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

作者： Gao, Guangju Liu, Jingshuai He, Yong Sun, Jianquan Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

This paper proposes a novel self-reconfigurable modular robot for search and rescue. The modular robot consists of two main structures, a planetary wheel driven robot body and a double unlocking system as the docking structure. By means of the planetary wheel structure, the robot has a strong flexibility to cross obstacles in a disaster environment. In virtue of the docking structure with double unlocking function, the self-reconfigurable ability has improved robot reliability. Further, the modular robot can be connected or disconnected according to the actual task requirements. Finally, to verify the stability and reliability, the finite element analysis is carried out. © 2021 IEEE.

关键词： Reliability analysis

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Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features

Linked Dynamic Graph CNN: Learning through Point Cloud by Li...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Kuangen Zhang Ming Hao Jing Wang Xinxing Chen Yuquan Leng Clarence W. de Silva Chenglong Fu Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China The University of British Columbia Vancouver Canada Tsinghua University Beijing China

ISBN: (纸本)9781665431545

Learning through a point cloud is attractive be-cause a point cloud contains geometric data and can help robots understand environments in a robust manner. However, a point cloud is sparse, unstructured and unordered, and its accurate recognition by a traditional convolutional neural network (CNN) or a recurrent neural network (RNN) is difficult. Hence, the present paper proposes a linked dynamic graph CNN (LDGCNN) to directly classify and segment a point cloud. The present work removes the transformation network, links hierarchical features from dynamic graphs, freezes the feature extractor, and retrains the classifier in order to increase the performance of LDGCNN. Also, the paper describes LDGCNN using theoretical analysis and visualization. The LDGCNN is able to classify a point cloud consisting of 1024 three dimensional points without normal vectors in the ModelNet40 dataset, achieving 92.9% accuracy, which is better than the state-of-art. The LDGCNN also achieves state-of-the-art segmentation performance in the ShapeNet dataset.

关键词： Point cloud compression Training Solid modeling Recurrent neural networks Semantics Object segmentation Feature extraction

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A Lightweight, Integrated and Portable Force-Controlled Ankle Exoskeleton for Daily Walking Assistance

A Lightweight, Integrated and Portable Force-Controlled Ankl...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yuquan Leng Guan Huang Lang Ma Yuepeng Qian Xinxing Chen Kuangen Zhang Chenglong Fu Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China National University of Singapore Singapore The University of British Columbia Vancouver Canada

ISBN: (纸本)9781665431545

Most of the powered lower-limb exoskeletons are bulky or complex to wear. Especially, some exoskeletons have constraints on human leg movement. These all limit their applicability to daily walking assistance. In this paper, the ankle joint is selected to be assisted based on the fact that ankle joint contributes most of the positive power in daily walking. Then, a lightweight, integrated and portable force-controlled ankle exoskeleton is designed and tested. The wearable parts of proposed exoskeleton can be completely integrated to shoes, with no other part add to human legs. This can reduce the wearing complexity and increase acceptability of the user. Time-sharing control method is adopted about admittance control and position control to assist ankle joint. The assistant curve imitates the biological moment of ankle joint and the maximal moment is set to 25% of the maximal moment of ankle joint. When human walks at 1.4 m/s, the result show that net metabolic cost with the exoskeleton can be reduced by 9.98% compared to without the exoskeleton, proving the effectiveness of the proposed exoskeleton.

关键词： Legged locomotion Costs Torque Mechatronics Exoskeletons Force Position control

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Double PWM coordinated control based on model predictive algorithm and power compensation

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International Journal of Reasoning-based intelligent systems 2019年第1期11卷 38-46页

作者： Fan, Bo Song, Lu Song, Shuzhong Pu, Jiexin Information Engineering College Henan University of Science and Technology Luoyang China Henan Provincial Laboratory on Intelligent Manufactory and Control Engineering Luoyang471003 China Henan Key Laboratory of Robot and Intelligent Systems Luoyang471003 China

