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

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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The A3C Algorithm With Eligibility Traces of Energy Management for Plug-In Hybrid Electric Vehicles

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IEEE Access 2025年 13卷 92507-92518页

作者： Dingyi Guo Guangyin Lei Huichao Zhao Fang Yang Qiang Zhang Shanghai Engineering Technology Research Center of SiC Power Device College of Intelligent Robotics and Advanced Manufacturing Fudan University Shanghai China Research Institute of Fudan University in Ningbo Ningbo China General Research and Development Institute China FAW Corporation Ltd. Changchun China

An energy management system is crucial for optimizing the performance and reducing fuel consumption of Plug-in Hybrid Electric Vehicles (PHEVs), which plays an important role in sustainable transportation. This paper presents a comprehensive study of the energy management problem for a selected PHEV, outlining key vehicle parameters such as battery state-of-charge (SOC), engine power, and energy consumption patterns. A mathematical formulation of the energy management problem is proposed to optimize fuel consumption while adhering to the vehicle’s operational constraints. The core of the paper focuses on the application of the Asynchronous Advantage Actor-Critic (A3C) algorithm to address this problem. To enhance the learning process, eligibility traces are introduced to refine the advantage function by weighting historical actions. This adjustment improves the decision-making process by making the action distribution more concentrated, which is crucial for optimizing fuel consumption. The proposed Asynchronous Advantage Actor-Critic Eligibility Traces (A3CET) algorithm incorporates these modifications and shows a significant improvement in fuel efficiency. During the training phase, it achieves 93.6% of the fuel optimality of the Dynamic Programming (DP) benchmark, and maintains 91.5% of DP’s performance during the testing phase. Moreover, A3CET reduces computation time by 62.5% compared to DP, and outperforms other EMSs in terms of both efficiency and real-time applicability.

关键词： Energy management Engines Batteries Reinforcement learning Prediction algorithms Motors Mechanical power transmission Heuristic algorithms Adaptation models Energy consumption

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A Novel Process Monitoring Framework Combined Temporal Feedback Autoencoder and Multilevel Correlation Analysis for Large-Scale Industrial Processes

SSRN

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SSRN 2024年

作者： Zhang, Cuicui Dong, Jie Zhang, Hongjun Peng, Kaixiang Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing University of Science and Technology Beijing Beijing100083 China

Modeling and monitoring generally face numerous challenges such as complex characteristics of multi-unit, temporal correlations and strong interaction among subblocks in large-scale industrial processes. To handle those challenges, in this paper, a novel process monitoring framework combined temporal feedback autoencoder and multilevel correlation analysis is proposed. Firstly, the large-scale industrial processes are decomposed into multiple subblocks in spatial order based on process knowledge. Secondly, an improved autoencoder with temporal feedback mechanism is constructed as local monitoring model to capture the important latent variables of each subblock. Then, considering the sequential transmission and correlations among subblocks in series, a multilevel correlation analysis method is employed to efficiently extract the unique features of subblock and the joint features of the whole process. Finally, the irregular contribution indices of the unique features and joint features are designed for hierarchical monitoring. The superiority of the proposed framework can be verified by Tennessee Eastman process. © 2024, The Authors. All rights reserved.

关键词： Process monitoring

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Zero-Shot Kidney Stone Segmentation Based on Segmentation Anything Model for Robotic-Assisted Endoscope Navigation 16th

Zero-Shot Kidney Stone Segmentation Based on Segmentation A...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Meng, Hui Chen, Lingkai Zhu, Shiqiang Fei, Chentao Zhang, Yong Zheng, Miaojuan Han, Jianda Song, Wei School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences 1 Sub-lane Xiangshan Hangzhou310024 China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Hangzhou ElephantChain Network Technology Co. Ltd. Hangzhou China Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou310018 China College of Artificial Intelligence and Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information Systems Nankai University Tianjin300350 China

