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Ocean Studies (ICOS), International Conference on

作者： Timoshenko Alexander Zuev Alexander Mursalimov Eduard Gribova Valeria Inzartsev Alexander Department of Automation and Robotics Laboratory of intelligent systems for marine robots Far Eastern Federal UniversityInstitute of Marine Technology Problems FEB RAS Vladivostok Russia Laboratory of Intelligent Systems Institute of Automation and Control FEB RAS Vladivostok Russia Laboratory of Control Systems Institute of Marine Technology Problems FEB RAS Vladivostok Russia

ISBN: (纸本)9781665473736

The paper proposes new ontologies for describing and diagnosing malfunctions of subsystems of autonomous underwater vehicles (AUV) as a part of the development of a previously created approach to the intelligent diagnosis of these vehicles using knowledge bases. To implement this approach, it is proposed to use two knowledge bases, one of which describes malfunctions, features for their identification and actions necessary to adapt to the negative consequences caused by the appearance of these malfunctions. The other database contains information about the main subsystems of the AUV, devices and replaceable modules, as well as the states they may be in. Knowledge bases are used in the work of the intelligent control-emergency system (ICES) on board the AUV, which allows for comprehensive functional diagnostics of all subsystems of the device, including payload modules, as well as adaptation to the consequences of emerging malfunctions, taking into account the actual cause-and-effect relationships between them. To test the system’s operability, a prototype was developed. The results of the prototype simulation showed that ICES performs the tasks and can be implemented on the onboard computers of existing AUV using well-known programming technologies.

关键词： Performance evaluation Autonomous underwater vehicles Simulation Knowledge based systems Prototypes Ontologies Programming

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Small Photoresist Defect Samples Augmentation Based on Generative Adversarial Network

Small Photoresist Defect Samples Augmentation Based on Gener...

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IEEE Information Technology, Networking, Electronic and Automation control Conference

作者： Guang Yang Zhihang Li Zhijia Yang Shuping Cui 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 Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Photoresist coating technology is an important part of the surface treatment of semiconductor wafers. The presence of bubbles in photoresist can seriously affect the quality of wafers, however, the lack of sufficient bubble samples makes intelligent automatic detection techniques impossible. To solve such a problem, we propose B-GAN based on adversarial generative network, and design a mapping network for potential encoding mapping and a synthetic network for high-quality bubble image generation, so that defective bubble samples can be automatically generated. Experiments prove that our method achieves excellent results.

关键词： Image coding Automation Image synthesis Resists Manuals Generative adversarial networks Coatings

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Fault Diagnostic Opportunities for Robot Servo Motors Using the Physics of Failure Analysis

Fault Diagnostic Opportunities for Robot Servo Motors Using ...

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International Conference on Reliability, Maintainability and Safety,ICRMS

作者： Bo Zhang Shan Liang Kai Wang Key Laboratory of Networked Control Systems Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Computer Science and Technology University of Chemical Technology Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350329988

ISBN: (纸本)9798350329995

Joint robots are widely used in various industries, such as aviation, aerospace, and automotive manufacturing. The performance degradation of servo motors can significantly affect the overall performance of the robots because of the core component of joint robots, and therefore, it is critical to detect and address servo motor failures promptly. In this study, we analyze some common causes and mechanisms of performance degradation in robot servo motors using the physics of failure analysis. We also discuss strategies for detecting and diagnosing faults using these mechanisms before the servo motors fail completely.

关键词： Degradation Industries Service robots Failure analysis Safety Manufacturing Reliability Servomotors Physics Automotive engineering

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Non-cooperative Stochastic Target Encirclement by Anti-synchronization control via Range-only Measurement

Non-cooperative Stochastic Target Encirclement by Anti-synch...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Fen Liu Shenghai Yuan Wei Meng Rong Su Lihua Xie Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou the Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. In extreme conditions, where GPS signals are not available, weight restrictions are present, and ground guidance is absent, the agents can rely solely on their onboard single-modality perception tools to measure the distances to the target. The distance measurement allows for creating a position estimator by providing a target position-dependent variable. Furthermore, the construction of the unique distributed anti-synchronization controller (DASC) can guarantee that the two agents track and encircle the target swiftly. The convergence of the estimator and controller is rigorously evaluated using the Lyapunov technique. A real-world UAV-based experiment is conducted to illustrate the performance of the proposed methodology in addition to a simulated Matlab numerical sample. Our video demonstration can be found in the URL https://***/EDVLvP-bk8M.

