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Application of a genetic algorithm in triangulation of a 3-D object surface

International Journal of Computers and Applications 2000年第2期22卷 73-77页

作者： Zhenyu, Chen Mbede, J.B. Yan, Zhou Dehua, Li Hanping, Hu State Commn. Res. Open Lab. Image P. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Intelligent Contr. and Robotics Lab. Dept. of Contr. Sci. and Engineering Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Second Artillery Institute Huazhong Univ. of Sci. and Technol. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. Second Artillery Institute China Wuhan University China Artificial Intelligence Department University of Edinburgh Inst. of AI and Pattern Recognition Huazhong Univ. of Sci. and Technol. China Cogn. Sci. Natl. Key Found. Res. P. Douala University Cameroon Tübingen University Germany Automatic Control Laboratory Darmstadt University Department of Service Cameroon Min. of National Education Robotics Laboratory Huazhong Univ. of Sci. and Technol. Wuhan China Assoc. Connectionnistes These-ACTH Rennes France IEEE Soc. of Robotics and Automation IEEE Society of Control Systems IASTED Assoc. Jeunes Chercheurs Robotique France Navy University of Engineering China Huazhong Univ. of Sci. and Technol. China Department of Computer Science Airforce Radar Acad. of Engineering China Inst. of P.R and AI Huazhong Univ. of Sci. and Technol.

This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of the surface of 3-D objects with a multi-objective optimization function to meet the needs of a wide range of applications. Further, a new crossover operator for triangulation and a new 3-D quadrilateral mutation operator are also introduced.

关键词： Object recognition

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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IEEE International Conference on robotics and Biomimetics

作者： Tian-Yu Xiang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Hong-Jun Yang Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The school of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau

ISBN: (纸本)9781665481106

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios.

关键词： Training Convolution Computational modeling Biological system modeling Streaming media Brain modeling Electroencephalography

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Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

Group Feature Learning and Domain Adversarial Neural Network...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Chen-Chen Fan Haiqun Xie Liang Peng Hongjun Yang Zhen-Liang Ni Guan’an Wang Yan-Jie Zhou Sheng Chen Zhijie Fang Shuyun Huang Zeng-Guang Hou State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China First People's Hospital of Foshan Foshan China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF- DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group- level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.

关键词： Deep learning Neural networks Medical services Human factors Sensitivity and specificity Feature extraction Robot sensing systems

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Design and Experiments of a Portable Robotic Ultrasound Scanning and Diagnosis System of Carotid Artery

Design and Experiments of a Portable Robotic Ultrasound Scan...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Longyue Tan Zhaokun Deng Mingrui Hao Pengcheng Zhang Xilong Hou Xiaolin Gu Chen Chen Shuangyi Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Lingshu Medical Company Suzhou China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Ultrasound (US) imaging is an important, non-invasive method for evaluating the carotid artery (CA), which plays a critical role in early screening cardiovascular and cerebrovascular conditions. However, it requires specialized training for sonographers, which is a challenge in primary healthcare. Robotic devices now enable semi-automatic or fully automatic US scans, reducing the need for skilled operators. This paper presents an innovative, economically viable robotic system specifically engineered for conducting US examinations of the human CA. This system is distinguished by its advanced human-machine interaction capabilities with artificial intelligence assistance, which facilitate semi-automatic scanning processes in primary healthcare settings. Building upon this, the study introduces a semi-automatic scanning approach designed to control the device’s operations, aiming at capturing highquality images of the key structures of CA. Employing the images obtained through this method, the research proposes a segmentation strategy, leveraging fine-tuning adjustments on the LiteMedSAM model to achieve semantic segmentation of the CA in the images. The outcome is a nuanced diagnostic tool capable of identifying and evaluating carotid stenosis. The mentioned device and methods are tested on human subjects and the results indicate success on detecting critical vascular structures and positive feedback on human-machine interaction from operators and patients.

