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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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On the Use of Torque Measurement in Centroidal State Estimation

arXiv

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arXiv 2022年

作者： Khorshidi, Shahram Gazar, Ahmad Rotella, Nicholas Naveau, Maximilien Righetti, Ludovic Bennewitz, Maren Khadiv, Majid Max-Planck Institute for Intelligent Systems Tübingen Germany Agility Robotics Oregon United States LAAS CNRS Toulouse France Tandon School of Engineering New York University United States Humanoid Robots Lab University of Bonn Germany

State-of-the-art legged robots are either capable of measuring torque at the output of their drive systems, or have transparent drive systems which enable the computation of joint torques from motor currents. In either case, this sensor modality is seldom used in state estimation. In this paper, we propose to use joint torque measurements to estimate the centroidal states of legged robots. To do so, we project the whole-body dynamics of a legged robot into the nullspace of the contact constraints, allowing expression of the dynamics independent of the contact forces. Using the constrained dynamics and the centroidal momentum matrix, we are able to directly relate joint torques and centroidal states dynamics. Using the resulting model as the process model of an Extended Kalman Filter (EKF), we fuse the torque measurement in the centroidal state estimation problem. Through real-world experiments on a quadruped robot executing different gaits, we demonstrate that the estimated centroidal states from our torque-based EKF drastically improve the recovery of these quantities compared to direct computation. © 2022, CC BY.

关键词： State estimation

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Learn Task First or Learn Human Partner First: A Hierarchical Task Decomposition Method for Human-Robot Cooperation

Learn Task First or Learn Human Partner First: A Hierarchica...

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IEEE International Conference on systems, Man and Cybernetics

作者： Lingfeng Tao Michael Bowman Jiucai Zhang Xiaoli Zhang Colorado School of Mines Intelligent Robotics and Systems Lab Golden CO USA GAC R&D Center Silicon Valley Sunnyvale CA USA

ISBN: (纸本)9781665442084

Applying Deep Reinforcement Learning (DRL) to Human-Robot Cooperation (HRC) in dynamic control problems is promising yet challenging as the robot needs to learn the dynamics of the controlled system and dynamics of the human partner. In existing research, the robot powered by DRL adopts coupled observation of the environment and the human partner to learn both dynamics simultaneously. However, such a learning strategy is limited in terms of learning efficiency and team performance. This work proposes a novel task decomposition method with a hierarchical reward mechanism that enables the robot to learn the hierarchical dynamic control task separately from learning the human partner’s behavior. The method is validated with a hierarchical control task in a simulated environment with human subject experiments. Our method also provides insight into the design of the learning strategy for HRC. The results show that the robot should learn the task first to achieve higher team performance and learn the human first to achieve higher learning efficiency.

关键词： Performance evaluation Conferences Reinforcement learning Control systems Resource management Task analysis Robots

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Improving Incremental Encoder Measurement: Variable Acquisition Window and Quadrature Phase Compensation to Minimize Acquisition Errors

Improving Incremental Encoder Measurement: Variable Acquisit...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： José Lima Ví tor H. Pinto A. Paulo Moreira Paulo Costa Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Polit&#x00B4 ecnico de Braganca Campus de Santa Apolonia Braganca Portugal INESC Technology and Science Porto Portugal SYSTEC (DIGI2) - Research Center for Systems and Technologies (Digital and Intelligent Industry Lab) Porto Portugal Faculty of Engineering of University of Porto Portugal

Motion control is an important task in several areas, such as robotics where the angular position and speed should be acquired, usually with encoders. For slow angular speeds, an error is introduced spoiling the measurement. In this paper there will be proposed two new methodologies, that when combined allow to increase the precision whereas reducing the error, even on transient velocities. The two methodologies Variable Acquisition Window and a Quadrature Phase Compensation are addressed and combined simultaneously. A real implementation of the proposed algorithms is performed on a real hardware, with a DC motor and a low resolution encoder based on hall effect. The results validate the proposed approach since the errors are reduced compared with the standard Quadrature Encoder Reading.

