resolve pressing, bending, torsional forces and acoustic vibrations. The superior sensing properties in conjunction with mechanical flexibility and robustness enabled real-time monitoring of blood pulses as well as detection of small vibration forces from music.

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.

An actuated force feedback-enabled laparoscopic instrument for robotic-assisted surgery.

BACKGROUND: Robotic-assisted minimally invasive surgery systems not only have the advantages of traditional laparoscopic instruments but also have other important advantages, including restoring the surgeon's hand-eye coordination and improving the surgeon's precision by filtering hand tremors. Unfortunately, these benefits have come at the expense of the surgeon's ability to feel. Various solutions for restoring this feature have been proposed. METHODS: An actuated modular force feedback-enabled laparoscopic instrument was proposed that is able to measure tip-tissue lateral interaction forces as well as normal grasping forces. The instrument has also the capability to adjust the grasping direction inside the patient body. In order to measure the interaction forces, strain gauges were employed. A series of finite element analyses were performed to gain an understanding of the actual magnitude of surface strains where gauges are applied. The strain gauge bridge configurations were calibrated. A series of experiments was conducted and the results were analysed. RESULTS: The modularity feature of the proposed instrument makes it interchangeable between various tip types of different functionalities (e.g. cutter, grasper, dissector). Calibration results of the strain gauges incorporated into the tube and at the base of the instrument presented the monotonic responses for these strain gauge configurations. Experimental results from tissue probing and tissue characterization experiments verified the capability of the proposed instrument in measuring lateral probing forces and characterizing artificial tissue samples of varying stiffness. CONCLUSION: The proposed instrument 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.

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.

An electromechanical based deformable model for soft tissue simulation.

OBJECTIVE: Soft tissue deformation is of great importance to surgery simulation. Although a significant amount of research efforts have been dedicated to simulating the behaviours of soft tissues, modelling of soft tissue deformation is still a challenging problem. This paper presents a new deformable model for simulation of soft tissue deformation from the electromechanical viewpoint of soft tissues. METHODS AND MATERIAL: Soft tissue deformation is formulated as a reaction-diffusion process coupled with a mechanical load. The mechanical load applied to a soft tissue to cause a deformation is incorporated into the reaction-diffusion system, and consequently distributed among mass points of the soft tissue. Reaction-diffusion of mechanical load and non-rigid mechanics of motion are combined to govern the simulation dynamics of soft tissue deformation. RESULTS: An improved reaction-diffusion model is developed to describe the distribution of the mechanical load in soft tissues. A three-layer artificial cellular neural network is constructed to solve the reaction-diffusion model for real-time simulation of soft tissue deformation. A gradient based method is established to derive internal forces from the distribution of the mechanical load. Integration with a haptic device has also been achieved to simulate soft tissue deformation with haptic feedback. CONCLUSIONS: The proposed methodology does not only predict the typical behaviours of living tissues, but it also accepts both local and large-range deformations. It also accommodates isotropic, anisotropic and inhomogeneous deformations by simple modification of diffusion coefficients.

Development of novel hybrid flexure-based microgrippers for precision micro-object manipulation.

This paper describes the process of developing a microgripper that is capable of high precision and fidelity manipulation of micro-objects. The design adopts the concept of flexure-based hinges on its joints to provide the rotational motion, thus eliminating the inherent nonlinearities associated with the application of conventional rigid hinges. A combination of two modeling techniques, namely, pseudorigid body model and finite element analysis was utilized to expedite the prototyping procedure, which leads to the establishment of a high performance mechanism. A new hybrid compliant structure integrating cantilever beam and flexural hinge configurations within microgripper mechanism mainframe has been developed. This concept provides a novel approach to harness the advantages within each individual configuration while mutually compensating the limitations inherent between them. A wire electrodischarge machining technique was utilized to fabricate the gripper out of high grade aluminum alloy (Al 7075T6). Experimental studies were conducted on the model to obtain various correlations governing the gripper performance as well as for model verification. The experimental results demonstrate high level of compliance in comparison to the computational results. A high amplification characteristic and maximum achievable stroke of 100 microm can be achieved.

Robust neural network motion tracking control of piezoelectric actuation systems for micro/nanomanipulation.

This paper presents a robust neural network motion tracking control methodology for piezoelectric actuation systems employed in micro/nanomanipulation. This control methodology is proposed for tracking of desired motion trajectories in the presence of unknown system parameters, nonlinearities including the hysteresis effect and external disturbances in the control systems. In this paper, the related control issues are investigated, and a control methodology is established including the neural networks and a sliding control scheme. In particular, the radial basis function (RBF) neural networks are chosen for function approximations. The stability of the closed-loop system, as well as the convergence of the position and velocity tracking errors to zero, is assured by the control methodology in the presence of the aforementioned conditions. An offline learning procedure is also proposed for the improvement of the motion tracking performance. Precise tracking results of the proposed control methodology for a desired motion trajectory are demonstrated in the experimental study. With such a motion tracking capability, the proposed control methodology promises the realization of high-performance piezoelectric actuated micro/nanomanipulation systems.