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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

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

ISBN: (纸本)9781728163215

The motor imagery (MI) based on electroencephalogram (EEG) via non-invasive Brain Computer Interface (BCI) is a promising interactive method to provide effective communication between the disabled and rehabilitation robotic device. Interactive efficiency of MI-based BCI largely relies on the performance of subject's motor imagery. In this work, we proposed a simple and effective paradigm based on visual and haptic modalities for a high-quality initialized and calibrated MI-BCI model. Five healthy male subjects participated in motor imagery experiments with two paradigms. The proposed paradigm utilized mixed cues based on the visual and haptic modalities where the haptic cues were provided by the mini vibration motors, and a traditional paradigm utilized the ordinary arrow visual cues. The analysis of the topographic maps was conducted to illustrate the differences of the EEG in both paradigms. The average classification accuracy of motor imagery in the proposed paradigm improved about 14% compared to the traditional paradigm. Furthermore, a subject completed the qualitative verification experiment of the trained model on a rehabilitation lower-limb exoskeleton which proved the feasibility of the proposed paradigm. © 2019 IEEE.

关键词： Brain computer interface

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Dynamic obstacle tracking based on high-definition map in urban scene

Dynamic obstacle tracking based on high-definition map in ur...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Li, Yuxiao Wang, Can Su, Zhilong Duan, Shengcai Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163215

The application of High-Definition map can realize centimeter-level position in urban scenes, the development of deep learning has made great breakthrough in the point cloud dynamic obstacle recognition. All these technologies make obstacle detection and tracking based on High-Definition map effectively realize in the modern smart city scene. It is different from the previous obstacle detection and tracking methods based purely on vision the using of High-Definition map can provide high-precision positioning and reduce the difficulty of point cloud classification. The using of lidar also solves the problem of up-to-scale in dynamic detection. In this paper, we put forward a new Multi-camera Lidar Point Cloud Map, we complete the map at normal speed on the highway and get a satisfactory result. At the same time, we also find a robust combination of traditional kalman filter, Hungary algorithm and current deep learning to solve dynamic obstacle tracking and detection. The experimental results show that our system can effectively complete the special problem of generating map and target tracking on urban scene. © 2019 IEEE.

关键词： Optical radar

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Boosting the Efficiency and Mechanical Stability of Organic Solar Cells Through a Polymer Acceptor by Reducing the Elastic Modulus

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advanced Energy Materials 2025年第0期

作者： Wang, Yan Yu, Han Zhao, Dan Liu, Wei Liu, Baoze Wu, Xin Gao, Danpeng Zhang, Dong Zhang, Shoufeng Sun, Xianglang Zhang, Chunlei Zhao, Chaoyue Fu, Yuchen Song, Wei Gong, Shaokuan Fu, Yuang Kwok, Chung Hang Ge, Ziyi Lu, Xinhui Chen, Xihan Xiao, Shuang Wong, Wai-Yeung Chai, Yu Yan, He Zhu, Zonglong Department of Chemistry City University of Hong Kong Kowloon999077 Hong Kong Department of Chemistry Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials Energy Institute and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom 999077 Hong Kong Hong Kong University of Science and Technology-Shenzhen Research Institute No. 9 Yuexing 1st RD Hi-tech Park Nanshan Guangdong Shenzhen518057 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Physics City University of Hong Kong Kowloon999077 Hong Kong Zhejiang Engineering Research Center for Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo315201 China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Department of Physics The Chinese University of Hong Kong Shatin999077 Hong Kong Center for Advanced Material Diagnostic Technology and College of Engineering Physics Shenzhen Technology University Guangdong Shenzhen518118 China Shenzhen Research Institute City University of Hong Kong Shenzhen518057 China

Organic solar cells (OSCs) are regarded as one of the most promising flexible power sources due to their lightweight and flexible properties, with the improvement of photovoltaic and mechanical performance. To improve the current density and power conversion efficiency (PCE), mPh4F-TS (TS) and PYSe2F-T (PA) are introduced into the binary host, PM6/mPh4F-TT (PM6/TT) as third components. It is demonstrated that the corresponding ternary devices, in both rigid and flexible devices, achieved superior efficiencies (19.6%/17.7% for PM6/TT+TS, and 19.2%/17.4% for PM6/TT+PA) outperform the binary counterparts (18.3%/16.4%). However, distinct differences in mechanical performance are observed between the polymer acceptor (PA) and small-molecular acceptor (TS). The PM6/TT+PA significantly improved the mechanical stability of flexible devices with a lower elastic modulus of 3.6 GPa, while the PM6/TT+TS resulted in the opposite effect with a higher elastic modulus of 5.5 GPa. Through in-depth investigation, a clear correlation between the elastic modulus, crack density, and mechanical stability of the active layer blends is successfully established, revealing the key role of reducing the elastic modulus in enhancing the mechanical stability of flexible OSCs. This study provides important guidance for the development of flexible photovoltaic devices with both high efficiency and mechanical robustness. © 2025 The Author(s). advanced Energy Materials published by Wiley-VCH GmbH.

