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2022 International Conference on 6G Communications and IoT Technologies, 6GIoTT 2022

作者： Piao, Jinnan Li, Dong Yang, Ming Liu, Jindi Yu, Xueting Li, Zhibo Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781665454841

When software defined network (SDN) is used to deploy the industrial control network, the deployment schemes are different in actual according to the different industrial production business, environment and capital cost. In this paper, we use the in-band management mode to deploy the SDN network in an industrial environment. We design four deployment schemes of the in-band management mode for the industrial SDN network according to the specific requirements of industrial environment and propose an algorithm for controller location selection in the in-band management network For the four schemes and the algorithm, we use the controller and switches to build a simulation environment according to different schemes and compare the performance between the in-band management and out-of-band management, which shows that the in-band management mode can set up a normal SDN industrial network with applicable network scale and throughput. © 2022 IEEE.

关键词： Internet of things

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A Task Offloading Method for Smart Instruments Based on Edge Computing 7

A Task Offloading Method for Smart Instruments Based on Edge...

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7th International Conference on Computer and Information Processing Technology, ISCIPT 2022

作者： Liu, Yang Zhang, Tianshi Xiao, Jinchao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110000 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The development of edge computing technology has enabled the promotion of traditional industrial instruments to smart instruments with collaborative working capabilities. Smart instruments are widely distributed in industrial production sites and can monitor working conditions in real time. Implementing multi-instruments collaborative computing on the edge side will help improve the responsiveness of industrial systems and reduce instrument maintenance costs. This paper proposes a task offloading method for smart instruments based on edge computing. Firstly, the instrument capability is evaluated based on the characteristics of the smart instrument. Then, the task scheduling of smart instruments is optimized based on Lyapunov algorithm. Finally the simulation verification is carried out. The experimental results show that the algorithm in this paper has a significant effect on improving computing power and reducing resource occupancy. © 2022 Copyright for this paper by its authors.

关键词： Edge computing

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Trajectory Planning for Formation Variation of UGVs in Cluttered Environment

Trajectory Planning for Formation Variation of UGVs in Clutt...

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International Conference on intelligent Transportation

作者： Jie Cheng Ruishuang Chen Zhihui Liang Shuan Ding Zaiyue Yang Shenzhen Key Laboratory of Control The-ory and Intelligent Systems School of System Design and Intelligent Manufacturing Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology (SUSTech) Shenzhen China

The problem of trajectory planning for formation variation of unmanned ground vehicles (UGVs) is challenging due to the coupled non-collision constraints that apply to multiple vehicles. To address this issue, an innovative frame-work based on the alternating direction method of multipliers (ADMM) is proposed in this paper. The framework decouples complex constraints into two categories: individual constraints and mutual constraints. The former category involves dynamic, formation, and static obstacle constraints, which can be solved in parallel by each vehicle. Convex feasible set (CFS) algorithm is employed in this paper to simplify the static obstacle constraints. The latter category deals with collision avoidance among vehicles during the variation process and can be solved by nonlinear solvers. The convergence of the ADMM for the optimization problem is proved in this paper. Compared with solving the entire problem directly, the proposed framework reduces computation time and improves the quality of solution.

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Research on Multiple Automated Guided Vehicles Path Planning to Reduce Default Time 2

Research on Multiple Automated Guided Vehicles Path Planning...

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2nd IEEE International Conference on Data Science and Computer Application, ICDSCA 2022

作者： Liu, Yiyang Yan, Shuaihua Hou, Zheng Zhao, Yang Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Kunshan Intelligent Equipment Research Institute Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China College of Information Science and Engineering Northeastern University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

This paper studies the path planning problem of multiple automated guided vehicles (multi-AGV) in a matrix manufacturing workshop. We proposed the multi-AGV path planning problem with default time, which is a variant of the classic path planning problem. In order to ensure the timely completion of each task, the default time of the task is explicitly considered. The goal is to obtain a conflict-free path with the minimum total default time, total time, and total path length. For these three goals, this problem is NP-hard. In order to solve this problem better, we established a multi-objective optimization model of total default time, total time and total path length of multi-AGV in flexible manufacturing environment. This paper proposes a multi-AGV path planning method that integrates game theory and A∗ algorithm. This method regards each AGV as a participant in the game model, through the default time obtained from each game dynamically adjusts the probability of each AGV planning the next path, and uses game theory to help the system dynamically select the most suitable AGV to plan the next path to improve the on-time delivery rate of tasks. Finally, the simulation results show that the multi-AGV path planning method integrating game theory and A∗ algorithm further reduces the total default time, total time and total path length of all AGVs. © 2022 IEEE.

关键词： Motion planning

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Improved Detection of Transmission Tower Equipment Using YOLOv7 with Saliency Data Augmentation

Improved Detection of Transmission Tower Equipment Using YOL...

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Clean Energy and Electric Power Engineering (ICCEPE), International Conference on

作者： Yingying Xu Chunhe Song Yong Sun Shimao Yu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences 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 University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

Transmission towers are critical infrastructure for power transmission, and the reliable operation of their equipment is essential to ensure electricity supply. However, the detection of transmission tower equipment faces challenges such as target overlap, large variations in size, small targets, and class imbalance due to the characteristics of the equipment. To tackle the identified issues, this study introduces a method based on YOLOv7 for the detection of transmission tower components, utilizing saliency-based data augmentation. Initially, an approach focusing on importance is employed to augment the sample size in categories with limited numbers, thereby enhancing the effectiveness of detection. Secondly, an adaptive adjustment module is introduced into the multi-head self-attention mechanism to dynamically adjust attention allocation based on the actual size of the targets and integrate contextual information, thus better handling targets with large size variations and overlaps. Finally, incorporating dynamic convolution within the backbone network significantly improves the ability to extract features from small objects. The outcomes of the experiments confirm the efficiency of the suggested approach in the smart surveillance of devices within intricate transmission settings, offering a reliable solution for maintaining power supply dependability.

