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IEEE Information Technology, Networking, Electronic and Automation control Conference

作者： Ke Zhou Xiaoming Wang Shijie Cui Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Nanning 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 Liaoning China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Time synchronization is a fundamental strategy for many operations such as data synchronous collection, timely fault tracking, distributed control, and real-time communication in Industrial Internet of Things (IIoT). Over the years, a wide variety of protocols and technologies have been proposed for different application scenarios. The accuracy, security, robustness, low cost, and so on, are the main focus for the design of time synchronization protocol. However, so far, there is no relatively unified standard for time synchronization in IIoT. To clarify the development of time synchronization technology, this paper surveys the latest research for time synchronization in various types of networks including Precision Time Protocol and wireless sensor networks. The future development of time synchronization in IIoT is also investigated.

关键词： Surveys Wireless sensor networks Protocols Accuracy Reviews Robustness Synchronization Security Standards Industrial Internet of Things

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Image Feature Matching Based on Spatial Constraints and Inertial Assistance for AGV Navigation in Digital Workshops 12

Image Feature Matching Based on Spatial Constraints and Iner...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Sun, Shijie Chen, Li Liang, Wei An, Haibo Zhang, Yinlong School of Artificial Intelligence Shenyang University of Technology Shenyang110020 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 Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of Cetc Jiangsu Nanjing210007 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

VSLAM shows promising potential for AGV navigation in digital workshops, thanks to its merits in low cost, easy configuration, high flexibility, and high accuracy. However, the AGV visual perception system could be easily affected by lighting changes, and body abrupt motions. The state-of-the-art visual feature matching methods may fail to fulfill the navigation tasks in presence of these challenging issues. In order to solve these problems, we propose a feature matching method based on feature space constraints and inertial assistance. In order to reduce the computational time, the spatial constraints are designed to delete the obvious false matches. Besides, the Inertia Assistance Visual Sample Consensus (IAVSAC) is proposed to reduce the computational time in discriminating the wrong feature matches. In order to ensure the reliability of the inertial data, the analysis of IMU noise has been performed. The best IMU configuration with the smallest noises is designed. Our proposed method has been evaluated on the developed AGV navigation platform. The results show that the feature matching accuracy rate and computational time of ours are more favorable than those of RANSAC and MSAC. © 2022 IEEE.

关键词： Navigation

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Gait Planning and Motion control Based on Vrep Simulation for Quadruped Robot 5

Gait Planning and Motion Control Based on Vrep Simulation fo...

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5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Knowledge Distillation on Driving Intention Generator: Learn Human-like Semantic Reasoning

Knowledge Distillation on Driving Intention Generator: Learn...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Wang, Hejun Dong, Jiajun Wang, Yunkai Jiao, Yanmei Xiong, Rong Zhejiang University The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China Hangzhou Normal University The School of Information Science and Engineering Hangzhou China

ISBN: (纸本)9798350327182

Semantic information plays a significant role in autonomous vehicles. However, it has remained open how to economically compute and efficiently incorporate semantic information into other frequently applied processes, such as construction of local navigation map, driving decision making and trajectories planning. To address this issue, we introduce Knowledge Distillation to convey semantic reasoning capability. Following the existing intention-guided framework, a semantically incorporated module associates given navigation instruction with acquired visual perception to synthesize driving intention. Our major contribution is to teach the driving intention generator how to propagate considering semantics implicitly. Specifically, we construct a semantic supervisor identical to the driving intention generator. The supervisor transfers semantic reasoning ability to the generator during training process, with which the generator is expected to perform stably and reliably even in extremely sophisticated surroundings or under frequently inaccurate navigation instruction. Through exhaustive experimental validation, our model demonstrates a superior robustness and adaptability compared to the state-of-the-art. Finally, we explain the intrinsic mechanism of our model with visualization. © 2023 IEEE.

关键词： Navigation

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Extraction algorithm for longitudinal and transverse mechanical information of AFM

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Nanotechnology and Precision Engineering 2022年第2期5卷 27-37页

作者： Chunxue Hao Shoujin Wang Shuai Yuan Boyu Wu Peng Yu Jialin Shi School of Information and Control Engineering Shenyang Jianzhu UniversityShenyang 110168China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institute of Robotics and Intelligent Manufacturing Innovation Chinese Academy of SciencesShenyang 110169China

The atomic force microscope(AFM)can measure nanoscale morphology and mechanical properties and has a wide range of *** traditional method for measuring the mechanical properties of a sample does so for the longitudinal and transverse properties separately,ignoring the coupling between *** this paper,a data processing and multidimensional mechanical information extraction algorithm for the composite mode of peak force tapping and torsional resonance is *** the basis of a tip–sample interaction model for the AFM,longitudinal peak force data are used to decouple amplitude and phase data of transverse torsional resonance,accurately identify the tip–sample longitudinal contact force in each peak force cycle,and synchronously obtain the corresponding characteristic images of the transverse amplitude and *** results show that the measured longitudinal mechanical characteristics are consistent with the transverse amplitude and phase characteristics,which verifies the effectiveness of the ***,a new method is provided for the measurement of multidimensional mechanical characteristics using the AFM.

关键词： Atomic force microscope Peak force tapping Torsional resonance Mechanical characteristic measurement Background subtraction algorithm Coupled mechanical model

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A fault detection method for AUV based on multi-scale spatiotemporal feature fusion 5

A fault detection method for AUV based on multi-scale spatio...