关键词： Organic solar cells

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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A lightweight soft exoskeleton in lower limb assistance

A lightweight soft exoskeleton in lower limb assistance

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Chinese automation Congress (CAC)

作者： Yu Zhang Zhuo Wang Chunjie Chen Tao Fang Ruimei Sun Yanjie Li School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China BeiJing Aerospace Wanyuan Science Technology Co. Ltd. Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The soft exoskeleton is a wearable robot which is functioned to improve the capacity of human walking or loading. The metabolic of human is increase with the additional load, one integrated design method is used to reduce this effect in this paper. Meanwhile, in order to enhance the convenience and decrease the disturbance to wearer, the wireless module is used to replace the signal line. The nonlinear characteristics and uncertainty of flexible material make the control of soft exoskeleton system be a challenge, a switching controller is proposed to deal with this problem in this paper. It consists of Iterative learning control (ILC) based stable controller and admittance control based unstable controller, functioned with wearers walking stable and not. An experiment of force tracking is conducted to verify the controller performance in this paper, and the result demonstrate that the tracking is well after 20 iteration. In order to evaluate the performance of assistance of soft exoskeleton, five health adult male take part in metabolic experiment By compare the metabolic rate between the wear soft exoskeleton and not wear, the metabolic rate are decrease 7.33%, 14.56% and 10.45% with the walking speed of 3km/h, 5km/h and 7km/h, which are corresponding absolute values decrease of 0.46W/kg, 1.60W/kg and 1.21W/kg respectively.

关键词： AC motors Mechanical cables Wireless communication Pulleys Microprocessors Micromechanical devices Germanium

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Auto-LEE: A novel autonomous lower extremity exoskeleton for walking assistance

Auto-LEE: A novel autonomous lower extremity exoskeleton for...

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2018 IEEE International Conference on Information and automation, ICIA 2018

作者： He, Yong Wang, Chao Li, Nan Fu, Ruiqing Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781538680698

Wearable exoskeletons have been proven to be efficacious in aiding walking for individuals suffering from lower limb mobility disorder. However, the application of most existing devices is limited to inconvenience of usage, e.g., complicated training and unnatural gait. This paper presents a novel autonomous lower extremity exoskeleton, Auto-LEE, for the purpose of improving the practicality of walking assistive devices as well as simplifying their application process. The developed exoskeleton consists of two robotic legs, and each of them has 5 active degrees-of-freedom (DOFs) to independently control the rotations of hip, knee and ankle joints in the sagittal and coronal planes, which enables the device to possess self-balancing ability and flexible gait. The modular design concept is introduced into the structure and hardware development of Auto-LEE, making it more convenient to be assembled and maintained. In order to validate the self-balancing walking ability, virtual prototype simulation and preliminary experiment on flat terrain are implemented. © 2018 IEEE.

关键词： Exoskeleton (robotics)

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Parallel Driving in CPSS:A Unified Approach for Transport automation and Vehicle Intelligence

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 577-587页

作者： Fei-Yue Wang Nan-Ning Zheng Dongpu Cao Clara Marina Martinez Li Li Teng Liu IEEE State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Advanced Vehicle Engineering Center Cranfield University Department of Automation Tsinghua University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Qingdao Huituo Intelligent Machine Company

The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel *** proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.

关键词： ACP theory connected automated driving cyber-physical-social systems(CPSS) iHorizon parallel driving parallel horizon parallel learning parallel reinforcement learning parallel testing

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Micro Channel for Analyzing Mechanical Adaption of Cancer Cell

Micro Channel for Analyzing Mechanical Adaption of Cancer Ce...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Pengyun Li Xiaoming Liu Xiaoqing Tang Masaru Kojima Jian Huang 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 Osaka University 1-3 Machikaneyama Toyonaka Osaka Japan Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

It was reported that cancer cells could biologically respond and adapt to the confined environment during migration in extracellular matrix (ECM). But it is not clearly studied that how cancer cells respond and adapt to confined environment in mechanical way. Thus, in this study, we designed and fabricated a kind of one-layer micro channel to mimic confined environment for analyzing mechanical adaption of cell. This micro channel could apply sequential mechanical stresses on a number of cells with high throughput, and the time of cells passing through five rows of micro channels were used to evaluate their adaptability to the sequential and confined space. In experiments, each cell suffered 20 times of mechanical stresses after passing through 20 squeezing channels arranged in 5 rows. We observed the sequential squeezing processes under microscope and investigated the passing time using a high-speed camera. The preliminary results indicated that cancer cells could pass through and adapt to the confined environment faster than normal cells.

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