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第41届中国控制会议

作者： Xiaohong Li Zhicheng Li Zaiyue Yang Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineeringand the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in UniversitiesSouthern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Bearing is a common and important component in the industry,and it is crucial to detect and diagnose its working *** to the characteristic the motion process of the bearing that is a periodic nonlinear system with sinusoidal input,a bearing fault detection method based on the nonlinear system with multi-frequency sinusoidal input is *** the dynamics modeling of the motion for the bearing,it is found that its input and output have the characteristics of almost periodic *** the periodogram-based asymptotic local fault detection method,the fault detection problem is transformed into a hypothesis test problem,and a threshold is obtained by the given confidence and the degree of freedom of a specific frequency *** method can effectively and quickly monitor the early minor faults in strong noise *** realization and performance of this bearing detection method are illustrated by the simulated vibration signal of the bearing.

关键词： Bearing dynamics model fault detection sinusoidal signal statistical methods

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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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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Wang, Zhuo Chen, Hui Yang, Fangliang Wang, Xiangyang Wu, Xinyu Chen, Chunjie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

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 (R2) 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, ρ, and R2 of 3.09± 0.90,\ 0.9911 ± 0.0052, and 0.9817± 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. © 2024 IEEE.

关键词： Clinical research

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ViT-based Terrain Recognition System for wearable soft exosuit

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Biomimetic Intelligence & robotics 2023年第1期3卷 57-64页

作者： Fangliang Yang Chunjie Chen Zhuo Wang Hui Chen Yao Liu Gang Li Xinyu Wua Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhen 518055China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Shenzhen 518055China SIASUN Robot and Automation CO. LtdShenyang 110169China

Terrain classification and force assistance strategies in complex environments have always piqued the interest of many *** wearable soft exosuits,inaccurate terrain recognition can easily introduce undesired assist forces that can easily injure the *** of these problems,we introduced a depth camera into the exosuit system,perform classification of terrain based on a Vision Transformer(ViT),and optimized the control algorithm,which is known as a ViT-Based Terrain Recognition System(TTRS).First,we used the Transformer algorithm to achieve a considerable classification effect in terrain *** also introduced terrain recognition as prior knowledge into the force assistance strategy of the exosuit,providing different force assistance to the exosuit in different ***,we performed human experiments with seven able-bodied people(six males and one female).The promising results demonstrate that our classification accuracy can reach 99.2%under six different terrains and that it can smoothly switch the force–assist curve in different terrains to better adapt to the complex terrain and improve the walking *** aforementioned terrain recognition algorithms and force–assist strategies may positively influence the study of soft exosuit,powered prostheses,and orthotics.

关键词： Vision Transformer(ViT) Depth camera Wearable soft exosuit Force assistance strategy Terrain recognition

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A Novel Feature Learning-based Bio-inspired Neural Network for Real-time Collision-free Rescue of Multi-Robot systems

arXiv

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arXiv 2024年

作者： Li, Junfei Yang, Simon X. Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada

Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this paper, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot provide a satisfactory solution to real-time responses to sudden environmental changes. The neurodynamic model is incorporated into the feature learning method that can use environmental information to improve path planning strategies. Task assignment and collision-free rescue trajectory are generated through robot poses and the dynamic landscape of neural activity. A dual-channel scale filter, a neural activity channel, and a secondary distance fusion are employed to extract and filter feature neurons. After completion of the feature learning process, a neurodynamics-based feature matrix is established to quickly generate the new heuristic rescue paths with parameter-driven topological adaptability. The proposed FLBBINN aims to reduce the computational complexity of the neural network-based approach and enable the feature learning method to achieve real-time responses to environmental changes. Several simulations and experiments have been conducted to evaluate the performance of the proposed FLBBINN. The results show that the proposed FLBBINN would significantly improve the speed, efficiency, and optimality for rescue operations. © 2024, CC BY.

