Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication

Cell mechanics is essential to cell development and function,and its dynamics evolution reflects the physiological state of cells.Here,we investigate the dynamical mechanical properties of single cells under various drug conditions,and present two mathematical approaches to quantitatively characterizing the cell physiological state.It is demonstrated that the cellular mechanical properties upon the drug action increase over time and tend to saturate,and can be mathematically characterized by a linear timeinvariant dynamical model.It is shown that the transition matrices of dynamical cell systems significantly improve the classification accuracies of the cells under different drug actions.Furthermore,it is revealed that there exists a positive linear correlation between the cytoskeleton density and the cellular mechanical properties,and the physiological state of a cell in terms of its cytoskeleton density can be predicted from its mechanical properties by a linear regression model.This study builds a relationship between the cellular mechanical properties and the cellular physiological state,adding information for evaluating drug efficacy.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Damage evolution and removal behaviors of GaN crystals involved in double-grits grinding

Elucidating the complex interactions between the work material and abrasives during grinding of gallium nitride(GaN)single crystals is an active and challenging research area.In this study,molecular dynamics simulations were performed on double-grits interacted grinding of GaN crystals;and the grinding force,coefficient of friction,stress distribution,plastic damage behaviors,and abrasive damage were systematically investigated.The results demonstrated that the interacted distance in both radial and transverse directions achieved better grinding quality than that in only one direction.The grinding force,grinding induced stress,subsurface damage depth,and abrasive wear increase as the transverse interacted distance increases.However,there was no clear correlation between the interaction distance and the number of atoms in the phase transition and dislocation length.Appropriate interacted distances between abrasives can decrease grinding force,coefficient of friction,grinding induced stress,subsurface damage depth,and abrasive wear during the grinding process.The results of grinding tests combined with cross-sectional transmission electron micrographs validated the simulated damage results,i.e.amorphous atoms,high-pressure phase transition,dislocations,stacking faults,and lattice distortions.The results of this study will deepen our understanding of damage accumulation and material removal resulting from coupling between abrasives during grinding and can be used to develop a feasible approach to the wheel design of ordered abrasives.

Characterizing permeability-porosity relationships of porous rocks using a stress sensitivity model in consideration of elastic-structural deformation and tortuosity sensitivity

Clarifying the relationship between stress sensitivities of permeability and porosity is of great significance in guiding underground resource mining.More and more studies focus on how to construct stress sensitivity models to describe the relationship and obtain a comprehensive stress sensitivity of porous rock.However,the limitations of elastic deformation calculation and incompleteness of considered tortuosity sensitivity lead to the fact that the existing stress sensitivity models are still unsatisfactory in terms of accuracy and generalization.Therefore,a more accurate and generic stress sensitivity model considering elastic-structural deformation of capillary cross-section and tortuosity sensitivity is proposed in this paper.The elastic deformation is derived from the fractal scaling model and Hooke's law.Considering the effects of elastic-structural deformation on tortuosity sensitivity,an empirical formula is proposed,and the conditions for its applicability are clarified.The predictive performance of the proposed model for the permeability-porosity relationships is validated in several sets of publicly available experimental data.These experimental data are from different rocks under different pressure cycles.The mean and standard deviation of relative errors of predicted stress sensitivity with respect to experimental data are 2.63%and 1.91%.Compared with other models,the proposed model has higher accuracy and better predictive generalization performance.It is also found that the porosity sensitivity exponent a,which can describe permeability-porosity relationships,is 2 when only elastic deformation is considered.a decreases from 2 when structural deformation is also considered.In addition,a may be greater than 3 due to the increase in tortuosity sensitivity when tortuosity sensitivity is considered even if the rock is not fractured.

Characteristics of laser-induced breakdown spectroscopy of liquid slag

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

Simulation of magnetization process and Faraday effect of magnetic bilayer films

We described ferromagnetic film and bilayer films composed of two ferromagnetic layers coupled through antiferromagnetic interfacial interaction by classical Heisenberg model and simulated their magnetization state,magnetic permeability,and Faraday effect at zero and finite temperature by using the Landau–Lifshitz–Gilbert(LLG)equation.The results indicate that in a microwave field with positive circular polarization,the ferromagnetic film has one resonance peak while the bilayer film has two resonance peaks.However,the resonance peak disappears in ferromagnetic film,and only one resonance peak emerges in bilayer film in the negative circularly polarized microwave field.When the microwave field’s frequency exceeds the film’s resonance frequency,the Faraday rotation angle of the ferromagnetic film is the greatest,and it decreases when the thickness of the two halves of the bilayer is reduced.When the microwave field’s frequency remains constant,the Faraday rotation angle fluctuates with temperature in the same manner as spontaneous magnetization does.When a DC magnetic field is applied in the direction of the anisotropic axis of the film,the Faraday rotation angle varies with the DC magnetic field and shows a similar shape of the hysteresis loop.

