Establishing the relationship between vibrotactile parameters and perceived intensity(PI)is of great significance in the regulation of desired sensations,which contribute to haptic interfaces in practical applications...Establishing the relationship between vibrotactile parameters and perceived intensity(PI)is of great significance in the regulation of desired sensations,which contribute to haptic interfaces in practical applications.Coin eccentric rotating mass(ERM)motors are routinely utilized due to their lightweight design and efficient ability to evoke strong tactile sensations.By adjusting input voltages,ERMs can yield different stimuli,but the effects of their physical properties on PI remain unclear.Thus,we developed the physical model of the ERM-skin system and optimized the parameters affecting PI.Moreover,the relationship between stimulus and perception was derived based on Fechner's law.Three experiments were conducted on fifteen subjects(ten males and five females,aged 24.40±2.87 years)to identify the vibration parameters and corresponding PI to verify the proposed PI model.ERMs in this study were attached to phantom skin/forearm with slim adhesive tape to minimize the interference.Experiment 1 performed vibration calibration with ERM attached to the skin phantom to simulate the actual configuration.Then,the relative and absolute PI of subjects on specific stimuli were acquired in Experiments 2 and 3.The fitting reliability of the proposed PI model was evaluated on fifteen subjects with R^(2) of 0.73±0.07 and root mean squared error(RMSE)of 1.35±0.28.Overall,the proposed PI model established the quantitative relationship between stimulus parameters(vibration parameters and physical parameters)and subjective PI,which provided theoretical support for ERM optimization and vibrotactile modulation.展开更多
Model-guided design of dielectric elastomer actuators(DEAs)is essential for enabling their application in soft robotics.However,current modeling methods primarily rely on the finite element method(FEM),which suffers f...Model-guided design of dielectric elastomer actuators(DEAs)is essential for enabling their application in soft robotics.However,current modeling methods primarily rely on the finite element method(FEM),which suffers from low computational efficiency.Additionally,the simulation-to-reality(Sim2Real)gap,mainly arising from variations in material properties and manufacturing processes,poses a significant challenge.In this work,we propose a data-driven modeling framework aimed at accurately and rapidly predicting voltage-induced displacements while minimizing the Sim2Real gap.The framework integrates a multi-layer perceptron(MLP)model,which serves as a computationally efficient surrogate for the FEM model,and a cycle-generative adversarial network(CycleGAN)model,which mitigates the Sim2Real gap by leveraging adversarial learning to process both simulation and experimental data.Dimensional analysis is performed to extend the framework's applicability across different DEA scales.The surrogate model delivers global predictions in just 0.8 s,achieving linear coefficients of determination(R^(2))of 0.99106 for release distance prediction and 0.99375 for actuation distance prediction compared to experimental results.Our model can quickly identify the feasible range of biaxial prestretch ratios required for generating the desired deformation,thereby streamlining the design process.Finally,a soft robotic gripper is designed and fabricated,demonstrating versatile object-grasping capabilities.展开更多
Parallel continuum robots(PCRs) have attracted increasing attention in the robotics community due to their simplicity in structure,inherence with compliance, and easiness of realization. Over the past decade, a variet...Parallel continuum robots(PCRs) have attracted increasing attention in the robotics community due to their simplicity in structure,inherence with compliance, and easiness of realization. Over the past decade, a variety of novel designs have been reported to enrich their diversity. However, there is a lack of systematic review of these emerging robots. To this end, this paper conducts a comprehensive survey on the mechanism design, kinetostatic modeling and analysis, and performance evaluation. For these robots, kinetostatic modeling plays a fundamental role throughout the design, analysis, and control stages. A systematic review of the existing approaches for kinetostatic modeling and analysis is provided, and a comparison is made to distinguish their differences. As well, a classification is made according to the characteristics of structure and actuation. In addition, performance evaluation on the workspace, stability, and singularity is also overviewed. Finally, the scenarios of potential applications are elaborated, and future research prospects are discussed. We believe that the information provided in this paper will be particularly useful for those who are interested in PCRs.展开更多
