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7th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Yang, Guang Liu, Zhifeng Wang, Jianjun Cui, Shuping 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 University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9798350334197

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. © 2023 IEEE.

关键词： Ships

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A Miniaturized Pneumatic Electrode Gripper for Robotic Cochlear Implant Surgery

A Miniaturized Pneumatic Electrode Gripper for Robotic Cochl...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Gao, Hongyan Zou, Yun Huang, Shaoping Chen, Weidong Gao, Anzhu Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Institute of Medical Robotics and Department of Automation Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665481090

Cochlear implant surgery is one of the most challenging surgeries due to the limited workspace and delicate inner structure. A traditional electrode implant instrument is a passive elastic forceps, which needs another forceps to help grip or release the electrode. It is not convenient to achieve delicate operations in a confined space. This paper develops a miniaturized pneumatic electrode gripper at the tip of forceps to achieve active gripping. It transmits the pressurized air through 3D-printed inner lumens to form two inflated soft bubbles to push the electrode to the rigid shell for accurate positioning. The finite element analysis is carried out to evaluate the shape of inflated soft bubbles with variable soft films. A prototype of a gripper with inner tortuous airways is fabricated using micro 3D printing technique, and experiments are carried out to validate its accuracy and demonstrate its ability to grip and release. Finally, robotic teleoperation using a robotic arm and a joystick is implemented for cochlear implant surgery. Results demonstrate that the developed miniaturized pneumatic gripper has successfully achieved electrode implantation in a 3D-printed cochlear phantom. © 2022 IEEE.

关键词： Electrodes

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UWIA-AODV: Improved Ad-Hoc On-Demand Distance Vector Routing Protocol for Underwater Acoustic Sensor Networks 42

UWIA-AODV: Improved Ad-Hoc On-Demand Distance Vector Routing...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Xueyi Liu, Meiqin Zhang, Senlin Dong, Shanling Zhejiang University National Key Laboratory of Industrial Control Technology Hangzhou310027 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9789887581543

In the past a few years, underwater acoustic networks have developed as a powerful technique used for variety of applications. Ad-Hoc On-Demand Distance Vector (AODV) is a popular and effective routing protocol, while it is not suitable for underwater usage due to the features of underwater acoustic channel such as low data rate, long delay, limited bandwidth and energy. In this paper, we propose an improved AODV based on implicit acknowledgement which employs broadcast features of nodes to overhear packets and retransmit those that fail to be transmitted. In addition, residual energy is considerded for energy balancing. Finally, the in-field experiment results shows that our proposed protocol can select the route with more residual energy for energy balancing. And our proposed protocol also outperforms AODV in terms of throughput and packet discovery ratio. So it is more suitable for underwater scenarios that require high transmission reliability. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Routing protocols

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Small Photoresist Defect Samples Augmentation Based on Generative Adversarial Network 6

Small Photoresist Defect Samples Augmentation Based on Gener...

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6th IEEE Information Technology, Networking, Electronic and Automation control Conference, ITNEC 2023

作者： Yang, Guang Li, Zhihang Yang, Zhijia Cui, Shuping 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 University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9781665460033

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. © 2023 IEEE.

关键词： III-V semiconductors

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy 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 Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Multi-Agent Q-Value Mixing Network with Covariance Matrix Adaptation Strategy for the Voltage Regulation Problem 42

Multi-Agent Q-Value Mixing Network with Covariance Matrix Ad...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Yiwen Zhang, Senlin Liu, Meiqin Dong, Shanling Zheng, Ronghao College of Electrical Engineering Zhejiang University Hangzhou310027 China Zhejiang University National Key Laboratory of Industrial Control Technology Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9789887581543

control and optimization of power systems typically involves schemes that utilize optimal power flow techniques and comprehensive modeling of various electrical components. However, the extensive integration of renewable energy sources and distributed energy resources makes it difficult to obtain accurate models, making the traditional model-based approaches more challenging. In this paper, we propose a Covariance Matrix Adaptation Q-value network mixing method (CMAQMIX), which is a novel model-free multi-agent reinforcement learning method that combines the advantages of Covariance Matrix Adaptation Evolution Strategy and the Q-value network mixing method to solve the problem of continuous action space. We establish a new multi-agent voltage regulation environment based on CityLearn framework to test the CMAQMIX method. The results show that our proposed method outperforms the Independent Proximal Policy Optimization method and can give immediate action response without the complete domain knowledge of the power system. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Optimization