关键词： Multipurpose robots

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intelligent Collective Escape of Swarm Robots Based on a Novel Fish-inspired Self-adaptive Approach with Neurodynamic Models

arXiv

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arXiv 2024年

作者： Li, Junfei Yang, Simon X. The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada

Fish schools present high-efficiency group behaviors through simple individual interactions to collective migration and dynamic escape from the predator. The school behavior of fish is usually a good inspiration to design control architecture for swarm robots. In this paper, a novel fish-inspired self-adaptive approach is proposed for collective escape for the swarm robots. In addition, a bio-inspired neural network (BINN) is introduced to generate collision-free escape robot trajectories through the combination of attractive and repulsive forces. Furthermore, to cope with dynamic environments, a neurodynamics-based self-adaptive mechanism is proposed to improve the self-adaptive performance of the swarm robots in the changing environment. Similar to fish escape maneuvers, simulation and experimental results show that the swarm robots are capable of collectively leaving away from the threats. Several comparison studies demonstrated that the proposed approach can significantly improve the effectiveness and efficiency of system performance, and the flexibility and robustness in complex environments. © 2024, CC BY.

关键词： Fish

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Probing Local Cellular Mechanics by Atomic Force Microscopy with Modified Spherical Tip

Probing Local Cellular Mechanics by Atomic Force Microscopy ...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Zhaofeng Zuo Xiaoming Liu Xiaoqing Tang Fengyu Liu Dan Liu Yuyang Li Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and the Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China

Abstract-The mechanical characterization of cell is important for knowing the physiological state and studying diseases of organism. While many approaches are available for measuring cellular elasticity, distinguishing the stiffness variation among different cellular areas is still a challenge. In this paper, we reported a method to modify spherical atomic force microscopy (AFM) tip for accurate measurement of Young’s modulus in several areas on single adherent living cancer cells (Hela cells). The micrometer size spheres were transported to an AFM probe tip by dual micropipettes and fixed by ultraviolet (UV) curable glue, which is reproducible and nondestructive to the cantilever. The force-displacement curves were measured along major axis of cells with the modified AFM probe in indentation experiments. The results demonstrate that the modulus value increased with the detected point approaching the nucleus center, and areas closed to nucleus showed have higher stiffness. Our study provides a quantitative method for static measurement of different locations of cell, and the result has the potential to reveal how intracellular structures have effect on the cell mechanical characterization.

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Analysis of the Obstacle-Crossing Capability for a Coupled Parallelogram Leg 16th

Analysis of the Obstacle-Crossing Capability for a Coupled P...

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16th International Federation of Theory of Machines and Mechanisms World Congress, IFToMM WC 2023

作者： Chen, Junpeng Pan, Yang Li, Mi Zhu, Renjie Gao, Meng Wang, Kun Xiao, Xinyu Deng, Lizi Dai, Jian S. Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education Centre for Advanced Mechanisms and Robotics Tianjin University Tianjin300350 China Centre for Robotics Research King’s College London LondonWC2R 2LS United Kingdom

ISBN: (纸本)9783031457692

This paper presents the kinematic analysis of a coupled parallelogram leg (CPL) used for locomotion in a hexapod robot. The ability to cross obstacles is a crucial functionality of legged robots, which heavily relies on their control algorithms and structural characteristics. In this study, we specifically focus on analyzing the CPL workspace and propose three performance metrics for assessing its obstacle-crossing (OC) capabilities: workspace utilization, effective obstacle-crossing area ratio, and maximum stride ratio. By calculating these metrics for two main walking directions, we thoroughly evaluate the CPL’s OC abilities. The results offer valuable insights into the maximum stride and the starting and ending footholds in each direction, providing a solid theoretical foundation for the development of flexible OC strategies. Prototype experiments successfully validate the accuracy of these calculations, further confirming the effectiveness of the proposed metrics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Kinematics

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Deep Learning-Guided Single-Cell Encapsulation through photo-crosslinking for advanced 3D Culture

Deep Learning-Guided Single-Cell Encapsulation through photo...