Illumination Adaptive Identification Algorithm of a Reconfigurable Modular Robot

Reconfigurable modular robots feature high mobility due to their unconstrained connection manners.Inspired by the snake multi-joint crawling principle,a chain-type reconfigurable modular robot(CRMR)is designed,which could reassemble into various configurations through the compound joint motion.Moreover,an illumination adaptive modular robot identification(IAMRI)algorithm is proposed for CRMR.At first,an adaptive threshold is applied to detect oriented FAST features in the robot image.Then,the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition method.After matching features,an improved random sample consensus algorithm is employed to eliminate the mismatched features.Finally,the reconfigurable robot module is identified effectively through the perspective transformation.Compared with ORB,MA,Y-ORB,and S-ORB algorithms,the IAMRI algorithm has an improvement of over 11.6%in feature uniformity,and 13.7%in the comprehensive indicator,respectively.The IAMRI algorithm limits the relative error within 2.5 pixels,efficiently completing the CRMR identification under complex environmental changes.

Understand anisotropy dependence of damage evolution and material removal during nanoscratch of MgF_(2) single crystals

To understand the anisotropy dependence of the damage evolution and material removal during the machining process of MgF_(2) single crystals,nanoscratch tests of MgF_(2) single crystals with different crystal planes and directions were systematically performed,and surface morphologies of the scratched grooves under different conditions were analyzed.The experimental results indicated that anisotropy considerably affected the damage evolution in the machining process of MgF_(2) single crystals.A stress field model induced by the scratch was developed by considering the anisotropy,which indicated that during the loading process,median cracks induced by the tensile stress initiated and propagated at the front of the indenter.Lateral cracks induced by tensile stress initiated and propagated on the subsurface during the unloading process.In addition,surface radial cracks induced by the tensile stress were easily generated during the unloading process.The stress change led to the deflection of the propagation direction of lateral cracks.Therefore,the lateral cracks propagated to the workpiece surface,resulting in brittle removal in the form of chunk chips.The plastic deformation parameter indicated that the more the slip systems were activated,the more easily the plastic deformation occurred.The cleavage fracture parameter indicated that the cracks propagated along the activated cleavage planes,and the brittle chunk removal was owing to the subsurface cleavage cracks propagating to the crystal surface.Under the same processing parameters,the scratch of the(001)crystal plane along the[100]crystal-orientation was found to be the most conducive to achieving plastic machining of MgF_(2) single crystals.The theoretical results agreed well with the experimental results,which will not only enhance the understanding of the anisotropy dependence of the damage evolution and removal process during the machining of MgF_(2) crystals,but also provide a theoretical foundation for achieving the high-efficiency and low-damage processing of anisotropic single crystals.

Dynamic Analysis and Parametric Optimization of Telescopic Tubular Mast Applied on Solar Sail

Large-scale solar sails can provide power to spacecraft for deep space exploration.A new type of telescopic tubular mast(TTM)driven by a bistable carbon fiber-reinforced polymer tube was designed in this study to solve the problem of contact between the sail membrane and the spacecraft under light pressure.Compared with the traditional TTM,it has a small size,light weight,high extension ratio,and simple structure.The anti-blossoming and self-unlocking structure of the proposed TTM was described.We aimed to simplify the TTM with a complex structure into a beam model with equal linear mass density,and the simulation results showed good consistency.The dynamic equation was derived based on the equivalent model,and the effects of different factors on the vibration characteristics of the TTM were analyzed.The performance parameters were optimized based on a multiobjective genetic algorithm,and prototype production and load experiments were conducted.The results show that the advantages of the new TTM can complete the deployment of large-scale solar sails,which is valuable for future deep space exploration.

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

3D printing of high-precision and ferromagnetic functional devices

The development of projection-based stereolithography additive manufacturing techniques and magnetic photosensitive resins has provided a powerful approach to fabricate miniaturized magnetic functional devices with complex three-dimensional spatial structures.However,the present magnetic photosensitive resins face great challenges in the trade-off between high ferromagnetism and excellent printing quality.To address these challenges,we develop a novel NdFeB-Fe_(3)O_(4) magnetic photosensitive resin comprising 20 wt.%solid loading of magnetic particles,which can be used to fabricate high-precision and ferromagnetic functional devices via micro-continuous liquid interface production process.This resin combining ferromagnetic NdFeB microparticles and strongly absorbing Fe_(3)O_(4) nanoparticles is able to provide ferromagnetic capabilities and excellent printing quality simultaneously compared to both existing soft and hard magnetic photosensitive resins.The established penetration depth model reveals the effect of particle size,solid loading,and absorbance on the curing characteristics of magnetic photosensitive resin.A high-precision forming and ferromagnetic capability of the NdFeB-Fe_(3)O_(4) magnetic photosensitive resin are comprehensively demonstrated.It is found that the photosensitive resin(NdFeB:Fe_(3)O_(4)=1:1)can print samples with sub-40μm fine features,reduced by 87%compared to existing hard magnetic photosensitive resin,and exhibits significantly enhanced coercivity and remanence in comparison with existing soft magnetic photosensitive resins,showing by an increase of 24 times and 6 times,respectively.The reported NdFeB-Fe_(3)O_(4) magnetic photosensitive resin is anticipated to provide a new functional material for the design and manufacture of next-generation micro-robotics,electromagnetic sensor,and magneto-thermal devices.

