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

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

International Conference on Advanced Robotics and Mechatronics (ICARM)

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

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.

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Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment

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arXiv 2023年

作者： Wang, Jingping Xu, Long Fu, Haoran Meng, Zehui Xu, Chao Cao, Yanjun Lyu, Ximin Gao, Fei State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Applicaion Innovation Laboratory Huawei Technologies Co. Ltd China

With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in R3 such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

Design of a Capsule Robot System for Gastric Hemorrhage Detection using Luminol

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Design of a Capsule Robot System for Gastric Hemorrhage Dete...

IEEE International Conference on Mechatronics and Automation

作者： Yuchen Mao Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Application Complicated Systems and Intelligent Robot Laboratory School of Electrical Engineering and Automation Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system.

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On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification

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arXiv 2022年

作者： Mu, Biqiang Chen, Tianshi Kong, He Jiang, Bo Wang, Lei Wu, Junfeng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Key Laboratory for NSLSCS of Jiangsu Province School of Mathematical Sciences Nanjing Normal University Nanjing China College of Control Science and Engineering Zhejiang University Hangzhou China

Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error method. For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping. In this paper, we report some new results on the embeddings/inverse embeddings of the QMIE method. Firstly, we present a general result on the frequency domain inverse embedding (FDIE) that is to find the inverse of the quadratic mapping described by the discrete-time Fourier transform. Then we show the relation between the TDIE and the FDIE from a graph signal processing perspective. Finally, motivated by this perspective, we further propose a graph induced embedding and its inverse, which include the previously introduced embeddings as special cases. This deepens the understanding of input design from a new viewpoint beyond the real domain and the frequency domain viewpoints. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

Two-Dimensional Borophene:Properties,Fabrication,and Promising Applications

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Research 2020年第1期2020卷 412-434页

作者： Zhongjian Xie Xiangying Meng Xiangnan Li Weiyuan Liang Weichun Huang Keqiang Chen Jianming Chen Chenyang Xing Meng Qiu Bin Zhang Guohui Nie Ni Xie Xiaobing Yan Han Zhang Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Institute of Microscale Optoelectronics and Otolaryngology Department and Biobank of the First Affiliated HospitalShenzhen Second People’s HospitalHealth Science CenterShenzhen UniversityShenzhen 518060China Shenzhen International Institute for Biomedical Research 518116 ShenzhenGuangdongChina National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices Key Laboratory of Digital Medical Engineering of Hebei ProvinceCollege of Electron and Information EngineeringHebei UniversityBaoding 071002China Nantong Key Lab of Intelligent and New Energy Materials College of Chemistry and Chemical EngineeringNantong UniversityNantong226019 JiangsuChina Center for Stretchable Electronics and Nanoscale Systems Key Laboratory of Optoelectronic Devices and Systems of Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Key Laboratory of Marine Chemistry Theory and Technology(Ocean University of China) Ministry of EducationQingdao 266100China

Monoelemental two-dimensional(2D)materials(Xenes)aroused a tremendous attention in 2D science owing to their unique properties and extensive ***,one emerging and typical Xene,has been regarded as a promising agent for energy,sensor,and biomedical ***,the production of borophene is still a challenge because bulk boron has rather intricate spatial structures and multiple chemical *** this review,we describe its excellent properties including the optical,electronic,metallic,semiconducting,photoacoustic,and photothermal *** fabrication methods of borophene are also presented including the bottom-up fabrication and the top-down *** the end,the challenges of borophene in the latest applications are presented and perspectives are discussed.

关键词： properties boron Bor

An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications

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arXiv 2022年

作者： Zhang, Junpeng Ju, Yue Mu, Biqiang Zhong, Renxin Chen, Tianshi School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China

Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling. Its state-of-art implementation is based on the state-space model realization of the spatial-temporal Gaussian process and its corresponding Kalman filter and smoother, and has computational complexity O(NM3), where N and M are the number of time instants and spatial input locations, respectively, and thus can only be applied to data with large N but relatively small M. In this paper, our primary goal is to show that by exploring the Kronecker structure of the state-space model realization of the spatial-temporal Gaussian process, it is possible to further reduce the computational complexity to O(M3 + NM2) and thus the proposed implementation can be applied to data with large N and moderately large M. The proposed implementation is illustrated over applications in weather data prediction and spatially-distributed system identification. Our secondary goal is to design a kernel for both the Colorado precipitation data and the GHCN temperature data, such that while having more efficient implementation, better prediction performance can also be achieved than the state-of-art result. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

An Improved Chimp Optimization Algorithm for Short-term Hydrothermal Scheduling

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IAENG International Journal of Computer Science

IAENG International Journal of Computer Science 2022年第3期49卷 666页

作者： Chen, Gonggui Wang, Shumin He, Yongsheng Shang, Wanfeng Long, Hongyu Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

The short-term hydrothermal scheduling (STHS) is a complex non-linear, non-convex and high-dimensional mathematical optimization problem. Due to the fact that the original chimp optimization algorithm (ChOA) is likely to fall into local optimum and has the disadvantage of low population diversity, an improved chimp optimization algorithm (IChOA) is proposed to solve the STHS problem. Firstly, Logistic-Tent chaotic mapping is employed to initialize the population and increase the population diversity. Then a new nonlinear convergence factor is introduced to make the optimization search process more applicable to STHS problems. Next, particle swarm algorithm (PSO) and gravitational search algorithm (GSA) are combined to enhance the shortcoming and balance the global search ability and local exploration ability of ChOA. Moreover, Cauchy mutation and opposition-based learning strategy are supplemented as perturbation interference when the optimal position is not updated to improve the ability to leap out of the local optimum. Finally, a graded optimization strategy is adopted for the constraint handing of dynamic reservoir balance and thermal power balance. Three standard hydrothermal power systems are utilized to verify the practicability and validity of the proposed method. Results of simulations reveal that IChOA has significant competitive advantages in addressing the STHS problem. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Particle swarm optimization (PSO)