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In stick-slip positioning, friction between the stick-slip surfaces plays a critical role in positioning accuracy. Positioning performance varies when the load to drive changes. In this paper, by using a form-closed cam, the driving unit is separated from the moving unit to eliminate load influence. The stage is capable of operating in either a stepping mode or a scanning mode. Numerical modeling was conducted to analyze the static and dynamic characteristics of the stage design. In experiments, a number of parameters were tested on the constructed stage, and positioning performance was measured via laser-doppler vibrometry. Experimental results demonstrate that in the stepping mode, the stage has a travel range of 2 mm with incremental step sizes ranging from 30 nm to 2.3 μm. In the scanning mode, the stage has a positioning resolution of 1.15 nm. The measured results also confirm that load variations on the stage have little influence on contact friction force and positioning performance. [ABSTRACT FROM PUBLISHER]

This paper describes a novel recognition algorithm which includes mean filter, Gaussian filter, Retinex enhancement method, and Ostu threshold segmentation method (MGRO) for the navigation of mobile robots with visual sensors. The approach includes obstacle visual recognition and navigation path planning. In the first part, a three-stage method for obstacle visual recognition is constructed. Stage 1 combines mean filtering and Gaussian filtering to remove random noise and Gauss noise in the environmental image. Stage 2 increases image contrast by using the Retinex enhancement method. Stage 3 uses the Ostu threshold segmentation method to achieve obstacle feature extraction. A navigation method based on the auxiliary visual information is constructed in the second part. The method is based on the artificial potential field (APF) method and is able to avoid falling into local minimum by changing the repulsion field function. Experimental results confirm that obstacle features can be extracted accurately and the mobile robot can avoid obstacles safely and arrive at target positions correctly. [ABSTRACT FROM AUTHOR]

In microsinjection robot system facing biological engineering, rapid seeking of the target cell and accurate location in view is effective in reducing the work of operator. It is necessary to realize automation microinjection. We propose a method of cells search based on visual. It can scan target plane automatically, identify the cells in target plane accurately and realize cells positioning by visual servo for further injection. Experiment results demonstrate this method can identify cells from the culture dish quickly and realize the cell positioning precisely. [ABSTRACT FROM AUTHOR]

Nanomanipulation inside a scanning electron microscope (SEM) has been employed to maneuver and characterize nanomaterials. Despite recent efforts toward automated nanomanipulation, it is still largely conducted manually. In this paper, we demonstrate automated nanomanipulation inside an SEM for a well-structured nanomanipulation task via visual servo control and a vision-based contact-detection method using SEM as a vision sensor. Four-point probe measurement of individual nanowires is achieved automatically by controlling four nanomanipulators with SEM visual feedback. A feedforward controller is incorporated into the control system to improve response time. This technique represents an advance in nanomanipulation inside SEM and can be extended to other nanomanipulation tasks. [ABSTRACT FROM PUBLISHER]

The robotic manipulation of deformable linear objects is a classic and challenging topic. Apart from synthetic objects, such as wires and cables, linear objects are also commonly found in biological cells and organisms. Biomanipulation of such objects is hampered by difficulties, such as limited degrees of freedom of micromanipulators and varied mechanical properties of the biological entities to manipulate. This article presents a robotic manipulation of human sperm, which are deformable cells with a linear shape. The shape and movement of the cell are recapitulated by our developed geometric and kinematic models. Under unfixed constraints between the end-effector and the cell, path planning is designed to update the manipulation point to control cell deformation. A state transition function is formulated in path planning to handle the stiffness variations of sperm without force sensing. A model-predictive controller is designed to minimize the orientation error and manipulation path length. To detect sperm tail for visual feedback, an accuracy of 98% was achieved via deep neural networks. The robotic manipulation of human sperm was performed using a standard clinical setup of a glass micropipette to rotate a sperm to the target orientation. Experimental results showed that robotic sperm rotation achieved an orientation error of 0.8 $^{\circ }$ , a tail curvedness of 0.14 $\mu$ m $^{-1}$ , and an operation time of 5.6 s, all significantly less than those of the manual approach. The less orientation error and tail curvedness after robotic rotation led to a significantly lower speed of sperm entering the micropipette during sperm aspiration, resulting in a higher success rate of 97% (versus 76% after manual rotation) for aspiration control. [ABSTRACT FROM AUTHOR]

Precise rotational positioning of end-effectors under microscopy is crucial for robotic micromanipulation. However, the end-effector is presently limited to a fixed orientation, which is manually set before a given micromanipulation task, lacking accuracy and versatility of in situ reorientation. In this article, we present a unified framework for rotationally positioning the end-effector in three dimensions by establishing a general rotational model, developing a detection method within the limited field of view under microscopy, and designing a three-loop control strategy that adapts to different experimental requirements and model parameters. In experiments, a standard angled micropipette was used as the end-effector to verify the validity of the proposed methods. The performance was evaluated experimentally where the micropipette was robotically rotated to an arbitrarily desired orientation with an average orientation error less than $2^\circ$. In the experiments of sperm manipulation, the in situ micropipette orientation control capability improved the success rate of sperm immobilization and achieved dexterous robotic sperm orientation for facile aspiration. [ABSTRACT FROM AUTHOR]

