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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Grey, Michael X. Ames, Aaron D. Liu, C. Karen School of Interactive Computing Georgia Institute of Technology AtlantaGA30332 United States Mechanical and Civil Engineering and Control and Dynamical Systems California Institute of Technology PasadenaCA91125 United States

ISBN: (纸本)9781509046331

Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions;they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the 'Randomized Possibility Graph', which uses high-level approximations of constraint manifolds to rapidly explore the 'possibility' of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, 'semi-unstructured' means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions. © 2017 IEEE.

关键词： Motion planning

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Compliant Impedance control for a Redundant Manipulator During Human Robot Interaction

Compliant Impedance Control for a Redundant Manipulator Duri...

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International Conference on Automation and computing (ICAC)

作者： Yiming Jiang Chenguang Yang Zhaojie Ju Andy Annamalai Honghai Liu Key Laboratory of Autonomous Systems and Networked Control South China University of Technology Guangzhou China Zienkiewicz Center for Computational Engineering Swansea University Swansea UK School of Computing University of Portsmouth Portsmouth UK Moray College University of Highlands and Islands Elgin UK

ISBN: (纸本)9781538648919

Robot control with a compliant motion behaviour is important to guarantee the safety of human robot interaction. In this paper, we aim to guarantee the desired task impedance of the end-effector and to ensure a compliant behaviour of a redundant robot during human robot interactions. A task space impedance control scheme is proposed by designing a transferred impedance error, such that the robot end-effector can be governed to follow a target impedance model. Additionally, a low-priority controller is designed using the null space projection, such that compliant joint motion is guaranteed without affecting the main task. The stability of the whole system is illustrated using a conditional Lyapunov theorem. Simulation studies based on a plane redundant robot is carried out to demonstrate the validity of the proposed controller.

关键词： Robots Impedance Task analysis Aerospace electronics Null space Torque Force

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P2P Over MANET: A Review and Its Conceptual Framework

P2P Over MANET: A Review and Its Conceptual Framework

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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Farkhana Muchtar Pradeep Kumar Singh Siti Nor Zawani Ahmmad Radzi Ambar Hafizul Fahri Hanafi Suzi Iryanti Fadilah Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Department of CSE &IT JayPee University of IT Waknaghat Solan Himachal Pradesh India Instrumentation & Control Engineering MITEC Johor Malaysia Faculty of Electronic & Electrical Engineering Universiti Tun Hussein Onn Batu Pahat Malaysia Faculty of Art Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Perak Malaysia School of Computer Sciences Universiti Sains Malaysia Gelugor Pulau Pinang Malaysia

ISBN: (纸本)9781728106489

Lately, Peer to Peer (P2P) over Mobile Ad hoc Network have become a hot topics. Thus, the main motivation of this paper is to review various techniques that have been used in previously reported research of Peer to Peer (P2P) over Mobile Ad hoc Network (MANET). From the said document, it is identified that all previously techniques used in P2P over MANET have one similarity; namely maintaining the use of TCP/IP stack as network solution, in which the source for high energy resources for their proposed solution. Hence, this paper proposed a new solution beyond the TCP/IP stack paradigms in order to improve energy efficiency in P2P over MANET, whereby through the use of Content Centric Networking (CCN). In detail, this paper has included conceptual framework of our suggested solution. It is hoped that this paper achieves its two objectives: (i) to present a literature review on the topic of P2P over MANET, and (ii) to provide conceptual framework of our suggested solution.

关键词： Mobile ad hoc networks Peer-to-peer computing TCPIP Overlay networks Internet Protocols

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Forecasting transportation network speed using deep capsule networks with nested LSTM models

arXiv

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arXiv 2018年

作者： Ma, Xiaolei Li, Yi Cui, Zhiyong Wang, Yinhai School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Civil and Environmental Engineering University of Washington Seattle98195 United States

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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Plenary Lectures

Plenary Lectures

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Mediterranean Conference on control and Automation (MED)

作者： Bart De Moor Yasamin Mostofi Maryam Kamgarpour Zdenko Kovačić Maja Cepanec Airlie Chapmar Mehran Mesbahi Department of Electrical Engineering (ESAT-STADIUS) KU Leuven Leuven Belgium Department of Electrical & Computer Engineering University of of California Santa Barbara CA USA Automatic Control Laboratory Swiss Federal Institute of Technology Zurich Zurich Switzerland University of Zagreb Faculty of Electrical Engineering and Computing University of Zagreb Faculty of Education and Rehabilitation Sciences University of Melbourne Australia University of Washington USA

The following topics are dealt with: control system synthesis; stability; nonlinear control systems; closed loop systems; robust control; feedback; Lyapunov methods; mobile robots; uncertain systems; and position cont... 详细信息

ISBN: (纸本)9781538674994;9781538678909

关键词： Robot sensing systems Electrical engineering Education Autism Robot kinematics Decision making

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A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products

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Procedia CIRP 2018年 72卷 1362-1367页

作者： Zhouyang Ding Zhigang Jiang Ying Liu Yan Wang Congbo Li Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science & Technology Wuhan 430081 China Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science & Technology Wuhan 430081 China Institute of mechanical and manufacturing engineering School of Engineering Cardiff University Cardiff CF24 3AA Department of Computing Engineering and Mathematics University of Brighton Brighton BN2 4GJ United Kingdom State Key Laboratory of Mechanical Transmission Chongqing University Chongqing 400030 China

Remanufacturing is considered as an important industrial process to restore the performance and function of End-of-Life (EOL) products to a like-new state. In order to help enterprises effectively and precisely predict the cost of remanufacturing processes, a remanufacturing cost prediction model based on big data is developed. In this paper, a cost analysis framework is established by applying big data technologies to interpret the obtained data, identify the intricate relationship of obtained sensor data and its corresponding remanufacturing processes and associated costs. Then big data mining and particle swarm optimization Back Propagation (BP) neural network algorithm are utilized to implement the cost prediction. The application of presented model is verified by a case study, and the results demonstrates that the developed model can predict the cost of the remanufacturing accurately allowing early decision making for remanufacturability of the EOL products.

