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IEEE International Conference on robotics and Biomimetics

作者： Xiangxiao Chai Shihua Wang Erzhen Pan Wenfu Xu School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The difficulty of moving from aquatic to aerial environment is one of the major challenges limiting the development of trans-medium multi-domain locomotion vehicles. In this paper, the concept of the transformable wheel, a feature commonly found in autonomous land vehicles (AL V s), is introduced into the design. Additionally, a flapping-wing robot equipped with multi-domain locomotion capability, which can not only realize aerial flight and surface navigation on water, but also ascend from water to land for subsequent takeoff is presented. The paper first introduces the system design of the robot and proposes the corresponding multi-domain locomotion strategy. Subsequently, the design of the transformable wheel mechanism is described in detail and the force analysis for the water-exit is conducted. The feasibility of the proposed strategy is experimentally validated, confirming the prototype's multi-domain locomotion performance finally. This research explores the feasibility of introducing the transformable wheel mechanism into trans-medium multi-domain vehicle platforms and provides a design example.

关键词： Legged locomotion Torque Limiting Navigation Propellers Roads Wheels Prototypes Motors system analysis and design

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Xinqiang Guo Yupeng Zou Shuo Zhang Mingxiang Luo Worawarit Kobsiriphat Xinyu Wu Meng Yin Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering China University of Petroleum East China National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.

关键词： Support vector machines Deep learning Accuracy Noise Gesture recognition Stroke (medical condition) Feature extraction Electromyography Convolutional neural networks Long short term memory

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive Control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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

作者： Guanfeng Yu Han Zhang Zehao Wang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities.

关键词： Pedestrians Accuracy systematics Wheelchairs Estimation Predictive models Real-time systems

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A novel End-effector Finger Rehabilitation Robot (EFRR) for stroke patients 7

A novel End-effector Finger Rehabilitation Robot (EFRR) for ...

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2021 7th International Conference on Manufacturing Technology and Applied Materials, ICAMMT 2021

作者： Hongbo, Wang Yu, Tian Baoshan, Niu Jiazheng, Du Junjie, Tian Parallel Robot and Mechatronic System Laboratory of Hebei Province Yanshan University Qinhuangdao066004 China Academy for Engineering and Technology Fudan University Shanghai200433 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China

This paper proposes a prototype of a novel end-traction finger rehabilitation robot with flexion/extension and adduction/abduction functions. The degree of freedom (DoF) and finger joint torque of a single-finger module has been analysed, and the calculation and selection of the motor driving force are completed. Finally, the design details of the mechanism and the hardware system of the robot are illustrated. This device minimized the mechanical size to some extent by using only one motor for a single finger, which also reduces the cost and increases the possibility of industrial application prospects. © Published under licence by IOP Publishing Ltd.

关键词： End effectors

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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Collaborative Fall Detection and Response Using Wi-Fi Sensing and Mobile Companion Robot

Collaborative Fall Detection and Response Using Wi-Fi Sensin...

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IEEE International Conference on robotics and Biomimetics

作者： Yunwang Chen Yaozhong Kang Ziqi Zhao Yue Hong Lingxiao Meng Max Q.-H. Meng Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering SUSTech Shenzhen China School of System Design and Intelligent Manufacturing SUSTech Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This paper presents a collaborative fall detection and response system integrating Wi-Fi sensing with robotic assistance. The proposed system leverages channel state information (CSI) disruptions caused by movements to detect falls in non-line-of-sight (NLOS) scenarios, offering non-intrusive monitoring. Besides, a companion robot is utilized to provide assistance capabilities to navigate and respond to incidents autonomously, improving efficiency in providing assistance in various environments. The experimental results demonstrate the effectiveness of the proposed system in detecting falls and responding effectively.

关键词： Navigation Biomimetics Collaboration Robot sensing systems Sensors Fall detection Wireless fidelity Monitoring Injuries Channel state information

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive Control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models Control systems Data models Nonlinear dynamical systems

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Optical modulation of repaired damage site on fused silica produced by CO2 laser rapid ablation mitigation

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Chinese Physics B 2020年第5期29卷 288-296页

作者： Chao Tan Lin-Jie Zhao Ming-Jun Chen Jian Cheng Zhao-Yang Yin Qi Liu Hao Yang Wei Liao State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China Research Center of Laser Fusion China Academy of Engineering PhysicsMianyang 621900China

CO2 laser rapid ablation mitigation(RAM)of fused silica has been used in high-power laser systems owing to its advantages of high efficiency,and ease of implementing batch and automated *** order to study the effect of repaired morphology of RAM on laser modulation and to improve laser damage threshold of optics,an finite element method(FEM)mathematical model of 351 nm laser irradiating fused silica optics is developed based on Maxwell electromagnetic field equations,to explore the 3D near-field light intensity distribution inside optics with repaired site on its *** influences of the cone angle and the size of the repaired site on incident laser modulation are studied as *** results have shown that for the repaired site with a cone angle of 73.3°,the light intensity distribution has obvious three-dimensional *** relative light intensity on z-section has a circularly distribution,and the radius of the annular intensification zone increases with the decrease of *** the distribution of maximum relative light intensity on y-section is parabolical with the increase of *** the cone angle of the repaired site decreases,the effect of the repaired surface on light modulation becomes stronger,leading to a weak resistance to laser ***,the large size repaired site would also reduce the laser damage ***,a repaired site with a larger cone angle and smaller size is preferred in practical CO2 laser repairing of surface *** work will provide theoretical guidance for the design of repaired surface topography,as well as the improvement of RAM process.

关键词： fused silica laser repaired surface optical modulation finite element method(FEM)

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Experimental Study on the Improvement of Yaw Stability by Coordination Control between the Caudal Fin and Anal Fin

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Journal of Bionic engineering 2022年第5期19卷 1261-1271页

作者： Jiang Ding Changzhen Zheng Chaocheng Song Qiyang Zuo Yaohui Xu Bingbing Dong Jiaxu Cui Kai He Fengran Xie College of Mechanical Engineering Guangxi UniversityNanning 530004China Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology Nanning 530004China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Shenzhen Key Laboratory of Precision Engineering Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China College of Mechanical Automation Wuhan University of Science and TechnologyWuhan 430000China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhen 518055China

Due to the unique locomotion,the head-shaking problem of biomimetic robotic fish inevitably occurs during rectilinear locomotion,which strongly hinders its practical *** this paper,we experimentally study this problem by proposing the method of coordination control between the caudal fin and anal ***,an untethered biomimetic robotic fish,equipped with an anal fin,a caudal fin and two pectoral fins,is developed as the experimental ***,a Central Pattern Generator(CPG)-based controller is used to coordinate the motions of the anal fin and caudal ***,extensive experiments are conducted to explore different combinations of the flapping frequencies,the flapping amplitudes,and the phase differences between the anal fin and caudal ***,through proper control of the anal fin,the amplitude of the yaw motion can be as small as 4.32°,which sees a 65%improvement compared to the scenario without anal fin,and a 57%improvement compared to that with a stationary anal *** paper provides a novel way to alleviate the head-shaking problem for biomimetic robotic fish,and first test this method on an untethered,freely swimming robotic platform,which can shed light on the development of underwater robotics.

关键词： Biomimetic robotic fish Anal fin Head-shaking Yaw stability

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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