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Ultrathin and Highly-Stable Rubber Electrodes Based on Island-Bridge Multi-Filler Conductive Network for Multilayer-Stacked Dielectric Elastomer Artificial Muscles

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SSRN 2024年

作者： Yu, Wei Chen, Wenqiang Yuan, Wenjing Li, Guoxian Meng, Chuizhou Guo, Shijie State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China School of Materials Science & Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology Hebei University of Technology Tianjin300130 China

Dielectric elastomer actuators (DEAs) have shown great promise in applications ranging from millimeter-sized haptic feedback to centimeter-scaled soft robots. However, past progress has been focused on the soft dielectric elastomers, and little attention has been paid to the compliant electrodes. Being stable to maintain conductive while enduring repeated large deformations and compliant to avoid stiffness increasing for the dielectric elastomer remains challenging. Moreover, the fabrication of multilayer DEAs is complex, hindering the practicality. Herein, an ultrathin (~500 nm) and highly stable acrylate rubber electrode based on unique island-bridge multi-filler conductive network is developed with outstanding electrical conductivity of 2.7 S cm-1, excellent stretchability of over 1200%, and superior cycling stability of 500,000 cycles at 30% uniaxial strain. An efficient stacking method based on conductive and dielectric layer co-stretching (CDCS) approach is proposed for assembling multilayer-stacked DEAs. The 5-layer DEA reaches a large actuation strain of >17% for lifting a heavy 1-kg load and can serve as biceps muscle to drive an artificial lower arm by a maximum rotation angle of 8°, demonstrating its potential in actuating humanoid robot in an essentially soft way. This conductive rubber electrode and CDCS method are promising for developing soft robotics and muscle-like actuators. © 2024, The Authors. All rights reserved.

关键词： Electrodes

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A Highly Sensitive Stretchable Constraint Fiber Mat Sensor with Tailored Near-Zero Poisson's Ratio for Very Early Stage of Battery Over Charge/Overdischarge Monitoring

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SSRN 2024年

作者： Liu, Guoqing Zhang, Yingying Li, Bo Guo, Shijie Yuan, Wenjing Meng, Chuizhou School of Materials Science & Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology Hebei University of Technology Tianjin300130 China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China

Battery state monitoring is crucial for safe operation and efficient management of lithium-ion battery (LIB) systems, but early stage of timely and accurate diagnosis remains challenging. Herein, we propose to utilize a highly sensitive stretchable strain sensor to in-situ monitor the slight strain variation of cells during charge and discharge, through which the state of the battery can be accurately reflected. The high sensing capability is realized by tailoring the Poisson’s ratio of low-modulus conductive fiber mats through a facile constraint manner. The non-uniform strain distribution together with the near-zero Poisson’s ratio synergistically result in superior sensing performance, including ultralow strain detection limit (0.006%), high strain sensitivity particularly under small strains (gauge factor of 116.1 between 0–2% strain), and high strain resolution under large deformations. When being practically utilized for LIB monitoring, the constraint fiber mat sensor can produce instant resistance response reflecting the cell capacity, and offer timely warning signal once the cell is overcharged or overdischarged. The developed stretchable strain sensor possesses comprehensive advantages of low modulus, tailorable small size, easy integration and conformality to cells, and little temperature interference, which would serve as a promising candidate for future battery state analysis and safety monitoring. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Deep-HFID: Deep Neural Network Based Hands and Face bio-metric Identification System Using Metric Learning

Deep-HFID: Deep Neural Network Based Hands and Face bio-metr...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Zahra Parvin Ashtyani Sara Ghanbari Navid Zare Mehdi Tale Masouleh Ahmad Kalhor School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665454537

This paper presents a device for person identification based on hand bio-metrics. The main objective of a person recognition device is to perform accurate identification at a reasonable production cost. The so-called Deep-HFID device is designed to meet these objectives as well as to provide users with ease of use. For the purpose of identifying individuals, this device uses hand and face bio-metrics. In terms of dimensions, this device has been designed to facilitate the use of a variety of individuals. For ease of deployment, the Deep-HFID system relies on a server for most of its processes. The routine tasks, like showing the graphical user interface are handled by a Raspberry Pi board. A dataset from the palm side of both hands from 110 individuals has been created. Using transfer learning, the six pre-trained state-of-the-art neural network architecture was fine-tuned and calibrated based on the F1 score and precision criteria. An augmented dataset containing samples from the palm side of 30 individuals with between fingers angles variation is collected. By applying the main and augmented dataset and using the center-based separation index as a complexity measure algorithm to rank pre-trained deep neural networks, a compression on the last layer of the best performed neural network, EfficientNetV2B1, is done which results in a more robust system without reducement in accuracy.