关键词： Remanufacturing Cost Prediction End-of-Life Products Big Data BP Neural Network

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A Low-Power Actor-Critic Framework Based on Memristive Spiking Neural Network

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IOP Conference Series: Earth and Environmental Science 2019年第3期252卷

作者： Yaozhong Zhang Xiaofang Hu Yue Zhou Wenbo Song College of Computer and Information Science Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China College of Electronic and Information Engineering Southwest University Chongqing 400715 China

Traditional deep reinforcement learning (DRL) algorithms consume much energy. Energy-efficient spiking neural networks (SNNs) are promising technologies to bulid a low-power reinforcement learning architecture. In this paper, an actor-critic framework based on memrisitive SNN is proposed. To convey and process information in SNN, spike encoding and decoding systems are created. Then, an improved learning algorithm based on spike-timing-dependent plasticity (STDP) learning rule is designed to combine actor-critic method with SNN. Moreover, this learning algorithm is also hardware-friendly. Besides, memristive synapse is designed to accelerate this learning algorithm. Finally, a continuous control problem is applied to illustrate the effectiveness of the proposed framework. The results show the proposed framework is prior to traditional methods.

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Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

Visual Place Recognition in Long-term and Large-scale Enviro...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Zhu, Jianliang Ai, Yunfeng Tian, Bin Cao, Dongpu Scherer, Sebastian School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781538644522

With the universal application of camera in intelligent vehicles, visual place recognition has become a major problem in intelligent vehicle localization. The traditional solution is to make visual description of place images using hand-crafted feature for matching places, but this description method is not very good for extreme variability, especially for seasonal transformation. In this paper, we propose a new method based on convolutional neural network (CNN), by putting images into the pre-trained network model to get automatically learned image descriptors, and through some operations of pooling, fusion and binarization to optimize them, then the similarity result of place recognition is presented with the Hamming distance of the place sequence. In the experimental part, we compare our method with some state-of-the-art algorithms, FABMAP, ABLE-M and SeqSLAM, to illustrate its advantages. The experimental results show that our method based on CNN achieves better performance than other methods on the representative public datasets. © 2018 IEEE.

关键词： Hamming distance

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Wavelet-based texture model for crowd dynamic analysis 23

Wavelet-based texture model for crowd dynamic analysis

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23rd IEEE International Conference on Automation and computing, ICAC 2017

作者： Wang, Jing Xu, Zhijie Cao, Yanlong Xu, Yuanping Faculty of Arts Computing Engineering and Sciences Sheffield Hallam University City Campus SheffieldS1 1WB United Kingdom School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom State Key Laboratory of Fluid Power Transmission and Control College of Mechanical Engineering Zhejiang University Hangzhou310027 China School of Software Engineering Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)9780701702618

Crowd event detection techniques aim at solving real-world surveillance problems, such as detecting crowd anomaly and tracking specific person in a highly dynamic crowd scene. In this paper, we proposed an innovate texture-based analysis method to model crowd dynamics and us it to distinguish the crowd behaviours. To describe complicated crowd scenes, homogeneous random features have been deployed in the research for behavioural template matching. Experiment results have shown that the anomaly appearing in crowd scenes can be effectively and efficiently identified by using the devised methods. © 2017 Chinese Automation and computing Society in the UK - CACSUK.

关键词： Template matching

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UAV assisted bridge defect inspection system 3rd

UAV assisted bridge defect inspection system

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3rd International Conference on Intelligence Science, ICIS 2018

作者： Yang, Shuzhan Shen, Zhen Wang, Xiao Bai, Tianxiang Ji, Yingliang Jiang, Yuyi Liu, Xiwei Dong, Xisong Li, Chuanfu Han, Qi Lu, Jian Xiong, Gang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China College of Engineering Peking University Beijing100871 China University of Chinese Academy of Sciences Beijing100049 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Expressway Qingdao266041 China Guangdong Launca Medical Device Technology Co. Ltd. Dongguan523808 China

ISBN: (纸本)9783030013127

Traditional bridge inspection methods require lane closures, inspection equipment, and most importantly the experiences and knowledge of the inspectors. This increases not only the inspection cost and time, but also the risk to the travelling public. Due to the lengthy and costly traditional bridge inspection methods, there has been an increasing backlog of inspection activities. In this research, we design an unmanned aerial vehicle (UAV) assisted bridge defect inspection system, in which a UAV can capture the image and transmit the information to the ground station for further analysis. The system can be divided into 2 subsystems: electromechanics & communication system, and image processing system. The electromechanics & communication systems ensure the self-locating, flight control, image transmission, and human intervention functions. The image processing system performs the image preprocessing, defect extraction, and provides the inspection report. This system, if put into practice, can save the cost up to 70%. We believe that the UAV assisted bridge inspection can be popular in the future. © IFIP International Federation for Information Processing 2018.

关键词： Image processing

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