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IEEE International Conference on robotics and Automation (ICRA)

作者： Shuai Wang Qingxiang Zhao Jian Chen Mingcong Chen Guanglin Cao Jian Hu Runfeng Zhu Danny Tat Ming Chan Ming Feng Hongbin Liu centre for Artificial Intelligence and Robotics (CAIR) Hong Kong Institute of Science and Innovation Chinese Academy of Sciences CAIR and CASIA Department of Biomedical Engineering City University of Hong Kong HKSAR School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China Department of Mechanical Engineering The Hong Kong Polytechnic University Department of Surgery The Chinese University of Hong Kong HKSAR Beijing Union Medical College Hospital

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Transorbital Neuro Surgery (TNS) offers a novel treatment towards the lesion inside skull pursuing minimal invasiveness. Most conventional TNS tools are rigid and straight, limiting the dexterity and accessibility in passing a small port. Bendable and steerable surgical tools provides an alternative for this issue. In this work, we proposed a dual-segment slender surgical robot arm for TNS, which is a Co-Axial Slender Tubular robot (CAST), and modelled it using novel approaches. Another contribution is tendon-mortise shaped slits along the axial direction, enhancing the overall stiffness. The bending of CAST is actuated by pushing/pulling distance, and the maximum diameter is only 1.7mm with high dexterity after mounting on a rigid robot arm. Experiments demonstrates that the proposed the slit design doubles the stiffness properties compared to traditional rectangle slit designs. The path-following task shows that the position error was maximally 3mm in open-looped control. Test on a skull model demonstrates that the whole system could successfully perform electrocoagulation procedure inside the depth of skull in a robotized manner effectively.

关键词： Medical robotics Limiting Instruments Robot vision systems Kinematics Manipulators Neurosurgery

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Fast Detection of Multi-Direction Remote Sensing Ship Object Based on Scale Space Pyramid

Fast Detection of Multi-Direction Remote Sensing Ship Object...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Ziying Song Li Wang Guoxin Zhang Caiyan Jia Jiangfeng Bi Haiyue Wei Yongchao Xia Chao Zhang Lijun Zhao School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Vehicle and Mobility Tsinghua University Beijing China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China

Ships in remote sensing images are usually arranged in arbitrary direction, small in size, and densely arranged. As a result, existing object detection algorithms cannot detect ships quickly and accurately. In order to solve the above problems, a lightweight object detection network for fast detection of ships is proposed. The network is composed of backbone network, four-scale fusion network and rotation branch. First, a lightweight network unit S-LeanNet is designed and used to build a low-computing and accurate backbone network. Then, a four-scale feature fusion module is designed to generate a four-scale feature pyramid, which contains more features such as ship shape and texture, and at the same time is conducive to the detection of small ships. Finally, a novel rotation branch module is designed, using balance L1 loss function and R-NMS for post-processing, to realize the precise positioning and regression of the rotating bounding box in one step. Experimental results show that the detection precision of our method in the DOT A remote sensing data set is compared with the latest SCRDet detection method, the precision is increased by 1.1%, and the operating speed is increased by 8 times, which can meet the fast detection requirements of ships.

关键词： Sea surface Shape Object detection US Department of Transportation Feature extraction Sensors Marine vehicles

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Gaussian Processes Incremental Inference for Mobile Robots Dynamic Planning ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9584-9589页

作者： Luka Petrović Filip Marić Ivan Marković Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Institute for Aerospace Studies Space & Terrestrial Autonomous Robotic Systems Laboratory Canada

Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. While such methods efficiently find solutions in static environments, they need to be ran from scratch multiple times in the presence of moving obstacles, which incurs unnecessary computation and slows down execution. In this paper, we propose a trajectory optimization algorithm that anticipates the movement of obstacles and solves the planning problem in an iterative manner. We employ continuous-time Gaussian processes as trajectory representations both for the mobile robot and moving obstacles for which future locations are predicted according to a given model. We formulate the simultaneous moving obstacles tracking and mobile robot motion planning problem as probabilistic inference on a factor graph. Since trajectories of moving obstacles are optimized concurrently to motion planning, the proposed approach works in a predictive manner. After computing the initial solution, we use incremental inference for online replanning after an estimate of the moving obstacle position is provided. Our experimental evaluation demonstrates that the proposed approach supports online motion generation in the presence of moving obstacles.

