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arXiv 2018年

作者： Sun, Yubo Lei, Yuqi Zou, Wulin Li, Jianmin Yu, Ningbo Institute of Robotics and Automatic Information Systems Nankai University Haihe Education Park Tianjin300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Haihe Education Park Tianjin300353 China Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States School of Mechanical Engineering Tianjin University Tianjin300072 China

Body weight support (BWS) is a fundamental technique in rehabilitation. Along with the dramatic progressing of rehabilitation science and engineering, BWS is quickly evolving with new initiatives and has attracted deep research effort in recent years. We have built up a novel gravity offloading system, in which the patient is allowed to move freely in the three-dimensional Cartesian space and receives support against gravity. Thus, the patients, especially for those that suffer from neurological injury such as stroke or spinal cord injury, can focus their residual motor control capabilities on essential therapeutic trainings of balance and gait. The realtime force control performance is critical for the BWS unit to provide suitable support and avoid disturbance. In this work, we have re-designed our BWS unit with a series elastic actuation structure to improve the human-robot interaction performance. Further, the 2 degrees of freedom (2-DOF) control approach was taken for accurate and robust BWS force control. Both simulation and experimental results have validated the efficacy of the BWS design and real-time control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Force control

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An intelligent Gripper Design for Autonomous Aerial Transport with Passive Magnetic Grasping and Dual-Impulsive Release

An Intelligent Gripper Design for Autonomous Aerial Transpor...

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IEEE/ASME International Conference on Advanced intelligent Mechatronics

作者： Usman A. Fiaz M. Abdelkader Jeff S. Shamma Department of Electrical & Computer Engineering University of Maryland(UMD) College Park. MD USA KAUST The Robotics Intelligent Systems & Control (RISC) Laboratory Thuwal KSA

We present a novel gripper design for autonomous aerial transport of ferrous objects with unmanned aerial vehicles (UAVs). The proposed design uses permanent magnets for grasping, and a novel dual-impulsive release mechanism, for achieving drop. The gripper can simultaneously lift up to four objects of arbitrary shape, in fully autonomous mode, with a 100% rate of successful drops. We optimize the system subject to realistic constraints, such as the simplicity of design and its sturdiness to aerial maneuvers, payload limits for multi-rotor UAVs, reliability of autonomous grasping irrespective of the environment of operation, active power consumption of the gripper, and its comparison with the existing technologies. We describe the design concepts, and the hardware, and perform extensive experiments in both indoor and outdoor environments, with two multi-rotor configurations. Several results, showcasing superior performance of the proposed system are provided as well.

关键词： Grasping Force Servomotors Grippers Payloads Torque Magnetic separation

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Evolutionary Dynamics and Individual Heterogeneity in Multi-agent networking systems 35

Evolutionary Dynamics and Individual Heterogeneity in Multi-...

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第35届中国控制会议

作者： ZHANG Jianlei CHEN Zengqiang LIU Zhongxin ZHANG Chunyan Department of Automation College of Computer and Control EngineeringNankai University Tianjin Key Laboratory of Intelligent Robotics Computer and Control EngineeringNankai University

ISBN: (纸本)9781509009107

Cooperative behaviors lie at the basis of human societies,yet its evolutionary origin still remains a perplexing *** exists a large evolutionary game literature exploring effects of population structures on evolutionary outcomes,but in most cases structured population models only employ one game alone.A relevant question is covered in this study thus concerns the effect of game diversity occurring along the edges linking two players situated on the *** basic structure of our mechanism provides more than one type of games available for the *** our game setting,the game model is assumed as a fixed property of each edge and will remain unaltered through game *** preliminary initiatives,we only consider two typical games（Prisoner’s dilemma game and Snowdrift game）,and all possible mixtures between them controlled by a *** performed simulations and theoretical analysis help us observe the remarkable improvement on cooperation levels in suitable parameter regions when the population are simultaneously provided with more than one type of ***,network heterogeneity helps to bring into existence of cooperator clusters and thus favors the survival of cooperators.

关键词： Collective Cooperation Evolutionary Game Theory Heterogeneity

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Outer-Linearization-Based Optimization Algorithm for Decentralized Control Design in Flexure-Linked H-Gantry

Outer-Linearization-Based Optimization Algorithm for Decentr...

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IEEE/ASME International Conference on Advanced intelligent Mechatronics

作者： Jun Ma Si-Lu Chen Wenyu Liang Xiaocong Li Chek Sing Teo Arthur Tay Abdullah Al Mamun Kok Kiong Tan Department of Electrical and Computer Engineering National University of Singapore Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Chinese Academy of Sciences Ningbo Zhejiang China Department of Electrical and Computer Engineering National Universit of Singapore Singapore Mechatronics Group Singapore Institute of Manufacturing Technology Singapore

The dual-drive H-gantry (DHG) is widely used for high-speed high-precision Cartesian motion control applications. Compared with the rigid-linked gantry design, the flexure-linked design is able to prevent the damage of the cross-arm because a small degree of rotation angle is allowed. Nevertheless, the flexure-linked design possibly induces the resonance of the gantry because of the chattering of control signals. To maintain the precision tracking of two carriages and minimize the chattering of control efforts, we aim to seek the most suitable flexure joints among the available ones and optimize the decentralized feedback controller parameters under parametric uncertainties. The mechatronic design problem is formulated as an H_2 guaranteed cost control problem. All the stabilizing feedback controller gains are parameterized over a convex set, from here, numerical procedures are developed to obtain the global optimum by means of an outer-linearization-based optimization algorithm. Experiments are carried out and the results successfully validate the optimality and the robustness of the proposed design approach.

