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检索条件"机构=Laboratory for Robotics and Intelligent Control Systems - LARICS"

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Development of a Tetris Playing Robot controlled by KNR

Development of a Tetris Playing Robot Controlled by KNR

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IEEE International Conference on Mechatronics and Automation

作者： Ming Wang Yanlu Zhang Huifang Dong Xu Li Junzhi Yu Department of Information and Electrical Engineering University of Shandong Jianzhu University Jinan China Department of Intelligent Robotics State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Robot plays an increasingly important role in daily life. This paper presents a vision-based wheeled robot for playing Tetris. How to control the robot is elaborated at length in this paper. The robot adopted a KNR controller as its core controller. With a CCD sensor, it identified colored blocks, lines and the destination. All algorithms are programed with LabVIEW. The experiments and the contest validated the effectiveness of the proposed vision-based wheeled robot system.

关键词： Mobile robots Cameras Robot sensing systems Image color analysis Servomotors Robot kinematics

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Information Modeling and Verification Method for Edge Computing of Power Internet of Things

Information Modeling and Verification Method for Edge Comput...

引用

IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Dawei Li Xiaolu Chen Chunhe Song Shimao Yu Start Grid Shanghai Information & Telecommunication Company Shanghai China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

There are various types of power Internet of Things terminals, and a unified edge-side information fusion model is the basis of data unification. This article describes the unified modeling method of power Internet of Things terminal information, and describes the construction method of the Internet of Things terminal information model from three aspects: attribute, message and service. Combined with the characteristics of the electric power Internet of Things information model, a model verification method is given. Finally, through the terminal data on the edge side of the power Internet of Things, the feasibility and effectiveness of the model verification method are verified.

关键词： Analytical models Computational modeling Data models Power systems Internet of Things Information technology Edge computing

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Distributed computing framework of intelligent sensor network for electric power internet of things

Distributed computing framework of intelligent sensor networ...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Dawei Li Ruimo Yu Chunhe Song Shu Li Gengtao Jia Xiaoli Zhou Start Grid Shanghai Information & Telecommunication Company Shanghai China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

The power IoT terminal environment is complex, and current smart sensor sensors have problems such as poor networking capabilities and large processing delays, and few specific edge-side automatic networking capabilities. This paper analyzes and elaborates the distributed intelligent sensor computing framework from the perspective of edge intelligent networking, designs a computing model for intelligent sensors of power Internet of Things terminals, and gives the edge-based deployment of convolutional neural network architecture, data aggregation, model training, and intelligent reasoning. Finally, the validity of the proposed framework model is verified by public sample data.

关键词： Training Analytical models Computational modeling Data aggregation Data models Internet of Things intelligent sensors

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Industrial robot rotate vector reducer fault detection based on hidden markov models

Industrial robot rotate vector reducer fault detection based...

引用

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zhang, Yinlong An, Haibo Ding, Xiaojian Liang, Wei Yuan, Mingze Ji, Chunyang Tan, Jindong Shenyang Institute of Automation Guangzhou Chinese Academy of Sciences Guangzhou511548 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Fifth Electronic Instituteof MIIT Guangzhou510610 China Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KonxvilleTN37996 United States

ISBN: (纸本)9781728163215

Reliable fault detection of rotate vector (RV) reducer is of paramount importance for the long-term maintenance of high-precision industrial robots. This paper proposes a Hidden Markov Model (HMM) based RV reducer fault detection using Acoustic Emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), the fault from the RV reducer is more complicated and undetectable due to its inherent inline and two-stage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but also the subsequent series of observations within the posteriori probability framework. Through this way, the random and unknown disturbance, which is common in the industrial scenarios, could be reduced. Besides, the HMM is also applied to separate the AE signal bulks within one cycle that has 39 subcycles, which is a critical step for AE signal pre-processings. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate that the proposed HMM based RV Reducer fault detection model can reliably and accurately detect reducer faults. © 2019 IEEE.

关键词： Hidden Markov models

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A New Approach for Simultaneous Localization of UAV and RF Sources (SLUS)

A New Approach for Simultaneous Localization of UAV and RF S...

引用

International Conference on Unmanned Aircraft systems

作者： Seyyed M. Mehdi Dehghan Hadi Moradi Advanced Robotics and Intelligent Systems laboratory Control and Intelligent Processing Center School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781479923779

In this paper, a new approach for Simultaneous Localization of UAV and RF Sources (SLUS) is proposed. In the UAV-based localization of RF sources, such as a cell phone in a search and rescue mission, the UAV navigation errors are a significant source of localization error. Although the use of GPS can reduce UAV localization error significantly resulting in more accurate RF source localization, however, if the GPS is not available temporarily or permanently, the accuracy of UAV-based localization decreases rapidly. The proposed approach solves these two connected problems, i.e. UAV localization and RF source localization, simultaneously to decrease the error of UAV position estimation and the error of RF sources localization. The prediction of UAV pose is done in parallel to RF source position prediction first. Then the predicted states are augmented and the augmented predicted state information is corrected in SLUS using the range and bearing observations. The simulation results show that this method can reduce RF source's position estimation error and prevent the divergence of UAV navigation in latitude and longitude channels. In the other words, the RF features available in the environment can be used to improve the UAV navigation.