关键词： motion planning trajectory optimization gaussian processes factor graphs incremental inference

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Position Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive Filtering Sliding Mode Observer

Position Sensorless Control of Permanent Magnet Synchronous ...

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Chinese Control and Decision Conference, CCDC

作者： Anna Wang Yingjie Xiong Tao Zhang College Of Information Science and Engineering Northeastern University Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665478977

In the position sensorless control strategy of permanent magnet synchronous motor (PMSM), the back-EMF method based on sliding mode observer (SMO) is favored because of its robustness. In this paper, a SMO based on the piecewise sigmoid function is designed. Based on the back-EMF observation error, the gain adaptation of the SMO is realized, which effectively reduces the high-frequency chattering problem of the conventional SMO. The low-pass filter of the conventional SMO is improved, and an adaptive filter based on the back-EMF low-pass filter is proposed. According to the back-EMF observation error, the cut-off frequency of the low-pass filter is changed in real-time, and the estimation of rotor position is improved. After Matlab/Simulink simulation verification, the control strategy is feasible and achieves a better control effect.

关键词： Adaptation models Low-pass filters Adaptive filters Rotors Sensorless control Observers Permanent magnet motors

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Development of micro-and nanorobotics: A review

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Science China(Technological Sciences) 2019年第1期62卷 1-20页

作者： YANG Jia ZHANG Chuang WANG XiaoDong WANG WenXue XI Ning LIU LianQing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Emerging Technologies Institute Department of Industrial & Manufacturing Systems Engineering University of Hong Kong

Micro-and nanorobotic is an emerging field of research arising from the cross-fusion of micro/nano technology and robotics and has become an important part of robotics. Micro-and nanorobots have the advantages of small size, low weight, large thrust-toweight ratio, high flexibility, and high sensitivity. Due to the characteristics distinguishing from macroscopic robots, micro-and nanorobots have stimulated the research interest of the scientific community and opened up numerous application fields such as drug delivery and disease diagnosis. In the past 30 years, research on micro-and nanorobots has made considerable *** article provides a comprehensive overview of the development of these robots. First, the application of the robots is reviewed. Then, the key components of the robots are discussed separately, covering their actuation, design, fabrication and control. In addition, from the perspectives of intelligence and sensing, clinical applications, materials and performance, the challenges that may be encountered in the development of such robots in the future are discussed. Finally, the entire article is summarized, and concepts for future micro-and nanorobots are described.

关键词： micro-and nanorobots magnetic actuation manipulation biohybrid robot

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Gesture Recognition Based on Attention and Dilated Causal Convolution

Gesture Recognition Based on Attention and Dilated Causal Co...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Zhou, Xin Liu, Jiaqing Huai, Yaping Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In recent years, based on surface electromyography (sEMG), great progress has been made in gesture recognition tasks, which is significant to the study of computer interaction and prosthesis control. Prior to this, many researches have focused on feature selection and time series modeling of data. However, it is difficult and complicated to manually perform feature extraction and feature selection on data. Besides, most of the time series modeling is based on recurrent neural networks with memory cell, that is, Long Short-term Memory (LSTM). Even though LSTM has memory gate, but cannot completely records historical information. In this paper, to address the above problems, we propose a Temporal Convolutional Network based on Attention mechanism (ATCN), in which Attention is used to characterize temporal features, and dilated causal convolution to capture more historical information. ATCN achieves 95.26% accuracy with this method on the public dataset NinaPro DB2, which exceeds the state-of-the-art accuracy of 5%. The experiments show that this approach is feasible and effective for gesture recognition tasks. Our contributions are as follows: Firstly, Attention mechanism is introduced into the field of prosthetic control. Then, ATCN is proposed for gesture recognition. Finally, ATCN achieves better results than LSTM on multiple public datasets. © 2022 IEEE.