关键词： Phase measurement Conferences Measurement uncertainty DC motors Hardware Hall effect Transient analysis

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Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

arXiv

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arXiv 2021年

作者： Hartmann, Valentin N. Orthey, Andreas Driess, Danny Oguz, Ozgur S. Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Group TU Berlin Germany Department of Computer Engineering Bilkent University Turkey

Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multirobot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world. Copyright © 2021, The Authors. All rights reserved.

关键词： robotics

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A framework for human-exoskeleton interaction based on sEMG interface and electrotactile feedback

A framework for human-exoskeleton interaction based on sEMG ...

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

作者： Duan, Shengcai Wang, Can Li, Mengyao Su, Zhilong Liu, Jiaqing Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering Chinese University of Hong Kong

ISBN: (纸本)9781665436786

The lower extremity rehabilitation exoskeleton provides a priceless opportunity for paraplegics to stand up and walk normally. In this paper, a framework for human-exoskeleton interaction with sEMG interface and electrotactile feedback is presented. Firstly, the exoskeleton recognises human motion intention through the Long Short Term Memory (LSTM) Neural Network based on surface electromyogram (sEMG). Secondly, electrotactile based on spatiotemporal coding scheme feeds back the exoskeleton states to the sensory skin, as a compensation for paraplegic's absent proprioception in lower limbs. Thirdly, physiological information, i.e. muscle fatigue, of arms is monitored and quantified with sEMG. Three healthy subjects participate the experiments including the motion intention recognition experiment, electrotactile perception experiment and online exoskeleton experiment. The online exoskeleton experiments demonstrate the feasibility and efficiency of the proposed framework. Furthermore, it is proved experimentally that the performance of collaborative control in rehabilitation human-exoskeleton interaction can be facilitated via the framework. © 2021 IEEE.

关键词： Long short-term memory

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Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-LSTM-Attention Network from Surface Electromyography

Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-...

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IEEE International Conference on robotics and Biomimetics

作者： Zhuo Wang Hui Chen Fangliang Yang Xiangyang Wang Xinyu Wu Chunjie Chen Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab 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 Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In various fields such as robotics, rehabilitation, biomechanics, human-machine interfaces, and clinical research, human motion prediction has extensive applications. A hybrid networks model based on Convolutional Neural Network-Long Short-Term Memory-Attention (CNN-LSTM-Attention) was employed in this paper to extract features from the high-dimensional space and time series of surface electromyography (sEMG) data for continuous prediction of knee angles. Five able-bodied subjects participated in the study, walking at a self-selected pace while six surface electromyography (sEMG) signals from muscles involved in knee flexion and extension, along with knee angles, were synchronously recorded. The performance was assessed using the root mean square error (RMSE), Pearson Correlation Coefficient $(r)$ , and coefficient of determination $(R^{2})$ between estimated and measured knee joint angles. Compared to the CNN, LSTM, and CNN-LSTM algorithms, the adopted method estimated knee angles more accurately, achieving an average RMSE, $\rho$ , and $R^{2}$ of $3.09\pm 0.90,\ 0.9911 \pm 0.0052$ , and $0.9817\pm 0.0108$ , respectively. As the advance time increased, the accuracy of knee joint angle estimation decreased, particularly when the advance time reached 100 ms, at which point the RMSE is 4.83 degress. In addition, the computing time of the prediction model was less than 1 ms, which was shorter than the advance time 100 ms it could predict, thereby facilitating real-time computation in practical environments. Compared to previous studies, the proposed method excelled at extracting features directly from raw signals, avoiding the need for handcrafted feature extraction. Consequently, the model effectively achieved continuous and accurate predictions of joint motion angles and demonstrated potential to reduce latency in the human-machine interface.

关键词： Knee Accuracy Computational modeling Predictive models Feature extraction Electromyography Convolutional neural networks Wearable devices Long short term memory Robots

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User distributions in shard-based blockchain network: queueing modeling, game analysis, and protocol design 21

User distributions in shard-based blockchain network: queuei...