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Dynamic Multi-Robot Path Planning in Narrow-Lane Environments with One-Way Constraint 6

Dynamic Multi-Robot Path Planning in Narrow-Lane Environment...

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6th International Conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Song, Zhenyu Zheng, Ronghao Zhang, Senlin Liu, Meiqin College of Electrical Engineering Zhejiang University Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xian710049 China

ISBN: (纸本)9798350371253

With the rapid development of the logistics industry, the path planning problem for automated guided vehicles (AGVs) in warehousing systems has become a prominent research topic. Traditional multi-robot path planning algorithms often perform poorly in narrow-lane environments found in warehouses. This paper proposes an improved dynamic multi-robot path planning algorithm specifically tailored for narrow-lane environments. By constraining the traversal directions within narrow lanes and dynamically determining the optimal directions, this algorithm achieves efficient multi-robot path planning and enhances the quality of robot paths. The effectiveness of the algorithm is theoretically proven in this paper. Extensive simulation data demonstrates that our algorithm efficiently generates high-quality paths in high-density narrow-lane warehouse environments. © 2023 IEEE.

关键词： Multipurpose robots

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An Efficient Learning control Framework with Sim-to-Real for String-Type Artificial Muscle-Driven Robotic Systems

arXiv

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arXiv 2024年

作者： Tao, Jiyue Zhang, Yunsong Rajendran, Sunil Kumar Zhang, Feitian The Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering the State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China The BSS Technologies Inc. Takoma ParkMD20912 United States

Robotic systems driven by artificial muscles present unique challenges due to the nonlinear dynamics of actuators and the complex designs of mechanical structures. Traditional model-based controllers often struggle to achieve desired control performance in such systems. Deep reinforcement learning (DRL), a trending machine learning technique widely adopted in robot control, offers a promising alternative. However, integrating DRL into these robotic systems faces significant challenges, including the requirement for large amounts of training data and the inevitable sim-to-real gap when deployed to real-world robots. This paper proposes an efficient reinforcement learning control framework with sim-to-real transfer to address these challenges. Bootstrap and augmentation enhancements are designed to improve the data efficiency of baseline DRL algorithms, while a sim-to-real transfer technique, namely randomization of muscle dynamics, is adopted to bridge the gap between simulation and real-world deployment. Extensive experiments and ablation studies are conducted utilizing two string-type artificial muscle-driven robotic systems including a two degree-of-freedom robotic eye and a parallel robotic wrist, the results of which demonstrate the effectiveness of the proposed learning control strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Fault Diagnostic Opportunities for Robot Servo Motors Using the Physics of Failure Analysis 14

Fault Diagnostic Opportunities for Robot Servo Motors Using ...

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14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Zhang, Bo Liang, Shan Wang, Kai 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: (纸本)9798350329988

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. © 2023 IEEE.

关键词： Servomotors

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Magnetic Flux Servoing for Precise Localization Based on Gradient Tensor Contractions

Magnetic Flux Servoing for Precise Localization Based on Gra...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Dean Martinović Matko Orsag Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems University of Zagreb Zagreb Croatia

This paper focuses on bridge inspections performed by intelligent unmanned aerial vehicles (UAV). For this, small data loggers are placed by the UAVs at the bridge, which have to be removed later. In our previous work we presented the magnetic localization method SRIOD, which not only detects the center of the data logger but also its orientation. In this work we proof SRIOD in a real application and let a robotic arm locate a magnetic target. The hardware involved is presented including the control strategy for the robot. The motions of the robot and the magnetic target are captured with 12 Optitrack cameras in order to validate the localization quality.

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