关键词： Decentralized control Optimization Uncertainty Feedforward systems Adaptive control Robustness Actuators

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Model-driven deep learning for physical layer communications

arXiv

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arXiv 2018年

作者： He, Hengtao Jin, Shi Wen, Chao-Kai Gao, Feifei Li, Geoffrey Ye Xu, Zongben National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan State Key Lab of Intelligent Technologies and Systems Tsinghua University Department of Automation Tsinghua University Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Institute for Information and System Sciences Xian Jiaotong University Xi'an710049 China

intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and demonstrated an impressive performance improvement in recent years. However, most of the existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, modeldriven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

arXiv

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arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Araujo, Helder Konukoglu, Ender Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Engineering Department Bogazici Univesity Turkey Institute for Systems and Robotics Universidade de Coimbra Portugal Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. Copyright © 2017, The Authors. All rights reserved.

关键词： Vision

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A new meniscus injury diagnostic model using fuzzy cognitive maps 24

A new meniscus injury diagnostic model using fuzzy cognitive...

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24th Mediterranean Conference on Control and automation, MED 2016

作者： Antigoni, Anninou P. Panagiotis, Poulios Peter, Groumpos P. Ioannis, Gliatis Laboratory of Automation and Robotics Department of Electrical and Computer Engineering University of Patras Greece Orthopedic Clinic Department of Medicine University of Patras Greece

ISBN: (纸本)9781467383455

The aim of this paper is to create an integrated meniscus injury diagnostic model, which by exploiting information about patient history and clinical examination made by physicians concludes to a final diagnosis using Fuzzy Cognitive Maps. Mechanisms of injury and symptoms combined with test results constitute the first level modelled with FCM. The output of this level decides if the patient has a meniscus tear or not. The second level decides if the injury is acute or degenerative with the help of risk factors using Competitive Fuzzy Cognitive Maps in order to conclude to only one decision. The results obtained by the model are very promising without requiring sending the patient for MRI (Magnetic Resonance Imaging) which is of utmost importance in diagnosing soft tissue injuries. © 2016 IEEE.

关键词： Fuzzy Cognitive Maps

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Deep ranking: Triplet MatchNet for music metric learning

Deep ranking: Triplet MatchNet for music metric learning

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rui Lu Kailun Wu Zhiyao Duan Changshui Zhang Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Beijing China Department of Electrical and Computer Engineering University of Rochester NY USA

ISBN: (纸本)9781509041183

Metric learning for music is an important problem for many music information retrieval (MIR) applications such as music generation, analysis, retrieval, classification and recommendation. Traditional music metrics are mostly defined on linear transformations of handcrafted audio features, and may be improper in many situations given the large variety of music styles and instrumentations. In this paper, we propose a deep neural network named Triplet MatchNet to learn metrics directly from raw audio signals of triplets of music excerpts with human-annotated relative similarity in a supervised fashion. It has the advantage of learning highly nonlinear feature representations and metrics in this end-to-end architecture. Experiments on a widely used music similarity measure dataset show that our method significantly outperforms three state-of-the-art music metric learning methods. Experiments also show that the learned features better preserve the partial orders of the relative similarity than handcrafted features.

关键词： Metric learning music similarity deep learning convolutional neural networks

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Micro-Nano Fabrication: Fabrication of Sub-Micrometer-Sized MoS2Thin-Film Transistor by Phase Mode AFM Lithography (Small 49/2018)

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Small 2018年第49期14卷

作者： Lianqing Liu Jialin Shi Meng Li Peng Yu Tie Yang Guangyong Li State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang 110016 China University of Chinese Academy of Science Beijing 100049 China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA 15213 USA

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Motion trajectory design and tracking control for underactuated Furata pendulum system

Motion trajectory design and tracking control for underactua...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Ancai Zhang Jianlong Qiu Chaomin Luo Chengdong Yang Zhenxing Li School of Automation and Electrical Engineering Linyi University Linyi Shandong China Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong (Linyi University) Linyi Shandong China Department of Electrical and Computer Engineering University of Detroit Mercy MI USA School of Information Science and Engineering Linyi University Linyi Shandong China

This paper discusses the motion trajectory design and tracking control problem for an underactuated mechanical system called Furuta pendulum. Firstly, the dynamic equations of a Furuta pendulum system are given. And the dynamic properties of this mechanical system are analyzed. Secondly, we construct a trajectory for the Furuta pendulum in its motion space based on the analysis properties. Afterwards, a tracking controller is designed to track the constructed motion trajectory. This guarantees the stabilization of the Furuta pendulum from an initial point to an objective point to be achieved. To demonstrate the validity of our proposed theoretical analysis results, a numerical example is presented finally.

关键词： Trajectory Tracking Mathematical model Mechanical systems Aerospace electronics Dynamics

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