关键词： RF source localization UA V navigation SLAM Kalman filter RSSI AOA

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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Automatic Cell Assembly by Two-fingered Microhand

Automatic Cell Assembly by Two-fingered Microhand

引用

2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Junnan Chen Xiaoming Liu Shengnan Dong Pengyun Li Xiaoqing Tang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Intelligent Robotics Institute School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing CHINA Department of Systems Innovation Graduate school of Engineering Science Osaka University Toyonaka Osaka Japan Global Alliance Lab The University of Electro-Communications Tokyo Japan

We have successfully achieved manipulation and assembly of microbeads having the size of 100μm diameter by hemispherical end-effectors with high stability and accuracy. The motivation of achieving assembly of actual cells lies in the great significance of it in tissue regeneration and cell analysis. Firstly, the most difficult problem we need to solve is the releasing problem caused by adhesion force. The viscosity on cell surface is much larger than the microbeads which makes cell releasing challenging. Secondly, the cell can generate its deformation, then contact area with end-effector will change during grasping process. This may influence the adhesion force and also bring problem to releasing. Thirdly, cell is much smaller, around 15μm in diameter, so we need to fabricate smaller end-effector to achieve successful manipulation and ensure the stability in the meantime. In this paper, we realize the manipulation by decreasing the adhesion forces and apply vibration to release a cell stably. We found the appropriate scale size for the end-effector is around 10μm diameter. It can not only grasp a 15μm cell but also bring little interference to the environment. As a demonstration of the proposed manipulation method, the repeated experiments were conducted to explore the dependence of adhesion force on the grasping distance, which can be helpful in the improvement of successful rate. Finally, we achieved automatic cell assembly using Hela cells.

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Event-Triggered Finite-Time Course control for Unmanned Surface Vehicles with Quantization

Event-Triggered Finite-Time Course Control for Unmanned Surf...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yiming Sun Yuqi Liu Xi Jin Changqing Xia Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Liaoning China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

This paper concerns the event-triggered finite-time course control problem of an unmanned surface vehicle (USV) under the network environment. To facilitate investigation, the nonlinear dynamic of the course control system is firstly modeled by a class of state-dependent uncertain systems. Then a output feedback framework of course control is established for the USV subject to wave-induced disturbance, where an event generator with dynamic triggering scheme and a logarithmic quantizer are introduced to save communication resources. By means of slack variable technique, a novel co-design criterion of output feedback controller and event-triggered scheme is developed to guarantee the course control system finite-time bounded with a desired ${\mathcal{H}}_{\infty}$ performance. Finally, the effectiveness of the proposed method is verified through a practical simulation case.

关键词： Industrial electronics Uncertain systems Quantization (signal) Simulation control systems Nonlinear dynamical systems Vehicle dynamics

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Accelerated identification method of industrial instrument based on yolov5

Accelerated identification method of industrial instrument b...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Shimao Yu Haiyang Zheng Chunhe Song Pengpei Gao Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665464697

There are many problems in industrial instrument detection, such as complex background and visual Angle, etc. Traditional machine learning methods are difficult to achieve high-precision object detection and real-time monitoring applications. This paper proposes an accelerated identification method of industrial instruments based on YOLOv5. First, sample data are collected on site for image annotation, and YOLOv5 network structure is designed for transfer learning training, so as to realize image recognition of four types of industrial instruments. After that, the model is accelerated based on TensorRT on Jetson TX2 platform. The speed of instrument online detection reached 30 fps and mAP0.5 is 0.989.

关键词： Training Visualization Computer vision Image recognition Instruments Computational modeling Transfer learning

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intelligent Leakage Inspection Method of Magnetic Mill Workshop based on Lightweight Yolov5

Intelligent Leakage Inspection Method of Magnetic Mill Works...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Shimao Yu Chunhe Song Yong Sun Pengpei Gao Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In the process of mining company beneficiation, large rotating equipment, pipelines and electrical equipment have many complex inspection areas and diverse inspection contents. The lack of intelligent solutions to the problem of pipeline leakage will cause serious consequences such as equipment shutdown if it is not found in time. This paper proposes a lightweight yolov5 intelligent leakage inspection method for the magnetic mill workshop. The inspection robot is used to continuously collect images of possible leakage points and transmit them to the background server. The improved yolov5 algorithm simplifies the model structure and realizes rapid and accurate identification of leakage targets with limited computing resources. The computational efficiency is increased by 44.98% with almost no loss of model accuracy. The mAP_0.5 of identifying large targets at leakage points reaches 97.96%.

关键词： YOLO Training Accuracy Conferences Magnetic resonance imaging Computational modeling Pipelines Inspection Servers Monitoring

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