关键词： Performance evaluation Training Face recognition Neurons Liquid crystal displays Libraries Servers

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Flexible, Stretchable, Breathable and Sweatproof All-Nanofiber Iontronic Tactile Sensor for Continuous and Comfortable Knee Joint Motion Monitoring

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SSRN 2022年

作者： Wang, Peng Liu, Jun Yu, Wei Li, Guoxian Meng, Chuizhou Guo, Shijie State Key Laboratory for Reliability and Intelligence of Electrical Equipment School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China

Running in a wrong way for years will cause progressive damage to the knee joints, which brings about a lot of pains to exercisers. Wearable sensors for continuously monitoring the knee condition are highly required. However, conventional flexible sensors built on solid substrates are inferior in comfortability during long time wearing. Herein, a highly flexible and stretchable iontronic tactile sensor based on an electrospun thermoplastic polyurethane nanofiber platform is developed. Thanks to the supercapacitive sensing mechanism and porous microstructure, high sensitivity, wide detection range, fast response and well stability and repeatability are achieved. The multilayer interlaced nanofiber network containing numerous micro-pores and micro-channels endows the sensor with air permeability, sweat inertness, thermal conductivity and biocompatibility, which ensures a comfort wearing for a long time. Practical wearable sensing of the knee motion during running in a real-time and continuous manner is demonstrated, including the monitoring of single point pressure by 3 individual sensor units on the rim of the knee and mapping of the spatial pressure distribution by a sensor array on the whole knee region. The health status of the knee can be timely judged, which is useful in warning the knee injury to achieve a correct fitness for exercisers. © 2022, The Authors. All rights reserved.

关键词： Joints (anatomy)

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The Analysis of Muscle-Synergy Similarity between Walking and Pedaling Based on Variational Mode Decomposition and Non-Negative Matrix Factorization

The Analysis of Muscle-Synergy Similarity between Walking an...

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IEEE International Conference on engineering, Technology and Innovation

作者： Wangyang Ge Jinhua She Shijie Guo Feng Wang Juan Zhao Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

Pedaling lower-limb rehabilitation robots are widely used in various stages of lower-limb rehabilitation because of their high safety and low requirements for patients' lower-limb strength. Researchers found that the activation sequence of the surface electromyographic (sEMG) signals of the muscles of the lower limb during pedaling is very similar to that of walking, which makes it possible to simulate the muscle force generation during walking by changing the pedal load during pedaling training. However, most pedaling lower-limb rehabilitation robots use constant loads, and it is of great importance to study the relationship between load and muscle synergy and to design load phase angle trajectories with higher muscle synergy consistency in the walking-pedaling motion to optimize the training effect of pedal-type rehabilitation robots. In this paper, we used variational mode decomposition (VMD) and nonnegative matrix factorization (NMF) to extract the muscle synergy features in each band of sEMG during walking and pedaling and to study the similarity of pedaling and walking and the influence of pedal load on muscle synergy consistency.

关键词： Legged locomotion Training Technological innovation Time series analysis Muscles Assistive robots Feature extraction Trajectory Safety Matrix decomposition

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Experimental Study on Motion Controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel robot

Experimental Study on Motion Controller based on NN-ARX and ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Hasan Jalali Hossein Damavandi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers.

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Conformational Design and Kinematic Analysis of a Reconfigurable Ankle Rehabilitation robot

Conformational Design and Kinematic Analysis of a Reconfigur...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and robots, ReMAR

作者： Jun Wei Haidong Ni Jianjun Zhang Hebei Key Laboratory of Robot Perception and Human-Machine Fusion School of Mechanical Engineering Hebei University of Technology Tianjin PR China Intelligent Rehabilitation Device and Detection Technology Engineering Research Center of the Ministry of Education Tianiin China

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

The ankle joint bears the most weight of the human joint, every year there are many people suffer from different types and degrees of ankle injuries, and their rehabilitation needs are diversified, in order to carry out targeted rehabilitation for ankle injuries, a reconfigurable ankle rehabilitation robot with seven modes is designed. Firstly, the skeletal structure of the human ankle joint as well as the form of movement were determined. Based on the physiological structure, size and movement mode of the human ankle joint, the reconfigurable ankle rehabilitation robot was configured and designed, and a parallel mechanism with seven rehabilitation modes was proposed, and the overall layout of the mechanism is introduced, and then the design of each branch chain of the mechanism is elaborated, and the switching between modes is introduced, and the degrees of freedom and the nature of motion of the mechanism are analyzed in each mode. Finally, the degree of freedom analysis was carried out based on the spiral theory; finally, the kinematic inverse solution of the mechanism in each mode was solved and verified by simulation, and the results showed that this reconfigurable ankle rehabilitation mechanism meets the demand of ankle rehabilitation.