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22nd International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2021

作者： Chen, Canhui Ma, Qian Chen, Xu Huang, Jianwei School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Pazhou Lab Guangzhou China School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Chinese University of Hong Kong Shenzhen Hong Kong

ISBN: (纸本)9781450385589

Sharding is one of the most promising and practical methods to achieve horizontal scalability of blockchain networks. However, the increasing number of cross-shard transactions in blockchain sharding protocols may degrade the system throughput. In this paper, we investigate how to distribute users properly in the shard-based blockchains to boost the system transaction performance. We first build an open Jackson queueing network model to capture users' transaction dynamics on shards. Then we cast users' interactions as a shard-based blockchain game, wherein each user aims to minimize its transaction confirmation time and transaction fee. We investigate the equilibrium of the game, and design a polynomial-time algorithm to find efficient equilibria with good system performance. We further design a novel sharding protocol with dynamic user distribution for the permissionless blockchain, and the protocol can maintain good performance in long-term dynamic environment. Extensive numerical results using realistic blockchain transaction data demonstrate that the proposed algorithm and the designed protocol can achieve superior performance for shard-based blockchains. © 2021 ACM.

关键词： Game theory

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Sleep Position Classification with a Compact Pressure Textile Sensor Mattress Using Convolutional Neural Networks

SSRN

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SSRN 2023年

作者： Breuss, Alexander Gnarra, Oriella Fujs, Manuel Schmidt, Markus Riener, Robert ETH Zurich Sensory-Motor Systems Lab Institute of Robotics and Intelligent Systems Tannenstrasse 1 Zurich8092 Switzerland Sleep-Wake Epilepsy Center NeuroTec Department of Neurology Inselspital University Hospital Bern Freiburgstrasse 3 Bern3010 Switzerland Ohio Sleep Medicine Institute 4975 Bradenton Avenue DublinOH43017 United States Spinal Cord Injury Center University Hospital Balgrist Zurich Switzerland

Robust body position classification during sleep is crucial for closed-loop robotic interventions in position-dependent sleep disorders. This work investigates a compact, custom-made textile pressure sensor to automatically classify recumbent body positions. We trained different convolutional neural networks on recordings from 21 participants, with a varying amount of personalized training data (i.e. from a generalized inter-person classifier towards fully personalized classifiers). The performances were compared using multi-fold cross-validation for the four classes supine, prone, lateral left, and lateral right, as well as for the binary classes supine vs. non-supine. For the generalized classifier, we could achieve an accuracy of 82.7% for a balanced test set. The personalized models for one male and one female user showed a higher accuracy, namely 95% and 92%. For the binary classifier, the personalized classifiers (male F1-score: 0.97, female F1-score: 0.94) outperformed the generalized classifiers (F1-score: 0.91). These results show that robust body position classification is possible using small-scale unobtrusive textile bedding sensors, pathing the way for future closed-loop interventions. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Covariance intersection to improve the robustness of the photoplethysmogram derived respiratory rate

Covariance intersection to improve the robustness of the pho...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jia Zhang Gaetano Scebba Walter Karlen Mobile Health Systems Lab Institute of Robotics and Intelligent Systems ETH Zurich Zurich Switzerland

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Respiratory rate (RR) can be estimated from the photoplethysmogram (PPG) recorded by optical sensors in wearable devices. The fusion of estimates from different PPG features has lead to an increase in accuracy, but also reduced the numbers of available final estimates due to discarding of unreliable data. We propose a novel, tunable fusion algorithm using covariance intersection to estimate the RR from PPG (CIF). The algorithm is adaptive to the number of available feature estimates and takes each estimates' trustworthiness into account. In a benchmarking experiment using the CapnoBase dataset with reference RR from capnography, we compared the CIF against the state-of-the-art Smart Fusion (SF) algorithm. The median root mean square error was 1.4 breaths/min for the CIF and 1.8 breaths/min for the SF. The CIF significantly increased the retention rate distribution of all recordings from 0.46 to 0.90 (p

关键词： Estimation Benchmark testing Robustness Correlation Biomedical monitoring Feature extraction Indexes

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