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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Ni, Jiangpeng Wang, Zhuo Li, Feng Su, Zhilong Cao, Wujing Chen, Chunjie Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Advanced Technology Chinese Academy of Sci-ences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Reliable environmental context prediction is critical for wearable robots (exoskeletons or prostheses) to assist terrain-adaptive locomotion. Inspired by the mechanism of human perception of the environment, the vision sensor can be introduced into the soft lower limb exosuit robot so that future environmental information can be used to improve the control. In this paper, a stable terrain classification and recognition system (TCRS) is designed for lower-limb soft exosuit robots. When the exosuit robot perceives the environment, it uses the residual network to train the terrain classifier to classify the terrain. During walking, the wearable soft exosuit robot collects the indoor environment, preprocesses, and marks the sampled images separately. The experimental results show that the classification accuracy is more than 90%, and the highest is 97.5%. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications. © 2021 IEEE.

关键词： Landforms

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Controllable distant interactions at bound state in the continuum

arXiv

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arXiv 2024年

作者： Tang, Haijun Huang, Can Wang, Yuhan Jiang, Xiong Xiao, Shumin Han, Jiecai Song, Qinghai Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen518055 China National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology Harbin150080 China Pengcheng Laboratory Shenzhen518055 China Collaborative Innovation Center of Extreme Optics Shanxi University Shanxi Taiyuan030006 China

Distant interactions at arbitrary locations and their dynamic control are fundamentally important for realizing large-scale photonic and quantum circuits. Conventional approaches suffer from short coupling distance, poor controllability, fixed locations and low wavelength uniformity, significantly restricting the scalability of photonic and quantum networks. Here, we exploit the intrinsic advantages of optical bound state in the continuum (BIC) and demonstrate an all-in-one solution for dynamically controllable long-range interactions. BIC metasurface can support a series of finite-sized quasi-BIC microlasers at arbitrary locations. The quasi-BICs microlasers have the same wavelength and are inherently connected through BIC waveguide. Consequently, the coupling distances in experiment increase significantly from subwavelength to tens of micrometers. Such long-range interaction in BIC metasurface enables scaling to two-dimensional architectures and ultrafast control of internal laser actions, e.g., non-Hermitian zero-mode lasing and enhanced optical gain. This research shall facilitate the advancement of scalable and reconfigurable photonic networks. © 2024, CC BY.

关键词： Location

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Adaptive admittance control of human-exoskeleton system using RNN optimization

Adaptive admittance control of human-exoskeleton system usin...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Lian, Pengchen He, Yong Ma, Yue Liu, Jingshuai Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

For wearable exoskeletons, ensuring safety and stability while reducing the effort of human lower limbs during walking remains a challenge. The level of safety can be guaranteed by constraining the exoskeleton robot to never exhibit any erratic behavior. But beyond that, the cooperative motion generated in human-exoskeleton interactions should be truly intuitive and should minimize the limitations of human performance. Therefore, this paper presented a new adaptive admittance control law, which not only ensures the stability of the lower limb exoskeleton in the constrained motion but also provides a very harmonious human-exoskeleton interaction. The proposed approach is applied to the movement of the exoskeleton containing the knee joint in the swing phase. The input of the admittance control system is the human-exoskeleton interaction force measured by one single-dimensional force sensor. The performance of the method was theoretically analyzed and discussed. Experiments were conducted in Matlab simulation environment to verify the effectiveness of aid to the swinging leg. Compared with the fixed admittance control, the adaptive admittance control system optimized by a Recurrent Neural Network(RNN) greatly improves the level of interaction between the exoskeleton and the wearer. © 2021 IEEE.

关键词： Recurrent neural networks

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A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction

A Self-supervised Contrastive Learning Method for Grasp Outc...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Chengliang Liu Binhua Huang Yiwen Liu Yuanzhe Su Ke Mai Yupo Zhang Zhengkun Yi Xinyu Wu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen

In this paper, we probe the proficiency of contrastive learning techniques in forecast grasp outcomes, without supervision. Employing a dataset that’s open to the public, we establish the effectiveness of contrastive learning techniques in accurately predicting grasp outcomes. More precisely, an impressive accuracy of 81.83% is achieved by the dynamic-dictionary-based method combined with the momentum updating technique, using data from just one tactile sensor, surpassing other unsupervised techniques. Our findings underscore the promise held by contrastive learning techniques in the domain of robotic grasping, while emphasizing the critical role played by precise grasp prediction in securing stable grasps.