关键词： Surveillance Poles and towers Data enhancement Power transmission Object detection Manuals Data augmentation Feature extraction Reliability Saliency detection

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A Fast-trained and Generalized Spiking Neural Network for Robotic Gas Source Localization

A Fast-trained and Generalized Spiking Neural Network for Ro...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Chiyang Liu Xinxing Chen Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems and the Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Mobile robots for Gas Source Localization (GSL) tasks are a safer alternative than human and animal rescuers in hazardous scenarios. Existing research primarily concentrates on rule-based algorithms or conventional artificial neural networks (ANNs). However, these approaches are either inapplicable in cluttered environments or undeployable due to their high energy consumption and the demand for substantial computational resources. This paper introduces the application of energy-efficient spiking neural networks (SNNs) to address robotic GSL tasks. A pipeline is proposed to train SNNs with deep Q learning and a pretrain-finetune paradigm. To facilitate the training process, a small dataset of gas dispersion is generated utilizing openFoam and GADEN, a high-fidelity simulator for gas dispersion. Data from a simplified plume model are leveraged to pretrain an ANN, the activation function of which gradually transitions from a bounded rectified linear unit (bReLU) to a step function. Subsequently, an SNN initialized with the ANN parameters undergoes finetuning on the GADEN-based dataset. The training pipeline significantly reduces training time compared to direct training of SNNs. The trained SNN is validated within the GADEN simulation environment and compared to three different models, demonstrating promising performance and superior generalization despite limited training data.

关键词： Training Location awareness Q-learning Mechatronics Pipelines Training data Spiking neural networks Data models Mobile robots Dispersion

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Design and Implementation of OpenWSN Hardware Platform 2

Design and Implementation of OpenWSN Hardware Platform

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Zhang, Shusheng Xie, Chuang Liu, Jun Zhao, Chun Yang, Zhijia Mao, Jizhi College of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350334784

This paper proposes a new wireless sensor network hardware platform based on OpenWSN project launched by the University of California, Berkeley. The hardware platform's processor uses a 32-bit microcontroller (STM32F103RCT6) produced by STMicroelectronics (ST), and the wireless module uses the WISA470 chip. The final experimental results show that this platform can support the Open WSN project very well. © 2022 IEEE.

关键词： Wireless sensor networks

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Research on two-step throttle characteristics of double-rotation valve port for hydraulic servo joint

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High Technology Letters 2021年第1期27卷 68-75页

作者： Jiang Lin Pan Xiaoyue Ren Lisheng Zhu Jianyang Chen Xinyuan Zhao Hui Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science and TechnologyWuhan 430081P.R.China Institute of Robotics and Intelligent Systems Wuhan University of Science and TechnologyWuhan 430081P.R.China

The hydraulic robot with large output torque is widely used in industry,however,its precision is not *** order to solve this problem,this paper presents a new structure of rotary valve with double-rotation valve port,which can improve the two-step throttle characteristics of the valve port,reduce the cavitation phenomenon of the valve port,and increase the output accuracy of the hydraulic servo ***,the internal flow field of the rotary valve is simulated by using the sliding grid technology of FLUENT software,and the changing rule of the throttle position in the working process of the structure is ***,compared with the simulation results of rotary valve with single-rotation valve port,it is shown that the two-step throttle characteristics of the structure are less affected by the change of the opening of the rotary valve,and the cavitation index of the joint valve port is ***,the influence of the rotation speed of the valve core,oil supply pressure and key dimension of valve core on throttle characteristics of rotary valve have been analyzed.

关键词： double-rotation valve port cavitation 3D sliding mesh transient solution the two-step throttle pressure changing frequency

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Autonomous, Mobile Manipulation in a Wall-building Scenario: Team larics at MBZIRC 2020

arXiv

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arXiv 2022年

作者： Vatavuk, Ivo Polic, Marsela Hrabar, Ivan Petric, Frano Orsag, Matko Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object’s global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick’s magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms. © 2022, CC BY-SA.

关键词： Brick

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Joint Algorithm of Message Fragmentation and No-Wait Scheduling for Time-Sensitive Networks

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 478-490页

作者： Xi Jin Changqing Xia Nan Guan Peng Zeng Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016 Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China Department of Computing Hong Kong Polytechnic UniversityHong KongChina

Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time ***-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort ***,due to inappropriate message fragmentation,the realtime performance of no-wait scheduling algorithms is ***,in this paper,joint algorithms of message fragmentation and no-wait scheduling are ***,a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal ***,to improve the scalability of our algorithm,the worst-case delay of messages is analyzed,and then,based on the analysis,a heuristic algorithm is proposed to construct low-delay ***,we conduct extensive test cases to evaluate our proposed *** evaluation results indicate that,compared to existing algorithms,the proposed joint algorithm improves schedulability by up to 50%.

关键词： Message fragmentation networked control system real-time scheduling time sensitive network

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