关键词： Long short-term memory

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Advances in atomic force microscopy for single-cell analysis

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Nano Research 2019年第4期12卷 703-718页

作者： Mi Li Ning Xi Yuechao Wang Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110016 China Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong 999077 China

Single-cell analysis has been considered as a promising way to uncover the underlying mechanisms guiding the mysteries of life activities, which con siderably complements traditio nal en semble assays and yields novel in sights into cell biology. The adve nt of atomic force microscopy (AFM) provides a potent tool for investigati ng the structures and properties of n ative biological samples at the micro/na no scale un der near-physiological conditions, which promotes the studies of single-cell behaviors. In the past decades, AFM has achieved great success in single-cell observation and manipulation for biomedical applications, demonstrating the excellent capabilities of AFM in addressing biological issues at the single-cell level with unprecedented spatiotemporal resolution. In this article, we review the recent advances in single-cell analysis that has been made with the utilization of AFM, and provide perspectives for future progression.

关键词： atomic force microscopy single-cell analysis cellular morphology cellular mechanics cellular manipulation

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Grand Challenges in the Verification of Autonomous systems

arXiv

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arXiv 2024年

作者： Leahy, Kevin Asgari, Hamid Dennis, Louise A. Feather, Martin S. Fisher, Michael Ibanez-Guzman, Javier Logan, Brian Olszewska, Joanna I. Redfield, Signe Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Complex and Autonomous Systems Research Group Thales UK Research Reading United Kingdom Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Department of Computer Science University of Manchester Manchester United Kingdom Renault Guyancourt France Department of Computing Science University of Aberdeen Aberdeen United Kingdom Department of Information and Computing Sciences Utrecht University Utrecht Netherlands School of Computing and Engineering University of the West of Scotland Glasgow United Kingdom U.S. Naval Research Laboratory WashingtonDC United States

Autonomous systems use independent decisionmaking with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE robotics and Automation Society Technical Committee for Verification of Autonomous systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them. © 2024, CC BY.

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Research on Industrial Control Network Security Measurement Method Based on GRU-SVM

Research on Industrial Control Network Security Measurement ...

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Chinese Automation Congress (CAC)

作者： Hengfei Chen Xianda Liu Chuan Sheng Hao Pan College of Information Engineering Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

Since the industrial Internet entered people's field of vision, industrial control network security has gradually received attention. Traditional network security measurement methods rely on the accuracy of situation sequences and historical situation values, and various factors in the network have a great influence on the results. However, compared with industrial control networks, the number and types of affected factors are less than those of the modern Internet. Therefore, based on the research on network security situational awareness, this paper proposes an industrial control network security measurement method model based on GRU-SVM. First, the data sets with threat type labels are classified, and then the similarity scoring mechanism and softmax function are introduced, to process the classification results. For the GRU-SVM classification module, the analysis of the simulation results shows that the convergence is better and the robustness is stronger. For the safety prediction measurement part, by comparing with different models, the mean absolute error and root mean square error are smaller and the reliability is higher.

关键词： Industrial control Simulation Measurement uncertainty Network security Predictive models Logic gates Robustness

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A Machine Learning Clustering Algorithm for Sensorless reduced Device Multilevel Inverter based active power filter control

A Machine Learning Clustering Algorithm for Sensorless reduc...

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Information Technologies & Smart Industrial systems (ITSIS), IEEE

作者： Lazhar Manai Achref Rihani Mongi Besbes Laboratory of Robotics Informatics and Complex Systems National Engineering School of Tunis University of Tunis El Manar B.P N° 37 Tunis Tunisia Higher Institute of Information and Communication Technologies B.P N°123 1164 Hammam Chatt- Tunisia University of Carthage

This article's primary goal is to minimize P.I regulators and maximize measurement tools while maintaining inverter capacitor voltage self-balance in order to lower costs and simplify controls. The second goal is to offer a reliable preventative solution for power grid issues. Numerical simulations and practical testing will be used to evaluate the effectiveness of the machine learning control of reduced device multilevel inverter-based active power filter control, and comparisons with alternative strategies are required to show its advantages…

关键词： Voltage measurement Regulators Machine learning algorithms Capacitors Clustering algorithms Machine learning Harmonic analysis

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