This article proposes a new eye-in-hand 3-D scanner-robot calibration approach to realize low data stitching errors during long-term continuous measurement. Eye-in-hand 3-D scanner–robot systems are commonly used for the complete measurement of an object under test (OUT) from multiple fields of view (FOVs). To align the multiple FOVs into a single coordinate system, marker-free stitching assisted by robot's positioning is attractive since it bypasses the cumbersome traditional fiducial marker-based method. Based on periodically capturing calibration images from a 2-D calibration target, scanner's and robot kinematic model's parameters are optimized. The challenges overcome in this article include: how to compensate for the center-detection error in scanner calibration; how to avoid the dependence of hand-eye calibration on DH parameters; and how to calculate an accurate world-to-robot transformation. These challenges were tackled by several new techniques including accurate scanner calibration with iterative refinement of control points, virtual arm-based scanner-robot kinematic modeling, and trajectory-based world-to-robot transformation calculation. Experimental results demonstrated a low-initial stitching error similar to the fiducial marker-based method (0.0446 mm versus 0.0542 mm) was achieved. The mean stitching error was effectively maintained to be < 0.1 mm during the continuous measurement with an average intermittent downtime of 78 s for recalibration. [ABSTRACT FROM AUTHOR]

In-situ SEM nanoindentation and nanoscratch testing methods are commonly used for mechanical characterization and investigation of the deformation and failure mechanisms of coating materials with micro-to nano-scale thicknesses. However, existing SEM-based integrated nanoindentation and nanoscratch instruments have two main limitations. First, the measured mechanical properties of the coating materials at micro-to nano-scale thicknesses are highly sensitive to surface roughness. Second, the existing SEM-based instruments lack the capability to acquire the morphology of residual imprints in real-time after nanoindentation and nanoscratching. In this study, a novel SEM-based integrated nanoindentation, nanoscratch, and atomic force microscopy (AFM) instrument, namely, NMT-AFM was proposed, developed and fabricated. The self-sensing piezoresistive cantilever (PRC) was selected as the AFM force sensor owing to its miniaturization ability. However, the resistance of the PRC sensor fluctuated because of the electron irradiation from SEM, resulting in the continuous drift of the PRC signal during SEM imaging. To overcome this limitation, a mechanism of PRC signal drift inside SEM was analyzed for the first time, and a PRC signal drift reduction method was proposed based on the mechanism analysis. The experimental results indicated that the PRC signal drift was reduced to 2 nm in 2 min by applied external voltage value U A of 30 V to modified PRC, which proved the proposed mechanism of PRC signal drift during SEM imaging. Finally, the X–Y fine nanopositioner angle calibration test using AFM calibration chip VGRP-UM and the nanoindentation/nanoscratch characterizations of the TiAlSiN coating material were conducted. • The work reports a new piezoresistive cantilever (PRC) signal drift reduction method during SEM imaging. • The PRC signal drift reduction method is based on the proposed P-channel JFET model of PRC during SEM imaging for the first time. • The PRC is chosen as the force sensor of AFM function in NMT-AFM instrument. [ABSTRACT FROM AUTHOR]

Nanopositioning stage, based on the stick–slip principle, is a hot research topic due to its unparallel merits such as compact structure, high resolution, and long travel stroke. However, most of the conventional stick–slip nanopositioning stages only have the ability to move along the horizontal directions. In addition, analysis of vertical stage movement is few. This paper proposes a novel stick–slip nanopositioning stage capable of moving upwards and downwards with an excellent performance. An adjusting unit was developed and integrated into the stage to effectively change friction force which is the key to the vertical stage movement. Theoretical and numerical analysis show that many parameters including revised driving signal contributes to the upward movement of stick–slip nanopositioning stage. Experimental results show that the stage has the capability of traveling up to 22 mm with an upward positioning accuracy of 7.00 nm and a downward positioning accuracy of 5.00 nm, respectively. The maximum speed for maximum speeds of stage for upward and downward motion were measured to be 2.70 and 0.77 mm/s, respectively. The maximum load of stage in vertical direction is 72 g. This stage design has a huge potential in applications requiring vertical manipulation while requiring nanometer positioning accuracies, high travel stroke, compact structure, and large load. [ABSTRACT FROM AUTHOR]

Instance segmentation of biological cells is important in medical image analysis for identifying and segmenting individual cells, and quantitative measurement of subcellular structures requires further cell-level subcellular part segmentation. Subcellular structure measurements are critical for cell phenotyping and quality analysis. For these purposes, instance-aware part segmentation network is first introduced to distinguish individual cells and segment subcellular structures for each detected cell. This approach is demonstrated on human sperm cells since the World Health Organization has established quantitative standards for sperm quality assessment. Specifically, a novel Cell Parsing Net (CP-Net) is proposed for accurate instance-level cell parsing. An attention-based feature fusion module is designed to alleviate contour misalignments for cells with an irregular shape by using instance masks as spatial cues instead of as strict constraints to differentiate various instances. A coarse-to-fine segmentation module is developed to effectively segment tiny subcellular structures within a cell through hierarchical segmentation from whole to part instead of directly segmenting each cell part. Moreover, a sperm parsing dataset is built including 320 annotated sperm images with five semantic subcellular part labels. Extensive experiments on the collected dataset demonstrate that the proposed CP-Net outperforms state-of-the-art instance-aware part segmentation networks., Competing Interests: Declaration of competing interest There are no competing interest for all authors., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)