Design and In Situ Additive Manufacturing of Multifunctional Structures

Multifunctional structures(MFSs)integrate diverse functions to achieve superior properties.However,conventional design and manufacturing methods—which generally lack quality control and largely depend on complex equipment with multiple stations to achieve the integration of distinct materials and devices—are unable to satisfy the requirements of MFS applications in emerging industries such as aerospace engineering.Motivated by the concept of design for manufacturing,we adopt a layer regulation method with an established optimization model to design typical MFSs with load-bearing,electric,heat-conduction,and radiation-shielding functions.A high-temperature in situ additive manufacturing(AM)technology is developed to print various metallic wires or carbon fiber-reinforced high-meltingpoint polyetheretherketone(PEEK)composites.It is found that the MFS,despite its low mass,exceeds the stiffness of the PEEK substrate by 21.5%.The embedded electrics remain functional after the elastic deformation stage.Compared with those of the PEEK substrate,the equivalent thermal conductivity of the MFS beneath the central heat source area is enhanced by 568.0%,and the radiation shielding is improved by 27.9%.Moreover,a satellite prototype with diverse MFSs is rapidly constructed as an illustration.This work provides a systematic approach for high-performance design and advanced manufacturing,which exhibits considerable prospects for both the function expansion and performance enhancement of industrial equipment.

Power Consumption Characteristics Research on Mobile System of Electrically Driven Large-Load-Ratio Six-Legged Robot

The electrically driven large-load-ratio six-legged robot with engineering capability can be widely used in outdoor and planetary exploration.However,due to the particularity of its parallel structure,the effective utilization rate of energy is not high,which has become an important obstacle to its practical application.To research the power consumption characteristics of robot mobile system is beneficial to speed up it toward practicability.Based on the configuration and walking modes of robot,the mathematical model of the power consumption of mobile system is set up.In view of the tripod gait is often selected for the six-legged robots,the simplified power consumption model of mobile system under the tripod gait is established by means of reducing the dimension of the robot’s statically indeterminate problem and constructing the equal force distribution.Then,the power consumption of robot mobile system is solved under different working conditions.The variable tendencies of the power consumption of robot mobile system are respectively obtained with changes in the rotational angles of hip joint and knee joint,body height,and span.The articulated rotational zones and the ranges of body height and span are determined under the lowest power consumption.According to the walking experiments of prototype,the variable tendencies of the average power consumption of robot mobile system are respectively acquired with changes in duty ratio,body height,and span.Then,the feasibility and correctness of theory analysis are verified in the power consumption of robot mobile system.The proposed analysis method in this paper can provide a reference on the lower power research of the large-load-ratio multi-legged robots.

Variable Curvature Modeling Method of Soft Continuum Robots with Constraints

The inherent compliance of continuum robots holds great promise in the fields of soft manipulation and safe human–robot interaction.This compliance reduces the risk of damage to the manipulated object and its surroundings.However,continuum robots possess theoretically infinite degrees of freedom,and this high flexibility usually leads to complex deformations when subjected to external forces and positional constraints.Describing these complex deformations is the main challenge in modeling continuum robots.In this study,we investigated a novel variable curvature modeling method for continuum robots,considering external forces and positional constraints.The robot configuration curve is described using the developed mechanical model,and then the robot is fitted to the curve.A ten-section continuum robot prototype with a length of 1 m was developed in order to validate the model.The feasibility and accuracy of the model were verified by the ability of the robot to reach target points and track complex trajectories with a load.This work was able to serve as a new perspective for the design analysis and motion control of continuum robots.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

A versatile humanoid robot platform for dexterous manipulation and human-robot collaboration

Humanoid robots have attracted much attention by virtue of their compatibility with human environments.However,biped humanoids with immense promise still cannot function steadily and reliably in real-world settings in the current state.Hence,rationally combining a humanoid robot with different stable mobile platforms is a favoured solution for diverse scenarios.Here,a new versatile humanoid robot platform,aiming to provide a generic solution that can be flexibly deployed in diverse scenarios,for example,indoors and fields is presented.Versatile humanoid robot platform incorporates multimodal perception,and extensible interfaces on hardware and software,allowing it to be rapidly integrated with different mobile platforms and end-effectors,only through easyto-assemble interfaces.Additionally,the platform has achieved impressive integration,lightness,dexterity,and strength in its class,with human-like size and rich perception,targeted to have human-intelligent manipulation skills for human-engineered environments.Overall,this article elaborates on the reasoning behind the design choices,and outlines each subsystem.Lastly,the essential performance of the platform is successfully demonstrated in a set of experiments with precise and dexterous manipulation,and human–robot collaboration requirements.