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IEEE International Conference on Mechatronics and Automation

作者： Yanfeng Zhao Kaijun Lin Haotian Yang Xinyi Dong Tao Sun Qing Shi Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institude of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Encapsulating single cells in hydrogels provides them with a relatively autonomous and controllable three-dimensional extracellular environment. This environment can facilitate cell interaction and proliferation, making it highly valuable in fields such as tissue engineering and single-cell research. However, traditional methods of single-cell encapsulation encounter limitations in terms of single-cell encapsulation rate and topography control, which hinder of the progress of single-cell encapsulation research. In this paper, we propose a novel method for printing microgel-encapsulated single cells using deep learning-guided DLP printing. By leveraging deep learning algorithms, we accurately capture and translate the real-time positional data of single cells into the printing system’s coordinate framework. Based on the single-cell positional information, a virtual digital mask is created via the OpenCV algorithm, which is then input into the digital microscope to complete the single-cell encapsulation. The single-cell encapsulation rate achieved by this method is 86.3%, which is about 2.87 times higher than that of the traditional method. Experimental results show that our method achieves high accuracy in single-cell encapsulation and the fabrication of microgels with arbitrary shapes, which hold significant importance for biological and medical applications.

关键词： Encapsulation Printing Fabrication Deep learning Three-dimensional displays Shape Hydrogels

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Design and Evaluation of a Novel Self-Adaptive Ankle Rehabilitation Exoskeleton with Elastic Modules 8

Design and Evaluation of a Novel Self-Adaptive Ankle Rehabil...

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8th IEEE International Conference on advanced robotics and Mechatronics, ICARM 2023

作者： Gao, Meng Chen, Junpeng Li, Mi Dai, Jian S. Southern University of Science and Technology Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Shenzhen518055 China Centre for Robotics Research King's College London LondonWC2R 2LS United Kingdom Centre for Advanced Mechanisms and Robotics Tianjin University Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education Tianjin300350 China

ISBN: (纸本)9798350300178

This paper proposes a novel ankle rehabilitation exoskeleton based on the research of human gait cycle, aiming to assist patients with neurological injury to recover quickly. The mechanism consists of two frames equipped with ankle joints and toe joint. The lower limbs and musculature system are correlated to propose the model. Incorporating spring modules are provided on the instep, the bottom of toes and either sides of the ankles which cushions lower limbs and reduces energy consumption. Kinematics and stress analysis are conducted to verify the feasibility of the model. Besides, the simulation, which incorporates the elastic elements and springs, is presented. Finally, the modular facilitates could easy integration with other rehabilitation modules for a comprehensive system. Overall, this research provides theoretical and engineering technical support for developing lower limb rehabilitation robots with adaptive stability, diversified movement functions, and human-computer interaction safety and health under different conditions. © 2023 IEEE.

关键词： Stress analysis

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Comprehensive Performance Optimization of a Novel Anthropomorphic Dexterous Hand

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IEEE robotics and Automation Letters 2025年第7期10卷 6816-6823页

作者： Ma, Yifan Shang, Weiwei Zhang, Fei Pang, Shunxiang Dai, Shuqing Yi, Zhengkun Li, Guotao Cong, Shuang University of Science and Technology of China Department of Automation Jinzhai Road 96 Hefei 230027 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen 518055 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing 100190 China

Grasp and in-hand manipulation of dexterous hands are essential for daily operational tasks. In this study, an optimized design method for a novel anthropomorphic dexterous hand is proposed, which improves the comprehensive performance of grasp and manipulability of the dexterous hand. The constraints of multi-fingered contact force and kinematics are established with full consideration of the applicability to the objects of different sizes and the shape characteristics of finger contact surface. The performance index of in-hand manipulation ability is proposed, and the optimization problem is constructed through the technique for order preference by similarity to ideal solution (TOPSIS) to achieve multi-objective optimization of precision grasp and in-hand manipulation. Finally, simulation and experiment verify the feasibility of our prototype and optimization method. The results show that the in-hand translation space and in-hand rotation range are increased by 20% and 8.5%, respectively. Our dexterous hand has excellent abilities of multi-fingered interaction and in-hand manipulation, which can perform 33 grasp gestures and two/three-fingered in-hand manipulation. This study provides a valuable reference for the design of dexterous hands with anthropomorphic functions. © 2016 IEEE.

关键词： design optimization Dexterous manipulation multifingered hands precision grasp

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