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Attention-enhanced BLSTM Network for Liquid Volume Estimation based on Tactile Sensing

Attention-enhanced BLSTM Network for Liquid Volume Estimatio...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yuanzhe Su Jingnan Wang Binhua Huang Xiaoyu Li Yiwen Liu Chengliang Liu Zhenning Zhou Zhengkun Yi Xinyu Wu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen

The task of liquid volume estimation based on tactile sensing is challenging. Previous researches mainly focused on the classification of different contents in the container, rather than achieving accurate estimation of a certain property of the contents in container, such as volume or viscosity. In addition, few studies are conducted for the regression task with advanced tactile sensors, but the performance is unsatisfactory. We propose a new deep-learning algorithm for processing the tactile signals collected from the photoresistors in the task of liquid content estimation. The proposed algorithm doesn’t need hand-crafted features. In addition, we use attention after BLSTM, so that more important features can be selected to achieve better performance for liquid volume estimation. We utilize a bi-directional Long Short-Term Memory (BLSTM) architecture, which allows for the incorporation of information from both preceding and subsequent time, thereby providing a more comprehensive and informative feature set for learning. Additionally, we employ an attention mechanism following the BLSTM, allowing for the selection of more critical features and improved performance in liquid volume estimation. To prove the effectiveness of our method, we conduct experiments utilizing tactile sensors on a physical robot, achieving superior performance in liquid volume estimation.

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HIGH QUALITY QUADRILATERAL MESH GENERATION BASED ON MEROMORPHIC QUARTIC DIFFERENTIALS 29

HIGH QUALITY QUADRILATERAL MESH GENERATION BASED ON MEROMORP...

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29th International Meshing Roundtable, IMR 2021

作者： Zheng, Xiaopeng Shi, Xin Wang, Chao Zhu, Yiming Luo, Zhongxuan Lei, Na Dalian University of Technology Liaoning Dalian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Beijing China Institute of Artificial Intelligence Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781733489010

Quadrilateral meshes generated for the industrial applications have to meet some special quality demands and it remains a tricky problem. We propose a novel method for high quality quad mesh generation based on the equivalence relation between quadrilateral meshes and meromorphic quartic differentials[1]. As long as more than one meromorphic quartic differential were obtained, the linear space extended by them is also available for us according to the meromorphic quartic differentials space theory. We can search the space for better quadrilateral mesh and the high quality mesh construction problem becomes a optimization problem. This paper gives the algorithm and carries out experiments under two different mesh quality constrains including mesh cell size constrain and feature lines constrain. The experimental results demonstrate the efficiency and efficacy of the algorithm. © 2021 held by the authors of the individual papers.

关键词： Mesh generation

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Structural Optimization of Photoacoustic Transducer with Pdms/Csnps Nanocomposite for Laser-Induced Nonlinear Surface Wave and its Application in Fatigue Crack Detection

SSRN

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SSRN 2023年

作者： Yao, Zhijun Li, Yehai Lv, Gaolong Chen, Dan Yang, Jian Guo, Shifeng Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic Shenzhen518055 China Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Ningbo China Ningbo315100 China

Laser ultrasonics is a promising non-contact inspection technique but faces the challenges of low signal-to-noise ratio and low amplitude under the non-destructive thermoelastic regime. In this paper, an ultrasonic surface wave modulation method based on laser ultrasonics and photoacoustic transducer (PAT) is proposed and combined with nonlinear wave mixing technique to achieve non-contact detection of fatigue crack. The PAT comprised of candle soot nanoparticles and polydimethylsiloxane (PDMS) as well as a line-arrayed laser source modulated by a slit mask are firstly introduced to generate narrowband surface wave with high intensity and SNR under the thermoelastic regime. The thicknesses of the PDMS layers of PAT are thereafter optimized by analytical, numerical and experimental investigations to acquire narrowband surface wave with pure fundamental frequency component. Two modulated ultrasonic surface waves with frequencies of 2.1 MHz and 2.9 MHz are excited on a fatigued aluminum plate to generate nonlinear mixed components, and the ultrasonic responses over the fatigue crack regions are acquired by using a scanning laser Doppler vibrometer. The localization of fatigue crack with microscale is eventually acquired by mapping the nonlinear parameter of the mixed components, which proves it a reliable and nondestructive technique to inspect the fatigue crack. © 2023, The Authors. All rights reserved.