ISBN: (纸本)9789819964888

Biomedical robotics has had a significant impact on flexible ureteroscopy (FURS) for the management of nephrolithiasis. The biomedical robots provide a suitable and safe platform for FURS. However, operating the robots expertly still represents a challenge for surgeons. Vision-based navigation is an effective means to simplify the operations of robots by surgeons, which has great potential to improve surgical efficiency. To achieve vision-based navigation, the localization of kidney stones in endoscopic images is the first step. In this paper, we propose a stone segmentation method (SSM) based on zero-shot learning, which consists of segmentation anything model (SAM), an image preprocessing module, and a mask postprocessing module. The SAM segments all objects in endoscopic images, which lack semantic information. The preprocessing module is designed to suppress interference of reflective area in endoscopic images, and the mask postprocessing module is used to filter noise masks. The experimental results on our collected endoscopic images indicate that our method achieves close accuracy on stone segmentation compared with supervised U-Net and VGG_U-Net, which significantly outperforms the Ostu and the SAM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Endoscopy

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Diagnosis of Localized Bearing Defects using Texture Analysis of Vibration Envelope Signals and Machine Learning

Diagnosis of Localized Bearing Defects using Texture Analysi...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Cong Dai Nguyen Fazal E Nasir Shahbaz Khan Sheraz Ali Khan Jong-Myon Kim Faculty of Radio-Electronic Engineering Le Quy Don Technical University Hanoi Vietnam Advanced Robotics and Automation Lab National Center for Robotics and Automation Peshawar Pakistan Institute of Manufacturing University of Engineering and Applied Sciences Swat Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan Department of IT Convergence University of Ulsan Ulsan South Korea PD Technology Ulsan South Korea

Bearings are important because they play a critical role in the operation of many machines and devices by reducing friction and enabling smooth motion between two moving parts. They are commonly used in rotating machinery, such as electric motors, turbines, pumps, and gearboxes, where they support and guide the rotating shafts. The primary cause of failure in wind turbines and induction motors is bearing faults, resulting in significant downtime and economic losses. Detecting these faults accurately and promptly is crucial to preventing unexpected shutdowns. Most of the existing methods for bearing fault detection rely on envelope analysis, which involves filtering and demodulating the raw signal, and Fourier analysis of the envelope signal to identify defect frequencies. Recent research has focused on improving the visibility of these frequencies by selecting an optimal band for filtering, using sub-band analysis and spectral kurtosis, which is a complex process and can be time-consuming. This paper presents a new approach to envelope signal analysis using texture analysis of the envelope signals that are projected as 2D grayscale images where each fault generates a unique texture. These textures are encoded using the local binary pattern operator and then used for fault classification. The method is tested on a publicly available seeded fault dataset.

关键词： Vibrations Fault diagnosis Shafts Time-frequency analysis Filtering Pumps Machine learning

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Review on Development Status, Challenges and Development Trends of Surgical Robots

Review on Development Status, Challenges and Development Tre...

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IEEE International Conference on Mechatronics and automation

作者： Liyuan Zhang Xiaoxuan Qi Yige Peng Sheng Bao Jianjun Yuan Shijie Guo Shanghai IC Technology & Industry Promotion Center Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Surgical robots are an important branch of medical robots. This paper analyzes the current development status of surgical robots from the perspective of application field classification, summarizes the challenges of various surgical robots such as laparoscopic surgical robots, orthopedic surgical robots, vascular intervention surgical robots, natural orifice transluminal endoscopic surgery (NOTES), and other specialized surgical robots, and predicts and prospects the future development of surgical robots, which provides intelligent support for domestic companies to develop the surgical robot industry. The research results indicate that surgical robot technology is in a period of rapid development, which has helped improve surgical accuracy and treatment effectiveness.

关键词： Industries Medical robotics Minimally invasive surgery Mechatronics automation Service robots Reviews

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A Real- Time Multi-Camera Auto-Adjustment Framework for Infrastructure Inspections

A Real- Time Multi-Camera Auto-Adjustment Framework for Infr...