关键词： Human computer interaction Training Ultrasonic imaging Mechatronics Semantic segmentation Buildings Imaging Medical services Robots Carotid arteries

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Magnetic-Actuated Flexible Instruments with Enhanced Bending Capability through Magnetic Distribution Optimization

Magnetic-Actuated Flexible Instruments with Enhanced Bending...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Yuanrui Huang Runyu Ma Jian Hu Mingcong Chen Jian Chen Hongbin Liu 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 Beijing China School of General Engineering Beihang University Beijing China Centre of AI and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

Magnetically-actuated flexible surgical robot has promising applications for ultra-narrow and tortuous orifices. However, maximizing its bending capability and manipulability remains a significant hurdle for clinical use. In this study, we introduce an objective function that can accurately predict a catheter's bending angle with given numbers of internal permanent magnets (IPMs) under various magnetic fields through iterative calculation. The optimal magnets distribution that yields the maximal controllable bending angle, could be determined by optimizing this objective function. The effectiveness of this method is validated through simulations and experiments. Additionally, we propose a simple control scheme to control the bending angle of the magnetically-actuated catheter based on the piecewise constant curvature model. The experimental results demonstrate that the proposed method significantly improves the motion accuracy of the catheter. The catheter with the optimized magnet distribution achieved superior tracking performance with an Integral Absolute Error (IAE) of 1.08 rad·s and maximal controllable bending angle of 150°.

关键词：

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Feature referenced tip localization enhanced by probability motion model for AFM based nanomanipulations

Feature referenced tip localization enhanced by probability ...

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IEEE International Conference on robotics and Biomimetics

作者： Shuai Yuan Lianqing Liu Zhidong Wang Ning Xi Yuechao Wang Zaili Dong Zhiyu Wang Zhibo Wang College of Information & Control Engineering of Shenyang Jianzhu University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Department of Advanced Robotics Chiba Institute of Technology Department of Electrical and Computer Engineering Michigan State University

ISBN: (纸本)9781457721366

The spatial uncertainties of tip positioning due to the nonlinearity of the PZT scanner and thermal drift hinder the further application of the AFM based nanomanipulation. This paper brings forward feature referenced tip localization enhanced by probability motion model to reduce the spatial uncertainties. An improved motion model is probabilistically built by incorporating the PI model, the creep model and the thermal drift model. For calibrating the accurate model parameters, the statistical experiments are designed and performed. Then the tip position is optimally estimated by combining with a local scan based feature sensing method. The simulation and corresponding experiments are performed to illustrate the validity and feasibility of the calibrated parameters and the algorithm.

关键词： Atomic Force Microscopy terminal interface processor nonlinearity based model parameters Drift models

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Kinetostatic Modeling of Compliant Mechanisms Via Reduced-Mode Cosserat Rod Model

SSRN

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SSRN 2023年

作者： Wu, Ke Wang, Wei Li, Xin Hu, Junzhe Chen, Pengyu Cheng, Ran Sun, Qidi Liu, Lifu Zhou, Luna Xu, Xin Zhang, Shucheng Deng, Zicheng Chen, Rui Zheng, Gang INRIA Lille-Nord Europe 40 Avenue Halley Villeneuve d’Ascq59650 France State Key Laboratory of Mechanical Transmission University of Chongqing Chongqing400030 China LS2N - équipe ReV - Robotique Et Vivant Institut Mines Telecom Atlantique Nantes France Chongqing University China Chongqing University University of Cincinnati Joint Co-op Institute Chongqing University Chongqing400044 China Center for Robotics Research University of Cincinnati CincinnatiOH45221 United States MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China Hong Kong

Compliant mechanisms (CMs), which are normally composed of rigid blocks and flexible members, utilize the elastic deflection of all these built-in flexible members, totransfer motion, force and energy. In the current literature, geometrically nonlinear Euler Bernoulli beam theory and its related methods are commonly used for modelingthese flexible members. However, its governing ordinary differential equation (ODE) only considers the main contributor to beam deflections: bending, without taking careof shear and axial stretch along the beam. To take care of more possible strains, Cosserat rod theory, serves as a better alternative for high accuracy. However, it stillsuffers from relatively complex modeling and time-consuming computation. In this paper, we propose a reduced-mode Cosserat rod model to bypass the need ofconducting complex modeling, providing a more compact formulation. The detailed derivations are presented step by step, and we also propose a mode selectionmethod to meet the requirements of different engineering scenarios. Numerical examples are provided to verify the reduced-mode model as well as the feasibility ofmodeling CMs. In particular, modeling flexible springs and related mechanisms has been proved valid via this proposed model. © 2023, The Authors. All rights reserved.