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A novel matrix approach for the stability and stabilization analysis of colored Petri nets

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Science China(Information Sciences) 2019年第9期62卷 102-115页

作者： Jiantao ZHAO Zengqiang CHEN Zhongxin LIU College of Computer and Control Engineering Nankai University Key Laboratory of Intelligent Robotics of Tianjin Nankai University

In this study, the stability and stabilization problem of a colored Petri net based on the semitensor product of matrices is investigated. First, the marking evolution equation of the colored Petri net in a Boolean algebra framework is established, and the necessary and sufficient condition for the stability of the equilibrium point of the colored Petri net is given. Then, the concept of the pre-k steps reachability set is defined and is used to study the problem of marking feedback stabilization. Some properties of the pre-k steps reachability set are developed. The condition of the stabilization of the colored Petri net is given. The algorithm of the optimal marking feedback controller is designed. The proposed method in this paper could judge the stability and stabilization of the colored Petri net by matrix approach. The obtained results are simple and easy to implement by computer. An example is provided to illustrate the effectiveness of the proposed method.

关键词： discrete event system colored Petri net semi-tensor product of matrices stability equilibrium point stabilization

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Weld Defects Detection by Neural Networks Based on Attention Mechanism

Weld Defects Detection by Neural Networks Based on Attention...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Guang Yang Zhifeng Liu Jianjun Wang Shuping Cui 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 Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Welding technology plays an indispensable role in ship construction, and its maturity has a direct impact on the quality of shipbuilding. The weld seam, being the weakest part of a welded structure, significantly affects the structural strength, service life, and safety reliability of ships. However, the current identification of weld defects in the ship industry relies on manual methods, which are subjective and prone to error. To address this issue, this paper presents a novel approach by collecting weld images from actual ship scenes, preprocessing them to eliminate noise and highlight defect features, and proposing a Transformer-based improved target detection model (T-OBJDETC) to enhance the recognition accuracy of the model.

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AI Enabled Automatic Mobile Robot intelligent Navigation in Construction with Obstacle Awareness

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IEEE Transactions on Automation Science and Engineering 2025年

作者： Zhang, Yinlong Liu, Yuanhao Cui, Yunge Zeng, Ziming Liang, Wei Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing101408 China Zeekr Group Zeekr Intelligent Driving Department Shanghai110078 China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen518055 China

In the construction industry, the integration of artificial intelligence (AI) and robotics has led to significant advancements in automating various tasks. One critical aspect is the intelligent navigation of automatic mobile robot (AMR) within construction sites, where dynamic environments pose challenges such as inaccurate robot state estimation, irregular and textureless obstacle detection. To solve these issues, this paper designs an AI enabled obstacle-aware AMR intelligent navigation approach while ensuring robot safety and efficiency. Specifically, the robot is equipped with the complementary RGB camera, inertial measurement unit (IMU) and wheel encoder to estimate the states (i.e., positions, orientations and velocities), which have been tightly fused in the optimization framework. Furthermore, the RGB-Depth images are jointly fed into the AI model. It combines of Mask-RCNN and multi-scale attention network, to detect and segment the obstacles, and estimate the corresponding relative depth. It should be noted that the system incorporates obstacle awareness mechanisms to dynamically switch the robot's velocities in response to obstacles in the environment, ensuring smooth and collision-free movement. Experimental results demonstrate the effectiveness and robustness of the proposed AI-enabled navigation system in real-world construction scenarios, showcasing its potential to enhance productivity and safety in construction automation. © 2004-2012 IEEE.

关键词： Construction industry

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Actor-Based Learning Framework for the Co-Optimization of Modular Cpps Scheduling and control with Stochastic Processing Times

SSRN

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SSRN 2023年

作者： Wan, Guangxi Dong, Xiaoting Dong, Qingwei He, Yunpeng Zeng, Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 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 Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

Scheduling with stochastic processing times is a kind of dynamic scheduling in production systems. The current scheduling optimization problem is formulated in the scheduling domain, which ignores the underlying synergies for optimization objectives between scheduling and control. To address this problem, we propose and formulate the problem of the co-optimization of scheduling and control with stochastic processing times (COSC-SPT) with a system respective view. To solve the COSC-SPT, we model the decision-making process as a Markov decision process and propose an actor-based learning framework to facilitate problem solving. To verify the feasibility of the proposed framework and the effectiveness of the formulated COSC-SPT, a real reducer assembly system is taken as a use case. The results of experiments demonstrate that the COSC-SPT provides better solutions than conventional scheduling problems. Keywords: Actor, cyber-physical production systems, optimization, scheduling, stochastic processing time. © 2023, The Authors. All rights reserved.

关键词： Markov processes

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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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A DDOS Attack Traffic Classification Model for Industrial Internet Based on CNN-LSTM

A DDOS Attack Traffic Classification Model for Industrial In...

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2022 Chinese Automation Congress, CAC 2022

作者： Wei, Weixuan Liu, Xianda Sheng, Chuan Feng, Ansong Shenyang University of Chemical Technology College of Information Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

With the continuous development and application of industrial Internet, the weakness of the security defense capability of industrial Internet has become increasingly prominent. This paper presents an end-to-end classification model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for DDoS attacks over industrial Internet. By self-learning the spatial and temporal characteristics of the original attack traffic, the model can avoid subjective errors that may occur due to manual selection of features. This method is a feasible means, with an accuracy rate of 99%. © 2022 IEEE.

关键词： Long short-term memory

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