Soft grippers due to their highly compliant material and self-adaptive structures attract more attention to safe and versatile grasping tasks compared to traditional rigid grippers.However,those flexible characteristi...Soft grippers due to their highly compliant material and self-adaptive structures attract more attention to safe and versatile grasping tasks compared to traditional rigid grippers.However,those flexible characteristics limit the strength and the manipulation capacity of soft grippers.In this paper,we introduce a hybrid-driven gripper design utilizing origami finger structures,to offer adjustable finger stiffness and variable grasping range.This gripper is actuated via pneumatic and cables,which allows the origami structure to be controlled precisely for contraction and extension,thus achieving different finger lengths and stiffness by adjusting the cable lengths and the input pressure.A kinematic model of the origami finger is further developed,enabling precise control of its bending angle for effective grasping of diverse objects and facilitating in-hand manipulation.Our proposed design method enriches the field of soft grippers,offering a simple yet effective approach to achieve safe,powerful,and highly adaptive grasping and in-hand manipulation capabilities.展开更多
Due to the lightweight and compliance, fabric-based pneumatic exosuits are promising in the assistance and rehabilitation of elbow impairments. However, existing elbow exosuits generally suffer from remarkable mechani...Due to the lightweight and compliance, fabric-based pneumatic exosuits are promising in the assistance and rehabilitation of elbow impairments. However, existing elbow exosuits generally suffer from remarkable mechanical resistance on the flexion of the elbow, thus limiting the output force, range of motion(ROM), and comfortability. To address these challenges, we develop a fabric-based soft elbow exosuit with an adaptive mechanism and composite bellows in this work. With the elbow kinesiology considered, the adaptive mechanism is fabricated by sewing the interface of the exosuit into spring-like triangle pleats, following the profile of the elbow to elongate or contract when the elbow flexes or extends. The composite bellows are implemented by further sealing a single blade of bellows into two branches to enhance the output force. Based on these structural features, we characterize the mechanical performance of different soft elbow exosuits: exosuit with normal bellows-NB, exosuit with adaptive mechanism and normal bellows-AMNB, exosuit with adaptive mechanism and composite bellows-AMCB. Experimental results demonstrate that by comparing with NB, the mechanical resistance of AMNB and AMCB decreases by 80.6% and 78.6%, respectively;on the other hand, the output torque of AMNB and AMCB increases to 120.3% and 207.0%, respectively, at50 k Pa when the joint angle is 120°. By wearing these exosuits on a wooden arm model(1.25 kg), we further verify that AMCB can cover a full ROM of 0°–130° at the elbow with 500 g weight. Finally, the application on a health volunteer with AMCB shows that when the volunteer flexes the elbow to lift a weight of 500 g, the s EMG activity of the biceps and triceps is markedly reduced.展开更多
The intrinsic compliance of soft materials endows soft robots with great advantages to achieve large deformation and adaptive interactions in grasping tasks.However,current soft grippers usually focus on the in-plane ...The intrinsic compliance of soft materials endows soft robots with great advantages to achieve large deformation and adaptive interactions in grasping tasks.However,current soft grippers usually focus on the in-plane large deformation and load capacity but ignore the effect of out-of-plane external loads,which may lead to instability in practical scenarios.This problem calls for stiffness design along multiple directions to withstand not only in-plane interacting forces with objects,but also unexpected outof-plane loads.In this paper,we design a new type of soft finger by embedding an endoskeleton inside the widely-used PneuNets actuator,and the endoskeleton layout is optimized to achieve a remarkable bending deflection and limited lateral deflection under combined external in-plane and out-of-plane loads.Based on the multi-objective topology optimization approach,the key structural features of the optimized endoskeleton are extracted and parameterized.The multi-material soft fingers are fabricated by the silicone compound mold method.Static and dynamic experiment results validate that the soft gripper with endoskeleton embedded exhibits remarkably improved out-of-plane stiffness,without sacrificing the in-plane bending flexibility,and leads to more stable grasping.展开更多
Prestretch plays an indispensable role in programming the voltage-induced deformation of dielectric elastomer actuators(DEAs).However,lacking rational design methods,the level of prestretch is usually determined throu...Prestretch plays an indispensable role in programming the voltage-induced deformation of dielectric elastomer actuators(DEAs).However,lacking rational design methods,the level of prestretch is usually determined through time-consuming experiments and trial-and-error tests.In this paper,we aim to quantitatively determine the optimal biaxial prestretch ratios for maximizing the voltage-induced displacement of interest.Based on the Lagrange method and adjoint sensitivity analysis,we obtain the derivative of the displacement field with respect to the principal prestretch ratios,in which the nonlinearities and complex electromechanical coupling of DEAs are rigorously taken into consideration.The derivative information allows for performing gradient-based optimization at a high convergence rate.We propose a comprehensive optimization framework connecting the nonlinear finite element analysis and sensitivity analysis to iteratively identify the optimal prestretch ratios.We validate our method on two classical types of DEAs,i.e.,planar DEAs with fixed boundaries and free-standing three-dimensional dielectric elastomer minimum energy structures(DEMES).The simulation and experiment results both show that remarkable improvements in concerned displacements are obtained compared with the non-optimized designs.展开更多
