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A Robust Surveillance System for AGV Using Forward-Downward Visual Measurements

A Robust Surveillance System for AGV Using Forward-Downward ...

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Chinese Control and Decision Conference, CCDC

作者： Sichao Zhang Wei Liang Xudong Yuan Haibo An Meng Zheng Yinlong Zhang 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 University of Chinese Academy of Sciences Beijing China Science and Technology on Information Systems Engineering Laboratory the 28th Research Institute of CETC Nanjing China

Automatic Guided Vehicle (AGV) has been widely used in the warehouse for transporting the bulky and heavy cargos. However, the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to, e.g., cyber attacks, unexpected blocks of the wireless signals. In order to ensure AGV running safely, this paper presents a visual surveillance system by making full use of the measurements from the forward and downward cameras. Specifically, the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image sequences. Besides, the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference frame. Apart from that, the AGV poses from the downward camera could correct scale and poses estimated the forward view. The proposed method has been extensively performed on the developed platform. The results proves the effectiveness in using the complementary forward-downward visual measurements for AGV security surveillance.

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Optimization method of Data interaction in power IoT based on particle swarm algorithm 2

Optimization method of Data interaction in power IoT based o...

引用

2nd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Wang, Tianyu Miao, Weiwei Zeng, Zeng Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China State Grid Jiangsu Electric Power Co. Ltd. Information Telecommunication Branch Nanjing China

ISBN: (纸本)9781665408868

The ubiquitous power Internet of things (IoT) constructs an intelligent service system with comprehensive perception, efficient response and flexible processing around all departments of the power system and with the support of communication technologies such as mobile Internet and artificial intelligence. With the rapid development of smart IoT perceptual devices and edge computing, it has become possible for perceptual devices and edge IoT agents to cooperate in processing tasks. The task completion time can be greatly reduced through the cooperative processing among multiple entities in the IoT. However, as the scale of the Internet of things and number of to-be-processed tasks expanding year by year, the simple task data interaction between devices can not meet the needs of the completion time. Therefore, it is an urgent problem to optimize data transmission and data interaction in the process of collaborative task processing among IoT devices. Motivated by this point, we propose an optimization method of data interaction in power IoT based on particle swarm algorithm. Evaluation on example IoT networks shows the effectiveness of our method to reduce overall task completion time. © 2022 IEEE.

关键词： Internet of things

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Research on prefabricated component production line mold platform combination method

Research on prefabricated component production line mold pla...

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2021 International Conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2021

作者： Han, Zhonghua Yu, Lingfeng Li, Bo Qi, Yuanwei School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China 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

In traditional prefabricated component manufacturing enterprises, there are often problems such as low utilization rate of the platform surface due to poor production plans of the enterprise, resulting in waste of production resources and low production capacity of the enterprise. In order to solve the problem of mold platform combination allocation in the production process of prefabricated component manufacturers, a mold platform combination allocation method based on the combination of machine learning and backtracking is proposed. The backtracking method is used to search for the theoretical best fit combination result, and the improved BL is used. The positioning algorithm simulates the placement process of the mold on the platform, and uses the Apriori algorithm to train the obtained data set to obtain the association rules contained in the frequent item set. When the enterprise re-produces, the prefabricated components are combined according to the association rules for production, and the utilization rate of the platform is improved. The simulation test is carried out with the example data of the prefabricated component manufacturing enterprise, which verifies the effectiveness of the combination allocation method of mold and platform combined with the backtracking method and the Apriori algorithm to solve the problem. © Published under licence by IOP Publishing Ltd.

关键词： Production control

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Service Encapsulation Method Based on Industrial Internet

Service Encapsulation Method Based on Industrial Internet

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作者： Li, Kai Pan, Hao Tong, Xing Li, Dong Wang, Ting Shenyang University of Chemical Technology Liaoning Shenyang110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Liaoning Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110016 China

Based on the analysis of software and hardware integration and highly heterogeneous infrastructure in the traditional industrial field, this paper puts forward the concept of service packaging engine, puts forward a unified service packaging model for Industry by using the Internet thinking design mode, and can complete the control of complex industrial field equipment by editing only simple logic in the programming interface. This article focuses on basic service encapsulation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer programming

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Design of a Novel Modular Flux Switching Permanent Magnet Resolver

Design of a Novel Modular Flux Switching Permanent Magnet Re...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Wenyuan Mi Jincheng Yu Zaixin Song Kuang Yang Zheng Cai Hang Zhao School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong SAR China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

Permanent magnet (PM) resolvers are highly employed with the ascent of the electric machine speed. In this digest, a novel modular flux switching PM (FSPM) resolver is designed based on the FSPM machine. By removing the stator yoke, the flux path in each tooth coil is fixed and the total harmonic distortion (THD) of the output voltage is reduced to only 0.37%. Moreover, the proposed 12/16 FSPM resolver can be adapted to fit different rotor poles under the same mechanical support with different modular units (e.g., 6/8 and 10/8 FSPM resolver). Therefore, it can be applied in electric machines with variable rotor pole pairs.

