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检索条件"机构=LABoratory of Intelligent Robotics Automation and Systems - LABIRAS"

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops 13

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

引用

13th IEEE Data Driven Control and Learning systems Conference, DDCLS 2024

作者： Zhang, Yanchao Geng, Hao Liu, Bin Zhang, Yinlong Yuan, Xudong Zhang, Sichao School of Information Science and Engineering Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 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)9798350361674

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation. © 2024 IEEE.

关键词： Extended Kalman filters

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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第42届中国控制会议

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network(TL-DNN) was proposed in this paper. Its specific implementation steps include(1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models(random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Magnetically Driven Capsule Robot for Multi-Targeted Biopsy and Drug Delivery in Stomach

Magnetically Driven Capsule Robot for Multi-Targeted Biopsy ...

引用

2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xu, Zhihui Wei, Tanyong Li, Zongze Huang, Ding Liu, Shoubin Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen China Southern University of Science and Technology Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350325706

Capsule robots have emerged as a promising solution for non-invasive and painless screening of gastrointestinal diseases, offering significant advantages in patient comfort and reduced risk of cross-infection. Despite these advantages, existing capsule robots fall short in their ability to conduct targeted biopsies and deliver drugs to specific gastric lesions. To address this limitation, this paper presents the design and development of a magnetically driven capsule robot that leverages visual feedback to achieve accurate targeting for biopsy and drug administration in gastric lesions. Internal magnets, responsive to an external gradient magnetic field, enable the controlled propulsion of the capsule robot. A kinetic model of the capsule robot is analyzed, allowing it to recognize lesion contours through the endoscopic camera. This recognition determines the rotation angle or small adjustments required for the capsule robot's attitude and positioning. Experimental results in a simplified in vitro stomach model showcase the capability of the capsule robot to achieve targeted biopsy and drug delivery. These results highlight the potential feasibility of the proposed capsule robot in enhancing diagnostic accuracy and treatment efficacy with broader applications within the realm of gastric healthcare. © 2023 IEEE.

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A Weighting Factors Autotuning Strategy for Model Predictive Torque Control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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第42届中国控制会议

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

关键词： Model Predictive Torque Control Weighting Factors Multi-objective Optimization Algorithm Permanent Magnet Synchronous Motor

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Weld Defects Detection by Neural Networks Based on Attention Mechanism 7

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

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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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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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IVR-SSM: Industrial Vehicular Robot Safe & Smart Move Using Wheel-Encoder and Inertial-RGB-Depth Measurements 30

IVR-SSM: Industrial Vehicular Robot Safe & Smart Move Using ...

引用

30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Liu, Yuanhao Liang, Wei Wang, Chu Zhang, Xin Gao, Huanbing Zhang, Yinlong 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 University of Chinese Academy of Sciences Beijing China School of Information and Electrical Engineering Shandong Jianzhu University Jinan China Shandong Key Laboratory of Intelligent Buildings Technology Jinan China

ISBN: (纸本)9798350391916

Safe & smart move (SSM) aims to help industrial vehicular robot (IVR), such as warehousing robots, running safely and smartly by detecting the obstacles in front and maintaining a safe distance & attitude. However, the traditional SSM methods suffer the drawbacks of limited ranges, inaccurate regression of safety distance & orientation between the robot and the forward unexpected obstacles, and the inappropriate IVR control strategies. To solve these issues, this paper designs an innovative real-time safety-aware framework using the complementary Wheel-Encoder and Inertial-RGB-Depth measurements to ensure the IVR running safely, reasonably and reliably. To estimate the robot running states, we take advantages of the complementary wheel encoder, monocular RGB images and inertial measurements to obtain the robot positions, orientations, angular and linear velocities in a tightly-coupled manner. To improve the safety relative distance & attitude estimation accuracy, we exploit the RGB and depth measurements in a task-oriented fashion. The forward obstacles with the specific contours are detected using the Mask-RCNN model, followed by the Gaussian Mixture Model to obtain the forward obstacle depth. Eventually, an IVR control strategy is designed to maintain the robot safety distance and orientation, and to keep the minimum damage to the robot mechanical system. The presented method has been evaluated on the developed industrial vehicular robot safety perception platform. The experimental results prove the competitiveness against the state-of-the-art methods. © 2024 IEEE.

关键词： Industrial robots

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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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intelligent Leakage Inspection Method of Magnetic Mill Workshop based on Lightweight Yolov5 3

Intelligent Leakage Inspection Method of Magnetic Mill Works...

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3rd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2024

作者： Yu, Shimao Song, Chunhe Sun, Yong Gao, Pengpei Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang 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)9798350355413

In the process of mining company beneficiation, large rotating equipment, pipelines and electrical equipment have many complex inspection areas and diverse inspection contents. The lack of intelligent solutions to the problem of pipeline leakage will cause serious consequences such as equipment shutdown if it is not found in time. This paper proposes a lightweight yolov5 intelligent leakage inspection method for the magnetic mill workshop. The inspection robot is used to continuously collect images of possible leakage points and transmit them to the background server. The improved yolov5 algorithm simplifies the model structure and realizes rapid and accurate identification of leakage targets with limited computing resources. The computational efficiency is increased by 44.98% with almost no loss of model accuracy. The mAP-0.5 of identifying large targets at leakage points reaches 97.96%. © 2024 IEEE.

关键词： inspection robot leakage inspection light-weight yolov5 object detection

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Optimization of Milling Robot Trajectory Based on Whale Optimization Algorithm 3rd

Optimization of Milling Robot Trajectory Based on Whale Opti...

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3rd International Conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2023

作者： Zhou, Yang Li, Qingxin Sun, Yong Software College Shenyang University of Technology Shenyang 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

ISBN: (纸本)9789819721993

In response to the problem of vibration and low work efficiency during robot milling, taking into account the work indicators of milling time and stability, combined with the kinematic constraints of the robotic arm, the time impact optimization problem is transformed into a multi-objective optimization problem. The paper proposes an improved whale optimization algorithm, which introduces a differential mutation strategy to improve the problem of traditional whale algorithms easily getting stuck in local optima. Compared with traditional whale optimization algorithms, the improved whale algorithm significantly improves the convergence speed and accuracy during the milling process. Finally, it is verified by a solid robot. The results showed that the pulse value of the planned trajectory was reduced by up to 99.76%, and the efficiency was improved by 47.62%, effectively avoiding robot vibration during the milling process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multiobjective optimization

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