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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Hannu Tuomisaari Juhana Peltonen Timo R Nyberg Xisong Dong Göte Nyman Business Innovation and Technology School of Science Aalto University Espoo Finland The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Behavioral Sciences University of Helsinki Finland

ISBN: (纸本)9781479905287

The core function of any profitable firm is capturing a share of the value that customer perceives in the firm's offering. Value capture is traditionally considered to relate to a competitive advantage at the firm level, but not the firm network level. However, the ever increasing role of information and knowledge in today's economy is profoundly changing how firms can create competitive advantages. Among these changes, we highlight reducing transaction costs in various areas of the economy, which drives the economy to organize more toward rapidly evolving networks or smaller firms. There is also an opposite trend for some internet firms to become larger due to economies of scale and due to network externalities. In this position paper, we examine the issue of value capture by firms that are increasingly small and operate in rapidly changing and evolving networks. We conclude by outlining future research on firm-level capabilities that are required to enable value capture in new forms of dynamic networks.

关键词： Economics Organizations Intellectual property Industries Communications technology Educational institutions

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RoboFold: A platform for Protein-folding Inspired Robot Swarms *

RoboFold: A platform for Protein-folding Inspired Robot Swar...

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WRC Symposium on Advanced robotics and Automation (WRC SARA)

作者： Feng Zhang Yuping Gu Shuai Yuan Yongliang Yang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning China School of Computer Science and Engineering and School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China

Natural swarms have inspired various controlling algorithms for swarm robotics, while few of them were programmed in a similar way as the natural swarms. Defining a reliable programming method is still a daunting challenge in swarm robotics. In nature, the information of 3D protein structure is programmed in the sequence of amino acids of the protein. To mimic the programming method of protein folding, we present a platform for investigating protein-folding inspired programming method for robot swarms. This platform includes three parts: a vibration machine, protein-like robot sequences, and recording devices. The preliminary results presented here demonstrated the effectiveness of this platform and the feasibility of protein-folding inspired programming method for robot swarms.

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Efficient Learning and control of String- Type Artificial Muscle Driven Robotic systems

Efficient Learning and Control of String- Type Artificial Mu...

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American control Conference (ACC)

作者： Jiyue Tao Sunil Kumar Rajendran Yunsong Zhang Feitian Zhang Dexin Zhao Tongsheng Shen The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China The National Innovation Institute of Defense Technology Beijing China The BSS Technologies Inc. Takoma Park MD USA The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper investigates the learning-based control of robotic systems driven by string-type artificial muscles. Due to the highly nonlinear dynamics of the actuators and the complicated mechanical structure, it is typically very challenging to design traditional model-based controllers that exhibit desired control performances. With the rapid development of machine learning techniques, deep reinforcement learning (DRL) algorithms have been utilized to control a variety of complex robotic systems. However, these DRL algorithms usually require a huge amount of training data and iterations to converge, which is generally unacceptable for the robotic systems of interest. Therefore, this paper designs an efficient learning-based control algorithm, aiming to improve the training efficiency for robotic systems driven by string-type artificial muscle actuators. Specially, two training improvements are proposed including imitation learning and data augmentation. This paper applies the proposed learning methods to three popular DRL algorithms and tests the control performances in three case studies using three string-type artificial muscle-driven robots, including a parallel robotic wrist, a two degrees-of-freedom (DOF) robotic eye and a robotic finger. Simulation results show that the proposed learning-based control methods significantly accelerate the convergence speed and improve the data efficiency in all the three case studies.

关键词： Artificial muscles Wrist Training Actuators Machine learning algorithms Imitation learning Fingers

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Generalized Visual-Tactile Transformer Network for Slip Detection

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IFAC-PapersOnLine 2020年第2期53卷 9529-9534页

作者： Shaowei Cui Junhang Wei Xiaocan Li Rui Wang Yu Wang Shuo Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Future Technology University of Chinese Academy of Sciences Beijing 100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai 200031 China

Slip detection plays a vital role in robotic dexterous grasping and manipulation, and it has long been a challenging problem in the robotic community. Different from traditional tactile perception-based methods, we propose a Generalized Visual-Tactile Transformer (GVT-Transformer) network to detect slip based on visual and tactile spatiotemporal sequences. The main novelty of GVT-Transformer is its ability to address unaligned vision and tactile data in various formats captured by various tactile sensors. Furthermore, we train and test our proposed network on a public and our visual-tactile grasping datasets. The experimental results show that our method is more suitable for sliding detection tasks than previous visual-tactile learning methods and more versatile.

关键词： Information sensor fusion Perception sensing Intelligent robotics Deep neural networks Visual-tactile fusion perception

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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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Mechanical Characterization Analysis of Kirigami Structure for Soft Robotic Design

Mechanical Characterization Analysis of Kirigami Structure f...

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Automation, control and robotics Engineering (CACRE), International Conference on

作者： Muhammad Niaz Khan Amin Lotfiani Umair Javaid Zhufeng Shao Department of Mechanical Engineering State Key Laboratory of Tribology Institute of Manufacturing Engineering Tsinghua University Department of Mechanical Engineering Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control Tsinghua University Beijing China Faculty of Engineering Technology University of Twente Enschede Netherland Robotics Institute Ningbo University of Technology Ningbo China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

This paper provides a detailed mechanical characterization and stress analysis of kirigami sheets across various structural configurations, highlighting their potential applications in soft robotics. The study examines linear, circular, triangular, and trapezoidal kirigami patterns through finite element simulations, complemented by experimental validation. Deformation parameters are systematically applied to both single and multiple units of kirigami sheets to assess their capacity for mechanical elongation and load sustainability. Additionally, friction analyses are discussed via simulation to explore the anchoring effects of these kirigami structures when applied to soft robots. The results of this study enhance our understanding of the mechanical properties of kirigami structures and their practical implications for soft robotics design.