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Bilateral Memory Consolidation for Continual Learning

Bilateral Memory Consolidation for Continual Learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xing Nie Shixiong Xu Xiyan Liu Gaofeng Meng Chunlei Huo Shiming Xiang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Baidu Inc. China Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation CAS

Humans are proficient at continuously acquiring and integrating new knowledge. By contrast, deep models forget catastrophically, especially when tackling highly long task sequences. Inspired by the way our brains constantly rewrite and consolidate past recollections, we propose a novel Bilateral Memory Consolidation (BiMeCo) framework that focuses on enhancing memory interaction capabilities. Specifically, BiMeCo explicitly decouples model parameters into short-term memory module and long-term memory module, responsible for representation ability of the model and generalization over all learned tasks, respectively. BiMeCo encourages dynamic interactions between two memory modules by knowledge distillation and momentum-based updating for forming generic knowledge to prevent forgetting. The proposed BiMeCo is parameter-efficient and can be integrated into existing methods seamlessly. Extensive experiments on challenging benchmarks show that BiMeCo significantly improves the performance of existing continual learning methods. For example, combined with the state-of-the-art method CwD [55], BiMeCo brings in significant gains of around 2% to 6% while using 2x fewer parameters on CIFAR-100 under ResNet-18.

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A non-interactive verifiable computation model of perceptual layer data based on CP-ABE 2

A non-interactive verifiable computation model of perceptual...

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2nd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Zhao, Jianming Miao, Weiwei Zeng, Zeng Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China State Grid Jiangsu Electric Power Co. Ltd. Information Telecommunication Branch Nanjing China

ISBN: (纸本)9781665408868

The computing of smart devices at the perception layer of the power Internet of Things is often insufficient, and complex computing can be outsourced to server resources such as the cloud computing, but the allocation process is not safe and controllable. Under special constraints of the power Internet of Things such as multi-users and heterogeneous terminals, we propose a CP-ABE-based non-interactive verifiable computation model of perceptual layer data. This model is based on CP-ABE, NPOT, FHE and other relevant safety and verifiable theories, and designs a new multi-user non-interactive secure verifiable computing scheme to ensure that only users with the decryption key can participate in the execution of NPOT Scheme. In terms of the calculation process design of the model, we gave a detailed description of the system model, security model, plan. Based on the definition given, the correctness and safety of the non-interactive safety verifiable model design in the power Internet of Things environment are proved, and the interaction cost of the model is analyzed. Finally, it proves that the CP-ABE-based non-interactive verifiable computation model for the perceptual layer proposed in this paper has greatly improved security, applicability, and verifiability, and is able to meet the security outsourcing of computing in the power Internet of Things environment. © 2022 IEEE.

关键词： Internet of things

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Trajectory Tracking Error Optimization based on Iterative Learning 4

Trajectory Tracking Error Optimization based on Iterative Le...

引用

4th IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2022

作者： Huang, Haoran Zhang, Tao Jia, Danping Zhang, Hualiang Yan, Hongyu Pei, Quanshen Ding, Fan Shenyang University of Technology School of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

The X-Y precision plane motion platform is widely used in various precision motion control fields. In this paper, to improve the tracking accuracy of the X-Y precision plane motion table, a high-level iterative learning control with forgetting factor is proposed, and it is combined with a kind of PD-type iterative learning control with low-pass filter, and the method is applied to the Simulink model of the linear motor of the X-Y precision plane platform, thereby improving the trajectory accuracy of the X-Y precision plane motion platform. The traditional ILC has the disadvantage that the more number of iterations, the more divergent tracking error is. The new ILC proposed in this paper can not only effectively reduce the noise, reduce the tracking error, but also eliminate the problem of error divergence caused by too many iterations. At last, the experimental results of high-order PD iterative learning with forgetting factor and low-pass filter, PD iterative learning with low-pass filter, ordinary PD iterative learning are compared. The results show that the high-order PD iterative learning with forgetting factor and low-pass filter has better tracking effect than the first-order iterative learning and other several algorithms. It can make the movement trajectory of the X-Y precision plane motion platform more stable and improve the accuracy. © 2022 IEEE.

关键词： Low pass filters

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Robust-Tube Model Predictive Control for Piezoelectrically Actuated Fast Mechanical Switches for Hybrid Circuit Breakers

Robust-Tube Model Predictive Control for Piezoelectrically A...

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Artificial Intelligence and automation Control (AIAC), International Conference on

作者： Yu Chai Chen Yang Huijuan Dong Jie Zhao Yuri A. W. Shardt Bingfeng Ju ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Department of Automation Engineering Technical University of Ilmenau Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%.

关键词： Vibrations Uncertain systems Uncertainty Simulation Switches High-voltage techniques Predictive models Trajectory Approximation methods Predictive control

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OptTopo: Automated set-point optimization for coupled systems using topology information

OptTopo: Automated set-point optimization for coupled system...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Gregor Thiele Theresa Johanni David Sommer Martin Eigel Jö rg Krü ger Department for Process Automation and Robotics Fraunhofer IPK Institute for Production Systems and Design Technology Berlin Germany Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany Department for Industrial Automation at the Institute for Machine Tools and Factory Management Technical University of Berlin Germany

The manufacturing sector has witnessed a rapid rise in the importance of energy-efficient operation. For finding optimal set-points for industrial facilities, optimization problems of increasing complexity occur. Key challenges are the leak of derivative information and the curse of dimensionality. For systematic reduction of the search-space by decomposition of the model, a methodology for the inclusion of topology knowledge in the optimization procedure is developed. An implementation of OptTopo (Optimization based on Topology), embedded in a testbed, demonstrates its advantages compared to popular out-of-the-box-optimization. OptTopo could be integrated in energy management software offering advanced set-point control for complex facilities.

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