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IEEE/SICE International Symposium on System Integration

作者： Chuong Phuoc Le Charles Ellison Steven Bunkley Hung La Anton Netchaev Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA

In this paper, we proposed a novel multi-camera auto-adjustment framework for infrastructure inspections, specifically 3D point cloud mapping via lidar-camera fusion. Our method uses a single controller for consensus target brightness and adaptive lighting. In addition, a vignette correction filter was created to apply to images outputted from cameras to avoid dark strips on the point cloud data. This approach is designed to be a versatile, real-time friendly, and platform agnostic software stack.

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AMR Navigation Based on Dynamic Control Sampling Space

AMR Navigation Based on Dynamic Control Sampling Space

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Advances in Electronics, Control and Communication Systems (ICAECCS), International Conference on

作者： Sara Bouraine Idriss Hasani Alla Djoudi Ahmed Gourine Boualem Kazed Division Robotics and Industrial Automation Center for Development of Advanced Technologies Baba Hassen Algeria Electronic and Automation department University BLIDA-1 Blida Algeria University of Science and Technology Houari Boumedienne Bab ezzouar Algeria

This paper addresses the problem of local navigation for autonomous mobile robots in partially observable dynamic environments. The main contribution of this work is the expansion of a sampling based approach to deal with the robot and environment dynamic constraints and integrate a safety distance to keep the robot away from collisions. The developed algorithm has been implemented on a robotic platform with a Robot Operating System based embedded architecture, where it has been tested and validated in an indoor environment.

关键词： Navigation Service robots Operating systems Heuristic algorithms Control systems Safety Planning

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Effect of laser shock peening on microstructure and wear resistance for 15-5 PH stainless steel

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Journal of Materials Research and technology 2025年 36卷 8953-8961页

作者： Jiajun Wu Xingze Lin Youbin Lai Wangwang Ding Jibin Zhao Hongchao Qiao College of Engineering Shantou University Shantou 515063 Guangdong China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen 518110 Guangdong China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 Liaoning China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 Liaoning China

Laser shock peening (LSP) is an advanced surface modification technology that can improve the mechanical properties of metallic parts effectively. In this work, the 15-5 PH stainless steel was selected as experimental material, and the experimental samples were treated by LSP with the laser energy of 5 J and 8 J, pulse width of 15 ns, and laser beam spot diameter of 3 mm. The mechanical properties, microstructural evolution, and wear resistance of experimental samples before and after the LSP treatment were investigated. The experimental results indicated that after the LSP treatment, the compressive residual stress was induced in the near-surface and the surface compressive residual stress was increased with laser energy. The microhardness of experimental samples were increased with laser energy, too. Since the mechanical properties improvement, the wear rate of the experimental samples were decreased, showing the better wear resistance after the LSP. In addition, the enhancement of wear resistance mechanism was systematically discussed.

关键词： Laser shock peening 15-5 PH stainless steel Microstructure Wear resistance

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Modeling Neural Representation of Social Recognition Information with Recurrent Neural Networks 17

Modeling Neural Representation of Social Recognition Informa...

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17th International Conference on Complex Medical Engineering, CME 2023

作者： Song, Fei Wang, Xinnian Zhan, Yang Tang, Fengzhen Si, Bailu State Key Laboratory of Robotics 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 School of Life Sciences Division of Life Science and Medicine University of Science and Technology of China Hefei230026 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Systems Science Beijing Normal University Beijing100091 China Chinese Institute for Brain Research Beijing102206 China

ISBN: (纸本)9798350316117

Social recognition, the ability to differentiate between familiar and unfamiliar individuals, is crucial for animals' survival and reproduction. Neuroscience research has predominantly focused on investigating the brain regions and circuits that regulate social recognition. However, a lingering question pertains to the precise mechanisms underlying the social recognition process. To address this question, we employed a recurrent neural network (RNN) to determine whether an agent can recognize familiar individuals without involving higher-level decision-making processes. This approach takes into consideration that animal behavioral performance may not directly mirror complex cognitive functions. The RNN was trained using the full-FORCE learning paradigm. Our simulation results revealed three distinct subsets of neurons with specific functions: one dedicated to representing novel individuals and two others representing the same social individual in both unfamiliar and familiar states. We propose a hypothesis regarding the potential memory computing processes involved in these operations. © 2023 IEEE.

关键词： Animals

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