关键词： Transducers

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Learning Compliant Assembly Strategy From Demonstration

Learning Compliant Assembly Strategy From Demonstration

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Sheng Liu Juyi Sheng Yongsheng Ou Southern University of Science and Technology (SUSTech) Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems (#2019B121205007)

Compared with robots, humans can complete the different assembly tasks of parts flexibly and quickly. By teaching robots with human experiences, not only the industrial assembling tasks can be resolved, but also many other robot applications can be realized. The pose adjustment stage is the most critical part of the peg-in-hole assembly process. This paper analyzes the contact force in the pose adjustment stage and calculates the control variables that affect the assembly motion. Based on the Gaussian mixture model (GMM), the nonlinear mapping relationship between the control variables and the state variables during the assembly is established using the human demonstration data, and the parameters of the model are solved by the expectation maximization (EM) algorithm, and thus the human-like compliant assembly is completed by a robot. In order to verify the effectiveness of the demonstration-learning algorithm, a peg-in-hole assembly experiment was carried out using KUKA manipulator. Finally, the experiment shows that the proposed learning-based method not only improves the efficiency of the robot peg-in-hole assembly but also makes the manipulator have a satisfied adaptive ability to the complex environments in the assembly process.

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Tunable Mid-Infrared Sensing Based on Graphene-Metal Hybrid Structure

SSRN

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SSRN 2024年

作者： Wang, Xiaowei Chen, Zhihui Feng, Guang Wang, Yang Li, Shan Yang, Yibiao Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Taiyuan Institute of Technology Taiyuan030008 China Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education Institute of Biotechnology Shanxi University Taiyuan030006 China

Improving the emission efficiency of molecules or quantum dots is one of the effective means to improve the sensitivity of sensing. Here we provide a graphene-metal hybrid structure to achieve sensitivity detection in mid-infrared. The graphene-metal hybrid structure well coupling with a dipole source in mid-infrared was constructed. And then the high quality resonance peak can be detected. For different molecular detection applications, the resonance window can be controlled by geometrical parameters of metal structure in the mid-infrared range. Meanwhile the resonance wavelengths can be adjusted with graphene's chemical potential which can be controled through a varying gate voltage, and the emission enhancement ratio was 501 times on average. The hybrid structures also show excellent results in different background refractive indices. The sensitivity of the hybrid structure as a refractive index sensor reaches 6233 nm/RIU. All of our analyses show that the hybrid structure can be used as a better mid-infrared sensor. © 2024, The Authors. All rights reserved.

关键词： Graphene

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Microfabricated Vapor Cells with Chemical Polishing and Two-Step Low-Temperature Anodic Bonding for Single-Beam Magnetometer

SSRN

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SSRN 2024年

作者： Wang, Yanbin Chen, Yao Yu, Mingzhi Ma, Yintao Han, Xiangguang Zhao, Libo Lu, Shun Guo, Ju Yang, Ping Lin, Qijing Research Institute for Intelligent Sensing Technology and System Xi’an710049 China School of Mechanical Engineering Xi’an Jiaotong University Xi’an710049 China School of Instrument Science and Technology Xi’an Jiaotong University Xi’an710049 China Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai Yantai264000 China

With the miniaturization of quantum sensors, the fabrication of alkali metal vapor cells based on micro-electromechanical systems (MEMS) has become increasingly important. However, the fabrication of MEMS vapor cells remains a challenge. In this study, a microfabricated millimeter-level vapor cell was fabricated and tested. The silicon cavity was fabricated by dry etching and chemical *** injection of alkali metal and buffer gas, a two-step low-temperature anodic bonding was *** first step was non-isothermal to keep the alkali metal in the low-temperature pole plate and the second step was isothermal to improve the bonding strength, enhancing the hermeticity. Then, the vapor cell was tested for sidewall roughness, bonding strength, and leakage rate. Subsequently, Rb D1 line absorption spectroscopy, stability, and the single-beam magnetometer signal were tested. The results show the fabricated vapor cell had high stability, providing a basis for the miniaturization of quantum devices. © 2024, The Authors. All rights reserved.

关键词： Cytology

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