It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUA...It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUAPt) from high-density surface electromyographic(sEMG) signals.However,the existing EMG decoding algorithms rarely obtain the spatial matching relationship between decoded motion units(MU) and designated muscles,and the control interface can only recognize the trained hand gestures.In this study,a semi-supervised HMI based on MU-muscle matching(MMM) is proposed to recognize individual finger motions and even the untrained combined multi-finger actions.Through automatic channel selection from high-density s EMG signals,the optimal spatial positions to monitor the MU activation of finger muscles are determined.Finger tapping experiment is carried out on ten subjects,and the experimental results show that the proposed s EMG decomposition algorithm based on MMM can accurately identify single finger motions with an accuracy of 93.1%±1.4%,which is comparable to that of state-of-the-art pattern recognition methods.Furthermore,the MMM allows unsupervised recognizing the untrained combined multi-finger motions with an accuracy of 73%±3.8%.The outcomes of this study benefit the practical applications of HMI,such as controlling prosthetic hand and virtual keyboard.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.91948302,52175021,52375021)the Key Research and Development Program of Science and Technology Department of Sichuan Province(Grant No.2023YFS0135).
文摘Establishing the relationship between vibrotactile parameters and perceived intensity(PI)is of great significance in the regulation of desired sensations,which contribute to haptic interfaces in practical applications.Coin eccentric rotating mass(ERM)motors are routinely utilized due to their lightweight design and efficient ability to evoke strong tactile sensations.By adjusting input voltages,ERMs can yield different stimuli,but the effects of their physical properties on PI remain unclear.Thus,we developed the physical model of the ERM-skin system and optimized the parameters affecting PI.Moreover,the relationship between stimulus and perception was derived based on Fechner's law.Three experiments were conducted on fifteen subjects(ten males and five females,aged 24.40±2.87 years)to identify the vibration parameters and corresponding PI to verify the proposed PI model.ERMs in this study were attached to phantom skin/forearm with slim adhesive tape to minimize the interference.Experiment 1 performed vibration calibration with ERM attached to the skin phantom to simulate the actual configuration.Then,the relative and absolute PI of subjects on specific stimuli were acquired in Experiments 2 and 3.The fitting reliability of the proposed PI model was evaluated on fifteen subjects with R^(2) of 0.73±0.07 and root mean squared error(RMSE)of 1.35±0.28.Overall,the proposed PI model established the quantitative relationship between stimulus parameters(vibration parameters and physical parameters)and subjective PI,which provided theoretical support for ERM optimization and vibrotactile modulation.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB4706500)the National Natural Science Foundation of China(Grant Nos.52275026,T2293725)+1 种基金the Shanghai Rising-Star Program(Grant No.24QA2704500)the Natural Science Foundation of Shanghai(Grant No.23ZR1427800)。
文摘Model-guided design of dielectric elastomer actuators(DEAs)is essential for enabling their application in soft robotics.However,current modeling methods primarily rely on the finite element method(FEM),which suffers from low computational efficiency.Additionally,the simulation-to-reality(Sim2Real)gap,mainly arising from variations in material properties and manufacturing processes,poses a significant challenge.In this work,we propose a data-driven modeling framework aimed at accurately and rapidly predicting voltage-induced displacements while minimizing the Sim2Real gap.The framework integrates a multi-layer perceptron(MLP)model,which serves as a computationally efficient surrogate for the FEM model,and a cycle-generative adversarial network(CycleGAN)model,which mitigates the Sim2Real gap by leveraging adversarial learning to process both simulation and experimental data.Dimensional analysis is performed to extend the framework's applicability across different DEA scales.The surrogate model delivers global predictions in just 0.8 s,achieving linear coefficients of determination(R^(2))of 0.99106 for release distance prediction and 0.99375 for actuation distance prediction compared to experimental results.Our model can quickly identify the feasible range of biaxial prestretch ratios required for generating the desired deformation,thereby streamlining the design process.Finally,a soft robotic gripper is designed and fabricated,demonstrating versatile object-grasping capabilities.