Intuitive Human-Robot-Environment Interaction With EMG Signals:A Review

A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive interaction.However,it still has a gap between scientific research and real-life applications.Previous studies mainly focused on EMG decoding algorithms,leaving a dynamic relationship between the human,robot,and uncertain environment in real-life scenarios seldomly concerned.To fill this gap,this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interaction(HREI)systems.The general processing framework is summarized,and three interaction paradigms,including direct control,sensory feedback,and partial autonomous control,are introduced.EMG-based intention decoding is treated as a module of the proposed paradigms.Five key issues involving precision,stability,user attention,compliance,and environmental awareness in this field are discussed.Several important directions,including EMG decomposition,robust algorithms,HREI dataset,proprioception feedback,reinforcement learning,and embodied intelligence,are proposed to pave the way for future research.To the best of what we know,this is the first time that a review of EMG-based methods in the HREI system is summarized.It provides a novel and broader perspective to improve the practicability of current myoelectric interaction systems,in which factors in human-robot interaction,robot-environment interaction,and state perception by human sensations are considered,which has never been done by previous studies.

Topology Structure Synthesis and Analysis of Spatial Pyramid Deployable Truss Structures for Satellite SAR Antenna

Many attentions for structural synthesis are paid to planar linkages and parallel mechanisms, while design novel pyramid deployable truss structure（PDTS） of satellite SAR mainly depends on experience of designer. To design novel configuration of PDTS, a two-step topology structure synthesis and analysis approach is proposed. Firstly, a conceptual configuration of PDTS is synthesized. Weighted graph and weighted adjacency matrix are established to realize topological description for PDTS. Graph properties are then summarized to distinguish differentia between PDTS and other type structures. According to graph properties, a procedure for synthesis conceptual configuration of PDTS is presented. Secondly, join relationship of components in a PDTS is analyzed. Kinematic chain and corresponding incidence/adjacency matrix are employed to analyze join relationship of PDTS. Properties and simplified rules of kinematic chain are extracted to construct kinematic chain. A procedure for construction kinematic chain of PDTS is then established. Finally, with this two-step approach all 11 rectangular pyramid deployable structures whose folded state is planar are discovered and their kinematic chains are constructed. Based on synthesis results, a novel deployable support structure for satellite SAR is designed. The proposed research can be applied to obtain some novel PDTSs, which is of great importance to design some novel deployable support structures for satellite SAR antenna.

Design Schemes and Comparison Research of the End-effector of Large Space Manipulator

The end-effector of the large space manipulator is employed to assist the manipulator in handling and manipulating large payloads on orbit.Currently,there are few researches about the end-effector,and the existing end-effectors have some disadvantages,such as poor misalignment tolerance capability and complex mechanical components.According to the end positioning errors and the residual vibration characters of the large space manipulators,two basic performance requirements of the end-effector which include the capabilities of misalignment tolerance and soft capture are proposed.And the end-effector should accommodate the following misalignments of the mechanical interface.The translation misalignments in axial and radial directions and the angular misalignments in roll,pitch and yaw are ±100 mm,100 mm,±10°,±15°,±15°,respectively.Seven end-effector schemes are presented and the capabilities of misalignment tolerance and soft capture are analyzed elementarily.The three fingers-three petals end-effector and the steel cable-snared end-effector are the most feasible schemes among the seven schemes,and they are designed in detail.The capabilities of misalignment tolerance and soft capture are validated and evaluated,through the experiment on the micro-gravity simulating device and the dynamic analysis in ADAMS software.The results show that the misalignment tolerance capabilities of these two schemes could satisfy the requirement.And the translation misalignment tolerances in axial and radial directions and the angular misalignment tolerances in roll,pitch and yaw of the steel cable-snared end-effector are 30mm,15mm,6°,3° and 3° larger than those of the three fingers-three petals end-effector,respectively.And the contact force of the steel cable-snared end-effector is smaller and smoother than that of the three fingers-three petals end-effector.The end-effector schemes and research methods are beneficial to the developments of the large space manipulator end-effctor and the space docking mechanism.