With analysis on double PWM structure though system's energy flow theory, active power and reactive power of rectifier are controlled by model predictive algorithm. System adopts the method of combine dynamic power's compensation with static power's compensation for system power in order to reduce system power error. A new power compensation algorithm is proposed to designs a new controller to replace PI controller of system's voltage loop, which can restrain fluctuation of DC bus voltage when load power suddenly varies and reduces DC-link's capacity of capacitance. As for inverter side, the system uses the method of rotor flux linkage oriented to control three phase asynchronous motor. Results of simulation can show that, system can realise the smallest tracking error of active power and reactive power by model predictive control, and restrain fluctuation of DC bus when load power suddenly changes. As the same time, the gird current's waveform is well. © 2019 Inderscience Enterprises Ltd.

关键词： Reactive power

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Admittance Control based Hemiplegic Exoskeleton Motion Control

Admittance Control based Hemiplegic Exoskeleton Motion Contr...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： He, Bailin Zhou, Xin Huai, Yaping Liu, Jiaqing Li, Pengbo Wang, Can Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In this paper, a method based on admittance control is developed to improve patients' comfort wearing an exoskeleton robot for rehabilitation training. In our previous work, we designed a modular and convenient hemiplegic exoskeleton robot. The mechanical structure will cause stiffness when it comes to contact with the human body in practical applications. We introduced admittance control to improve the flexibility of the exoskeleton robot and improve the subjects' comfort. This paper considers coupling of model between the exoskeleton and the human body as the human-computer interaction force of the system at the main human-computer interaction point. Measuring the interaction force between exoskeleton members and limbs, the work uses the virtual admittance model to correct the system's inherent characteristics and studies the control method's stability with Lyapunov's theory. The experiment collected the subject movement information which was converted the movement information of the exoskeleton. Human-computer interaction forces in the thigh and calf were measured while the exoskeleton assisted the subject's movement. The experimental results show that adding the admittance controller reduces the human-computer interaction force from 5g to 2g and 6g to 3g, respectively. © 2022 IEEE.

关键词： Human computer interaction

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Accurate Parameter Estimation of Time-varying Nuclear Radiation Field with Mobile Sensors

Accurate Parameter Estimation of Time-varying Nuclear Radiat...

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第40届中国控制会议

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

In this paper, the optimization strategy of mobile sensors and the parameter estimation of time-varying nuclear radiation field are investigated. Based on the measurement model with white Gaussian noise, Fisher information matrix is derived as an information metric. At each time step, finding the best mobile sensor coordinates that contain the amount of maximal information in the 4-Dimension space is formulated as a constrained nonlinear optimization problem, which is able to minimize the trace of Cramer-Rao Lower Bound. Further, an efficient heuristic algorithm called Augmented Lagrangian Genetic Algorithm is adopted to solve the above optimization problem. Then, the parameters of nuclear radiation field are estimated by a maximum likelihood estimator. The proposed sensor coordinates moving strategy has better estimation performance than random coordinates strategy, which is demonstrated via simulation results.

关键词： Optimal sensor coordinates Fisher information matrix(FIM) Cramer-Rao Lower Bound(CRLB) Augmented Lagrangian Genetic Algorithm(ALGA) Maximum likelihood estimate(MLE)

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Distributed Vehicle Platoon Control with Leader Selection Strategy

Distributed Vehicle Platoon Control with Leader Selection St...

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第40届中国控制会议

作者： Zikai Ouyang Zhicheng Li Shibo Chen Zaiyue Yang Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology

This paper investigates the control problem of vehicle platoon with leader selection strategy. The current research on vehicle platoon control is based on the leader-follower communication structure. Under such structure, each follower tracks the state of its front vehicle to maintain a proper distance and form a platoon. However, the disadvantage of this fixed communication structure is that followers are short of capacity of resisting disturbance when the first vehicle(FV) of platoon is disturbed. To solve this problem, a vehicle platoon model where each follower is able to take any front vehicle as its leader is proposed. Based on this model, the corresponding controller and leader selection strategy are designed. That is, first, a controller considering the string stability of vehicle platoon with various communication topologies is designed;then, each follower changes its connecting vehicle in the communication link to reduce the impact of disturbance on FV when FV suddenly slows down or speeds up. The simulation results show the effectiveness of the control method and the strategy.