基金supported by the National Key R&D Program of China(Grant No. 2022YFB4701200)the National Natural Science Foundation of China(NSFC)(Grant Nos. 52022056 and 51875334)the Innovation Foundation of the Manufacturing Engineering Technology Research Center of Commercial Aircraft Corporation of China(Grant No. COMAC-SFGS-2023-41)。
文摘Parallel continuum robots(PCRs) have attracted increasing attention in the robotics community due to their simplicity in structure,inherence with compliance, and easiness of realization. Over the past decade, a variety of novel designs have been reported to enrich their diversity. However, there is a lack of systematic review of these emerging robots. To this end, this paper conducts a comprehensive survey on the mechanism design, kinetostatic modeling and analysis, and performance evaluation. For these robots, kinetostatic modeling plays a fundamental role throughout the design, analysis, and control stages. A systematic review of the existing approaches for kinetostatic modeling and analysis is provided, and a comparison is made to distinguish their differences. As well, a classification is made according to the characteristics of structure and actuation. In addition, performance evaluation on the workspace, stability, and singularity is also overviewed. Finally, the scenarios of potential applications are elaborated, and future research prospects are discussed. We believe that the information provided in this paper will be particularly useful for those who are interested in PCRs.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB4701204the Natural Science Foundation of China under Grants 52205031,52305013,and 52022056.
文摘Soft grippers due to their highly compliant material and self-adaptive structures attract more attention to safe and versatile grasping tasks compared to traditional rigid grippers.However,those flexible characteristics limit the strength and the manipulation capacity of soft grippers.In this paper,we introduce a hybrid-driven gripper design utilizing origami finger structures,to offer adjustable finger stiffness and variable grasping range.This gripper is actuated via pneumatic and cables,which allows the origami structure to be controlled precisely for contraction and extension,thus achieving different finger lengths and stiffness by adjusting the cable lengths and the input pressure.A kinematic model of the origami finger is further developed,enabling precise control of its bending angle for effective grasping of diverse objects and facilitating in-hand manipulation.Our proposed design method enriches the field of soft grippers,offering a simple yet effective approach to achieve safe,powerful,and highly adaptive grasping and in-hand manipulation capabilities.
基金supported by the National Natural Science Foundation of China (Grant Nos. 52025057 and 91948302)the Science and Technology Commission of Shanghai Municipality (Grant No. 20550712100)。
文摘Due to the lightweight and compliance, fabric-based pneumatic exosuits are promising in the assistance and rehabilitation of elbow impairments. However, existing elbow exosuits generally suffer from remarkable mechanical resistance on the flexion of the elbow, thus limiting the output force, range of motion(ROM), and comfortability. To address these challenges, we develop a fabric-based soft elbow exosuit with an adaptive mechanism and composite bellows in this work. With the elbow kinesiology considered, the adaptive mechanism is fabricated by sewing the interface of the exosuit into spring-like triangle pleats, following the profile of the elbow to elongate or contract when the elbow flexes or extends. The composite bellows are implemented by further sealing a single blade of bellows into two branches to enhance the output force. Based on these structural features, we characterize the mechanical performance of different soft elbow exosuits: exosuit with normal bellows-NB, exosuit with adaptive mechanism and normal bellows-AMNB, exosuit with adaptive mechanism and composite bellows-AMCB. Experimental results demonstrate that by comparing with NB, the mechanical resistance of AMNB and AMCB decreases by 80.6% and 78.6%, respectively;on the other hand, the output torque of AMNB and AMCB increases to 120.3% and 207.0%, respectively, at50 k Pa when the joint angle is 120°. By wearing these exosuits on a wooden arm model(1.25 kg), we further verify that AMCB can cover a full ROM of 0°–130° at the elbow with 500 g weight. Finally, the application on a health volunteer with AMCB shows that when the volunteer flexes the elbow to lift a weight of 500 g, the s EMG activity of the biceps and triceps is markedly reduced.
