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Development of micro-and nanorobotics: A review

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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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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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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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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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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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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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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State Observation of Affine-in-the-States systems with Unknown Time-Varying Parameters and Output Delay

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IFAC-PapersOnLine 2022年第12期55卷 341-346页

作者： Alexey Bobtsov Nikolay Nikolaev Romeo Ortega Denis Efimov Olga Kozachek Department of Control Systems and Robotics ITMO University Kronverkskiy av. 49 Saint-Petersburg 197101 Russia Departamento Académico de Sistemas Digitales ITAM Ciudad de México México INRIA Univ. Lille CNRS UMR 9189 - CRIStAL F-59000 Lille France Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia

In this paper we address the problem of adaptive state observation of affine-in-the-states time-varying systems with delayed measurements and unknown parameters. The development of the results proposed in the [Bobtsov et al. 2021a] and in the [Bobtsov et al. 2021c] is considered. The case with known parameters has been studied by many researchers—see [Sanz et al. 2019, Bobtsov et al. 2021b] and references therein—where, similarly to the approach adopted here, the system is treated as a linear time-varying system. We show that the parameter estimation-based observer (PEBO) design proposed in [Ortega et al. 2015, 2021] provides a very simple solution for the unknown parameter case. Moreover, when PEBO is combined with the dynamic regressor extension and mixing (DREM) estimation technique [Aranovskiy et al. 2016, Ortega et al. 2019], the estimated state converges in fixed-time with extremely weak excitation assumptions.

关键词： Nonlinear time-varying systems state observer delay systems parameter estimation

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A Novel Signals and Features Fusion Method Based on Fisher Vector for Severity Assessment in Parkinson's Disease

A Novel Signals and Features Fusion Method Based on Fisher V...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Yubo Sun Yuanyuan Cheng Zhizhong Zhu Jinrui Liu Zhenhui Guo Ningbo Yu College of Artificial Intelligence Nankai University Tianjin China Department of Rehabilitation Medicine Tianjin Huanhu Hospital Tianjin China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

Parkinson's disease (PD) is a progressive neurode-generative disorder that requires accurate severity assessment to facilitate proper medication and interventions. This study proposes a novel method for PD severity assessment by fusing bilateral surface electromyography (sEMG) signals and features using Fisher vector. The sEMG signals were measured from the tibialis anterior (TA) and lateral gastrocnemius (GL) muscles in 28 PD patients. Preprocessing was performed to remove industrial frequency noise and low-frequency trend, followed by feature extraction using a customized feature generator that included time-domain and frequency-domain features. The extracted features and preprocessed sEMG signals were then vectorized using the Fisher vector and fused as input to the classifier. Our results demonstrated that considering bilateral features is more favorable than unilateral features for the PD severity assessment. Moreover, the proposed method significantly enhanced the classification accuracy, with a remarkable classification accuracy of 96.00% achieved through the combination of our proposed method with a decision tree classifier.

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Fully Robotized 3D Ultrasound Image Acquisition for Artery

Fully Robotized 3D Ultrasound Image Acquisition for Artery

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mingcong Chen Yuanrui Huang Jian Chen Tongxi Zhou Jiuan Chen Hongbin Liu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Biomedical Engineering and Imaging Sciences King's College London London UK

Current imaging of the artery relies primarily on computed tomography angiography (CTA), which requires contrast injections and exposure to radiation. In this paper, we present a method for fully autonomous artery 3D image acquisition using a linear ultrasound (US) probe and a 6 DoFs robot arm with a 3D camera. Robotic vessel acquisition can minimize tissue deformation and permit the reproduction of scans. Additionally, the robotic-based acquisition can provide more precise vessel position data that can be utilized for 3D reconstruction as a preoperative image. The first scanning point is determined by the 3D camera using a neural network for leg area estimation. A visual servo algorithm adjusts the in-plane motions using a cross-sectional vessel segmentation produced by a neural network with a UNet structure, while a US confidence map regulates the in-plane rotation. The robot is equipped with impedance control to maintain a constant and safe scan. Experiments on a leg phantom and a volunteer indicate that the robot can follow the vessel and modify its position to provide a sharper US image. The average error of phantom scanning in y-axis and z-axis are 0.2536mm and 0.2928mm, respectively, while the root means square error (RMSE) of contact force in the volunteer experiment is 0.2664N. In addition, a 3D vessel reconstruction demonstrates the possibility of robotic US acquisition as a preoperative image.

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