关键词： Boundary value problems

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CapsuleBot: A Novel Hybrid Aerial-Ground Bi-Copter Robot with Two Actuated-Wheel-Rotors

arXiv

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arXiv 2023年

作者： Zheng, Zhi Cai, Qifeng Wang, Jin Xu, Xinhang Cao, Muqing Yu, Huan Li, Jihao Meng, Jun Lu, Guodong State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Institute of Zhejiang University Zhejiang University Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh15217 United States Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

This paper presents the design, modeling, and experimental validation of CapsuleBot, a novel hybrid aerial-ground bi-copter robot designed for long-endurance and low-noise operations. CapsuleBot combines the maneuverability of a bi-copter in the air with the low power consumption and low noise of a two-wheel self-balancing robot on the ground. To achieve this, we design an innovative mechanical structure named the actuated-wheel-rotor, which uses a servo motor and a brushless motor to function as both a tilting rotor in the air and an actuated wheel on the ground. CapsuleBot is equipped with two actuated-wheel-rotors, enabling it to achieve hybrid aerial-ground propulsion using only four motors, with no additional motors required compared to a bi-copter. Additionally, we develop comprehensive dynamics and control systems for both air and wheel mode, based on the bi-copter model and the two-wheel self-balancing robot model. A prototype of CapsuleBot is constructed, and its performance in terms of low power consumption and low noise is validated through experiments. Challenging tasks demonstrate CapsuleBot’s capability to climb steep, fly over cliffs, and traverse rough terrains. Copyright © 2023, The Authors. All rights reserved.

关键词： Antenna grounds

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Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation

arXiv

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arXiv 2023年

作者： Li, Shilei Shi, Dawei Lou, Yunjiang Zou, Wulin Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China Xeno Dynamics Control Department Xeno Dynamics Co. Ltd Shenzhen518055 China

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Kalman filters

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Model-Based Shape Estimation and Closed-Loop control of Magnetic Continuum Robot Based on Monocular Vision

Model-Based Shape Estimation and Closed-Loop Control of Magn...

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IEEE International Conference on robotics and Biomimetics

作者： Junfeng Zhu Yuanrui Huang Jian Chen Hongbin Liu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre of AI and Robotics Hong Kong Institute of Science Innovation Chinese Academy of Sciences School of Biomedical Engineering and Imaging Sciences King's College London UK

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Magnetic continuum robots (MCRs) show promising potential for applications in narrow lumens, such as vascular interventions. Accurate shape sensing of these robots is crucial for the success of interventional surgeries. However, the submillimeter dimensions of MCRs poses significant challenges for integrating additional sensors. An alternative approach is shape estimation from images, yet the lack of precise MCR kinematics can make this process time-consuming. In this work, we propose a novel MCR design featuring a rectangular tip capable of specific planar bending, along with a corresponding kinematic model and a mechanical model. Additionally, we introduce a model-based shape estimation method that enables the estimation of the MCR's shape from a single-view image without the need for external markers. Experimental results demonstrate that our method can estimate an MCR's shape with a mean absolute error (MAE) of $\mathbf{0.58}\pm \mathbf{0.41}\mathbf{mm}$ within 85ms, facilitating real-time application. Furthermore, closed-loop control based on this method has achieved an end position error below 11% of the tip's total length, highlighting the accuracy and practicality of our approach for surgical interventions.

关键词： Solid modeling Shape Computational modeling Magnetic resonance imaging Estimation Kinematics Robot sensing systems Real-time systems Continuum robots Magnetosphere

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