基金supported by the National Natural Science Foundation of China (Grant Nos.52275026 and 91948302)the State Key Laboratory of Structural Analysis for Industrial Equipment (Grant No.GZ21117)。
文摘The intrinsic compliance of soft materials endows soft robots with great advantages to achieve large deformation and adaptive interactions in grasping tasks.However,current soft grippers usually focus on the in-plane large deformation and load capacity but ignore the effect of out-of-plane external loads,which may lead to instability in practical scenarios.This problem calls for stiffness design along multiple directions to withstand not only in-plane interacting forces with objects,but also unexpected outof-plane loads.In this paper,we design a new type of soft finger by embedding an endoskeleton inside the widely-used PneuNets actuator,and the endoskeleton layout is optimized to achieve a remarkable bending deflection and limited lateral deflection under combined external in-plane and out-of-plane loads.Based on the multi-objective topology optimization approach,the key structural features of the optimized endoskeleton are extracted and parameterized.The multi-material soft fingers are fabricated by the silicone compound mold method.Static and dynamic experiment results validate that the soft gripper with endoskeleton embedded exhibits remarkably improved out-of-plane stiffness,without sacrificing the in-plane bending flexibility,and leads to more stable grasping.
基金the National Natural Science Foundation of China(Grant Nos.52275026 and 51905340)the State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(Grant No.GZ21117)。
文摘Prestretch plays an indispensable role in programming the voltage-induced deformation of dielectric elastomer actuators(DEAs).However,lacking rational design methods,the level of prestretch is usually determined through time-consuming experiments and trial-and-error tests.In this paper,we aim to quantitatively determine the optimal biaxial prestretch ratios for maximizing the voltage-induced displacement of interest.Based on the Lagrange method and adjoint sensitivity analysis,we obtain the derivative of the displacement field with respect to the principal prestretch ratios,in which the nonlinearities and complex electromechanical coupling of DEAs are rigorously taken into consideration.The derivative information allows for performing gradient-based optimization at a high convergence rate.We propose a comprehensive optimization framework connecting the nonlinear finite element analysis and sensitivity analysis to iteratively identify the optimal prestretch ratios.We validate our method on two classical types of DEAs,i.e.,planar DEAs with fixed boundaries and free-standing three-dimensional dielectric elastomer minimum energy structures(DEMES).The simulation and experiment results both show that remarkable improvements in concerned displacements are obtained compared with the non-optimized designs.
基金supported in part by the China National Key R&D Program(Grant No.2018YFB1307200)the National Natural Science Foundation of China (Grant Nos.51905339&91948302)。
文摘It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUAPt) from high-density surface electromyographic(sEMG) signals.However,the existing EMG decoding algorithms rarely obtain the spatial matching relationship between decoded motion units(MU) and designated muscles,and the control interface can only recognize the trained hand gestures.In this study,a semi-supervised HMI based on MU-muscle matching(MMM) is proposed to recognize individual finger motions and even the untrained combined multi-finger actions.Through automatic channel selection from high-density s EMG signals,the optimal spatial positions to monitor the MU activation of finger muscles are determined.Finger tapping experiment is carried out on ten subjects,and the experimental results show that the proposed s EMG decomposition algorithm based on MMM can accurately identify single finger motions with an accuracy of 93.1%±1.4%,which is comparable to that of state-of-the-art pattern recognition methods.Furthermore,the MMM allows unsupervised recognizing the untrained combined multi-finger motions with an accuracy of 73%±3.8%.The outcomes of this study benefit the practical applications of HMI,such as controlling prosthetic hand and virtual keyboard.
