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18th International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2018

作者： Ma, Jun Chen, Si-Lu Liang, Wenyu Teo, Chek Sing Tay, Arthur Al Mamun, Abdullah Tan, Kok Kiong SIMTech-NUS Joint Lab. on Precision Motion Systems Department of Electrical and Computer Engineering National University of Singapore Singapore117582 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Agency for Science Technology and Research Singapore Institute of Manufacturing Technology Singapore138634 Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo Zhejiang315201 China

ISBN: (纸本)9780995775121

Dual-drive H-gantry is widely used in many industrial processes that require high-speed and high-precision Cartesian motion. Unlike 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 possible. However, the flexure-linked design may induce the resonant modes of the gantry because of high frequency 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 while optimizing the decentralized feedback controller parameters under model uncertainties. In this way, good tracking performance is achieved without costly system re-design. In this work, we formulate the mechatronic design problem as an H2 guaranteed cost control problem. All the stabilizing feedback controller gains are parameterized over a convex set, and the global optimum is obtained by means of an outer-linearization-based algorithm. Experiments are conducted and the results successfully validate the effectiveness of the proposed design approach. © 2018 CURRAN-CONFERENCE. All rights reserved.

关键词： Convex optimization Dual-drive H-gantry Flexure Mechatronic design Optimal control Parallel mechanism

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Efficient Inverse Kinematics for Redundant Manipulators with Collision Avoidance in Dynamic Scenes

Efficient Inverse Kinematics for Redundant Manipulators with...

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IEEE International Conference on robotics and Biomimetics

作者： Liangliang Zhao Jingdong Zhao Hong Liu Dinesh Manocha Department of Computer Science and Electrical & Computer Engineering University of Maryland at College Park MD USA State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (纸本)9781728103785;9781728103778

We present a novel iterative algorithm for inverse kinematics of redundant manipulators in dynamic environments. Our approach uses an iterative approach to generate valid configurations and selects an inverse kinematics solution using a rank-based selection strategy. To avoid the moving obstacles, we combine our inverse kinematics method with a collision avoidance scheme. We highlight the performance of our algorithm on different high DOF manipulators in a dynamic scene.

关键词： Kinematics Manipulator dynamics Collision avoidance Iterative methods End effectors Trajectory

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Petri Net Control Method for Pipe-line systems and Its Implementation via CIF3 ⁎

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IFAC-PapersOnLine 2019年第24期52卷 261-266页

作者： YuHao Fu JiLiang Luo Huifeng Wu Jianhong Ye Yi-sheng Huang College Information Science and Engineering Huaqiao University Xiamen 361021 China College Information Science and Engineering Huaqiao University Xiamen 361021 China and also with Fujian Engineering Research Center of Motor Control and System Optimal Schedule Xiamen 361021 China Institute of Intelligent and Software Technology Hangzhou Dianzi University Hangzhou 310018 China Computer Science and Technology Huaqiao University Xiamen 361021 China and also with Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou 362000 China Department of Electrical Engineering National Ilan University Taiwan

As for a pipe-line system subjected to complex operations, an approach is proposed to synthesize the controller via Petri nets (PNs) such that the plant is run as concurrently as possible and the loads of equipments are balanced. A P-timed and labeled PN is designed to model the whole process of a pipe-line system, where tasks, including transporting material from one tank to another and cleaning tank, are represented by operational places, and level sensors amounted in tanks are represented by labels assigned to transitions. Further, monitor places are designed to resolve the conflict relations among tasks due to the shared valves and pipes. An method is presented to translate an PN controller into a CIF3 (Compositional Interchange Format) model, which can be converted into a PLC program in the CIF3 tool. A beer filtration plant is taken as an example to illustrate the approach, and its simulation experiments are carried out to verify the theoretic results.

关键词： Chemical plant Valve control Petri net CIF3

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Collision-free kinematics for redundant manipulators in dynamic scenes using optimal reciprocal velocity obstacles

arXiv

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arXiv 2018年

作者： Zhao, Liangliang Zhao, Jingdong Liu, Hong Manocha, Dinesh State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Department of Computer Science and Electrical & Computer Engineering University of Maryland at College Park MD United States

We present a novel algorithm for collision-free kinematics of multiple manipulators in a shared workspace with moving obstacles. Our optimization-based approach simultaneously handles collision-free constraints based on reciprocal velocity obstacles and inverse kinematics constraints for high-DOF manipulators. We present an efficient method based on particle swarm optimization that can generate collision-free configurations for each redundant manipulator. Furthermore, our approach can be used to compute safe and oscillation-free trajectories in a few milli-seconds. We highlight the real-time performance of our algorithm on multiple Baxter robots with 14-DOF manipulators operating in a workspace with dynamic obstacles. Videos are available at https://***/view/collision-free-kinematics. Copyright © 2018, The Authors. All rights reserved.

关键词： Inverse problems

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Self-adaptation of chimera states

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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

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Remaining Useful Battery Life Prediction for UAVs based on Machine Learning

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IFAC-PapersOnLine 2017年第1期50卷 4727-4732页

作者： Mansouri S.S. Karvelis P. Georgoulas G. Nikolakopoulos G. Robotics Group Control Engineering Division of the Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå SE-97187 Sweden Laboratory of Knowledge and Intelligent Computing Department of Computer Engineering Technological Educational Institute of Epirus Arta Greece

Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions. © 2017

关键词： Battery Machine Learning Prediction Remaining Useful Life UAVs

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Joint learning of discriminative low-dimensional image representations based on dictionary learning and two-layer orthogonal projections

arXiv

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arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Dense dilated network with probability regularized walk for vessel detection

arXiv

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arXiv 2019年

作者： Mou, Lei Chen, Li Cheng, Jun Gu, Zaiwang Zhao, Yitian Liu, Jiang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430081 China Cixi Institute of Biomedical Engineering Chinese Academy of Sciences Zhejiang315201 China UBTech Research UBTech Robotics Corp Ltd Guangdong518055 China Department of Computer Science and Engineering Southern University of Science and Technology Guangdong518055 China Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Zhejiang315201 China

The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. However, most of the algorithms neglect the connectivity of the vessels, which plays an important role in the diagnosis. In this paper, we propose a novel method for retinal vessel detection. The proposed method includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection. The dense dilated network integrates newly proposed dense dilated feature extraction blocks into an encoder-decoder structure to extract and accumulate features at different scales. A multi-scale Dice loss function is adopted to train the network. To improve the connectivity of the segmented vessels, we also introduce a probability regularized walk algorithm to connect the broken vessels. The proposed method has been applied on three public data sets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method outperforms the state-of-the-art methods in accuracy, sensitivity, specificity and also area under receiver operating characteristic curve. Copyright © 2019, The Authors. All rights reserved.

关键词： Diagnosis

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Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

arXiv

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arXiv 2018年

作者： Petković, Tomislav Puljiz, David Marković, Ivan Hein, Björn University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Kaiserstraße 12 Deutschland KarlsruheDE-76131

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual reality

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