关键词： Ankle Spirals Layout Government Kinematics Switches Assistive robots

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A Nonlinear MPC-Net Optimization Framework for Wheeled humanoid robots with Whole Body Dynamics

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IEEE Transactions on Automation Science and engineering 2025年

作者： Li, Guoxin Liu, Xingjian Wu, Xiaoyu Tang, Qirong Zhang, Hao Li, Zhijun Shi, Peng Tongji University School of Mechanical Engineering Translational Research Center Shanghai YangZhi Rehabilitation Hospital Shanghai Sunshine Rehabilitation Center Shanghai200092 China Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Shanghai201804 China State Key Laboratory of High-Performance Precision Manufacturing Dalian116024 China Tongji University School of Mechanical Engineering Shanghai201804 China National University of Singapore Department of Biomedical Engineering 117583 Singapore Tongji University Department of Control Science and Engineering Shanghai201804 China The University of Adelaide School of Electrical and Mechanical Engineering SA5005 Australia Obuda University Budapest1034 Hungary

The task performance of mobile manipulators can be significantly enhanced by whole-body control and optimization in complex scenarios. Due to the nonlinear properties of whole-body dynamics and parameter uncertainty, modeling accurate system dynamics is essential in addition to designing an effective control strategy. However, traditional control methods have high computational costs and fail to deal with the parameter errors caused by model linearization. To address these issues, we propose a model predictive control (MPC)-Net, a learning-based approach that facilitates rapid online optimization by combining deep learning with multiple MPCs. Firstly, we develop a parameter identification algorithm based on a deep neural model to estimate the unknown dynamics parameters. Although the control performance of the MPC approach positively correlated with the prediction horizon, a long horizon would result in additional computational costs. Thus, MPC-Net is constructed by combining multiple sub-MPC issues, and the nonlinear coefficients are obtained by using a deep neural network-based optimization framework. Furthermore, MPC-Net generates the solution by combining the outputs of multiple sub-MPC problems using the nonlinear transformation of learned coefficients. Experiments are conducted on a mobile manipulator, which demonstrates the proposed MPC-Net-based optimization control offers fast efficient computation and low tracking error performance. © 2004-2012 IEEE.

关键词： Predictive control systems

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Effect of the configuration and concentration of B-doping on the thermal properties and structural stability of graphene: A theoretical study

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Diamond and Related Materials 2025年 156卷

作者： Li, Dongbo Da, Liangguo Huang, Kaifeng Li, Dong Yu, Liang Wang, Haini Yu, Yaqi Wu, Long Tang, Yunqing School of Mechanical and Electrical Engineering Huainan Normal University Huainan232038 China Human-computer collaborative robot Joint Laboratory of Anhui Province Huainan232038 China School of Mechanical Engineering Shandong University Jinan250061 China State Key Laboratory of Advanced Equipment and Technology for Metal Forming Shandong University Jinan250061 China Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Shandong University Jinan250061 China

The thermal properties and structural stability of B-doped graphene were investigated by employing density functional theory (DFT), ab initio molecular dynamics (AIMD), and molecular dynamics (MD) simulations, with a focus on the effects of doping configuration and concentration. DFT calculations indicate that B-doping reduces the cohesive energy of graphene, consequently diminishing its structural stability. Among the three B double-doping configurations, only the para configuration exhibits a phonon dispersion without imaginary frequencies, whereas both meta and ortho configurations show imaginary frequencies, indicating structural instability. The AIMD results further demonstrate that B-doped graphene preferentially undergoes cleavage at B[sbnd]B bonds, leading to decreased structural stability. MD results show that B-doping significantly reduces the thermal conductivity of graphene. At a doping concentration is 0.6 %, the thermal conductivity of meta and para configurations decreases to 48.08 % and 54.6 % of that of the pristine graphene, respectively. The results of phonon properties show that B-doping reduces the phonon participation rate and induces strong phonon localization at the B-doped site. These results and conclusions will provide valuable theoretical insights for the thermal design of graphene-based semiconductor devices. © 2025 Elsevier B.V.

关键词： Temperature distribution

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Experimental Study on Neural Network-ARX and ARMAX Actuation Identification of a 3-DoF Delta Parallel robot for Accurate Motion Controller Design

Experimental Study on Neural Network-ARX and ARMAX Actuation...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Saeed Rahimi Hasan Jalali Mohammad Reza Hairi Yazdi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665420952

This paper investigates the problem of actuator identification of a 3-DoF Delta parallel robot, by means of linear AutoRegressive Moving Average with eXogenous input (ARMAX) and nonlinear dynamic Neural Network AutoRegressive with eXogenous input (NN-ARX) methods. To this end, the ARMAX and NN-ARX approaches are used to develop a scheme which is capable of identifying a model for each actuator. Based on the ARMAX structure, an accurate model of the actuation system is derived. The model is then trained and tested using the data collected from a real robotic setup. Using a dynamic neural network capabilities, an identification and prediction scheme is designed for modeling the nonlinear dynamic behavior of the system. The NN-ARX is trained based on the collected data from the system, and the new trajectory data is used to validate both methods. By considering the results of experimental implementations, three servo motors are demonstrated to have different dynamical behavior which was expected to happen from the outset, due to uncertainty in fabrication of motors component and gearbox. In the identification and prediction stages, the Root Mean Square Errors (RMSE) index is used to validate and analyze the performance of each method using the validation data from new trajectories. In terms of predicting the output of the system, NN-ARX performed better than ARMAX with RMSE of 0.001441, compared to ARMAX with RMSE of 0.0886. Due to the high accuracy of the obtained models. thus they can be used in the design of motion controllers and modelling disturbances in the system.

关键词： Actuators Parallel robots Artificial neural networks Predictive models Mathematical models Trajectory Nonlinear dynamical systems

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