关键词： Automation Deformation Friction Soft robotics Finite element analysis Sustainable development Stress

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Tracking control of a compliant XY nano-positioner under plant uncertainty using a transfigured loop-shaping H∞ controller

Tracking control of a compliant XY nano-positioner under pla...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hui Tang Yangmin Li Qinmin Yang Department of Electromechanical Engineering Faculty of Science and Technology University of Macau Taipa Macao China Overseas Distinguished Professor at the State Key Laboratory of Robotics of Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Control Science and Engineering Zhejiang University Hangzhou China

This paper presents tracking control of a compliant XY nano-positioner using a transfigured loop-shaping H ∞ (TLSH) control strategy, which is capable of achieving satisfied positioning performance. The employed compliant nano-positioner is featured with a symmetric 4-PP (P represents prismatic joint) structure in which four-bar flexures are designed as the prismatic joints, meanwhile two embedded piezoelectric ceramic actuators (PZT) are adopted. Based on the identified plant transfer function of the nano-positioner, the presented control strategy and the traditional PID controller are implemented with different input scanning frequencies of 0.5 Hz and 2.5 Hz for comparisons. Both the simulation results and the practical experimental results demonstrate that the presented TLSH control method obviously performs better than the traditional PID method in the presence of plant model uncertainty, which will be applied to perform practical micromanipulation tasks.

关键词： Mathematical model Uncertainty Hysteresis Nanobioscience Actuators Robustness

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Design of the control station system for the search and rescue robot

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Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2010年第8 SUPPL.期31卷 245-248页

作者： Wang, Minghui Wang, Nan Li, Bin State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang 110016 China School of Information and Control Engineering Shenyang Jianzhu University Shenyang 110168 China

According to the application requirements of search and rescue robots, the design criterion of control station system is proposed. Based on human-robot interaction, the architecture of this system is constructed. The control station system for the search and rescue robot is designed, which has some features such as multiple perceptive information, flexible control and strongly interaction. The experiments on the simulated disaster environment demonstrate that this control station system can control motion and exchange multi-channel information in the complex environment, which indicates the feasibility and validity in the fields of search and rescue.

关键词： Human robot interaction

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Improved Cascade Mask RCNN Single Character Detection Algorithm for Cast Pipe

Improved Cascade Mask RCNN Single Character Detection Algori...

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Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Fuli Xu Yu Wang Yan Jiang Huaizhen Wang Yong Cui Ye Tao Yisheng Wang Qi Zhang School of Software Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Shanghai Ballsnow Intelligent Technology Co. Ltd Shanghai China Shenyang Zhongke Microcyber Technology Co. Ltd Shenyang China

Due to the interference of blurry background, character occlusion, and low background contrast in the Cast Pipe character images, the detection accuracy and real-time performance cannot meet the requirements of industrial scenes. To address these issues, this paper presents an improvement based on the Cascade Mask RCNN. Firstly, the Channel Attention Mechanism Module (Squeeze-and-Excitation, SE) is embedded into the ResNet structure. By constructing a bottleneck structure with two fully connected layers, the normalized weights are applied to each channel feature to enhance the network representation ability. Secondly, a bottom-up structure is introduced after the Feature Pyramid Networks (FPN) to transmit the strong localization information from lower-level features to higher-level semantic features. Thirdly, an adaptive pooling layer is added to allocate the candidate regions generated by the Region Proposal Network (RPN) to different scale feature maps. Additionally, skip connections are introduced between the bottom-level features and the highest-level features of each stage to reduce model parameters while retaining global representation capabilities. The improved algorithm is tested on a cast pipe dataset, achieving an average detection accuracy (mAP) of 98.7%, which is 2.7% higher than that of Cascade Mask RCNN. The accuracy indicates that the improved performance is superior to the original algorithm.

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On stability of sampled-data extended state observer for networked systems

On stability of sampled-data extended state observer for net...

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Asian control Conference

作者： Yuan Huang Junzheng Wang Dawei Shi Jian Xue State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China Harvard John A. Paulson School of Engineering & Applied Sciences Harvard University Cambridge MA USA

ISBN: (纸本)9781509015740;9781509015733

The problem of sampled-data state observation is considered in this work. A nonlinear continuous-time system with internal uncertainty and external disturbance is introduced, for which the sampled-data observation problem based on an extended state observer is formulated. We first introduce the framework of the observed system, in which the output measurement is sampled and transmitted to extended state observer periodically. Then based on this framework, the observation performance of the sampled-data extended state observer (SD-ESO) is investigated. For the observer considered, the uniformly ultimately bounded property has been guaranteed. Furthermore, a co-design method for the sample interval and high gain parameter of SD-ESO is provided to achieve the desired observation performance. The obtained theoretic results are validated through the numerical simulations.

关键词： Observers Uncertainty networked control systems Convergence Distributed databases Gold Australia

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