关键词： Vehicle platoon leader selection strategy distributed control

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Power Sharing and Voltage Deviation Restriction for Multi-Bus DC Microgrids

Power Sharing and Voltage Deviation Restriction for Multi-Bu...

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IEEE Conference on Decision and Control

作者： Handong Bai Zhancheng Liu Hongwei Zhang School of Electrical Engineering Yellow River Conservancy Technical Institude Kaifeng Henan P.R. China School of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan P.R. China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Guangdong P.R. China

Power sharing and voltage regulation are fundamental but conflict control objectives of DC microgrids. This paper presents a distributed control strategy to achieve adjustment of the control objectives from accurate power sharing to accurate voltage regulation. At the same time, the bus voltage of critical node is regulated to reach the rated value of the DC microgrid. Based on this control strategy, steady state characteristics of the closed-loop system are analyzed. For a given DC microgrid, the proposed control method is verified experimentally.

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Terrain recognition and gait cycle prediction using IMU

Terrain recognition and gait cycle prediction using IMU

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

作者： Wang, Zhuo Zhang, Yu Ni, Jiangpeng Wu, Xinyu Liu, Yida Ye, Xin Chen, Chunjie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SZ DJI Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9781665436786

It is well known that terrain recognition and gait cycle prediction are important for powered exoskeleton. However, only a few works have focused on the concerns of complexity of the control system caused by using redundant sensors. In this paper, only two IMU sensors are applied to collect information of the angle and angular velocity of the hip joint in the situation of level-ground walking, ramp ascent, and ramp descent. Based on information acquired from these two IMU sensors, two methods are proposed to achieve terrain recognition. One method uses the angle of the hip joint when the two legs intersect as the threshold of terrain recognition. It can identify the terrain (level-ground walking, ramp ascent, ramp descent) during stable walking, but it cannot recognize the transitional terrain (from level-ground walking to ramp ascent, from ramp ascent to ramp descent, and so on) and its robustness is limited. The other method selects the angle and angular velocity of the hip joints as the eigenvector, and uses SVM for terrain recognition. The accuracy of terrain recognition is improved from 69.7% to 100% after introducing the Gaussian kernel function instead of Linear kernel function. For gait cycle prediction, Wiener one step prediction is applied in predicting the GC. Compared to actual GC, the error from predicted GC based on mean prediction is more than 8.0%, while the error from Wiener on step prediction is less than 4.35%. © 2021 IEEE.

关键词： Forecasting

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Time-frequency decomposition-based weighted ensemble learning for motor imagery EEG classification

Time-frequency decomposition-based weighted ensemble learnin...

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

作者： Zheng, Liangsheng Ma, Yue Li, Mengyao Xiao, Yang Feng, Wei 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 Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781665436786

Motor imagery brain-computer interface system based on Electroencephalogram (EEG) is an effective way to help the disabled recover part of their motor abilities. However, decoding the movement intention contained in the EEG signal accurately presents many challenges. In this paper, we propose a time-frequency decomposition-based weighted ensemble learning (TFDWEL) method, which aims to improve the classification performance of motor imagery EEG signals. The TFDWEL method divides the EEG signal into multiple subsets, and uses four time-frequency processing methods to extract the time-frequency sub-bands of each subset. Then the feature extraction model and classifier model of each subset trained by the common spatial pattern (CSP) algorithm and the support vector machine (SVM) algorithm are used to build a set of base learners. The least square error estimation method is used to learn the weight of each base learner, and finally the weighted summation method is used to obtain the final decision. The classification performance of the TFDWEL method is evaluated on the BCI Competition IV Data Set 2b, and the results show that the classification accuracy of 81.58% can be obtained. Superior classification performance indicates that the TFDWEL method can be used in further research to help the rehabilitation of the disabled. © 2021 IEEE.

关键词： Electroencephalography

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