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5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Xia, Shaoxuan Zhou, Xiaofeng Shi, Haibo Li, Shuai 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 University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510662520

Autonomous underwater vehicles (AUVs) are important equipment for ocean development and exploration. To ensure the task implementation of AUV, faults shall be detected in time. We propose a fault detection method based on multi-scale spatiotemporal feature fusion (MSF) for the time-varying characteristics and multiple correlation characteristics of AUV monitoring data. First, we apply a variety of sampling and data processing methods to generate monitoring windows with different scales along the time axis. Then, a composite feature extraction method is proposed to obtain temporal and spatial features simultaneously, and a feature pyramid of temporal and spatial information is formed. We use bidirectional long short-term memory (BiLSTM) to obtain the time-series characteristics of a single monitoring variable, and convolutional neural networks (CNN) to obtain the implicit spatial relationship characteristics among multiple monitoring variables. Next, we use an adaptive feature fusion method to solve the inconsistency in different feature scales, which can adaptively suppress the possible conflict information of different scale features. Finally, we use a fully connected network to detect the fault of the fused features. The fault detection experiment of Haizhe AUV shows the effectiveness and superiority of the proposed method. © 2023 SPIE.

关键词： Autonomous underwater vehicles

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A Battery control Strategy for Sequential Utilization of Energy Storage systems in Power Batteries

A Battery Control Strategy for Sequential Utilization of Ene...

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Bowen Li Shijie Cui Lizhong Zhu College of Automation and Electrical Engineering Shenyang Ligong University Liaoning 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 Liaoning China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

With the widespread adoption of energy storage systems utilizing power batteries, battery lifespan degradation has become a primary constraint on system performance. To address this issue, this study proposes a lifespan-optimized battery control strategy. Firstly, a battery lifespan calculation model for the energy storage system is established, taking into detailed consideration key parameters such as battery state of Charge (SOC). Secondly, a control strategy based on SOC for charging and discharging is designed, aiming to minimize battery lifespan consumption to the greatest extent possible. Finally, experimental results demonstrate that the proposed control strategy significantly improves the lifespan of the energy storage system compared to existing strategies. This research provides an effective means of control for the long-term stable operation of power battery systems.

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An Optimization Method for SLAM Feature Point Extraction in Fuzzy Scenes 10

An Optimization Method for SLAM Feature Point Extraction in ...

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10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Wang, Yunmeng Cui, Long Liu, Zaoming Liu, Nailong Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Beijing Institute of Control Engineering Science and Technology on Space Intelligent Control Laboratory Beijing China

ISBN: (纸本)9798350309379

In order to solve the problem that image blur is easily caused by camera shaking of micro-robots during surgical navigation, which leads to the accumulation of SLAM errors, this paper studies the feature point extraction optimization method based on deep learning in fuzzy scenes, proposes an improved defuzzification network, and finally applies the network to the front end of SLAM, and conducts experimental verification on the open data set TUM. The results show that the proposed algorithm significantly improves the accuracy of front-end feature extraction and matching. © 2023 IEEE.

关键词： environment perception feature extraction fuzzy detection Monocular vision SLAM Surgical navigation

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Flexible job shop machines and AGVs cooperative scheduling on the basis of DQN algorithm

Flexible job shop machines and AGVs cooperative scheduling o...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Dong Xiaoting Wan Guangxi Zeng Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China 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: (数字)9798350316537

ISBN: (纸本)9798350316544

In flexible job shop, the transportation time and path of Automated Guided Vehicles (AGVs) greatly impact the production cycle time. However, traditional flexible job shop scheduling problems (FJSSP) mainly focus on job sequencing and machine selection, neglecting the effect of AGVs transportation on the production process. This often leads to suboptimal scheduling solutions that are difficult to guide the shop floor operation effectively. To address this issue, this paper formulates a cooperative scheduling model that integrates the AGVs scheduling into traditional flexible job shop scheduling problem, abbreviated as the FJSMAS problem, with the objective of makespan minimization. Furthermore, by designing state features, action space, and reward function, the FJSMAS problem is transformed into a Markov decision process, a deep reinforcement learning algorithm (DRL) is then adopted to solve it. The feasibility of the proposed FJSMAS model and the effectiveness of DRL algorithm in solving the FJSMAS problem are verified through benchmark’s instances.

关键词： Greedy algorithms Sequential analysis Job shop scheduling Remotely guided vehicles Transportation Production Benchmark testing

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Intelligent electricity consumption forecasting and electricity-theft analysis method based on deep learning 2

Intelligent electricity consumption forecasting and electric...

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2nd International Conference on Electronic Information Engineering and Computer Technology, EIECT 2022

作者： Yu, Shimao Song, Chunhe Gao, Pengpei Zheng, Haiyang Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang Institute of Automation 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)9798350399561

Residential electricity consumption data have great mining value for electricity-theft analysis and electricity consumption forecasting. This paper designs an intelligent electricity consumption forecasting and electricity-theft analysis method based on deep learning. Firstly, the power consumption data of a single user is converted into multi-period data through preprocessing, and the data forecasting is realized by designing a deep neural network model. The electricity-theft data are first amplified through user's power data and electricity-theft labels. And then the time series are converted into two-dimensional tensors for classification and recognition by deep CNN. The classification accuracy of electricity-theft reaches 92.2% compared with RNN method. © 2022 IEEE.

关键词： Forecasting

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